Tech companies, including those in Silicon Valley and others outside of the set like Microsoft, have been through various guises through the years. Plenty of the old tech giants, like IBM and Toshiba, continue to enjoy success in the 21st century due to a chameleon-like quality that keeps them innovating and bringing in new revenue streams.

Evolve or die

The Microsoft CEO Satya Nadella wrote these words in his first email to company employees when he took the reins from Steve Ballmer.

In this opening salvo, he set out his vision that the next decade of computing would be dominated by the proliferation of devices and the rise of more accessible "intelligence" – hinting at the rise of AI in some form.

The company that he was to lead would embark on a strategy that would "harness the power of software" and deliver this through a network of connected devices for every organization and individual. The innovation that he referenced largely came in the form of a move away from a desktop-oriented mindset and one that was more embracing of the cloud.

Pivoting for AI

As the decade since this email wore on, Microsoft stayed true to that mantra. And, in the last few years, Nadella led the company through another significant shift in light of the rise of AI.

When ChatGPT burst onto the scene, Microsoft rushed to forge ties with the company and has aggressively added AI into its core products and services – from Bing to Windows.

While Azure continues to scale at a tremendous pace, surpassing $100 billion in revenue, the company has seen a mixed picture on the AI front. That's not due to a lack of demand, but to a shortage of physical space and energy to power these services and to expand capabilities in model training, deployment, and usage.

Humanoid robots are on the rise, with a slate of companies building several different prototypes. Whether they're designed to be on the production line or engineered to roam around your house performing various chores, different kinds of machines are on the cusp of mass production.

Setting expectations

Australian roboticist Brooks remarked on the appearance of robots in the first of his own three laws of robotics, inspired by Isaac Asimov, in a blog post that he self-published.

The co-founder of robotics company iRobot, which created the Roomba robot vacuum, Brooks explained that the anthropomorphization of humanoid robots – both by engineers and by consumers – could be counterintuitive.

While the form is necessary to gain pubic acceptance, engineers must deliver on performance and capabilities. Adopting a similar form to that of humans may imply a level of humanlike intelligence or competence in navigating the real world. Should robotics companies fail on that count, people will see straight through the ruse.

Robots of mass production

Brooks' laws focus on the competency and capabilities of robots and the need for creators to ensure that they are trained, that they're capable, and that they don't remove human agency.

Plenty of highly polished demos, recorded promotional videos, and on-stage appearances have done well to highlight the fact that these machines are not ready for prime time – despite impressive capabilities in highly controlled environments. That's evidenced by a series of 'robot fails' when they're introduced into real-world settings.

Companies, however, say they're improving the AI technology powering these machines, as well as the dexterity of appendages. They plan on ramping up production toward the end of this year, with the first machines set to be deployed in commercial settings by 2027.

As Tim Cook prepares to vacate his role as Apple CEO on September 1st and John Ternus assumes control, people have asked me what Cook is like and how he compares to Ternus. I can't say I know either man well, but I do know them and have, over the years, had the opportunity to speak to each of them.

In due time, both will be part of a very small club: men who have stepped into running one of the biggest and most important tech companies on the planet. When Cook took over, it was shortly before one of Apple's most important launches: the iPhone 4S and, not coincidentally, Siri (yes, the original Siri). 15 years later, Ternus becomes Apple CEO at another pivotal moment: the launch of the first folding iPhone (maybe called the iPhone Ultra) and the full release of Siri AI.

How Cook in his day and Ternus next month deal with consequential moments is surely tied to their personalities. Leaving aside the scripted moments and the strategic imperatives that drive such days, it's who these men are that define the events. I'm not sure I ascribe to the idea that a company reorients itself in the image of its CEO, but it's hard to argue with the notion that who a person is does impact products, partnerships, and corporate culture. Even relationships with people like me (media journalists) will vary depending on leadership.

Cook and his Apple legacy

I thought about all of this as I watched Cook struggle a bit this week with a question from CBS News Correspondent Jo Ling Kent, who was interviewing the outgoing Apple CEO at the opening of a new Mac Mini plant in Houston, Texas, the first such US plant in ages to build consumer-grade Macs on US soil.

Kent reminded Cook that he once said Apple co-founder and former CEO Steve Jobs' legacy was innovation and wondered what his would be.

Anyone who knows Cook or has spoken to him even a little bit would know that he's not the self-aggrandizing type and is not obviously introspective. In my conversations, I found a genial and extremely polite man who could be a bit guarded (though he is far more approachable than Steve Jobs ever was). He's quick with a handshake and smile, but not one to slap you on the back and share a joke or a secret.

I somehow knew that Cook would not be defining his own legacy. Instead, he told Kent that should be left to others, but he hoped that "people say I was a good and decent man, then I feel like I will have achieved something."

In direct interactions and for much of his Apple tenure, I think Cook's basic decency was on display. Far more so than his predecessor, Cook spoke about Apple as a force for good, helping people, protecting the environment, and adding DEI principles to Apple's corporate policies that remain in place to this day.

Cook's reputation for goodness and decency may have taken a hit in public discourse when he appeared to align himself and Apple with divisive US President Donald Trump. The work has mostly been part of an effort to bring manufacturing back to the US, though some might argue Apple was strong-armed into it by Trump's threat of exorbitant tariffs. In fact, the completion of the Mac Mini plant is a direct result of the promises Cook made to Trump.

The complication of legacy and compromise

I, like many others, have struggled to reconcile the Cook I know with the man I saw handing Trump a golden trophy. Cook has since said the trophy was not a political statement and later told Esquire, "I’ve interacted with governments all around the world, some that I have very different views on. But I think until you engage, you never know — you never understand — where somebody else is coming from. And you have no influence at all."

This statement dovetails neatly with Cook's southern personality, which is focused on politeness and compromise, but also an iron will that allows you to ride the middle while still getting things done.

Cook would never tell you what he thinks of Trump, certainly not now or even when he becomes Executive Chairman on September 1, though I doubt Cook would be surprised if he heard Trump say he's unconcerned if people think he's been a good and decent man.

Which brings me to Ternus. Over the years, I've spoken to him mostly about products and technology, but have had a few moments where we just chatted (briefly), and I could immediately see how different he is from Cook. Ternus seems quicker to laugh, joke, and is maybe a little less guarded and focused on politeness. Perhaps it's his bi-coastal background (born in California, schooled in Pennsylvania), but there's no southern gentility here.

Apple CEO is not, fundamentally, a personality-driven role, not since Jobs, a true iconoclast, died. Cook has ultimately been more of a traditional CEO whose personality came second to the job. Ternus will surely be no different.

Whatever Cook's true legacy (services, health, Apple Watch, a trillion-dollar valuation), it'll soon be Ternus's time, and his moment to start building his own legacy, one that will also be judged and declared by others.

The global stage of innovation is once again shifting, and where Silicon Valley once dominated over all, other equally powerful regions are rising up to challenge it.

Sadiq Khan recently spoke out on the dynamic politics in the US and claimed it was a key reason why tech talent and VC is being driven to the UK in competition against Silicon Valley.

As more investment and talent are drawn to the UK, the volume of R&D activity carried out here will only grow, and with it, the number of businesses turning to R&D tax relief to fund that innovation.

But under the watchful eyes of HMRC, whose growing scrutiny is felt by all across the industry, the need for robust claims is more important than ever.

R&D activity must be properly captured and evidenced, yet while the underlying innovation is real and substantive, the claims can run into trouble.

If the documentation was incomplete, the evidence was gathered retrospectively in a rush, and the finance and technical teams had never properly aligned on what needed to be captured or when, the claim is left operationally weak.

This is where strong, solid R&D activity is undermined by the operations and processes behind the claim.

The R&D disconnect

In my experience, the most common point of failure is rarely businesses’ technical knowledge. There’s often a disconnect between those individuals conducting R&D and the people preparing the claim. If we think about it, technical and engineering teams do not naturally think in the language of tax legislation – and why should they?

It’s not in their wheelhouse, and frankly, it isn’t a requirement of their day-to-day role. Equally, finance and tax functions often do not have the depth of technical understanding needed to translate innovation activity accurately into a compliant claim. This gap can leave the whole process vulnerable.

The challenge that businesses face is compounded by poor documentation and reactive evidence gathering, which remain some of the biggest operational weaknesses across the market. HMRC has been increasingly clear that it expects evidence captured at the time the R&D activity occurs, not reconstructed after the financial year has closed, as this leaves it open to incorrect recall and inaccuracies.

My advice to any business approaching its R&D claim in 2026 would be listen to HMRC, take it seriously and be proactive. Think about your R&D activities and how you are recording them throughout the year, not just at year end.

The pressure created by increased HMRC scrutiny

The R&D tax ecosystem has undergone substantial change in recent years, with the introduction of the merged scheme, changes to rates, new compliance requirements, and all under the shadow of HMRC’s growing scrutiny.

Its approach is far more rigorous, more targeted and it’s more likely to release enquiries into claims today than it was five years ago. The merged scheme is designed to bring greater consistency and clarity for businesses making claims, but in the short term, navigating HMRC’s expectations requires more robust operational processes.

The critical word is quality. For today’s claimants, this means ensuring claims are compliant with legislation and carry minimal risk of enquiry. It also means the quality of service, so making it as easy as possible for teams to provide the information needed for the claim, ensuring nothing is missed and maximizing the claim from a compliance perspective.

Those two things should work in tandem to successfully create a robust claim that doesn’t demand unnecessary workload from those involved and ultimately produces a better outcome.

Establishing the conditions to claim with confidence

As a means of making the claims process easier, businesses have turned to AI. There are well-documented, tangible benefits to deploying this technology across the claims process, but only when it’s tightly cornered off with guardrails. Yes, AI tools can make the compilation of claims faster and smoother, but when it’s used to write the narratives and technical descriptions, there’s a greater risk of inaccuracies, and it’s something that HMRC is cracking down on. The technology is only as good as the underlying data and the human judgement applied to it.

But we’re clearly heading in the right direction. HMRC’s new levels of scrutiny are ultimately there to set a better standard for R&D claims and filter out those with false claims. We exist in a volume-driven era, where the focus has previously been to build and submit claims quickly and at scale, rather than prioritizing quality. The UK has so much to offer when it comes to innovation, and the shifts taking place reflect a broader maturing of the market as we cement our place on the global stage.

Any business destined to become the foundation of the country’s global success is investing their own R&D processes, giving their innovation the backing it deserves to enable cyclical investment from R&D tax relief. We know the operational gap is where good claims break down, so closing it is where the real competitive differentiation now lies.

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A small business owner receives a call from a customer.

An email that appeared to come from the business led the customer to a fraudulent website, and their personal information may have been compromised.

What makes situations like this so damaging is that the owner never knew the risk existed.

The domain used in the attack had been registered for a campaign years earlier and left quietly active, sitting outside anyone's management, until someone else found a use for it.

Situations like this rarely begin with a major security breach. More often they start with something small: a forgotten domain, an outdated email configuration, or a digital asset nobody realized was still active.

AI makes finding those unnoticed weaknesses all too easy for attackers. According to KnowBe4's 2025 Phishing Threat Trends Report, 82.6% of phishing emails now show some use of AI, a 53.5% increase year-over-year. Attackers are adopting the same automation businesses use to improve efficiency, and the speed advantage has shifted. The good news is that businesses can use the same advances in AI to identify many of these risks before attackers do.

But the speed of detection is only part of the challenge. The harder problem is visibility. You can't secure what you don't know you own.

More and more, small businesses manage domain names, websites, email systems, social channels, and third-party tools, and as those layers expand, the gaps between ownership and oversight become easier to miss.

Forgotten domains are a common example. Domains created for promotions, campaigns, or discontinued services often stay active long after their purpose disappears. Left unmanaged, they become blind spots that attackers exploit through phishing, impersonation, and brand abuse, and they can quietly erode credibility well before any breach, since a domain that no longer resolves correctly signals neglect to customers and machines alike.

Simply put, many business owners no longer have a complete view of the digital assets they own or the vulnerabilities that come with them.

Security needs to be embedded, not bolted on

Small business owners are focused on serving customers, growing revenue, and running their businesses. They are not thinking about DNS records, certificate renewals, or dormant subdomains during their day. Nor should they have to.

But many do not have the option. According to VikingCloud's 2026 research, 84% of SMB owners manage cybersecurity themselves, often without dedicated training or expertise.

The most effective security strategies are built into the infrastructure which businesses depend on every day, rather than added after problems arise. There are three layers where this matters most: the domain, which serves as a business' identity online; the website, where customers form opinions about credibility and trustworthiness; and email, which remains one of the most important channels for customer communication and one of the most common targets for impersonation and fraud.

Embedding security into the solutions businesses already use helps them maintain visibility and confidence without constant manual oversight of all of those moving parts.

Trust is now measured by both people and machines

Today’s cybersecurity conversation always comes back to trust. That’s what SMBs want to win by securing their online business. It is the engine of their success.

We know that trust is one of the most important competitive advantages a business can have: according to McKinsey reporting on digital trust, 40% of consumers have completely pulled their business from a company after discovering the organization was reckless with customer data, and 10% of consumers will cut ties with a brand immediately upon learning of a data breach, regardless of whether their own personal information was actually compromised or stolen.

For years, trust online was primarily a human judgment. Customers visited a website, received an email, or interacted with a brand, and decided whether it appeared credible. Today they still make those decisions, but they are no longer the only ones making them.

Search engines, AI assistants, and automated systems increasingly evaluate trust signals on behalf of users. Roughly two-thirds of Google searches now end without a click, according to Similarweb clickstream data analyzed by SparkToro. Trust is no longer just a customer's perception; it is becoming part of how businesses get discovered.

Credibility is no longer determined solely by what customers see. Domain resolution, certificate validity, email authentication records, and the consistency of businesses’ online presence all feed into the assessments that influence search rankings, AI-generated recommendations, and discovery across the platforms customers use every day. A business that doesn’t deliver on these fronts may be overlooked long before a customer ever decides whether to trust it.

Trust and security are now competitive infrastructure

For decades, businesses viewed security as a defensive function, meant to reduce risk and respond to threats. That perspective is changing.

Trust and security now influence customer acquisition, retention, reputation, and long-term growth. In the AI era, trust is no longer just a security outcome. It is a business strategy. As AI accelerates both innovation and risk, customers have become more selective about who they engage with and where they share their information. Credibility is difficult to earn and almost impossible to buy back once lost.

At Network Solutions, we have spent decades helping businesses establish and protect their digital identities. One lesson remains consistent: investing in trust early creates advantages competitors struggle to replicate.

Businesses that stand out in the years ahead won't simply adopt more AI. They'll build trust into every layer of their digital presence. The business owner who took that call from a customer deserved a better security infrastructure, not a better incident response after the fact.

Technology will continue to evolve, and so will the threats. Trust will only become more valuable. The businesses that thrive won't simply adopt more AI; they'll build stronger foundations for trust. That's where our industry needs to go next.

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The recent release of Anthropic Mythos is a wake-up call for the tech industry – and the fact that Anthropic themselves chose not to release it publicly speaks volumes about the level of risk we have now reached. AI agents have evolved from chatbots with upgraded capabilities to effective employees with database access, API keys, and system privileges.

However, the security protecting them is built on the same strategy that failed to stop ChatGPT jailbreaks in 2023. And this time, there’s no human to review an agent’s output, just an autonomous agent carrying out commands in a silo.

AI agents are reshaping enterprise systems and the way work gets done. Securing them requires an equally fundamental shift in thinking. Ultimately, now that agents act independently, resilience must be rooted in foundational controls, including hardware-level and lower-stack security, to be ready when the higher-level safeguards fail.

How AI agents expand the attack surface

Before agentic AI, the biggest AI risks were bad recommendations, inappropriate responses, and conversational data exposure. Human oversight acted as a safeguard for every action, and AI systems operated without direct access to sensitive information. The primary concern was reputational damage rather than risks to underlying infrastructure.

When Anthropic released the Model Context Protocol (MCP) in November 2024, it established a standardized framework that allows AI agents to connect to databases, file systems, and enterprise tools. But within eight months, a critical vulnerability emerged (CVE-2025-49596, CVSS9.4), triggering emergency security responses across the industry.

The risk came from four factors working together. Autonomy means agents can decide and act without human review. Privileged access gives them credentials, tokens and file system permissions. Machine-speed execution leaves little time for human intervention. And cross-system reach means one compromised agent can move across connected environments.

Together, these factors expanded the attack surface far beyond what traditional security controls – even AI-enabled ones – were built to manage.

Why software-only defences keep falling short

The industry is moving quickly to secure AI agents, but the response largely mirrors a familiar approach: adding more layers of software. Most companies are focusing on two main layers: input guardrails – implementing more software tools designed to stop malicious instructions from ever reaching AI agents, and permissions and monitoring – limiting what compromised agents can access.

It’s the same strategy the industry had relied on for decades: deploy quickly, remain agile, and address vulnerabilities as they emerge. Both methods operate inside the software trust boundary.

But history shows this approach often ends the same way: with the need for hardware-layer protections. In the 1990s and 2000s, network security responded to software exploits by deploying additional software layers. Breaches persisted until organizations eventually adopted hardware-enforced network segmentation.

The same pattern played out with endpoint security in the 2000s and 2010s. As malware evolved to bypass detection, the response was behavioral analysis, sandboxing, and endpoint detection and response. Yet more software. Breaches continued until TPM (Trust Platform Module) chips and hardware-enforced secure boot became widely adopted. Cloud security, in the 2010s and 2020s, followed a similar path.

A common lesson runs through each of these domains: when the software trust boundary is compromised, the hardware layer – where data actually lives – must be secured too.

The case for hardware-level security

This time, we cannot afford to learn slowly. Agents are already being connected to the systems that business rely on for their daily operations. Incidents like the MCP critical vulnerability and recent reports of a data leak caused by a Meta AI agent show how quickly the risks can become real.

Guardrails, permissions, and monitoring are necessary, but they are insufficient, and they represent the security layers that history shows will eventually be bypassed. Effective defense requires a third layer – one that exists beyond the software trust boundary and provides oversight at the hardware level, where sensitive data is ultimately stored and processed.

Hardware Root of Trust serves as the final security barrier, helping contain breaches before they escalate into a full system compromise. As the number of companies using AI agents continues to grow, security needs to move deeper than the application layer.

The industry has already learned that software alone cannot secure complex systems – it should not wait for a major compromise to learn the same lesson again.

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In today’s supply chain, a week is not merely a long time. It can be the difference between protecting margins and absorbing avoidable cost; maintaining availability and losing sales; or preserving customer trust and explaining why another commitment has been missed.

Geopolitical tensions, tariff changes, economic uncertainty and supplier instability are no longer occasional interruptions to an otherwise predictable operating environment.

They increasingly overlap, interact and move faster than traditional planning cycles.

The external risk picture supports that conclusion. The World Economic Forum’s Global Risks Report 2026 identifies geoeconomic confrontation as the leading risk for both 2026 and the period to 2028. Half of the experts surveyed expect the global outlook over the next two years to be turbulent or stormy.

This is creating a decision-speed challenge at the heart of global commerce.

Research with supply chain leaders, found that only 20% can develop and deploy a response to a geopolitical disruption within 24 hours. A further 38% require more than a week.

During those seven days, transport costs can change, capacity can disappear, inventory can become stranded and competitors can secure alternative sources of supply. By the time a response has passed through every functional review and approval, the original assumptions may already be obsolete.

The critical question is not whether an organization can see disruption. It is whether it can convert that signal into an executable enterprise decision while there is still time to influence the outcome.

Visibility is not the same as readiness

For many years, supply chain transformation focused on improving forecasts, optimizing individual functions and reducing cost. Those disciplines remain important, but many of the operating models surrounding them were designed for a more predictable world.

Today’s disruptions cut across procurement, manufacturing, logistics, inventory, commercial planning and finance simultaneously. Yet many organizations still manage them through separate systems, functional metrics and sequential decisions.

Consider a tariff change. It may require the business to reassess sourcing, production, inventory deployment, pricing, customer allocation and margin exposure. If each function develops its own answer before the enterprise reconciles the trade-offs, valuable time is lost.

A dashboard may reveal the disruption quickly, but it does not determine which customers to prioritize, what financial exposure is acceptable or who has the authority to act.

Visibility without decision rights is simply faster awareness of the same problem.

Confidence is becoming polarized

The research also reveals a decline in overall confidence. Only 66% of leaders now consider their supply chains ready for the future, compared with 73% in 2025.

46% describe themselves as highly optimistic about their supply chains. These leaders are also more likely to report shared cross-functional KPIs, integrated data and stronger end-to-end visibility.

Optimism itself does not create better performance. Rather, confidence appears to reflect the capabilities these organizations have already built.

Less optimistic organizations are 2.3 times more likely to experience slow data sharing and integration, 2.5 times more likely to operate in functional silos and four times more likely to describe their supply chains as disjointed.

The divide is therefore not simply technological. It is organizational and operational. Connected businesses are better positioned to develop a common understanding of events, evaluate enterprise-wide consequences and align teams around one response.

From unified data to unified decisions

Unified data platforms are now the most widely adopted new technology in the survey, deployed by 51% of organizations. Adoption rises to 64% among the more optimistic cohort, compared with 40% among less optimistic leaders.

That foundation is essential, but it is only the beginning.

The real value emerges when common data supports connected decisions across planning and execution. This enables organizations not only to identify disruption, but also to assess its end-to-end impact, evaluate alternative responses and orchestrate action across the network.

Technology provides the foundation, but achieving decision speed also requires shared enterprise outcomes, clearly defined decision ownership, agreed intervention thresholds, pre-modelled scenarios and the authority to act.

European businesses are already adapting. European Commission survey evidence found that 27% of EU firms had changed, or planned to change, their strategies in response to tensions, disruption or policy changes in foreign markets. Among those adapting, 38% were changing sourcing or destination countries, while 22% were increasing inventory buffers.

Resilience is no longer an abstract ambition. Businesses are actively reconfiguring supply networks—but every adjustment carries consequences for cost, working capital, service and risk. The faster those trade-offs can be understood across the enterprise, the more choices leaders retain.

In grocery and consumer goods, the response window is narrower still because shelf life, availability and promotional commitments leave little room for delay.

AI can accelerate decisions - but foundations determine value

AI will play an increasingly important role in reducing the time between signal, insight and action. Machine learning and predictive AI are already used by 45% of respondents, with another 29% implementing them. Generative AI adoption has doubled from 12% to 24%, while agentic AI remains at an earlier stage, with 8% reporting live deployment.

AI can continuously monitor conditions, identify exceptions, evaluate scenarios and recommend responses at a scale human teams cannot match. When connected to planning and execution, it can also help translate those decisions into coordinated action across the network.

However, autonomy is not a shortcut around unresolved transformation challenges. AI operating across fragmented data, conflicting objectives and unclear decision rights may accelerate activity without improving outcomes.

Before giving AI agents greater scope to act, leaders must be confident in the data, guardrails, accountability and operating model surrounding those decisions. The question is not simply, “Can the agent act?” It is: “Should it act, within what boundaries and in pursuit of which enterprise outcome?”

The new measure of resilience

No organization can predict every disruption. A more practical measure of resilience is how quickly the organization can understand what has changed, determine what matters, make the necessary trade-offs and mobilize a coordinated response.

That is the real 24-hour test.

The organizations pulling ahead are not simply those with more information. They are those that can connect intelligence, decisions and execution quickly enough to act while choices still exist.

In an increasingly volatile world, resilience will not be determined by who sees the disruption first. It will be determined by who can make - and execute -the best decision before the window to respond closes.

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Agentic AI’s prolific use of tokens can create sizeable, unexpected costs for organizations. But saving on token costs without factoring in risk can be a fatal step.

As upfront prices for flagship artificial intelligence models continue to shrink, organizations have begun to wise up to the hidden costs they encounter with agentic AI models.

Specifically, the costs of tokens, which may look tiny when viewed as individual charges, can add up exponentially as AI agents become more active, leaving organizations with hefty AI expenditures they may not have anticipated.

This is putting CISOs in something of a bind. If they seek to save money on inference costs, primarily driven by token generation incurred by agentic AI, they may increase their security risk and accumulate hidden technical debt that puts their Agentic Development Lifecycle (ADLC) in jeopardy. It’s a problem that many CISOs may not have factored into their security budgets, but it cannot be left unaddressed.

The effectiveness of automated security processes is being impeded by fragmented pricing across the AI landscape, whether we’re talking about hyper-optimized nano models (essentially lightweight, yet powerful models built for a specific use, like Google’s Nano Banana 2 image generator) or premium reasoning engines, like Salesforce Atlas or OpenAI o3.

Organizations do have to keep a close eye on token costs to prevent them from spiraling, but CISOs also need to examine how agentic AI is affecting their security.

The hidden costs of AI agents

Erratic pricing has been a trademark of generative AI pretty much from the beginning.

About two years after OpenAI released ChatGPT, the Chinese company DeepSeek shook up the AI market with the release of a powerful, open-weighted large language model whose training parameters were publicly available, allowing users to customize the model and build on the cheap compared with other generative AI models.

ChatGPT-maker OpenAI and other AI companies started doing the same, and suddenly, the costs of using GenAI systems dropped off a cliff. In fact, prices fell faster for GenAI than for any other technology in history.

The emergence of agentic AI has introduced some stealth costs into the equation, however. The costs of agentic software range from free for open-source models to enterprise agents, with prices that vary from one-time fees (roughly $15,000 for basic models to more than $1 million for global enterprise models) to monthly subscriptions (which can range from a few thousand to $13,000 or more).

But those costs are fixed. Inference costs are another story: they scale with usage and can amount to 90% of AI lifecycle costs.

Tokens come into play when an AI agent requests processing from GenAI models, which charge agents for processing information. At a glance, the costs may appear inconsequential. Input tokens generally range from 15 cents to $5 per million requests. Output tokens, which require slightly more processing, cost from about 60 cents to $25 per million.

They may start small, but can add up in no time, thanks to AI agents that work very quickly, autonomously, and unpredictably. They are designed to interact with systems and other agents throughout the enterprise. A single action might generate scores of LLM calls. Token use, which has grown exponentially with the use of AI agents, has already increased IT budgets by about 20% according to recent estimates.

The accelerating cost of agentic AI is prompting CISOs to look for ways to save money where they can, and one way is to identify LLMs that charge the least per token. But what they may not be considering are the risk factors associated with those LLMs. If CISOs concern themselves only with the costs, they may open themselves up to security risks.

But better security doesn’t necessarily have to cost more. Depending on what they’re using agentic AI for, they may find they don’t always have to trade security for lower token costs.

Getting costs (and risks) under control

There are a few things organizations can do to help stop token costs from getting out of hand, including:

Match Agents and LLMs to the Job at Hand. Commodity AI systems can cost little or nothing, but they lack the deep reasoning for complex security synthesis. But not every application or function within the organization requires a reasoning engine. You can set up agents to work with low-cost LLMs on low-risk projects, while preserving higher-cost LLMs for critical tasks. It’s also worth being aware of which agents are likely to request more LLM calls.

Factor Risk Scores in Choosing Agents and LLMs. The security implications of using AI can’t be ignored. When developing a budget plan, include risk factors.

Monitor Workflows. Keeping a close watch on workflows can help you track costs and performance, allowing you to better understand which tools work best in which situations.

Lean on Human Oversight. Despite agentic AI’s autonomy, in fact, because of agentic AI’s autonomy, forgetting about the importance of the human element is risky business. Teams need thorough upskilling in secure development, with clearly defined ownership roles. And they must be given prominent oversight roles throughout the ADLC.

Agentic AI is fast becoming integral to enterprise operations, and organizations must control its associated costs. But a race to the bottom on token pricing creates hidden technical debt. Instead, CISOs need to weigh security performance when choosing AI tools as part of establishing an up-to-date security maturity model and an AI governance policy that emphasizes performance, costs, and risk management.

Only that approach allows agentic AI to be deployed without either breaking the budget or putting your entire organization at risk.

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When my co-founders and I started building our platform, the prevailing industry consensus was that the future of AI belonged exclusively to a tiny handful of elite, hyper-capitalized technology labs. The dominant narrative insisted that massive centralized scale and closed proprietary control were the only viable paths to frontier capabilities.

Today, that multi-billion-dollar bet on proprietary infrastructure is facing a massive market disruption. From my perspective as a founder, the era of treating AI as a rented utility is rapidly drawing to a close, replaced by an urgent global demand for open-source independence and data sovereignty.

The first major driving force behind this change is the reality of corporate accounting. Being fully dependent on a cloud provider for the core infrastructure of cognitive capabilities has transformed from a convenient beginning into a huge liability in strategic and security terms. It simply does not make sense to remain in a permanent closed-door monopoly.

As shown by recent research carried out by a scholar at UC Berkeley, moving an enterprise project from a proprietary API to open-source cuts the cost of computation from $3,000 down to $31. Recent reports say that this economic revolution takes place all over the world due to the fact that the quality difference between open and closed systems has become non-existent.

Independent LMSYS leaderboard shows the leaders of open source solutions to be just two per cent behind the best proprietary systems, such as Claude Opus.

Sovereign AI

Beyond the obvious economic advantages, the call for open architecture has become highly geopolitical. Both within the government sector and the business sector alike, institutions are now realizing that there are real risks involved in being tied to a foreign company's products. We are starting to see how such friction points manifest themselves through governmental actions.

In Germany, the increasing conflicts between Bavaria and Microsoft regarding data protection issues and regulations illustrate precisely why businesses cannot afford to be tied into closed systems and rely on overseas hyperscalers.

This is why I am such an avid supporter of Sovereign AI, where one is able to have complete and absolute control of their own data, their own models, and their own jurisdictions instead of continually leasing it from a third party.

This fast deconstruction will result in a great wave of stranded investments due to the immense amounts of capital being channeled into centralized and monolithic data centers.

The trend of technology is no longer towards a reliance on hyper-scale. With open source becoming so much more efficient and smaller in its footprint, there is no longer any need to do everything within a few large-scale server farms.

Engineering advancements mean that highly specialized architectures can do all of the heavy lifting locally or in a distributed network of various hardware configurations. There will be no competition between monolithic centers meant for renting proprietary compute cycles and sovereign, local networks run by companies themselves.

Open source

It is no secret that I believe that attempts to tame AI by limiting access to closed models will always boomerang against the interests of the proprietary software developer. Once there is a looming possibility that they may lose access or encounter political restrictions on export, they simply get pushed into going for open models that they will own and control themselves in their jurisdiction.

That is the whole idea of the open source philosophy. The results of scientific work and technical progress cannot be confined to just a few private labs of major corporations. With an open model, everyone can adjust its parameters for their particular use cases.

Applications of open model architecture clearly demonstrate just how much open-model architectures can achieve in terms of adaptation to a particular localized purpose. Closed-model architectures will surely remain temporarily ahead in individual benchmarks, especially in terms of the most complex and experimental functions. However, most companies do not require a Porsche on each and every trip.

What they require is a highly advanced model that is completely optimized for a specific task, which they can always have access to without being dependent on the decisions of a couple of tech leaders.

Whereas AI has the potential to revolutionize all aspects of our collective existence as well as business operations in the world, such a process cannot be controlled by a handful of providers in any way that would be safe. The general market simply wouldn’t tolerate it. It would be too risky for any contemporary company to tie its strategic development path to a single provider.

That is exactly the reason why technology leaders are pouring resources into infrastructure that is neutral and open. We can’t just invest in software and hardware; we have to invest in sovereignty.

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Each year, the Mobile Industry Awards shine a spotlight on the organizations pushing the sector forward. From operators and MVNOs to wholesale providers and technology innovators, the winners and shortlisted companies are recognized for delivering better experiences, stronger services and more innovative approaches to an increasingly competitive market.

What's striking, however, is that many of the qualities celebrated have become almost invisible to customers. In a world where connectivity has become increasingly commoditized, mobile providers are no longer defined by who has the fastest network or the cheapest tariff alone.

Instead, they reflect something far more difficult to see: the operational excellence that underpins every customer interaction.

That raises an interesting question: In 2026, what does "good" actually look like in mobile services?

For years, operators have measured success through familiar metrics. Network availability, coverage, latency and service level agreements remain critical measures of operational performance, but they only tell part of the story.

Today's customers don't experience mobile services through dashboards showing network uptime. They experience them through the outcomes those networks enable - whether they could activate an eSIM without friction before a flight, if changing their tariff took minutes rather than days, whether roaming worked automatically, or if resolving a billing query required multiple conversations with customer support.

The network may be performing flawlessly while the customer walks away frustrated. That's because the definition of "good enough" has fundamentally changed.

Beyond network performance

Reliable connectivity remains essential, but it has become the baseline rather than the differentiator. Across many mature markets, customers now expect consistently high levels of coverage and performance. As networks continue to improve, distinctions based purely on technical capability become harder to perceive. Instead, customer expectations have expanded to encompass every digital interaction surrounding the core mobile service.

Consider the lifecycle of a typical customer. They discover a provider online, complete digital identity verification, activate an eSIM, manage their account through an app, receive personalized offers, adjust their plan as their circumstances change, expect seamless roaming when abroad, contact support when necessary, and increasingly interact through automated digital channels.

Each of these moments contributes to their perception of service quality, but none of them is defined solely by signal strength. This is where traditional performance metrics begin to fall short. An operator can meet every contractual SLA while still delivering a fragmented customer experience if these touchpoints fail to work together consistently.

The industry's challenge, therefore, is no longer simply maintaining network performance, but delivering consistency across hundreds of smaller interactions that collectively shape customer trust.

Complexity has become telecom's defining challenge

Delivering these experiences has become considerably more difficult than many people realize. Today's mobile services are built on highly interconnected ecosystems. Behind what appears to be a simple customer interaction may sit cloud platforms, legacy operational systems, multiple software vendors, wholesale partnerships, application programming interfaces (APIs), AI-powered decision engines, digital engagement platforms, regulatory processes and cybersecurity controls.

Every additional capability creates new opportunities to innovate, but also introduces another layer of operational complexity. The challenge is not that these technologies exist - many have significantly improved what operators can deliver - it’s understanding how they interact.

A seemingly straightforward change to a customer proposition, for example, can have implications across billing systems, customer relationship management platforms, digital applications, compliance processes, provisioning workflows and partner integrations. Organizations are increasingly operating within environments where small changes can create unexpected consequences elsewhere.

It’s not that operators lack capability - they often possess sophisticated technology estates and highly skilled teams. What they often lack is clarity. Without clear visibility across increasingly interconnected systems, it becomes difficult to understand where customer friction originates, which operational processes are introducing delays, or how individual technology decisions affect the overall customer experience.

This is where micromoments come in. The small interactions that, when executed well, build trust and loyalty over time. For a customer, a micromoment might be receiving a relevant offer exactly when they need it, increasing their data allowance instantly while travelling, or having a billing anomaly resolved before it becomes a complaint. These micromoments are the true measure of service quality, and they are only possible when complexity is managed effectively behind the scenes.

The result is that complexity itself becomes the barrier to delivering simplicity for customers.

Complexity behind the scenes

The rapid evolution of cloud-native technologies, automation and AI has transformed what is possible within telecommunications. These innovations have reduced deployment times, accelerated development cycles and opened the door to entirely new business models. They have also encouraged a narrative that launching or modernizing mobile services is becoming increasingly straightforward.

There is truth in this: many technical barriers have undoubtedly been lowered. However, there is an important distinction between making technology easier to deploy and making services easier to operate. Complexity has not disappeared; it has just moved behind the scenes.

Customers rightly expect effortless digital experiences, but delivering those experiences requires sophisticated orchestration across systems that continue to grow in scale and diversity. The providers achieving the best customer outcomes are not those avoiding complexity altogether; they are those managing it effectively enough that customers never notice it exists.

This is where modern BSS and MVNE platforms have an increasingly important role to play. The objective is not to eliminate complexity, but to manage and abstract it so that customers experience simplicity, even when the underlying operational environment is becoming more complex. By creating better coordination between systems, processes and partners, operators can deliver more consistent experiences without exposing customers to the complexity behind the scenes.

Operational excellence is rarely visible when everything works as intended. But the lack of it becomes apparent when organizations struggle to introduce new products, resolve customer issues quickly, adapt to changing regulation or respond confidently to evolving market demands.

In many respects, successful mobile services are defined less by the absence of complexity than by an organization's ability to absorb it without passing it on to customers.

The new competitive advantage

As connectivity continues to mature, competition is shifting. Coverage and performance still matter, and price will always influence customer choice. Yet increasingly, these are becoming expected rather than exceptional.

Differentiation now comes from an organization's ability to evolve rapidly while maintaining a consistently high-quality customer experience. That means introducing new services faster, responding more effectively to market changes, integrating emerging technologies without disrupting existing operations, maintaining compliance across increasingly complex regulatory environments and giving customers confidence that every interaction will be straightforward.

In other words, operational agility is becoming just as important as network capability. It also changes how organizations should think about investment. Rather than asking only whether infrastructure can support future services, operators must increasingly ask whether their operational architecture allows them to deliver those services consistently across every customer touchpoint.

Competitive advantage is increasingly linked not to the size of a technology estate or the number of digital capabilities an organization has, but to how effectively those components work together to create a coherent customer experience. That requires visibility, governance and a deep understanding of complexity - not because complexity is valuable in itself, but because managing it well enables simplicity for customers.

We have seen this in practice with operators that have expanded their role beyond connectivity alone by embedding mobile services into broader ecosystems of everyday value, such as retail benefits, partnerships and lifestyle services.

Customers are more likely to engage with brands that are present in more moments of their daily lives. Differentiation comes from creating value that customers cannot easily find elsewhere and building stronger relationships beyond the mobile service itself.

Redefining what "good" looks like

The mobile industry has spent decades asking whether networks are good enough. Today, the more relevant question is whether the services built on top of those networks are.

As customer expectations continue to rise, quality can no longer be measured solely through traditional operational metrics. It must also be judged by how seamlessly organizations bring together the many systems, partners and processes that sit behind every interaction.

Customers are not consciously evaluating network architectures or operational models. They are judging the experience in front of them. They notice whether joining is easy, whether changes happen instantly, whether support feels connected, whether digital journeys are intuitive and whether the service simply works when they need it.

The providers that stand out in the years ahead will therefore be those that recognize that "good enough" is no longer defined by any single metric. It is defined by the countless moments in which operational complexity either reaches the customer or, through careful design and disciplined execution, remains completely invisible.

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As governments around the world seek to regulate artificial intelligence (AI), they are creating a patchwork of laws and policy frameworks that organizations operating across multiple jurisdictions must be aligned to – and understand.

In the European Union, for example, the AI Act – described as the “first-ever legal framework on AI” – takes a risk-based approach as part of a bid to “foster trustworthy AI in Europe”.

While in the US, the federal government has taken an altogether different stance. In March 2026, the White House published its National Policy Artificial Intelligence (AI) Framework, which, among other things, prioritizes deregulation in favor of innovation.

But the picture is becoming increasingly complex at state level. In July 2026, Illinois signed one of the country’s most comprehensive AI safety laws, requiring large AI developers to introduce transparency frameworks, independent third-party audits and formal risk mitigation measures. The legislation follows similar moves in California and New York, adding further momentum to a growing patchwork of state-level governance.

The divergence is not limited to these powerhouses on either side of the Atlantic. According to a recent briefing note published by the House of Commons Library, the UK “does not have any AI-specific regulation or legislation covering AI as a technology”.

Instead, it reports that AI is “regulated in the context in which it is used, through existing legal frameworks, such as financial services legislation”.

A global response to AI

Recognizing the current direction of travel, in July 2026, the United Nations convened its first Global Dialogue on AI Governance, arguing that international cooperation is essential in a world where AI systems, data and economic impacts routinely cross national borders.

“Artificial intelligence is reshaping economies, societies, and daily life,” it said. “Its opportunities are real. So are its risks.

“No country can address either alone. The AI Dialogue exists to ensure that governance reflects the priorities of all nations, not just the most technologically advanced and that the benefits of AI are shared by all,” it said.

So, while policymakers continue to debate the shape of future governance, it begs the question: how can enterprises continue to innovate and evolve when there is so much regulatory uncertainty?

Stop thinking about governance as compliance

For me, the answer is to take a more pragmatic approach. That means first understanding that governance is not the same as compliance. And second, that organizations need to stop treating it simply as a response to regulation.

Instead, business leaders need to understand that governance establishes a framework for who is accountable for AI, how decisions are made, how risks are managed and how systems are monitored over time.

While it’s true that specific regulations may differ from one jurisdiction to another, the fundamental principles that will deliver ‘good AI’ remain remarkably consistent.

Think about it for a moment. How often do AI projects begin with the same set of questions? Is this risky? Who owns it? Who signs it off? What data can we use? How will it be monitored? What happens if something goes wrong?

When you’re faced with a barrage of uncertainty, is it any wonder that projects stall even before they start?

But with the right governance in place, many of these questions have already been answered. Instead of repeatedly reinventing the wheel and debating the same issues, organizations can focus on the task at hand.

This is what good governance looks like. And it’s why it has the ability to remove friction, speed up decision-making and allow organizations to move faster.

The trust advantage

At an operational level, it gives teams an agreed starting point and set of rules. But the benefits extend beyond the organization itself.

Sharing these frameworks with customers, regulators, investors and other stakeholders is a sure sign that governance has been built in from the ground up rather than bolted on as an afterthought.

In doing so, organizations can show that they are taking accountability, oversight and risk seriously. And in an era where confidence in AI remains fragile, that trust can become a competitive advantage.

For anyone looking to implement governance, there are five key areas you need to establish.

Establish clear ownership: Every AI initiative should have a clearly defined owner – someone who is responsible not only for deployment but also for its ongoing governance.

Put oversight mechanisms in place: Organizations need processes to monitor, challenge and review AI systems, particularly as they become more autonomous.

Define your risk appetite: Not every AI application carries the same level of risk. That’s why organizations need to establish clear criteria for assessing use cases before deployment.

Create accountability from the outset: Build accountability from day one. Teams should know who makes decisions, who approves deployments and who owns the outcome.

Review and adapt continuously: AI governance is not a one-off exercise. Review your framework regularly as technologies, regulations and risks evolve.

While each of these has its place on the AI governance leaderboard, if I were pushed to say which is the most important, I would have to opt for clear ownership. Why? Because without clear accountability, governance risks becoming everyone's concern but nobody's responsibility. And when that happens, it can lead to paralysis.

As I said at the beginning, while policymakers may still be figuring out legislation, there is nothing to stop organizations from adopting good governance. In fact, regulatory uncertainty makes good governance more important, not less.

The organizations that establish the right structures, processes and accountability now will be best placed to adapt as the regulatory landscape continues to evolve.

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The computing industry was once unrecognizable from the one we see today, with the idea of discipline centered around writing code treated as some kind of fantasy. That was before industry pioneer Margaret Hamilton coined the term while leading the flight software team for NASA's Apollo space program in the 1960s.

Pioneering an industry

Hamilton was describing the process by which she coined the term "software engineering" and, by doing so, defined an entire computer science discipline, in an interview.

Rather than an interview for any media outlet, the transcript for this interview appeared in the first edition of the computer science textbook 'Fluency With Information Technology: Skills, Concepts, & Capabilities' by Lawrence Snyder and Ray Laura Henry.

She recalled during this conversation a particularly memorable moment in which a highly respected hardware guru explained to everyone in the meeting that the process of building software should also be considered an engineering discipline – just like hardware. That's because she and her colleagues had earned the respect of others in the room as being in an engineering field in their own right.

Teething issues

Before Hamilton coined the term and defined many of the processes and best practices that drove the software achievements at NASA during the Apollo mission, the approach to computing was in a state of disarray.

People, at the time, valued physical hardware and the components fitted underneath far more than the code that nobody could see. Programming, in a sense, wasn't seen as a real science until Hamilton came along.

During her time at NASA, she created vital on-board flight software, with a particular highlight being her error-detection-and-recovery system. Her contributions were immortalized in the 2022 short film 'To Go to the Moon', in which she was portrayed by Dominque Roberts.

This attitude led to a software crisis in the 1960s through the 1980s, with many projects running over budget and schedule and with the software that was published containing critical bugs, with costly consequences. This was the direct impact of a lack of professionalization in the field.

For years, the PC was a tool that waited for instructions. Even the most advanced software still needed someone to open it, tell it what to do and check the result. AI agents are beginning to change this model.

Rather than simply responding to prompts, AI agents can help users pursue goals across multiple steps. They retain awareness of previous activity and use approved tools and automate portions of workflows within organizational guardrails.

This shift is changing the way organizations think about PCs. For years, the buying criteria were straightforward: performance, reliability, security and cost. The question was whether a device could run the software employees needed.

Now, organizations also need to consider whether a device can support employees working alongside AI agents. That makes a fleet refresh about more than specifications alone.

The PC is no longer just a tool

That collaboration changes the role a device plays in the working day. Traditionally, a PC has been a tool that waits for instructions, responding when an employee opens an application, enters information or starts a task.

AI agents create the opportunity for a more continuous and proactive working relationship, where the device can help employees navigate workflows, surface relevant information and connect work across activities.

Today, that might mean preparing for meetings or helping draft reports. Tomorrow, it could mean helping a project team maintain a shared understanding of a complex program over months, identifying risks, tracking commitments and surfacing relevant information before someone even thinks to search for it.

In that sense, the PC becomes more than a gateway to applications; it’s a platform that helps people navigate their working day by connecting information, insight and actions across tasks.

Delivering that experience requires more than running a browser and a spreadsheet. Agents need to maintain the thread of work across tasks, access information securely and support multiple AI-driven processes without compromising performance or governance.

That creates a new set of requirements and makes fleet decisions more strategic than they have traditionally been. If the infrastructure is not ready, organizations risk limiting both the effectiveness of the agent and employees' trust in it.

Why where AI runs matters

As AI becomes a larger part of everyday work, organizations are paying closer attention to where processing takes place. Advances in PC hardware mean more AI processing can take place locally on the device, which is particularly important given AI agents will likely need to operate continuously and handle sensitive information.

This is particularly important in regulated sectors. A financial services firm reconciling client data or a hospital summarizing patient records needs confidence in how data is processed, stored and governed. Running more AI workloads locally can provide organizations with greater flexibility in how they meet those requirements.

That brings the conversation back to the role of the PC. If organizations want employees to work effectively alongside AI agents, devices need to support AI experiences securely, connect activities across workflows and deliver those capabilities in a way that aligns with organizational governance requirements.

Technology is only part of the answer

Employees are ready for this shift too. Research has identified a “transformation paradox” in which employees are often more ready to work with AI tools than the organizations around them are prepared to support.

Only 30% of AI-using UK employees believe their organization's leadership is clearly and consistently aligned on AI strategy, while 13% say they are actively rewarded for redesigning how they work with AI. The result is a growing gap between what people can do with AI and what organizational structures, incentives and processes are designed to support.

Hardware alone will not close that gap, but it is one of the more straightforward parts of the problem to solve. Fleets that aren’t ready to run agents locally, securely and consistently simply add friction on top of the cultural and leadership challenges organizations already face.

As AI agents become a more familiar part of the working day, the role of the PC is changing. For decades, devices were primarily judged on their ability to run applications efficiently. Increasingly, they will be judged on how effectively they help people work with AI. In that environment, the PC becomes more than a gateway to applications; it becomes a platform for connecting information, intent and action.

Organizations that recognize that shift early will be best placed to capture the opportunities AI creates.

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There are more than 70,000 AI companies operating today.

Most of them will not exist in five years.

Before you can see why — or figure out whether yours is one of them — you need a distinction the market keeps blurring.

Strip away the pitch decks and there are really only two types of AI system being built today.

The first is AI infrastructure: the orchestration and governance technology that makes AI usable at scale. In plain terms, this is the plumbing — agent frameworks, model routing, evaluation and monitoring tools, guardrails, and the controls that let a large organization use AI safely.

It sits between the foundation models and the end user, and it is where an enormous amount of venture money is going right now.

The second is the surface application: the tool an actual person uses to do actual work. The underwriting assistant, the contract reviewer, the sales copilot. The thing with a login screen and a job to do.

What I see in the market is a blending of the two. Some firms are selling architecture.

Some are selling tools. Many are trying to sell both, on the theory that owning the whole stack is the safest position.

And while this market is filled with tremendous exuberance with seemingly everyone starting an AI company, I am very skeptical that many of these firms will ever see profitability as history offers a strong counter.

We've run this experiment twice.

The past and the future

The dot-com era ran the first version of this experiment, and its final tally is worth stating plainly. Researchers estimate that roughly 50,000 startups were founded in the United States between 1998 and 2002 to commercialize the internet.

Of those, something like 8,000 attracted venture funding. About 1,700 internet-related companies made it to an IPO across the whole era — 585 in 1999 and 2000 alone — and at the peak, only about 14 percent of the tech companies going public were profitable.

By late 2002, most internet stocks had lost more than three-quarters of their value and roughly 1.7 trillion dollars had been wiped out. And the number of enduring, large-scale winners from that entire cohort — Amazon, eBay, Priceline, Expedia — you can count on two hands. Run the funnel: 50,000 founded, 8,000 funded, 1,700 public, fewer than ten giants.

A real gold rush works the same way: a few strike it rich, some make a living, and most go home with less than they brought. This is important to remember for everything that follows.

If that funnel looks like a quirk of one bubble, it is not — it is how markets distribute winnings everywhere. Hendrik Bessembinder at Arizona State studied every U.S. stock since 1926, more than 25,000 companies, and found that the best-performing 4 percent account for all of the net wealth the stock market has ever created; the other 96 percent, taken together, did no better than Treasury bills.

Just 90 companies — a third of one percent — produced more than half of it, and the majority of stocks lost money outright over their lifetimes. The market wins; almost no individual company does. Keep that in mind every time someone tells you AI will create trillions in value. It will. That says nothing about whether any particular company captures a dime of it.

The example of cloud

Cloud computing is the sharper rerun. In the early days there were hundreds of cloud providers and a thriving ecosystem of middleware companies selling the connective tissue — provisioning tools, management layers, monitoring platforms.

Today three companies control roughly two-thirds of the cloud market, and their share grows every year.

And here is the part that matters for AI: the middleware layer did not consolidate alongside the platforms. It was absorbed by them. The hyperscalers built the management consoles, the monitoring, the orchestration, and shipped it as a feature. The companies whose entire business was cloud plumbing were acquired cheap or squeezed out.

Meanwhile, the application layer on top of that consolidated infrastructure exploded. Thousands of SaaS companies built durable, profitable businesses without owning a single server. The bottom of the stack ended up in a few hands. The top produced thousands of winners.

It has already happened once inside this stack

If cloud feels like ancient history, look at the data layer — the foundation every AI system sits on. That consolidation already occurred, and it finished recently. The "modern data stack" boom of the last decade funded hundreds of startups selling pipelines, catalogs, transformation tools, and warehouses.

Today the independent tier has settled to exactly two companies at scale: Snowflake and Databricks, each running at roughly five billion dollars in annual revenue, with the hyperscalers’ native offerings holding most of the rest of the market. Nearly everyone else was acquired, absorbed as a platform feature, or left scraping for the remainder.

And notice the shape it settled into. The top five data platforms — Snowflake, BigQuery, Redshift, Databricks, and Microsoft’s offering — hold roughly two-thirds of the market. That is almost exactly where cloud landed: three players, about two-thirds of the market, a long tail fighting over the rest.

Two different layers, a decade apart, ending in the same proportions. That is not a coincidence. It is what happens when competing takes huge capital and the platforms can build whatever sits next to them. Expect the AI orchestration layer to end up the same way.

The consolidation was driven as much by the buyer as by the vendors. Large enterprises learned that scattered data is expensive data: every additional platform meant another copy of the truth, another integration, another security review, another contract.

So CTOs stopped buying data tools one team at a time and started making strategic platform decisions — pick one or two providers, consolidate the estate onto them, and hold that line. A single source of truth became an explicit architectural goal at most large companies, and once thousands of enterprises were making that same decision, the market had no room left for a long tail of vendors.

Look at what it took for Snowflake and Databricks to survive that consolidation: enormous capital, the fact that customers’ data lives on their platforms and is costly to move, and deep ties into how their customers work every day. You can survive as an independent alongside the hyperscalers — but only by becoming one of the few names a CTO puts on the strategic list, and almost nobody makes that list.

The same consolidation is coming for AI

Apply that pattern to the two types of AI company and the forecast writes itself.

The infrastructure layer — orchestration and governance — will consolidate down to a few. Not because the current tools are bad, but because this layer sits directly in the expansion path of the biggest players in technology. The model providers and hyperscalers have every incentive to build orchestration, evaluation, and governance into their platforms, and they are already doing it. Every capability that today justifies a standalone infrastructure startup is a roadmap item at a company with a hundred times the resources and a direct line to the same customers.

If you are building an architecture-only solution, this is the uncomfortable implication: you are likely to be taken out by one of the big players. Maybe you get acquired, if you are early and lucky. More often, the platform simply builds what you sell and includes it for free. Either way, orchestration and governance alone is not a business you can hold. The only real question is how long you have.

Which leaves the application layer as the open field. And this is the counterintuitive part: infrastructure consolidation is good news for application builders. When orchestration and governance become cheap, standardized, and built into the platforms, the cost of building a serious AI application collapses — just as commodity cloud ignited the SaaS boom. We are already seeing a massive increase in the number of AI applications getting built, and most will likely not survive.

Better software, worse odds

Part of what makes this cycle different is how little it costs to enter. Building serious software used to take millions in capital and a room full of engineers — a filter that limited how many companies could even try. Today a handful of people with AI tools can ship in weeks what took a funded startup a year.

So new ventures are multiplying, not because there are more good ideas, but because the cost of trying has collapsed. The scale tells the story: more than 70,000 AI companies operate globally today, roughly 18,000 to 30,000 of them in the United States alone.

The comparison to the dot-com era’s 50,000 is not perfectly apples to apples — that was a five-year founding total for one country, this is a snapshot of companies operating worldwide right now — but the order of magnitude is the same, this wave is global, and the count is still climbing.

Here is the twist that makes the coming shakeout more brutal, not less: the software being built is genuinely good. This is not the dot-com era, where half-finished products hid behind splashy marketing. The tools are now so powerful that quality is the baseline — which means quality has stopped differentiating anything. When every product is polished, capable, and shipped fast, none of that separates you from the next founder who did the same thing last month.

And that is precisely why so few founders see the danger. Every one of them genuinely believes they are building something singular — and by their own measure, they are right. They compare their product to what came before: the clunky incumbent, the manual process, the way the work used to get done. Against that benchmark it looks revolutionary.

What they never compare it to is the tens of thousands of other teams looking at the same models and the same problems, building virtually the same thing at the same time. Measured against the past, every AI product is remarkable. Measured against the field, almost none are. More entrants than either previous cycle, all building excellent software, almost none of it distinguishable.

That is the setup for the largest culling yet, and it will run almost entirely on the moats, because there is nothing else left to separate the winners from the losers.

The delusion of special

I see this up close. I have this conversation with application founders every week, and it always goes the same way. They believe the quality of what they built is their moat: the product works, customers love it, nothing else on the market feels as good.

All of that can be true, and none of it protects them. Quality can be copied. The same tools that let them build an excellent product in months let a competitor build one in weeks. A few founders have built something that truly stands alone, but I just can’t see many finding a way to real profitability.

There will be some winners, but I think they will need to rest on three key differentiators:

1. Data. Not data you scraped or licensed — proprietary data your business generates by operating: claims histories, transaction flows, patient outcomes. If your system gets smarter from data competitors cannot obtain at any price, you compound. If you are building on the same public internet as everyone else, you do not.

2. Distribution. If you already own the customer relationship — an installed base, a trusted brand, an embedded sales channel — you can put an AI product in front of buyers faster and cheaper than any startup. This is why incumbents are more dangerous in this cycle than the last one. The startup has to build the product and buy the audience. The incumbent only has to build the product.

3. Integration into workflows. The one people underestimate. Companies that wire themselves into how work actually gets done — the approvals, the systems of record, the daily habits of thousands of employees — become painful to remove even when a rival ships something better. Switching costs are not glamorous, but they have protected enterprise software for thirty years, and they will protect AI applications too.

Have one of these and you can build a durable business on commodity infrastructure. Have two and you can build a great one. Have none and you are likely running out of time.

Your toughest competitor is your customer

And here is what makes the application layer even harder than the dot-com or SaaS eras: surface applications are not just competing with other vendors. They are competing with the companies they are trying to sell to. The same commodity infrastructure that makes it easy for a startup to spin up an AI application makes it just as easy for the buyer to build one internally.

Every enterprise pitch now runs into a question that barely existed in the SaaS era: why would we buy this when a small internal team could build it in a quarter?

And here is the uncomfortable part. The three advantages that decide the application winners — distribution, proprietary data, embedded workflows — are precisely what the buyer already has. The enterprise owns its data. It is its own distribution. It controls its own workflows. The customer starts the build-versus-buy conversation holding every moat you are trying to claim.

A surface application does not just need to be better than its competitors. It needs to be so much better than what the customer could build themselves that buying beats owning — and that bar rises every time the underlying infrastructure gets easier to use.

Know which company you are

I am not going to pretend to know which specific firms win. But the structure of the outcome is already visible, because we have now watched it three times — dot-com, cloud, and the data layer: infrastructure consolidates to a few, applications proliferate, and the survivors are the ones holding data, distribution, or workflow integration that cannot be copied.

So the first question is not "is my product good?" It is "which of the two companies am I?" If you are infrastructure, your realistic endgame is being bought or being bypassed — plan accordingly. If you are an application, the model is not your moat and the product probably is not either.

So what is?

The good news, and who gets it

One clarification before closing, because everything above can read as pessimism about AI itself. It is the opposite. The technology will create enormous value, and the markets built on it will grow. The open question is who keeps that value, and a century of evidence gives a consistent answer: mostly the consumers of a technology, not its producers.

William Nordhaus at Yale measured this across decades of American innovation and found that producers capture only about 2 percent of the total value their innovations create — the rest flows to the people and businesses that use them. Railroads transformed the economy and ruined most of their investors. Airlines moved the world and destroyed capital for a hundred years. The internet made a handful of platforms rich — and made every company that deployed it more productive.

This cycle is already tracing the same shape: the infrastructure layer consolidates, prices its scarcity, and books historic profits, while the application layer competes and hands its margin to the buyer.

That is the real ending of this story. The coming massacre of AI companies and the coming growth of the AI economy are the same event, seen from opposite sides of the table. If you sell AI, the funnel is your problem and the moats are your only defense.

If you buy AI, the competition among 70,000 firms is working precisely in your favor: every improvement, every price cut, every copied feature moves value from their side of the table to yours. The bad news in this article is only bad depending on which chair you sit in.

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AI-generated content and increasingly fragmented communications are changing how organizations assess digital evidence. For investigators, establishing authenticity is becoming as important as analysing the evidence itself.

For years, digital evidence carried an assumption of authenticity. A document was presumed genuine unless there was reason to believe otherwise.

A screenshot reflected a conversation. A photograph captured a moment in time.

But that assumption is becoming harder to sustain.

Advances in artificial intelligence and editing tools have transformed how digital content is created and shared. At the same time, investigations increasingly rely on evidence gathered from messaging platforms, cloud services and personal devices.

Together, these developments have made questions of authenticity far more prominent.

Before evidence can be relied upon, investigators need to understand where it came from, how it was obtained and whether it accurately reflects the underlying source material.

The end of assumed authenticity

Digital evidence remains central to most investigations. Documents help establish timelines and decision-making. Communications provide insight into intent and behavior. Yet both can present challenges that are not immediately visible.

A document may appear final and authoritative while representing only one version of a longer history. Earlier drafts may be unavailable, edits may leave few traces, and supporting material may never be disclosed.

Communications create similar difficulties. Screenshots and message extracts can appear highly persuasive because they seem direct and contemporaneous. Yet they often lack context. Messages may be missing, conversations may be incomplete, and edits or deletions may not be visible.

In some circumstances, entire exchanges can be fabricated before they are ever disclosed. The issue is not that digital evidence has become inherently unreliable. Rather, reliability cannot be assumed simply because something looks convincing.

Why screenshots deserve scrutiny

Screenshots are among the most commonly encountered forms of digital evidence, particularly in workplace investigations and disputes.

Consider two screenshots of what appears to be the same WhatsApp conversation. Both display realistic timestamps, the same participants and a seemingly authentic exchange. Yet the content differs in a crucial respect: one version appears to confirm a contractual agreement, while the other suggests different terms altogether.

To a casual observer, both screenshots may appear equally credible. For investigators, however, the image itself is only part of the story.

Questions about when the screenshot was created, who captured it and whether it can be traced back to the original data source are often more important than the image itself. Without access to the underlying message database, device or backup, it can be difficult to establish with confidence whether a screenshot accurately reflects the original conversation.

This is why screenshots are rarely treated as definitive evidence in isolation. Their value often depends on whether they can be corroborated by the underlying source data.

Deepfakes and synthetic content

The rise of generative AI has added another layer of complexity. Manipulated images and videos are not new, but the tools needed to create them have become far more accessible. Content that once required specialist skills can now be produced in minutes.

While sophisticated deepfakes remain relatively uncommon in most corporate investigations, their existence has changed expectations around digital evidence. Authenticity can no longer be taken for granted.

As a result, organizations increasingly need processes that establish whether content is genuine before it is used to support important decisions.

Testing evidence, not accepting it

In response, investigators are placing greater emphasis on validating evidence before drawing conclusions. The starting point is often provenance: understanding how material was obtained and whether it came directly from a source system. Evidence collected from original sources will generally carry more weight than material provided selectively by individuals.

Investigators also look for signs that content may have been altered. Metadata, file properties and technical inconsistencies can sometimes reveal issues that are not visible at first glance.

Just as important is understanding what may be missing. A message thread viewed in isolation can tell a very different story from the same conversation viewed in full.

These questions help establish how much confidence can reasonably be placed in a particular piece of evidence.

Confidence matters

Digital evidence rarely falls neatly into categories of authentic or inauthentic. More often, investigators are assessing levels of confidence.

A complete WhatsApp conversation extracted directly from a device and verified through forensic analysis will naturally carry more weight than a handful of screenshots supplied by one individual. Both may support the same conclusion, but the strength of the underlying evidence is different.

This distinction is important because investigations often involve decisions being made before every uncertainty has been resolved. Understanding the reliability of the available evidence helps organizations make those decisions on a firmer footing.

Trust in a digital world

Digital evidence remains one of the most valuable sources of information available to investigators. What has changed is the level of scrutiny required before that evidence can be relied upon.

Documents, messages and media files continue to play a central role in establishing facts and understanding events. However, appearance alone is no longer a reliable indicator of authenticity.

As digital content becomes easier to manipulate and generate, investigators need to pay closer attention to where evidence originated and how it can be verified.

In a world where convincing content can be created with increasing ease, trust is becoming a central part of the investigative process.

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Among the giants of the digital world, hyperscalers like Amazon, Google, Meta and Microsoft will collectively allocate more than $500 billion in capital expenditure to AI infrastructure this year.

When a handful of players invest upwards of half a trillion dollars in data centers, primarily to support AI, that tells us they see a ton of demand.

We can say with total certainty that they have every intention of recouping their investment (and then some).

Indeed, the voracious demand expected from enterprises for AI capability is what has made hyperscalers, frontier labs and GPU makers darlings of the stock market.

However, their anticipated revenues are the future cash flows of companies, including those in industrial sectors like manufacturing, energy and transportation.

The Cost Shift We’re Not Talking About Enough

Up until recently, much of the discussion around AI costs has focused on training large language models. The cost of training just one OpenAI model, GPT-4, for example, is said to have been north of $100 million.

That’s a big number. It’s also largely irrelevant to the industrial organizations that will never train a frontier model themselves.

Inference, the process of applying models in live operating environments to generate recommendations, support decisions and take actions, is a different matter, however. Yet it’s largely absent from most boardroom conversations.

Unlike training, which mostly represents a single upfront investment, inference tasks to support decisions and operational execution run continuously.

As companies shift from experimentation to large-scale deployments of AI across the value chain, inference will soar as a share of workloads and IT budgets.

Tokenomics Take Root

Most businesses do not understand the true cost of AI from a financial point of view. Industry needs a new economic calculus: “tokenomics.”

Tokens—the units through which generative AI systems process and generate information—are the standard mechanism to measure and charge for AI consumption. They’re like the kilowatt-hours for which electric utilities charge their customers.

An AI agent that performs tasks across large operational datasets consumes lots of compute (and storage, networking, electricity, water and more), and the total bill reflects that increased usage.

As companies move more and more to agentic systems, these costs will mount quickly. Agentic workflows require several model calls for each task, involve large context windows and need multiple reasoning loops. By their nature, they are many times more compute-intensive than the generative AI tools most enterprises have used so far.

A Familiar Pattern

Recent research suggests the AI inference market will more than double over the next five years.

Many of us witnessed a similar pattern earlier in our careers during the move to the cloud. In the 2010s when cloud computing was reaching scale, the same economic phenomenon was at play: unit costs dropped sharply while total usage skyrocketed.

The result was, contrary to many expectations, significantly increased total IT spending. There were many business benefits associated with cloud, but it didn’t mean that total costs declined.

The coffers of hyperscale cloud providers swelled. Those industrial enterprises that did not accurately project cloud consumption curves were caught off guard. Today, tokenomics is proceeding on a similar trajectory, only faster.

By now, CFOs understand well the economics of cloud computing and how to optimize the variable cost structures they represent. A similar level of budgetary scrutiny is appropriate for AI.

Token prices are declining as a result of intense rivalry and efficiency improvements. Open-source frameworks, custom silicon and innovative development methods are driving down model costs rapidly, especially in China.

However, as companies look to integrate AI into mainstream industrial operations, plant floors and logistics networks, the absolute number of tokens generated will far outstrip gains in per-unit costs. What looks like a bargain on a per-transaction basis today will look very different as industry marches towards greater autonomy.

A Pragmatic‍‌‍‍‌ AI Approach

Moreover, data-rich environments like industrial plants are sensitive to latency in ways that consumer and enterprise applications are not. Continuous inference over a network of sensors, industrial equipment and control systems requires a very different kind of computation than running a chatbot. The data volumes are different. The infrastructure requirements are different. And the economics are different.

This should not be taken as an argument against the deployment of AI in industrial operations. On the contrary, with a pragmatic mindset about AI’s costs, we can harness the exciting innovations AI introduces and enable all-new use cases.

The good news, though, is that tokenization does not have to mean runaway consumption. Where models are invoked, how often they run, what context they draw on and how intelligence is distributed across edge, cloud and hybrid environments all affect cost.

Importantly, AI in industry is not monolithic. Lighter-weight models, edge-based processing and other techniques will continue to improve the efficiency of AI.

Tried-and-true AI-based predictive maintenance, computer vision and process optimization solutions all provide clear illustrations of mature, winning business cases. Generative, agentic, physical and multimodal AI, however, herald something different and more complex financially.

The upside of AI in industry is significant. So are the potential costs.

They should be accounted for on the same spreadsheet.

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The European Commission has just moved to classify Amazon's and Microsoft's cloud businesses, AWS and Microsoft Azure, as "gatekeepers" under the Digital Markets Act. The designation is reserved for platforms with significant market power, even though neither meet the usual size thresholds for that label.

Regulators pointed instead to entrenched market positions, steep switching costs, and the growing weight of AI tools and partnerships in locking customers in.

This is not only a cloud computing issue. It reflects a broader shift in how the world’s largest technology companies exert influence across digital life. Their power increasingly comes not just from attracting large audiences, but from controlling the infrastructure, platforms and distribution systems on which those audiences and other businesses depend.

The same pattern is visible across the media landscape. YouTube, Google, Instagram, Facebook and Amazon rank among the most influential companies across mainstream and social media. Their lead is not simply a product of popularity. It is reinforced by their control of the channels through which information is created, distributed and discovered.

No room for new players

There's a clear divide between this top tier and everyone else, and right now nothing sits in between. The handful of brands that come closest are themselves long-established, globally dominant companies, not new entrants nearing a breakthrough, and even they trail well behind.

This suggests the market is characterized less by active competition and more by a settled hierarchy of platforms. That line separates an established class of technology platforms from the rest of the market, including some of the world's other largest companies.

Perplexity AI is the one worth watching. Its sentiment across social and traditional media already beats Google, TikTok, and Apple, carried by a chief executive who's a near-constant presence in debates about AI and search. However, its influence remains concentrated within specialist and tech communities rather than broader mainstream media.

Keep growing at this pace, and assuming other media issues don't change opinion, it could close much of the gap within a couple years. For now, every one of these platforms sits a long way outside the established top tier.

The self-reinforcing lead

McKinsey found that Generative AI was the primary source of insight for 44% of its users last year. That shifts even more influence to the companies that either build the AI systems shaping discovery, or supply the content those systems draw on.

A similar dynamic is emerging beyond Big Tech. Among leading brands, those appearing most consistently in AI-generated answers aren't necessarily those dominating current media coverage, but those most deeply embedded in AI training data.

Once a brand becomes part of an AI model's underlying knowledge, visibility starts to reinforce itself. Discovery increasingly favors what AI already knows, making it progressively harder for challengers to break through.

Regulatory pressure hasn't opened much of a door either, at least not yet: Apple and Meta have faced significant Digital Markets Act penalties, and several of the biggest platforms that carry below-average sentiment are weighed down by antitrust cases.

Those moves acknowledge how concentrated digital power has become, but they remain reactive rather than having any real redistribution of influence. The influence rankings barely shift because the underlying advantages - audience scale, data, ecosystems and AI integration - remain intact.

What would it take to shift the balance?

The evidence points to a bigger shift than simply market concentration. The platforms at the top aren't just attracting the largest audiences; they're increasingly shaping the infrastructure through which digital services are built and discovered.

AWS and Azure underpin huge parts of the internet. Google, YouTube and ChatGPT increasingly influence how information is surfaced. Apple's and Google's mobile ecosystems determine distribution.

These layers of infrastructure create reinforcing advantages across cloud, search, and device ecosystems. Those advantages reinforce one another in ways that are difficult for standalone competitors - including Reddit, Perplexity AI, Bluesky, Snapchat, and Quora - to replicate.

That's why there are so few credible alternatives today. Perplexity may continue closing the gap in AI search, and regulators may succeed in limiting some anti-competitive behavior. But neither addresses the broader reality that influence has become cumulative across multiple parts of the technology stack.

Until that changes, the question isn't whether Big Tech faces competition, it clearly does. It's whether any competitor can challenge the system of advantages that keeps the current leaders at the top.

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With all the buzz around nation-state threats, it’s easy for organizations to focus on threats outside the business – and forget about risks that can spiral outwards from within.

Whilst GenAI tools have introduced undeniable efficiencies for employees, these platforms have also introduced a new class of risk: two in three UK organizations admit they can’t track whether employees are sharing data via approved tools.

Most of the time, employees aren’t sharing sensitive data because they have malicious intentions. They are uploading sensitive information – like contracts, client proposals or supplier agreements to models like ChatGPT and Claude to save time on routine tasks.

Almost all (93%) of CEOs across the globe have adopted generative AI to some extent in the past 12 months (PwC). What’s concerning is that much of this activity is happening without any oversight, in the browser – meaning organizations are failing to track the flow of company information, including when and where it’s uploaded.

This is spiraling into serious risk for businesses.

First, because employees may inadvertently share credentials or other access details with public LLMs, which could result in unauthorized access if the model is compromised.

Second, uploading personal data to LLMs can trigger compliance breaches with laws like GDPR and the Data Use and Access Act – resulting in costly fines as well as reputational damage.

To take control of this issue, leaders will need to implement tools and technologies that provide visibility and control over usage at both the browser and the application levels.

The incentive problem

Employees don’t need more mandatory cybersecurity training – the problem is incentive. Many company-owned gated LLMs are still in the pilot stage, falling short of the speed and precision offered by public alternatives.

While the majority of employees understand the risks, 35% of UK businesses admit data sharing through external tools takes place – indicating many would rather ‘throw caution to the wind’ than waste valuable time using slower tools.

But the risks of this behavior – particularly in highly regulated sectors like financial services, could mean unsanctioned LLMs become 'hidden icebergs’ in an organization. Concealed, but capable of causing catastrophic damage upon impact – like inadvertently exposing customer transaction histories or credit scores.

Part of curbing Shadow AI use in the enterprise therefore starts with designing approved AI tools that integrate easily with existing platforms (for example, Microsoft 365 and Google Workspace). These tools should be continuously improved based on user feedback, ideally avoiding excessive restrictions that make the tool frustrating to use.

But the fact is, nearly two-thirds of organizations are currently stuck in the pilot stage when it comes to their AI initiatives and haven’t started to scale across the enterprise (McKinsey). So, what can organizations do today to gain control of the Shadow AI problem?

The solution – tools to bring unsanctioned AI usage under control

You can’t control what you can’t see, which is why organizations need a real-time view of who, or what, is accessing what data, from which devices, and where it’s being shared.

Next-gen identity security platforms can help organizations to gain an immediate understanding of how employees interact with consumer AI tools like ChatGPT, Claude, and Gemini, tracking interaction frequency and monitoring document uploads.

Once high-risk behavior is identified, organizations can then automate corrective actions, redirect users to secure AI alternatives, or prompt users to justify their business use case before proceeding.

Visibility will become even more important with the emergence of ‘nested’ agents. In this scenario, employees might believe they’re only interacting with a single AI agent, but that chatbot may delegate tasks to multiple underlying agents. The organization has no visibility into how many downstream agents or services its information is being shared with.

An identity security tool makes these identities ‘discoverable’ via a real-time ‘agent ledger’. This ledger acts as a complete, unchangeable trace of all agent activities and interactions. It also applies controls to each agent in the database.

Only the absolute minimum privilege required for a task is granted, at the exact moment it is needed, and for the shortest possible duration. In this way, agent permissions don’t automatically ‘cascade’. If an agent wants to connect with another agent, it must be verified by the system first.

Closing the visibility gap

Shadow AI is more than a tooling problem: it’s an identity problem. Organizations can close the ‘visibility gap’ by using tools that track interaction frequency, block sensitive document uploads, and prompt employees as well as AI agents to justify their business case before they use unsanctioned tools.

Once organizations know which tools are being used, what data they're accessing, and where that information goes, they can apply effective guardrails to secure behaviors – both human and non-human. A simple inventory of AI agents is not enough; now, organizations need to move beyond flat inventories and develop an understanding of the context and relationships that surround every agent.

In essence, identity security platforms become adaptive – moving from static to dynamic, real-time approaches to access. This is helping organizations to operationalize zero trust by ensuring that no identity, human or non-human, is trusted by default.

In the era of AI agents, securing identity has become a prerequisite for innovation.

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As governments and organizations rethink their reliance on foreign-owned IT infrastructure, data sovereignty has become a boardroom priority.

However, the conversation has become overly focused on where data is stored, overlooking the legal, operational and resilience factors that determine whether organizations are truly in control.

Whether through misunderstanding or a deliberate attempt to mislead, the term data sovereignty is often misused.

Data residency and data sovereignty are being conflated, despite being very different. Data residency is about the physical location of data, while data sovereignty is much broader and also includes legal jurisdiction, operational control, resilience, governance and the ability to manage risk.

Concerns about dependence on foreign-owned digital infrastructure have brought added urgency to issues of digital independence and control over critical technology.

In response, various vendors - particularly the big US-based hyperscalers - are now repositioning themselves with “sovereign” alternatives based primarily on where they store customer data.

While this might address some of their customers’ needs, reducing sovereignty to a question of geography creates a misleadingly simple narrative: if an organization moves data to the “right” country, it will somehow become compliant. In reality, the core issue is not just where data should be hosted, but understanding who may seek access to it and who ultimately controls the infrastructure supporting it.

This misunderstanding is giving rise to what could be described as “data sovereignty washing”, with simplified claims that don't reflect legal or operational reality.

Data sans frontières

Governments the world over have well-established legal mechanisms for requesting information held in other jurisdictions. While data residency influences which laws apply and how requests are handled, it does not provide immunity from lawful access or eliminate international cooperation.

An example is the US CLOUD Act. Under certain conditions, it enables US authorities to request data from US service providers even when it is stored outside the United States. So, even if a UK or European-owned organization hosts data with a US-owned provider in a UK or European data center, it may still be reachable under US legal process. Being physically ‘local’ doesn’t change that.

The US is far from unique in this regard. Many other countries have legislation in place allowing authorities to access data for law enforcement or national security purposes, often supported by cross-border agreements and established legal processes.

The risk is that enterprises treat location as a complete sovereignty strategy, rather than one element of it. Threat actors care about the value of the data, not geography. As a result, organizations can spend significant time and money migrating data to new locations while leaving their biggest security risks fundamentally unchanged.

A more useful starting point is to ask what risks the organization is actually trying to reduce. That shifts the focus and any subsequent changes in approach away from maps, and towards threat modelling, which provides the right context for meaningful conversations about sovereignty.

Start with the threat model

Different organizations have fundamentally different threat models. A local retailer, a multinational bank, a defense contractor and a government department are unlikely to share the same priorities, even if they all process sensitive information.

For some organizations, regulatory compliance or data residency requirements may be the primary concern. For others, resilience against cyberattack, protection of intellectual property, or reducing dependence on a particular technology provider may be far more important. It’s generally a matter of sector-specific and business priorities.

So, rather than simply asking where data is stored, leaders should consider who ultimately controls the infrastructure, how dependent they are on individual cloud providers, what happens if services become unavailable, and whether they retain sufficient visibility and control over critical systems. If any of this raises operational or regulatory concerns, it may be sensible to adjust strategy.

The resilience paradox

An unintended consequence of pursuing absolute data localization is that it can reduce resilience. Organizations often improve availability and data recovery by maintaining geographically separate copies of critical data. Restricting everything to a single jurisdiction can reduce those options.

Decisions about sovereignty should therefore factor in availability, confidentiality, and integrity – all of which are important. The most effective approaches recognize that resilience sometimes requires carefully managed distribution rather than rigid localization.

Consider this scenario: an organization has ensured all their email is stored within a single jurisdiction to meet sovereignty objectives. Months later, their provider has a major outage in that one region, and they lose access to their email for days. Even worse, a serious data loss event affects their backups, which are also stored in the same region. If they had optimized for resilience instead, their data would have been safe and available throughout.

Even though the organization successfully addressed one aspect of sovereignty, they weakened another by reducing their ability to recover critical business information. That's ultimately the difference between treating sovereignty as a marketing claim and treating it as a genuine risk management exercise.

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The past two years have seen unprecedented adoption of generative AI. This was driven largely by LLM platforms such as Claude, which reported 28 million paying US customers in March.

In the corporate world and startups, this has put the pressure on companies to invest in AI, to keep up with the latest models and bolster productivity and efficiency. But now that the world has adopted AI, a stark readiness gap is emerging as governance and AI skills lag behind. AI fluency is now a baseline expectation for employees as leaders feel the pressure to prove the returns on their hefty investments.

Tokenmaxxing is the latest AI trend to come under fire as businesses want employees to use more AI in their work. In essence, this means boosting AI input to maximize AI output. On the surface, it sounds efficient and harmless. But underneath, it poses major security risks.

Tokenmaxxing explained

So what actually is tokenmaxxing?

A ‘token’ is a unit of data processed by an AI model. For example, a word or character inputted into an LLM search. So ‘tokenmaxxing’ quite simply means over-engineering generative AI prompts to get the most out of one search input. This can be anything from overly detailed prompts, to overloading an LLM chat with information, to asking an AI model for step-by-step breakdowns, as opposed to short summaries.

The trend is driven by businesses as leaders face pressure to prove the ROI of AI. It became a tongue-in-cheek benchmark of AI performance. Some companies, such as Meta, even gamified tokenmaxxing, measuring and ranking AI usage and citing the highest scorers ‘Token Legends’.

Their assumption is that AI usage means being AI-forward. But instead, companies need to consider the value they get from AI tools. They also need to keep security at the center of the conversation.

The hidden costs of tokenmaxxing

Although it might look like harmless corporate showboating, this trend poses a wide range of security risks given that LLM vendors are 52% more likely to be designated as “high risk” than traditional SaaS. This is due to access to sensitive data, IP, and internal workflows, so it’s imperative that businesses have oversight of how employees use these resources.

Rapid adoption of AI has led to an experimentation mindset. This is a positive shift from an innovation standpoint, but from a compliance perspective, a ‘trial and error’ approach is more error than trial.

The legacy tech systems most major enterprises are still reliant on are controlled by procurement and security teams and were not designed for the agility of AI technology. Meaning both innovation and compliance are lagging behind. It’s the latter that’s causing major concerns in the cybersecurity world.

When employees face mounting pressure to get the job done, they won’t wait for security teams to approve new tools, which results in shadow AI. This is where unmanaged, unapproved AI tools operate inside company environments without oversight.

Industry data shows that 70% of 16k cybersecurity customers currently have some form of shadow AI lurking within their organization, largely due to AI tools introduced through improper procurement channels that now have access to company data without oversight or guardrails.

There’s also been a 36% increase in shadow IT year-on-year, with organizations discovering, on average, around 140 Shadow IT tools accessing their environment within 90 days of connecting to the platform.

The bottom line is that AI adoption is drastically outpacing governance, and employees are prioritizing speed over control.

How businesses can defend against Shadow AI

The core issue isn’t tokenmaxxing itself; it’s businesses' inability to keep up with employee demand for speedy access to the latest AI tools. This ultimately results in friction between the desire to safely onboard new tools and the ongoing pressure to use AI.

When security teams intervene and revoke access to unmanaged tools, employees just reinstall them. Industry data finds that within a 30-day period, the average enterprise sees employees reinstall revoked tools 100+ times. Within one year, it happens 1,000 times.

To bolster defenses, organizations must design their procurement systems to match the speed of AI innovation, so they can keep up with the rate of AI usage, as demonstrated by so-called ‘token legends’.

Three actions businesses can take now

The more generative AI gets adopted in the corporate world, the more employees will face pressure to adopt and prove its ROI. Tokenmaxxing is just one hype within this wider picture. Businesses need to act fast to stop the gap between experimentation and control widening.

Three things leaders and compliance teams can kickstart today to bolster defenses against shadow AI are:

• Shrinking vendor review timelines so they match the speed of AI adoption
• Set up continuous monitoring systems to detect threats caused by tokenmaxxing before they jeopardize safety
• Implement employee training and policies for AI usage to ensure employees don’t expose sensitive data or IP

The new mandate is matching speed with governance, and it’s up to security teams to lead the charge.

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I’ve had the Sky Glass Gen 2 TV setup in my living room since April of last year. To begin with, I wasn’t quite sure whether I was going to get on with Sky’s flagship TV. It was quite different from a lot of TVs in its price range, boasting a relatively chunky build, a unique OS, and I'd be swapping my trusty soundbar for its built-in Dolby Atmos sound system.

For the uninitiated, Sky Glass models provide a captivating proposition for Sky customers. It acts as an all-in-one TV hub, with access to live channels over your network connection (rather than a satellite or aerial), various streaming and smart apps, and more.

So would I recommend buying the Sky Glass Gen 2 instead of one of the best OLED TVs or best mini LED TVs in its price range? I’ll take you through my experience as a user, break down its picture and audio performance, and tell you whether I think it's truly worth the asking price.

The user experience: how does the Sky Glass Gen 2 stack up after a year?

Let’s start by talking about the user experience you get with the Sky Glass Gen 2.

As I said, it aims to serve as an all-in-one hub for Sky customers, and in my view, it pulls this off with ease. The user interface is laid out in a simple and practical way, making it seamless to scroll through each channel, access a comprehensive TV guide, tap into apps like Netflix and HBO Max, and switch between various sources.

The crowning achievement of the Sky Glass Gen 2’s experience, though, is its voice control. I’m not usually a big fan of these — I’ve never really got on with Alexa for controlling devices, or Bixby on Samsung phones, for instance.

But Sky nails it on its Glass TVs. You can either say ‘hello Sky’ or press the mic button on the remote control, and ask for specific apps or channels, or even issue a more general command such as ‘show me movies with cats’. I always find that I get accurate, helpful results, and I rarely experience accidental triggers of voice controls or irrelevant answers.

The interface also looks really clean, and accessing settings is quick and seamless. Just press the ‘…’ button, and you can swiftly alter picture mode, sound settings, toggle speech enhancement, and more.

More generally, I just find Sky's OS to be a lot more user-friendly than a lot of its rivals, such as VIDAA OS on my Hisense TV or even Google TV on TCL models I've tested. Sky's OS just feels sleeker, more organised, and its pin-point voice controls make everyday viewing feel much more seamless.

Other than the occasional plug for a Sky exclusive, there's also little in the way of ads — something the aforementioned rivals push onto the home screen more than I'd like.

One issue I’d previously had was that advanced settings were buried deep within the main menu, but Sky has updated this, making them accessible via the ‘…’ button as well. This means I can easily change network settings, alter viewing preferences, and make advanced picture adjustments (such as changing Dolby Vision or HDR picture modes).

I’ve also found that having TV channels available through my home network is a big plus. The quality of broadcasts is excellent (more on that later), and I’ve experienced very few network issues during my time with the TV — you need 25Mbps download speeds to get 4K streaming, which is no problem in a city. But if you ever do encounter issues, there’s an option to fall back on an aerial connection, which is always handy.

This isn’t to say I’ve experienced no hiccups whatsoever. On occasion, some channels have failed to load, and apps haven’t displayed on the main menu. But this has been a very rare thing, and has always been fixable by unplugging the TV to restart it.

Something else that I love about the Sky Glass Gen 2, is that you get bundled subscriptions to select streaming apps, such as Netflix, HBO Max, Disney+, and Hayu — so long as you have a Sky Ultimate TV subscription, which is £24 per month.

That’s an exceptional deal, and has opened up a lot of shows and movies that I’d previously missed out on. Sure, it's the standard (with ads) subscription for these services, but as someone who typically buys 4K Blu-ray if I want the best viewing experiences for things I really cherish, that’s fine by me for everything else.

Finally, I have to say that the Sky Glass Gen 2 is extremely easy to set up and get started with. It comes with plastic feet that slot into the TV without needing to mess around with screws or fiddly stands.

Picture & audio quality: a solid all-round performer

Right, so the user experience is great on the Sky Glass Gen 2, but how does it perform when it comes to picture and audio?

First things first, it's important to understand the tech inside this model. It's a QLED TV that harnesses the power of local dimming to create a vibrant picture with solid black levels to match. The Glass Gen 2 added more dimming zones, greatly boosts brightness, and provides wider viewing angles than its predecessor, and the results are pretty good overall.

Are you going to get the inky blacks and eye-popping colors that a TV like the LG B6 can deliver? No, but as a QLED model, the Sky Glass Gen 2 still really impressed me, and it's brighter than LG's model, making it good for well-lit living rooms.

I'd consider its picture to be competitive against similarly priced mini-LED options from the likes of Hisense and TCL most of the year around — though in the sales or on Black Friday, you might be able to get a more premium TV from those models for a similar price, which might edge the Glass 2 out.

The Glass Gen 2 supplies good contrast and color accuracy, decent black levels, and impressive motion-handling, making it a versatile option for all kinds of content. Whether I’m watching a blockbuster Premier League match, indulging in the quality of the best 4K Blu-ray players, or just watching a YouTube video, the Sky Glass Gen 2 is able to give me the vibrant colors and detailed picture that I need.

You get Dolby Vision, HDR10, and HLG support, meaning you can enjoy most video content at its best, although it's worth noting that there’s no HDR10+.

My main gripe with the Sky Glass Gen 2, performance wise, is its restrictive 60Hz refresh rate. As a keen gamer, this means I’m unable to play titles that offer 120fps gameplay at the peak of their powers.

Don’t get me wrong, 4K 60fps is good enough for the vast majority of my favorite PS5 and Nintendo Switch 2 titles, but if you want advanced gaming settings and buttery 120Hz refresh rates, you may want to look elsewhere.

On the audio side, things get really interesting, though. The TV has a Dolby Atmos soundbar system built-in, which uses a 3.1.2 channel configuration. This means you get: three outward-firing speakers; a pair of woofers for bass output; and a pair of upward-firing speakers for more vertical, immersive sound.

If you’ve not got room for a standalone soundbar, or you don’t want to spend more on one, then it’s a practical solution without question. It offers much deeper bass and clearer dialogue than you’d expect from most TVs' built-in speakers. The system also whips up relatively expansive sound, ideal for movies with striking Dolby Atmos effects.

But in honesty, the built-in sound system can’t compete with a top-class soundbar. If you want rippling bass, crystal clear mids, and expressive highs — and if you want fantastic music playback to boot — I’d still recommend investing in one of the best soundbars, such as the Samsung HW-Q800F, or a larger surround sound option like the JBL Bar 1300MK2.

One unintentional benefit of the built-in system is that it provides some height below the screen to easily slot in your own soundbar, if you do want an upgrade. For example, I have the spectacular Marshall Heston 120 sitting in front of it, with plenty of space between the bottom of the screen and the soundbar.

Wrapping up: is the Glass worth the cash?

So, is the Sky Glass Gen 2 worth picking over rivals from the likes of LG, Samsung, and Sony? For me, the answer is yes — it offers reliable performance, intuitive functionality, and serves as a great hub for Sky TV and streaming services.

OK, if your number one concern is getting the best picture quality possible, and you’re happy with a Sky box (including the Sky Stream, which has the same core software as the Glass 2, though isn't quite as snappy), then there are some cheaper OLEDs and mini-LED TVs that outperform the Glass Gen 2.

But if you want an accomplished entertainment hub that still performs well across the board, and even comes with a built-in sound system, then the Sky Glass Gen 2 is well worth the outlay. Sky's trick here is that it says this is an all-in-one system, needing just one cable to get started, and I think it achieves that.

The Sky Glass Gen 2 costs £699 for the 43-inch version, £949 for the 55-inch, and £1,199 for the 65-inch model. For reference, I own the latter, and I think it certainly earns its price, all things considered.

It's also worth noting that the Sky TVs have flexible purchase structures — you can buy them outright or pay for them via monthly installments, which in itself can be a big plus.

However, one thing to consider is that purchasing the TV on its own will result in a fairly restrictive user experience. You’ll have to pay a subscription fee for a number of Sky TV products — such as Sky Sports, Sky Cinema, UHD & Dolby Atmos, and Ad Skipping — to get the best out of your Sky Glass, and these costs can really add up.

But if you’re all in on the Sky experience, or if you’re an existing customer, I think the Glass Gen 2 is a great TV all things considered, and after more than a year with it, I have no intention of substituting it out.

No more needles. Until the Google Pixel Watch 5 arrived as part of Google's Made by Google 2026 event, the only way most people could monitor their blood sugar's responses to food and exercise with smart tech was to use a continuous glucose monitor, or CGM. A small puck with a Bluetooth receiver inside and a spike to connect to your blood, it projects your blood sugar levels to your phone in real time.

Very useful — and originally developed — for diabetics, it was also quickly co-opted by the wellness sector and marketed as a way to curb blood sugar spikes. I attended a product launch for one of these devices in a fancy hotel, where a celebrity chef talked us through how to make healthy canapés that wouldn't spike our blood sugar, As proof, we could see our blood sugar broadcast to our phones in real time, monitored by the needles in our arms.

Too much sugar increases the body's insulin resistance, leading to fat storage, while exercise uses your body's glucose stores as fuel. Simple enough in theory, but I found the constant readings from the CGM easy to obsess over during my trial period, checking my graphs after every meal and mentally planning how much exercise would be needing to offset the effect of those spikes. I am not diabetic.

The Google Pixel Watch 5 is heralding in, as part of its passive-tracking Health Guardian suite of features, a non-invasive Insulin Resistance Trends feature for Pixel Watches 3 and above. According to Google: 'The tool uses passive sensors on your wrist to track monthly shifts in your metabolic health, reserving formal lab work for when you're at the doctor's office.

'The goal of this new feature is to help you take control of your health journey, serving as a gentle reminder to pause and check your routines,' continues Google's press release. 'When this Health Guardian feature detects sustained, elevated insulin resistance trends over the month, it alerts you with a gentle nudge to help you adjust your daily habits and get your metabolic health back in its sweet spot.'

'Metabolic health' refers to the process of your body turning food into energy. A healthy body turns food and sugar into energy very effectively, while an unhealthy body works harder to mitigate those blood sugar spikes, leading to excess blood sugar being stored as fat rather than fuel. This results in weight gain and in some cases, Type-2 diabetes.

The feature isn't on watches yet (it'll arrive next month) and there's no indication of exactly how Google has been able to wrangle its optical LED sensors to manage this non-invasive Insulin Resistance Trends monitoring.

In its press materials, Google has been keen to emphasize that 40% of Americans are living with 'invisible shifts in their metabolic health'. The feature might help the US obesity crisis with a two-pronged assault; education about insulin resistance, and providing an accessible way to monitor this growing health problem without sticking a needle in your arm

However, just as I could see myself compulsively checking my blood sugar graphs, I worry that this might become yet another metric for otherwise-healthy smartwatch users to obsess over. Like weighing yourself every day or taking GLP-1s, weight management techniques can quickly transform from a healthy, positive thing into an addiction.

Research published in the Cardiovascular Digital Health Journal in 2020 has identified a trend we've seen before in health tech users: an obsession with metrics, streaks and optimization.

The researchers said that although wearables provide lots of actionable data and improve real-time surveillance of disease, they found "another aspect of the digital health revolution that has not yet received due attention: the unanticipated and potentially negative effects of wearable devices on patients’ psychological health, quality of life, and health care utilization."

Just recently, our writers Becca Caddy and Alex Blake wrote about their own experiences with this phenomenon, with Caddy stating "What I didn't understand at the time was how thin the line between discipline and obsession can be. You can cross it gradually, one habit and one goal at a time, until something that started as a genuine attempt to look after yourself becomes another source of pressure, anxiety and control."

The messaging around weight management can be difficult to get right, and as much as the feature is likely to improve wearer's health, I worry Insulin Resistance Trends could become quite a triggering metric for people at risk of disordered behaviors.

To Google's credit, however, the glimpse of the feature we've seen so far isn't a moment-to-moment graph. Instead, it shows month-to-month general trends, presumably to avoid people checking the feature too much. There's also a lot of good to be done around educating users about the relationship between blood sugar, insulin, weight gain, and diabetes.

Regardless, when the feature does arrive next month, I hope thought is put into its execution so we can have as many of the good consequences as possible, while avoiding the bad.

First released in 1972, the general-purpose programming language C is one of the most important in the history of computing and underpins many of the modern systems that we use daily. Its creator, the late Dennis Ritchie, laid the foundations for much of the world's technical infrastructure in his early work, and C was a major element of his contribution.

Laying foundations

Ritchie summarized the history and impact of the programming language he pioneered while working at Bell Labs in a history paper that he presented in 1993.

His comments describing C highlighted its strange syntax, confusing declarations, and minimal rules base as evidence of its quirkiness. The flaws, meanwhile, arose in the fact that there were no built-in bounds for checking memory, with bugs frequently leading to crashes, alongside some small errors that led compilers to act in unpredictable ways.

However, the success of C is unquestionable, with the language running fast and mapping well to chips, and the language finding itself at the heart of many widely used operating systems including Unix, Windows and Linux.

The evolution of language

Ritchie's C was also the precursor to a host of more modern languages including C++, Objective-C, Java, C#, PHP, JavaScript, and Go. Categorized as being under the 'C family', these languages inherit the basic syntax structures, but each will have its own use case and functionality in the modern world.

Despite C being firmly established in the programming hall of fame, it's no longer the world's most popular programming language.

A global index on the most used programming languages shows C in 11th position as of November 2025, with JavaScript taking top spot as the most used language among developers, followed by HTML/CSS, SQL, and Python.

Meta CEO Mark Zuckerberg has released his manifesto for the future of AI, entitled, "The Future is for Everyone", outlining the two paths he believes the technology could take.

On the one hand, Zuckerberg paints a picture of AI being centralized by big institutions and governments purely for the benefit of themselves. On the other hand he paints a picture of Meta (a big institution) leading humanity into a new golden age of limitless bounty and potential.

At the expense of my sanity, I’ve read the whole thing and dissected the key points of his proposals, and what they might mean for you.

Meta is the only one who can stop AI being centralized

To start, I will warn you that there is one key thing missing from Zuckerberg’s AI manifesto: substance.

The introductory paragraphs are the same empty words regurgitated by big tech leaders and AI gurus the world over: “The number of valuable things superintelligence can invent to help achieve your goals is unlimited”, et cetera, et cetera.

The manifesto then moves on to questioning the power structures and ‘centralization’ that threaten the future development and distribution of superintelligence, arguing that the best way to keep the balance of superintelligence in favor of people is by putting it in the hands of everyone.

But there is a glaring contradiction. Zuckerberg’s manifesto positions the US, its allies, and in some cases Meta, as those who should determine how AI can be used. A renewed perspective of the ‘global policeman’ for the AI era it seems, especially as Zuckerberg praises US efforts to limit the AI capabilities of its geopolitical rivals.

Simultaneously, he positions his manifesto, and Meta, as the guiding principles on how the technology should be used, powered, and trained: “If our [Meta’s] beliefs and principles lead, then the balance of power will favor individuals and a better future for everyone,” he writes. Who exactly are these “individuals” Mr. Zuckerberg?

“Meta is the company primarily focused on building personal superintelligence for everyone. Most other labs are focused on building AI for companies, governments, or other institutions, so if those labs lead, then the balance of power will favor larger institutions over individuals.”

The message is clear: only Meta holds the keys to humanity's salvation.

Sketchy stats and promises of jobs

“Recent statistics suggest it may be more likely that individuals' capability growth could match or outpace automation, in which case people will gain the ability to do many new things before their current jobs change. This would lead to a healthy balance and potentially even job growth.”

The statistics Zuckerberg cites aren’t referenced. He also doesn’t clarify if the “healthy balance” accounts for new entries to the job market, or if it will only account for those whose jobs are replaced.

He further argues that workers will have access to “personal superintelligence that expands their capabilities” to be used “as a tool of invention” to produce “incredibly valuable new things”. More empty words to soothe those yet to be replaced.

AI needs regulating, but not your data, your health, or the environment

Zuckerberg also picks and chooses where regulations on AI and data should apply. He writes that “American labs have to comply with many additional restrictions on training data”, and that this is harming US hegemony in the AI industry. He then states that “policy must reduce this additional friction”, essentially calling for these restrictions to be lifted.

Publicly available data can currently be used for AI training, but it's the private stuff that’ll really deliver the full power of superintelligence, or so Zuckerberg would want you to believe.

But don’t worry. Meta will be sure to keep all your personal and private data safe once superintelligence has been delivered.

“We will build a fully private mode where even Meta cannot see or grant access to your information,” Zuckerberg promises. It’ll be offered for “free” too, or “as affordably as possible”. My money is on the latter.

Zuckerberg also complains about how government policy and regulations are causing the US to fall behind in AI development, capacity, and deployment. He calls on the US to “accelerate the ability to build infrastructure – whether that's energy, data centers, or silicon”, despite there being growing national opposition in the US to new AI infrastructure.

Sorry Mr. Zuckerberg, but didn’t you earlier say that, “People also check and balance the power of institutions including businesses and governments”?

Oh, the humanity!

Overall the manifesto reads like a desperate plea, frontloaded with promises offering everyone unlimited happiness while hiding Meta’s true intentions behind the confusing wording of an out-of-touch billionaire.

Zuckerberg's perspective is sickeningly optimistic, written through rose tinted glasses poisoned by wealth, privilege, and power. It praises this undefined ‘superintelligence’ as if it were an apathetic deity, with only Zuckerberg and Meta having the knowledge to ensure it is used for good.

The majority of adults in the US don't want AI infrastructure for very valid reasons. It IS replacing jobs, it IS harming communities, and it IS damaging ecosystems.

What good is promising superintelligence to billions of people around the world when there are far more basic and beneficial problems to solve?

You can use AI to search up your symptoms and get a (potentially hallucinated) AI diagnosis, but you can’t afford healthcare. You can use AI to learn all about why energy prices are rising, but that bill just keeps going up. You can use AI to learn about crop failures and where the poverty line currently sits, but you still can’t afford food.

Zuckerberg seems to believe that AI agents are a need and not a want. That it will somehow advance human intelligence in step with superintelligence.

I could have used an AI agent to write this article.

I could outwardly trust that AI would have covered all the key points, and added the necessary context. But if I had done that I wouldn’t be bristling with new questions, new knowledge, and new things to look into.

Sure, the AI would have suggested some other questions or perspectives to pursue, but they wouldn’t be mine and they wouldn’t be human.

Our existence is defined by constantly pursuing new questions and theories, not having all of them answered immediately in a neat, overly-polite bullet-pointed list.

But if superintelligence can provide a better answer on what it means to be human, I'll eat my hat.

Many organizations believe they are successfully leveraging AI tools to strengthen their security posture.

Investment is rising, AI is being embedded across multiple workloads and workflows, and governance frameworks continue to expand, giving the impression on the surface that progress is being made.

But beneath this AI adoption lies an unseen problem: a growing gap between what organizations believe about their infrastructure security, and what they can actually evidence.

Confidence is rising, but so are breaches

According to a global 2026 Hybrid Cloud Security Survey, which gathered the views of more than 1,000 Security and IT leaders, 93 percent have invested in new security technologies, yet despite this, breach rates have hit their highest point. Sixty-five per cent of organizations experienced a data breach in the past 12 months, an 18 percent rise year on year, and a near 40 percent rise over three years.

These worrying statistics are starting to ring alarm bells, highlighting that within organizations we are starting to see an ‘illusion of security’ creeping in. Organizations are investing heavily in security tools, yet still seem to lack clear visibility into the outcomes of their investments. In other words, leaving security to be measured by what has been implemented, rather than what can actually be verified.

A perfect storm of conditions has brought us to this point: AI adoption has scaled faster than governance and security teams can keep pace with, and hybrid cloud has added another dimension of complexity that few organizations have fully reckoned with.

AI is now embedded across enterprise environments, accelerating not just how organizations operate, but how risk moves through them. As adoption has outpaced oversight, the consequences are starting to surface, with nearly half of organizations surveyed reporting a rise in AI-related insider threats, including data leaks, and unsanctioned use (shadow AI). Perhaps surprisingly confidence hasn't reduced. Many organizations continue to classify their AI security posture as "defined" or "integrated," despite evidence portraying a radically different story.

That confidence often rests on assumptions, and the scale of the disconnect is striking; AI is now involved in 83 percent of security incidents, spanning external attacks, internal exposures, and direct targeting of AI systems. As threats move faster across increasingly fragmented and distributed environments, assumptions about what’s secure quickly fall apart and without clear visibility into how data moves and systems behave, organizations cannot reliably manage risk.

The warning signs

No single indicator reveals a false sense of AI security, but several recurring patterns make it obvious.

The first is investment without impact. Many organizations are expanding their security stacks, yet detection and response times are still moving in the wrong direction. More than 40 percent report that it now takes longer to detect and investigate breaches than it did previously, and that's not a coincidence.

When signals are spread across systems that don't connect, teams spend longer piecing together what happened rather than acting on it. Adding tools without improving visibility doesn't create more clarity. It creates more data, and more data without context is just more noise to wade through not to mention more false positives and perhaps even worse more false negatives.

The second is an inability to trace incidents back to their source. More than one in four organizations cannot determine the root cause of a breach, which means incidents are being closed out without fully understanding how they happened. The consequences are predictable: nearly one-third of organizations report multiple incidents within the same year. Without traceability, there is no learning, and without learning, the same gaps simply get exploited again.

The third is the visibility gap opening in AI-driven environments. With nearly three-quarters of organizations reporting limited visibility into AI-driven data flows, which span APIs, models, cloud services, and distributed infrastructure that is dynamic by design and doesn't map cleanly onto traditional monitoring approaches. Security teams can often see that something happened, but simply can’t get to the how or the why. That gap between knowing an incident occurred and understanding it, is exactly where the illusion lives.

Moving from illusion to evidence

In response to growing complexity, organizations are collecting more telemetry than ever; metrics, events, logs and traces (MELT data), but more data does not necessarily equate to better understanding. These signals each offer only a partial view: one measures performance, another records activity, other flags issues after the fact.

None of them, on their own, are able to explain how systems behave as a whole. What’s missing is the connective tissue, the context that shows how these signals relate, how one event triggers another, and how issues propagate across the environment. Without that, organizations aren’t gaining insight; they’re just accumulating noise.

Shattering the AI ‘security illusion’ requires a shift from reactive security to proactive monitoring and real-time observation of how systems behave. Security leaders agree, with more than 90 percent of organizations reporting that complete visibility across data in motion is critical to their successful security outcomes.

This is where network-derived telemetry becomes essential. Unlike logs, which show what systems say they’re doing, network telemetry proves what is happening: how data moves, how systems interact, and how threats develop. It's the shift from assumption to evidence and then proof, and it's the only foundation solid enough to build real security on. The network is the source of truth for today’s security teams.

AI security must be measured, not assumed

AI is reshaping the threat landscape, enabling faster, more adaptive attacks while increasing the complexity of enterprise environments. But defenders are not without their own advantages.

The same technologies fueling attacks are already supporting security teams, automating detection, accelerating response, and investment in these capabilities continues to grow.

But let there be no mistake: investment is not proof. Security must be measured by the ability to observe, understand, and validate what is actually happening across the environment.

In the age of AI, the real risk is not organizations underinvesting in security, but the belief that they are secure without the evidence to prove it.

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To comply with the EU AI Act's Article 50(2) Code of Practice on Transparency of AI-Generated Content, Anthropic has announced that “New models will mark AI-generated content from day one”.

This is a remarkable step for Claude, because it not only applies to any images it generates, which are relatively easy to watermark, but also to any text it generates.

Anthropic says that Claude models will have an “imperceptible watermark” embedded directly into generated text at the model level. It says the mark should survive copying/pasting and minor edits.

Anthropic has not yet publicly explained the exact algorithm or released a detector, so we can only speculate for now about how it might be doing this and its effectiveness.

Hemorrhaging customers

Personally, I think that Claude is about to start hemorrhaging customers, unless the marking is relatively easy to circumvent, or all the other major AI players immediately follow suit.

If every piece of text it produces will now be easily identified as AI, then Claude becomes useless as a tool to a lot of people who are currently using it to generate text and are not being entirely honest about where that text came from.

And then there’s the issue of using Claude to proofread your human-written text — will your text now be flagged as AI if you accept Claude’s editing advice?

Your initial reaction to that might be “Good! You should be forced to reveal when AI has written something, and it’s about time people started writing on their own again!”, and you’d be entirely justified in that opinion.

But while it remains the only one of the big three AIs that’s doing this, I think we’ll see a lot of people switch to either ChatGPT or Gemini, because they don’t want to be revealed as using AI in their work.

How is watermarking plain text even possible?

We don’t know exactly how Anthropic is doing its marking with text yet, but my best guess is statistical watermarking during token generation rather than hidden Unicode characters or metadata. Imagine that at every point Claude is choosing among several perfectly reasonable next words:

e.g. The movie was excellent / superb / terrific / impressive.

Normally it chooses according to the model's probability distribution. A watermarking system can secretly divide possible tokens into preferred and non-preferred groups using a key. Claude then gives a tiny statistical nudge toward the preferred group.

One word tells you nothing. But across 500 or 1,000 words, a detector with the key can ask if the text is choosing the preferred tokens significantly more often than chance would allow. If yes, then there's statistical evidence it came from the watermarked model.

So, the watermark is more like a faint statistical fingerprint distributed across hundreds of choices, which would also explain how it can survive a copy-and-paste. You’re essentially copying the fingerprint along with the words.

But can you crack the code?

Since Anthropic hasn’t released an AI text detector yet, it’s impossible to know how easy this code will be to break just by changing a few words. For instance, if you put your 1,000-word Claude article into another LLM and wrote “Rewrite this completely in different words while preserving the meaning”, would it then be impossible to detect as AI?

I’d also be interested to know how long a piece of text has to be before it can be marked in this way, and as soon as a detector is made available, I’ll be testing it.

Perhaps the bigger issue is that Claude has done this to comply with Article 50(2) of the EU AI Act. From August 2, 2026, providers of generative AI systems that produce text, images, audio, or video are required to make those outputs machine-readable and detectable as artificially generated or manipulated, insofar as that is technically feasible. Existing systems get a limited transition period until December 2, 2026 for this particular requirement.

A note on Anthropic’s statement confirms that the watermarking additions will be applied retroactively to all existing Claude models, not just any new models it produces.

Anthropic's new system is explicitly a response to those rules, and it says the watermark will apply globally, not just when Claude is being used in Europe. Broadly speaking, OpenAI and Google face the same requirement if they want to offer qualifying generative-AI systems in the EU.

They don't necessarily have to copy Anthropic's method of using statistical text watermarking, but they will need to produce output that is machine-readable and detectable, and the technical solution should be effective, interoperable, robust and reliable as far as technically feasible.

I’ve contacted OpenAI and Google for comment, and will update this article if I receive it. For now, I think if Anthropic embarks on this path as the only one of the major three AI chatbots to do so, it could have a disastrous effect on its customer base.

Update: While OpenAI declined to provide specific comment on this story it did direct me to a statement online, where it states:

"Consistent with our commitments under the European Commission’s Code of Practice on Transparency of AI-generated content, our goal is to expand provenance signals to all modalities including text, so customers and developers have clear ways to meet their own transparency obligations as standards and tooling continue to mature."

Evaluating AI agents in production tends to focus only on positive results. Did the agent complete the task? Was the output accurate? Did the demo go well?

The answers to those questions matter, but they miss the case that determines whether an enterprise can actually trust agents with real work: what happens in the 30% of instances where something goes wrong?

In financial services, for example, an autonomous agent that mishandles a money-movement workflow doesn't turn into an innocuous support ticket. It creates legal liability that can extend across an entire business.

In healthcare, unchecked data access calls can compromise patient safety and lead to HIPAA violations. Highly regulated industries can’t treat failure as a minor inconvenience. And when the stakes are categorically higher than in most other sectors, it changes what "production-ready" means.

The problem? Most popular agent frameworks were built by teams focused on connecting large language models to reasoning loops and evaluation. While valuable, it's not the same discipline as distributed systems engineering.

Few of these frameworks were built by people who spend their careers thinking about recovery, consistency and fault isolation. Many give little thought to what happens when an agent fails mid-flight, and that gap can be very expensive once agents are handling real-world transactions.

Replaying From Scratch Doesn't Work

When a sales agent moves through a 10-step workflow and fails at step nine, the naive recovery approach is to start the entire flow over from step one. That sounds harmless until you account for what each step actually costs. Every restart means re-running every LLM call that already succeeded, burning through token spend for work that was already done correctly. At scale, that inefficiency turns a single bug into a real financial problem.

It also produces a worse experience for the people and systems downstream. In a regulated workflow, it’s sloppy work that becomes a compliance and audit problem waiting to surface.

The fix is durable execution: checkpointing that captures progress at each meaningful step, so recovery means resuming from step nine, not replaying the whole sequence. This has become standard practice in distributed systems, and agent orchestration needs to follow suit.

When you’re evaluating agent frameworks, here’s the question you should ask: if an agent fails partway through a long-running task, does the system resume from where it left off, or does it start over? The answer will tell you the difference between frameworks built for production and those built for demos.

There’s an Access Problem Nobody Talks About

Security in agentic systems presents its own version of this challenge, and it starts with a question that sounds basic but that most organizations can't seem to answer: can you prove exactly who or what did what, and when?

That question is harder to answer as agents act on behalf of other agents, which act on behalf of humans. Each layer of delegation adds ambiguity about accountability. And when you add MCP servers into the mix, the exposure compounds. MCP gives language models access to company records, patient data and internal systems. The vast majority of MCP servers in production today connect to some kind of database, and the common mistake is granting broad access to that entire data store rather than narrowly scoping what each server can see.

That distinction matters when something goes wrong. If a system with broad access suffers a breach or a supply chain compromise, the attacker gains access to the entire environment. Scoped access, where an MCP server or agent can only reach the specific data it needs for the task at hand, is one of the most overlooked design decisions in agentic architecture right now. It's also a relatively cheap problem to fix before deployment, yet one of the most expensive to fix after a breach.

Identity adds another layer. Many organizations still use traditional authentication protocols that were made for human users logging into applications, not for autonomous systems that act at machine speed and scale. Without verifiable identities for each agent and each component, malicious code can impersonate a legitimate part of the system and operate undetected.

What’s needed here is what’s known as cryptographic attestation: a tamper-proof record of everything that happened in the system tied to the specific identity that did it. That record lets you replay the system's state after the fact and determine with certainty that a specific piece of code accessed a specific system at a specific moment. Why? Because that identity was managed and enforced in real time. It's the difference between a policy that says a component “should” be trusted and a system that can prove what it actually did.

Design to Limit the Blast Radius, Not Just Patch the Damage

There's also a problem with how most organizations think about vulnerabilities. The industry's attention is almost entirely on patching known CVEs, and that's necessary. But at the same time, it misses something important: a vulnerability only becomes a known CVE after a breach has already occurred. Patch management is inherently reactive and does nothing while a system is actively compromised, by which point malicious code is already trying to move laterally through the network.

This is why runtime enforcement deserves far more attention. The goal isn't just prevention; it's containment. If a system is compromised, can you detect that a component is behaving abnormally and immediately restrict its access, even before you’ve identified or patched the underlying flaw? Limiting the blast radius in real time, rather than relying solely on detection after the fact, is what separates a contained incident from a full-scale breach.

Zero trust principles should be applied specifically to AI workloads, not just inherited from traditional cloud-native security protocols. That means strict authentication and authorization for every agent and MCP server, clear policies on what each component is allowed to access, and controls that stop compromised components from sending data outside the organization.

You Need to Prove Safety, Not Just Function

This isn’t pessimism toward AI agents; it's about engineering maturity. Every distributed system that has matured into something enterprises trust with critical workloads, from databases to cloud infrastructure, went through this same evolution. They go from optimizing for common cases to designing explicitly for the rare, expensive failure.

Agentic AI is now at that point. The organizations that get it right will be able to sit down with an auditor or a regulator and demonstrate, with evidence, exactly how their systems behave when something breaks:

- Durable recovery that doesn't waste a single completed step.

- Access that's scoped to the task, not the whole database.

- Identity that can be cryptographically verified, not just assumed.

- Containment that activates in real time, not after the fact.

These are the bars that enterprises serious about deploying AI agents at scale need to meet.

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The integration of artificial intelligence (AI) into everyday work and life has prompted businesses and regulatory bodies to take action. AI is a force introducing entirely new categories of threat, making heightened cyber resilience essential. However, amidst the panic to get in front of this, it should be noted that not all the hype is entirely accurate.

The underlying vulnerabilities organizations face today are largely the same ones they faced five or ten years ago: unpatched systems, weak identity controls, excessive privileges, insecure third-party integrations.

In some cases, these weaknesses have existed for some time, and most likely, will continue to exist because the first fundamental constraint of computer science is that the removal of all vulnerabilities is impossible. Therefore, no amount of tooling or budget will ever make any business 100% secure. Equally, that doesn’t mean improvements should simply be dismissed - especially in the age of AI.

Speed, not novelty

While not introducing anything inherently new, there is still some cause for concern surrounding AI. The nature of existing weaknesses remains the same. However, AI does significantly change the speed and scale at which they can be identified and exploited. Tasks that once required time, skill, and persistence can now be automated, accelerated, and in some cases delegated.

The barrier to entry has been lowered for less sophisticated actors to operate with greater efficiency and success.

We are already seeing early signs of this shift. Elements of the attack lifecycle can be automated, be that reconnaissance or lateral movement. Nation-state actors have begun experimenting with using these systems to coordinate multi-stage operations, and we’ve recently seen a fully autonomous attack take place, without any human supervision.

At the same time, more familiar techniques are being enhanced rather than replaced. Malware can be generated or iterated more quickly to evade detection. Social engineering has become more convincing through deepfakes, and phishing campaigns have become easier to scale.

None of this represents a fundamentally new playbook, simply the acceleration of an existing one.

The distraction problem

The distinction between novelty and speed matters because it shapes how organizations respond. If AI is treated as a novel and exceptional threat, it encourages a reactive mindset. Security teams are pushed towards finding “AI-specific” solutions, often at the expense of addressing longstanding gaps in their environment. In practice, those gaps still remain the most reliable entry points for attackers.

The risk that the current level of attention on AI creates, is a form of strategic distraction. Boards and executives are rightly asking questions about AI risk, but those conversations can become detached from the basics. Patch management programs remain inconsistent. Asset inventories are incomplete. Third-party exposure is poorly understood. Identity and access management remains fragmented across systems.

These are the same issues that security professionals were tackling before the advent of AI and the technology does not take them off the board. If anything, these become more consequential as the speed of exploitation increases.

The right response

It is worth being clear about the limits of control. No organization will ever be 100% secure. There will always be unknown vulnerabilities, many of which the new frontier models will be able to fish out.

However, the idea that AI introduces risk that can be entirely “solved” is misleading. Even if advanced models identify previously unknown weaknesses, the response remains the same as it has always been: prioritize, remediate and reduce exposure over time.

For defenders, matching this increased tempo requires a combination of discipline and adaptation. Established practices such as red teaming and tabletop exercises need to evolve to incorporate AI-enabled scenarios. Incident response teams need to be prepared to handle new forms of evidence, including those generated or manipulated by AI systems.

In addition, training programs need to reflect the growing sophistication of social engineering, particularly where deepfakes and voice cloning is concerned.

AI-driven detection and response capabilities can play an important role, particularly in identifying patterns at scale. But they are not a substitute for secure-by-design principles, robust access controls, or a clear understanding of where critical data resides. Therefore, organizations should observe caution about over-rotating towards new tools without addressing foundational weaknesses.

The expansion of the attack surface through enterprise AI adoption adds another layer of complexity. Threat actors are already targeting AI workflows directly, exploiting vulnerabilities in software development environments, and using techniques such as prompt injection to manipulate system behavior.

In some cases, malicious instructions can be embedded within otherwise benign content, triggering unintended actions when processed by an AI system.

Again, these developments are best understood as extensions of familiar concepts. Input validation, supply chain risk, and data integrity have always been central to security. AI introduces new contexts in which these issues manifest, but not entirely new categories of risk.

From a governance perspective, this reinforces the need for clarity rather than novelty. Boards should be focused on defining risk tolerance, ensuring accountability, and maintaining visibility over how AI is used within the organization.

This includes integrating AI considerations into existing risk frameworks rather than treating them as a separate domain. Legal, technical, and communications teams need to be aligned, particularly in scenarios involving misinformation or synthetic media, where response speed is critical.

What matters now

There is value in the current focus on cyber risk. Increased attention at the board level can drive investment and accountability in ways that were previously difficult to achieve. But that attention needs to be directed towards the right problems. Treating AI as an entirely new threat risks misallocating resources and overlooking the vulnerabilities that are already present.

AI will continue to evolve and so will the ways in which it is used by both attackers and defenders. In cyber security, progress is often less about discovering new answers and more about applying existing ones with greeted consistency and speed.

The organizations that navigate this shift most effectively will be those that remain grounded in a clear understanding of what has and has not changed.

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In June, Keir Starmer announced at London Tech Week that tech firms have until September to introduce device controls that prevent children from sending and receiving sexually explicit images. The plan requires on-device or client-side scanning, and has been framed as a child safety measure. For enterprise security teams, it raises a different set of questions entirely.

For enterprises, the proposal challenges one of the core assumptions underpinning modern cybersecurity: that devices can be trusted to process sensitive information without external inspection.

If software is required to scan content before it is encrypted or transmitted, it introduces new attack surfaces and weakens the privacy and integrity that businesses depend on to protect intellectual property and confidential information.

It’s a concern being felt widely across the industry, with organizations including Signal and the British Computer Society issuing statements warning that client-side scanning can create systemic vulnerabilities that could ultimately be exploited by cybercriminals and other malicious actors, fundamentally reshaping digital trust.

As organizations prepare for the September deadline, they must equip teams with the necessary skills to evaluate potential risks that client-side scanning could cause, adapting security strategies where necessary.

Why the proposed plan has led to conversations around security

The greatest risk posed by mandatory on-device scanning is the disruption of trust architecture on which enterprise security depends. Modern mobile security frameworks, including mobile device management (MDM), endpoint protection and zero-trust access controls, are built on the assumption that the operating system functions as a controlled and trusted layer.

Enterprises use this trusted foundation to enforce security policies and verify the integrity of managed devices.

Introducing a government-mandated scanning agent beneath or alongside that trusted layer fundamentally changes the security model. Once an additional privileged component can inspect device content, the integrity of the operating system can no longer be assumed, making the security controls that sit above it less reliable.

This structural change to the trust boundary that underpins enterprise mobile security, creates new opportunities for exploitation if the capability is ever compromised or repurposed.

The operational problems for security teams

The consequences extend beyond security architecture into day-to-day enterprise operations. The underlying issue is not privacy, which is what the government’s plan is concerned with, but architecture. A scanning agent at the operating system level sits outside the boundaries that enterprise security teams are able to govern, disrupting security processes that organizations rely on to verify whether endpoints remain in a trusted state.

Government-run scanning agents also create the potential for compliance failures, particularly in highly regulated sectors where organizations must demonstrate control over how sensitive data is processed and monitored. If the scanning function operates outside the managed work profile used by enterprise MDM platforms, it remains effectively invisible to IT administrators.

As a result, security teams have no practical mechanism to audit its behavior or control how it acts with secure data. This also disrupts device attestation - the process by which MDM platforms verify a device is in a known, trusted state - which could cause managed devices to be flagged as non-compliant and blocked from corporate resources.

In an environment where visibility and assurance are fundamental principles of cyber defense, introducing a privileged component that falls outside enterprise oversight creates a blind spot that weakens, rather than strengthens, organizational security.

While intended to improve online safety, client-side scanning raises significant concerns for organizations. Because it requires privileged access to device content and is widely considered incompatible with true end-to-end encryption, it could weaken security, increase the risk of sensitive data exposure, and force organizations to navigate difficult trade-offs between compliance and protecting critical systems.

The lack of clear exemptions for legally privileged, healthcare and financial data also leaves regulated sectors facing uncertainty over how to meet competing legal obligations.

What organizations can do to prepare

Organizations cannot afford to treat this proposal as a policy issue alone. Security leaders should already be engaging with the compliance and governance issues it raises, assessing how any mandated changes to mobile operating systems could affect device trust, regulatory obligations and existing security controls.

This also means investing in the skills needed to assess security risks at the architectural level, rather than simply responding to threats after they emerge.

Security teams will need a deeper understanding of operating system security models, trusted execution environments, encryption, endpoint architecture and mobile device management, enabling them to evaluate how changes to core platform designs could affect an organization's overall security posture.

Building these capabilities will help organizations make informed decisions about adopting new technologies, understand the implications of regulatory changes, and identify potential weaknesses before they become exploitable.

As trust increasingly becomes embedded within the architecture itself, having teams with the expertise to assess and challenge these foundations will be just as important as the security tools used to defend them.

Safety cannot erode trust

As the debate surrounding the UK’s on-device scanning proposal continues, the conversation must move beyond the framing of privacy versus safety and consider the wider architectural consequences for enterprise environments.

Weakening the trusted computing model on which modern mobile security is built risks introducing new vulnerabilities, reducing visibility for security teams and undermining confidence in the platform’s organizations depend on.

Making the internet safer for children is crucial, but achieving that goal should not come at the expense of the security foundations that businesses rely on every day. The challenge for policymakers and technology providers is not just to implement new safeguards, but to do so without eroding the trust that underpins the wider digital ecosystem.

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Enterprise internet security vendors and experts have spent many years trying to understand human behavior.

As a result, there is now a wide variety of very effective tools and processes that help distinguish legitimate user activity from behavior that may indicate a compromised account or malicious activity.

Behavioral analytics has come a very long way.

The underlying principle is that behavior is often a stronger indicator of compromise than the use of credentials alone.

In this context, behavioral analytics establishes a strong baseline for individual users over time, including the systems they access, typical login patterns, data usage, administrative actions, API activity and various other interactions.

Any significant deviations can trigger investigation.

A security disconnect

But as we all know, things are changing very fast. The rapid move from GenAI assistants to autonomous AI agents has introduced a new kind of enterprise actor, one that combines non-human identity with autonomy, dynamic decision-making and the ability to execute multi-step actions across systems. Existing security models were not designed with this combination of characteristics in mind.

Clearly, AI agents are now of particular concern thanks to their ability to execute tasks autonomously, access multiple applications, retrieve information, make decisions within defined parameters and complete multi-step workflows.

To say the adoption of AI agents across the enterprise space is dramatic is to put it very mildly. Gartner predicts that by the end of 2026, 40% of enterprise applications will incorporate task-specific AI agents, compared with fewer than 5% in 2025. In order to work, many of these agents will be given identities, permissions, credentials and access to sensitive business systems. It stands to reason that security issues will follow.

Unlike human users, however, most organizations have little understanding of what constitutes expected or abnormal behavior for autonomous identities. That helps explain why Gartner's 2026 Hype Cycle views Agentic AI Security as an emerging discipline, and why governance and behavioral monitoring capabilities are still in their early stages of development.

It also creates a potentially serious disconnect organizations have mature behavioral intelligence for people but comparatively little for AI agents, despite both increasingly operating as trusted identities within enterprise environments.

Agents of change

But is the difference between human and AI behavior really that important? In the pre-AI era, human behavioral analytics relied on relatively stable patterns. Most users worked predictable hours, accessed a consistent set of applications, connected from familiar locations and performed activities aligned with their role. After all, humans are creatures of habit, including in the workplace environment where the vast majority would never do anything malicious.

AI agents are fundamentally different because, unlike employees, they may legitimately operate continuously rather than during business hours. Activity at 3 am is not inherently suspicious, but a human employee being online at that time might raise a red flag, particularly if it occurs outside normal activity patterns.

Agents can also interact with dozens of services and APIs within seconds as part of a single workflow, while high activity volumes should be expected rather than seen as exceptional.

Yes, many agents act on behalf of users, but they also make independent decisions about how best to complete a task within defined parameters. This creates an additional layer of abstraction between the user request and the actions ultimately performed.

Permissions are also likely to become more nuanced as organizations expand the range of what agents are allowed to do. An agent designed to perform a relatively simple task, such as retrieving information, may later gain the authority to trigger much more complex and consequential business processes. That’s fine, but what if the associated security processes don’t develop at the same rate?

Bringing all these issues together, the challenge is not just to ask whether behavior looks unusual in human terms, but also to understand whether it is unusual for that specific agent.

As a result, organizations must also establish behavioral baselines for AI agents in the same way they have done for human users, while recognizing that the characteristics of those baselines will be fundamentally different and subject to change at any point.

Monitoring should also consider whether the sequence of actions taken remains consistent with the agent's intended objective, because individually legitimate actions can, when combined, produce unintended or harmful outcomes.

What should organizations monitor?

Having established that monitoring AI agents is a specific requirement, organizations then need to put processes in place to ensure their behavior remains within whatever guardrails they have established, and that begins with visibility.

This means clearly understanding which AI agents exist, what identities they have, which systems they can access and the purpose they are intended to fulfill. Behavioral baselines can then be restricted to the applications an agent typically accesses, the APIs it calls, the data it retrieves or modifies, the business processes it supports and the level of privilege it normally exercises.

But, for every agent, context is critical. Within reason, asking a finance agent to access accounting systems is normal, whereas the same agent interacting with software development or HR software platforms may pose a different level of risk. As with human users, the objective is not simply to identify isolated events but to recognize meaningful deviations from an agent's established operating pattern.

The point is that monitoring should complement existing security controls, such as identity management and governance as well as policy enforcement, among other options. The processes and technologies should provide an additional layer of understanding of how authorized AI identities operate within the enterprise.

Without these controls in place, we can expect to see many more headlines about ‘rogue agents’ and the potentially dire consequences of losing control over their behavior.

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Ask any business what it actually does and the answer is almost always specific. A quarrying company extracts rock. A peatland conservation organization restores peatlands. A retailer sells items. The answer is not "we send emails" or "we have meetings."

This distinction between what makes a business unique and the common operations surrounding it has always mattered. More than two centuries ago, Adam Smith recognized that productivity comes from specialization.

Workers focusing on narrow tasks consistently outperformed generalists, and economies grew by dividing labor into ever finer slices. Businesses succeeded not by doing everything, but by becoming exceptionally good at one thing.

Artificial intelligence (AI) doesn’t change this principle. If anything, it reinforces it. So why does much of today’s AI discussion assume the opposite?

The prevailing belief is that increasingly capable general-purpose models will eventually become the best solution for almost every task.

While these systems will undoubtedly transform how organizations operate, there are strong economic and technical reasons to argue that the greatest competitive advantage will come not from general AI, but from specialized systems built around the work that makes each organization unique.

Universal workplace infrastructure

General-purpose AI models are rapidly being adopted across every industry. These tools summarize documents, write code, answer questions, analyze data and automate routine knowledge work well enough that not choosing to use them will soon become a competitive disadvantage.

But their greatest strength also creates their greatest limitation. When every organization has access to the same capabilities, those capabilities stop being a differentiator. Email transformed business, but no company gains competitive advantage simply by having email. Cloud computing became essential infrastructure, but it does not distinguish one organization from another.

General-purpose AI is likely to follow the same path. As these models become ubiquitous, they will increasingly resemble infrastructure – in other words, essential for remaining competitive, but insufficient for pulling ahead.

The obvious question then becomes: where will competitive advantage come from?

Specialist work requires specialist AI

The answer lies in the work that businesses actually exist to do.

General-purpose models excel because they are trained on tasks performed by millions of people. Specialist work is different.

Going back to my opening analogy, a quarrying company does not just need help with emails. It needs to optimize blast patterns based on geological conditions, monitor crusher efficiency in real time and match production to market demand.

A peatland conservation organization does not just need help writing reports. It needs to map erosion across thousands of hectares of remote terrain from aerial imagery, then plan restoration interventions accordingly.

These are not simply harder versions of general tasks. They are fundamentally different problems requiring specialized data. And this is where general-purpose models begin to struggle.

The reasons for this are not primarily about intelligence, but economics. There is way more training data available for common business tasks than for specialized industrial or scientific ones.

More importantly, frontier AI labs have enormous commercial incentives to optimize their capabilities for features that millions of customers will use. By comparison, niche use cases are a harder sell from the perspective of return on investment.

But while the economics of model development may more immediately favor general capabilities, the economics of competitive advantage do not.

Specialization creates strategic value

Today, switching between AI providers is reasonably easy. Many organizations continue to move freely between ChatGPT, Claude, Gemini and other models as new capabilities emerge. Because these systems remain broadly interchangeable, changing providers carries relatively little cost.

But that will not always be true. In fact, that flexibility will survive only as long as the models remain largely generic. Once they acquire organizational memory, switching providers becomes far more difficult.

The next generation of AI is likely to move beyond simply responding to prompts. Instead, models will increasingly learn how organizations operate: how decisions are made, how workflows evolve and how years of accumulated expertise are applied in practice.

This goes far beyond larger context windows, searchable document repositories or vector databases. These technologies allow models to retrieve information. They do not fundamentally change the model itself. True organizational memory means the system adapts to the business over time.

In its strongest form, the model learns continuously, gradually embedding the organization's knowledge into how it reasons and makes decisions.

That creates the potential for a lasting competitive advantage – but only if the organization retains ownership of that accumulated memory rather than effectively renting it from an AI provider. Otherwise, organizational memory becomes a source of vendor lock-in rather than a strategic asset.

Building for the specific

This is why specialized AI deserves a much larger piece of today's conversation.

Across multiple industries we have found that the greatest competitive value comes not from deploying the largest available model, but from designing systems around specific operational problems.

For a major quarrying operation, we developed a computer vision system that monitors rock size distribution on crusher conveyors, detecting hidden downtime that amounted to five to ten percent of operational hours.

For peatland restoration, we built a tool that maps erosion from aerial imagery and 3D depth data, dramatically accelerating what was previously slow and expensive manual assessment.

The list goes on.

Each system delivers value because it was designed around a particular problem: trained on business-specific data, using a model architected to the exact task. In many cases, the models used were smaller, faster and less computationally demanding than general-purpose alternatives. In this sense, specialization is not about adding complexity. It is about removing everything that does not serve the specific goal.

The economics haven't changed

As AI matures, the distinction between general and specialized systems will become increasingly clear. General-purpose AI tools will handle the operational tasks shared by every organization. These will become table stakes, like internet access or accounting software. Necessary, but not distinctive.

The activities that truly define a business – the specific niche it exists to fill – will come to rely on AI built for that purpose. This is not a prediction about technology. It is simply the economics of specialization applied to a new generation of tools. Just as businesses create value through specialization, so too will the AI that powers them.

For organizations looking to capture that value, the time to build is now. As generic AI becomes commoditized, providers themselves will increasingly turn their attention to specialized domains, narrowing the window for businesses to establish their own enduring advantage through specialization.

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AI is being pushed as a friend: your new sidekick that tackles the legwork you don’t have time for anymore. Great!

But what happens when you realize that this sidekick that helps is also scaling new ways that can directly hurt your business?

AI is both friend and foe in ways most consumers and brands are not prepared to handle.

Enter: AI imaging tools. These tools have become incredibly sophisticated and easily accessible, especially after recent upgrades. They can produce multiple high-quality images from a single prompt – no design background or expensive technology required.

The tools designed to help everyday users move quickly are now equally valuable to bad actors looking to make their schemes more convincing and harder to detect – and even consumers who feel pushed to abuse retail policies.

The age of AI-powered abuse is here: seeing should no longer be believing.

AI has democratized fraud

Businesses have long relied on photos and documentation to validate returns and refund requests. For a while, this logic was sound: the time, skill, and cost required to manufacture evidence acted as a barrier for most consumers, but the dam is breaking.

With modern image-generation tools, a malicious actor or your next-door neighbor doesn’t need design skills or specialist software. A single prompt can generate realistic receipts and damaged product images, turning the dial up on return abuse, refund fraud, and friendly fraud.

In fact, the Merchant Risk Council (MRC) found that over the past year, 57% of merchants reported increasing rates of refund and policy abuse. Retail’s global multi-billion-dollar fraud problem just became that much more costly, thanks to AI.

The speed and scale at which these images are produced, altered, and tested are staggering. With AI, fraudsters and abusers can now tailor claims to different merchants, test variations, and scale attacks across multiple accounts and thousands of merchants in short order.

The challenge is that AI-powered abuse is not being driven by one type of actor. Retailers are facing pressure from both organized fraud rings that deliberately exploit systems at scale and everyday consumers who are using new tools to push the boundaries of return and refund policies. While the motivations and sophistication levels are different, both create additional complexity for businesses trying to protect customers while preventing abuse.

Organized fraud groups are increasingly treating policy abuse as a scalable business model. Rather than relying on a single fraudulent claim, these groups look for weaknesses in retailer processes, create multiple accounts, and coordinate activity across merchants. AI-generated images make these operations more effective by providing convincing evidence that supports false claims, helping bad actors appear as genuine customers.

Forter has seen coordinated returns abuse operations where fraud rings used AI-altered images to support claims of product damage, including minor cracks, dents, and faulty components. In one case, Forter stopped a $1.5 million returns abuse operation over Christmas 2025 that used AI-altered damage images to make fraudulent refund requests appear legitimate. When not caught, these abusers resell the quality merchandise after getting refunded for the purchases.

The scale of these attacks is what makes them particularly challenging. A single suspicious claim may not reveal much, but when activity is connected across accounts, devices, behaviors, and merchants, a much bigger picture emerges. Fraud rings can spread activity across multiple retailers, making each individual interaction harder to identify without broader context.

AI has blurred the lines between fraud and abuse

But it’s not just organized criminals who are trying to cash in on these capabilities. The accessibility of AI image tools means everyday opportunistic shoppers are also starting to use them for their own gains. A shopper who wants to avoid the cost of a return, claim a refund for an item they damaged themselves, or take advantage of a flexible policy can now create realistic supporting evidence in seconds.

This creates a growing grey area for retailers. Not every questionable claim comes from a professional fraudster, and not every policy abuser operates with the same level of intent or organization. However, the impact is the same: businesses must spend more time and resources determining which return claims are from trusted customers and policy abusers, while legitimate customers risk facing additional friction as retailers respond to rising abuse.

Fighting AI with AI

Companies may be quick to tighten their policies to curb this AI-powered abuse. But shortening return windows, charging for returns, or delaying refunds reduces abuse at the expense of good customers. The goal has to be identifying the bad actor, not penalizing the good customer. Unfortunately, traditional measures like static rules and manual review processes can't operate at the speed or volume AI-powered fraud now demands.

Detection must operate across the full context – identity, device behavior, transaction history, account age, claim patterns – to catch what any individual piece of evidence would miss. Machine learning is the only realistic path to doing that at scale, in real time, without creating so much friction that legitimate customers are turned away.

Visual evidence needs to be augmented with commerce context and intelligence. A damaged phone screen from a long-term customer with one prior return means something very different than a three-day-old account submitting its fifth claim this week across dozens of retailers.

Businesses need to know with confidence who is behind the claim to assess what’s legitimate and what’s not.

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The image most organizations still have of a cyberattack is fundamentally outdated.

We tend to picture hackers battering down digital walls by exploiting software vulnerabilities or launching convoluted ransomware campaigns. Yet some of the most damaging breaches today involve something far less dramatic.

An attacker enters through the front door using valid credentials, passes authentication checks, and proceeds through the environment as if they are a completely legitimate employee.

Recent attacks targeting public sector organizations in the UK have once again demonstrated just how easy it is to get into an environment if you have the right credentials.

Earlier this year, hackers managed to breach systems used by the UK Foreign Office and local councils using stolen login credentials.

As compromised credentials become increasingly easy to buy and sell on the dark web, organizations face a different sort of challenge: determining whether the person behind a successful login is actually who they claim to be.

Authentication is no longer enough

For years, organizations viewed authentication as a decisive security event. A user entered the correct password, perhaps completed a multi-factor authentication challenge, and was granted access.

The problem is that attackers have, as ever, have found a way around.

Large-scale phishing campaigns, infostealer malware, session hijacking techniques and credential harvesting operations have made legitimate account access easier to acquire than ever before. The UK's Cyber Security Breaches Survey 2025 found that phishing remains the most common cyber threat facing organizations, affecting 85% of businesses that experienced a breach or attack.

For many cybercriminals, phishing is simply the first step in a wider economy built around stolen identities. Once compromised, credentials and authentication tokens are routinely traded on dark web forums, giving attackers a ready-made route into trusted environments.

So why do we continue to treat a successful authentication as proof that a user can be permanently trusted?

In reality, authentication provides only a snapshot in time. It confirms that a user presented the correct credentials at a specific moment. It does not prove that the individual behind the keyboard remains the same person throughout their activity, nor does it account for changing risk factors once access has been granted.

The rise of continuous trust

The concept of Zero Trust isn’t a new principle, but it reflects a broader recognition that trust must be earned repeatedly, not granted indefinitely.

Continuous trust models assume that every request, transaction, and interaction carries some degree of risk. Instead of relying solely on login events, security controls continuously assess whether behavior remains consistent with an individual's expected identity and context.

For example, an employee logging in from their usual location during working hours may initially present low risk. However, if that same account suddenly begins accessing systems it has never touched before, or exhibiting behavior inconsistent with historical patterns, trust levels should automatically decrease.

The critical question is no longer "Did this user authenticate correctly?" but rather "Does this activity continue to make sense?"

Behavior tells a story that credentials cannot

One of the most promising developments in modern cybersecurity is the growing ability to analyze behavioral signals in real time.

Every user leaves behind a digital fingerprint through their actions. They access particular applications, work within predictable timeframes, interact with specific datasets, and follow recognizable workflows. Even small deviations can provide valuable indicators of potential compromise.

Machine learning and advanced analytics increasingly allow security teams to identify these anomalies at scale. The goal is not simply to detect malicious activity but to recognize when behavior no longer aligns with an established baseline.

This is particularly important because attackers who obtain legitimate credentials often attempt to blend into normal operations. They move carefully, avoid triggering conventional alerts, and exploit the fact that many security systems are designed to detect intrusion rather than impersonation.

Behavioral intelligence offers a much-needed layer of scrutiny that credentials alone cannot provide.

Importantly, this approach also helps reduce reliance on static indicators of compromise, many of which become obsolete quickly.

Why identity security sits at the heart of business resilience

Identity-related attacks are increasingly becoming the biggest threat to business continuity. Modern organizations depend on interconnected digital infrastructures spanning employees, contractors, partners, suppliers, and customers. The compromise of a single trusted identity can create a pathway into multiple systems and services.

Security strategies should focus on limiting unnecessary privileges, continuously validating access rights, reducing identity sprawl, and establishing clear visibility across the entire identity ecosystem.

Just as importantly, organizations must recognize that identity is dynamic. Employees join, leave, change roles, gain new permissions, and interact with new applications constantly. Security controls need to evolve at the same pace.

The organizations most resilient to future threats will be those that understand identity as a living system rather than a static credential database.

The future of cybersecurity starts with questioning trust

The next generation of cyberattacks will be defined by attackers blending in rather than breaking in. That shift demands a fundamental rethinking of cybersecurity from first principles.

In an environment where identities are constantly targeted, trust can no longer be binary. It cannot be granted once and forgotten. Instead, trust must become dynamic, measurable, and continuously verified.

The future belongs to organizations that recognize authentication as the start of a security conversation, not its conclusion.

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Australian singer-songwriter Nick Cave has enjoyed a long and successful career as a frontman, musician, and vocalist. His influence is far-reaching, and he's attracted many fans through the decades, although one online encounter prompted a massive response to the rise of AI-generated impersonations of his style and work.

Art through suffering

Months after OpenAI launched ChatGPT for the first time, Nick Cave wrote a lengthy critique on his blog about the use of this tool by fans in replicating some of his work.

In his blog, Cave opined about the origins of music and the conditions needed to generate genuinely human-centric music – and suffering, namely the "internal human struggle of creation" – is the key ingredient.

As far as he's concerned, he added, algorithms don't feel. He admitted that ChatGPT was, at the time, in its infancy and will always have further to go. But he was steadfast in his views that any AI-generated lyrics, or even full songs, would never capture an audience's attention in the same way that innate human artistry could.

Cloud sounds

Despite Cave's views, that hasn't abated the world from experimenting with AI-generated music – and there's been an explosion in both 'slop' as well as catchy machine-made music.

There's been a huge rise in the number of tools that can generate music, including tools like Canva's built-in generator, Gemini's Lyria 3, and specialist tools like Suno AI.

It's no surprise that Deezer suggested in November last year that a third of songs uploaded to its platform were AI-generated. The situation has escalated recently, however, with its in-house AI detection tool now finding that figure rising to more than 50% of daily uploads, amounting to 90,000 songs.

Apple may or may not be working on a mostly-glass 20th anniversary iPhone. This long-rumored passion project would not necessarily transform the iPhone into a fully translucent device, but would extend the front and back glass panels so that they almost meet around a far thinner metal border.

But like a rabbit hiding inside the secret compartment of a magician's hat, the device, depending on your perspective, might not exist and may only appear through the force of sheer will or alchemy.

What's not in question is that the iPhone will celebrate 20 years of existence that started on January 9, 2007, the day Apple founder and CEO Steve Jobs held it aloft at Macworld in San Francisco. That it didn't ship for another six months is unlikely to dissuade Apple from engaging in some introspection and external celebration.

That might include a glassy iPhone.

On one side, we have an analyst stating that, owing to poor manufacturing yields, Apple scrapped plans for the 20th anniversary device. On the other side, we have Apple soothsayer Bloomberg's Mark Gurman claiming the glass iPhone is on track. In the middle, though, is the reality of a device that challenges the limits of physics and promises more glass than metal.

I think it's safe to say that Apple will ultimately produce an exciting, albeit practical, 20th anniversary device that we may get off-schedule in mid-2027. If the design is particularly challenging, then it may be a limited run, priced like a museum piece, and mostly never used by the relative handful or so Apple enthusiasts who manage to score one.

Buried under the [rumor] mill

The entire saga, though, led me to a different realization. I mean, what are we even talking about here? This conversation about a 20th anniversary iPhone is raging (and has been for years) just weeks before Apple unveils not just its iPhone 18 line, but the highly anticipated folding iPhone or iPhone Ultra.

That's a big deal, even more so because the flexible device will be held up not by long-time CEO Tim Cook, but by John Ternus, who will have officially taken over the reins just days before (September 1). It's a huge moment for the company and the tech industry.

And yet we're still obsessed with "what's next?"

No doubt, most tech companies suffer from this a little bit, but nothing like the insatiable curiosity that's dogged Apple since the launch of the first iPhone. The tech rumor mill was, in some ways, developed simply to support all the leaks and guesswork surrounding much of what Apple does, especially as it pertains to the iPhone.

This impulse didn't take years to develop. It was on full display in 2007. This October 9, 2007, story mused about whether or not the next iPhone would feature an Intel chip. This CNet feature obsessed somewhat presciently over a potential name change ("the 2G iPhone"). And the 2009 YouTube video went on an iPhone design deep dive, even touching on the never-delivered "iPhone Nano".

Apple, I'm certain, doesn't mind the attention, and as long as the leaks are not breaking the law or revealing clearly stolen information, they're happy to let the rumors percolate. For those working on these new devices, however, there must also be some level of frustration.

Just imagine if you're working on a big project and your customers say, "Yeah, great, but what about the next one?" Or every time you serve dinner, your family asks what you're making for tomorrow or begins speculating on what you might make and how it'll obviously be better than what you just served.

Apple can never get away from attention, turning our distractible heads toward the future.

When I attend an Apple iPhone launch event, I always laugh at how stories about the next iteration appear right after the launch of the still new iPhone — one I haven't even reviewed yet.

None of this serves consumers, who are usually still struggling to understand their current devices and maybe some of the new hardware currently on offer. Talking about a distant and sometimes improbable future only confuses and probably frustrates them.

Who is this serving?

Lately, the impulse to look past the present is creating this sonic dissonance wherein the pundits cannot even agree on the fundamentals of an uncertain future. They're free to make proclamations, even wildly different ones, because Apple will naturally never tell them (or us) otherwise. The information is so contrasting that it becomes useless (even if markets sometimes move on these rumors).

It's gotten so out of hand that we're already discussing the iPhone Ultra 2 and the Ultra 3. That's right, the unreleased folding phone has, according to rumor, been greenlit for second and third iterations. This is the only scientific proof we have that vacuums have weight and, in fact, get heavier over time. (Narrator: Pure vacuums have zero mass and weight.)

What if we stopped trying to fill these information vacuums with pure speculation and unfounded guesswork? What if we stopped fixating on the "what's next" and stuck with the "what's new," especially at least until we understand the impact of the new thing?

How, for example, will the folding iPhone, which may or may not be called Ultra, fare? It too is entering an uncertain future where it will compete with the well-established Samsung Galaxy Z8 line, especially that oddball Samsung Galaxy Z Fold 8.

As you may have noticed, for as popular as Samsung is, it doesn't really suffer or enjoy a rumor mill in quite the same way. Yes, there are always rumors about the next Galaxy line, but they're usually confined to the upcoming model release and not two or more years down the line.

I'm excited about Apple's next iPhone and am honestly over speculating about anything beyond that. Perhaps you and Apple are too.

Control is one of the easiest words in enterprise AI to misuse.

A business may download a model, run it in its own cloud and negotiate favorable terms, and yet still remain dependent at the points that matter.

Access can create useful freedom. It does not, by itself, prove operating control.

I have learned to treat sovereignty as an operating discipline, not a nationality badge.

If an organization cannot prove what it is running, who can change it, what it depends on and how it can be retired, its control is largely assumed.

These five questions turn an ambiguous claim into a practical test for procurement, security and leadership teams.

1. Can you prove exactly what you are running?

A model name and version number on an architecture diagram are not provenance. Teams need a traceable origin, an artefact identifier, a cryptographic hash or signature, the approved configuration and a record of every material change. That record should include fine-tunes, safety layers, prompt templates, retrieval sources and any post-deployment adjustments that affect behavior.

The harder question is operational: who approved the change, who can make the next one and how quickly can the previous state be restored? If a supplier can alter behavior without the customer seeing the change, or rollback depends on goodwill, control is borrowed. The evidence should be simple enough to inspect during an incident, not buried in a quarterly assurance exercise.

2. Can you verify the weights and every critical dependency?

Model weights matter, but they are only one layer of an AI system. The inference engine, libraries, drivers, orchestration tools, safety filters, retrieval components, monitoring services and update channels all shape how it operates. Open weights may remove one dependency while introducing several others.

Every critical component therefore needs an owner, a known version, a license, an update route, a vulnerability response and a credible substitute. Integrity checks should run when the system is built, when it is deployed and after a suspected incident. A bill of materials is useful only when it connects inventory to verification and action. A long list of components with no decision rights is documentation, not control.

3. Who controls the infrastructure and operating conditions?

Location is not the same as control. A model can sit in a domestic data center whilst essential decisions remain elsewhere. Teams should map who operates the compute, network, identity system, encryption keys, logs and administrative tools. They should know which party can pause, patch, throttle, inspect or revoke the service, and under what conditions.

Owning the building is not enough if a supplier retains remote administration, a proprietary control plane or the only route to scarce accelerators. Backups and telemetry matter too: where they go, who can read them and how long they persist. The strongest test is a degraded-mode exercise. When capacity disappears, credentials are compromised or a provider becomes unavailable, can the organization continue safely and make its own decisions?

4. Which law, license and contract governs the stack?

Technical architecture and legal architecture form the same control map. The relevant questions cover the law, license and contract governing the weights, hosted services, support, telemetry and any fine-tuned assets. They also cover subcontractors, audit rights, incident notification, unilateral changes, export constraints, and the rights available at exit.

Data residency does not settle jurisdiction, and a familiar supplier name does not settle enforceability. Legal, procurement, and security teams should be able to answer from the same evidence set. Contradictions between the contract and the system design are not paperwork problems; they are design defects. The useful test is not whether a supplier appears trustworthy today, but which rights survive a dispute, acquisition, service withdrawal or regulatory change.

5. Can you switch, stop and dispose safely?

No AI strategy is controlled until its exit has been tested. Could the organization move to another model, runtime, or provider without rebuilding the entire service? Can it export configurations, evaluation sets, prompts, logs, and fine-tuning artefacts in usable formats? Can it revoke identities and tokens, remove deployed copies, clear checkpoints and caches, and still retain the evidence required for audit or investigation?

Safe stopping deserves the same attention as fast starting. The exit plan should name triggers, owners, sequence, time limits, and recovery targets, then be rehearsed before dependence becomes difficult to unwind. If switching exists only in a contract slide, it is not a credible option. Disposal is the final proof that control includes ending a dependency without creating a new operational or security risk.

Control must be evidenced, not declared

The answer to these questions will rarely be a clean yes or no. Control has degrees, and different workloads justify different dependencies. What matters is that each answer is evidenced, assigned to an owner and tested again as the stack changes.

Open access, local infrastructure and strong contracts can all reduce risk. None substitutes for the ability to prove what is running, verify its dependencies, identify who can intervene, understand which rules bind it and leave safely.

That is less glamorous than a sovereignty label, but it is the difference between an AI architecture an organization can use and a stack it can genuinely govern.

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When you ask businesses about how they are innovating their ways of working, one word will come up time and again: Agile. Agile delivery. Agile workflows. Agile everything.

The term has been so overwhelmed by buzzwords, supposed best practice guides, and misguided strategy documents that it has almost lost all meaning.

In fact, if many organizations that believe they’re operating in an Agile manner were to take a step back and take an objective look at their ways of working, they would quickly come to realize they have not moved away from legacy command-and-control structures.

In this fog of misunderstanding, a series of myths has taken hold that have made the role of Agile professionals far less clear-cut, making it harder for organizations to put this invaluable methodology into practice.

One root of these problems lies in a tendency for people to narrow Agile’s scope, approaching it as a tick list of processes to adhere to rather than a fundamental shift in how people work and deliver for a business.

It is critical that Agile professionals equip themselves to fact-check these myths as they come up in their work. Every failed project done in Agile’s name diminishes the luster attached to the methodology, increasing the risk that future businesses will reject this powerful way to improve program and project management.

Myth 1 – “Agile delivery leaders are just administrators”

Agile professionals are often seen principally as bureaucratic note-takers, or schedulers – the people who organize everyone else’s work.

This view misses the point: their real value is as facilitators and protectors from distraction. Good Agile professionals create the space for teams to focus, and align delivery with outcomes rather than just activity.

Myth 2 – “Agile professionals are the scrum police”

Some believe Agile delivery operates within a top-down hierarchy, with Agile professionals in place to enforce frameworks and control how team members spend their time.

This is quite wrong: Agile professionals work with teams, coaching them to make better decisions, remove friction, and build confidence in delivery – without resorting to outdated command-and-control methods.

Myth 3 – “Agile delivery must be technical”

It’s a common assumption that Agile delivery leaders require a deep technical background. Technical awareness can certainly help, supporting a deeper understanding of the team’s tasks and challenges, but it isn’t the core of the job.

The real skill lies in understanding people and processes, then creating the conditions for specialists to succeed. In most projects, the need is for strong communication and facilitation across the entire team rather than strategic decision-making or technical troubleshooting.

Indeed, in some circumstances ‘hands-on’ Agile delivery leaders who think they know best can create conflict within teams, damaging morale and motivation in the process. Think of it like athletics: coaches don’t need to run faster than the athletes; their task is to help the athletes run faster.

Myth 4 – “Certification equals capability”

A certificate may prove that somebody knows about an Agile framework, but it does not demonstrate that they’re capable of applying it in practice.

The best Agile professionals succeed through experience, attitude, and ‘soft’ skills. They know from experience how to adapt Agile values and principles to particular circumstances; when to challenge people, and when to simply step back and get out of their team’s way.

Like any profession, being truly effective at the job requires skill, practical experience, and professional characteristics appropriate for the role. A piece of paper doesn’t make you agile; effectiveness with people and process does.

Myth 5 – “Agile delivery leaders are cheerleaders”

Contrary to popular belief, Agile delivery isn’t principally about arranging team socials or keeping everyone upbeat during stand-ups.

The value lies in keeping people aligned and productive under deadline pressure and/or changing priorities; after all, no plan survives contact with the enemy. Agile delivery leaders are there to create stability within an uncertain environment: they help teams stay focused on what matters, and shield them from distractions so they can maximize time spent ‘in the zone’.

That said, there is value in boosting morale – and championing a team’s achievements is one way to do this. According to the “broaden-and-build” theory of psychologist Barbara Fredrickson, positive emotions broaden our thought-action repertoires, enabling us to develop skills and resources that improve our long-term productivity.

One famous study from the "Journal of Labor Economics" showed that happiness at work leads to improved performance, especially in jobs where creativity and initiative are key.

Myth 6 – “Agile delivery leaders must remove all blockers for their teams”

While impediment removal is without doubt a core responsibility for an Agile delivery leader, the reality is that teams solve most of their own issues – perhaps with the assistance of a little coaching or support.

Nonetheless, sometimes a challenge is too big, distracting or political for them to handle, and Agile delivery leaders must step in – often by representing the team with stakeholders and at governance layers: this can be helpful in reducing the ‘noise’ around the team and the number of distracting interactions that take team members away from the core task.

As captured in the proverb: “Give a man a fish, and you feed him for a day; teach a man to fish, and you feed him for a lifetime,” effective Agile delivery leadership is about enabling, not babysitting.

True Agile

So Agile is a much more subtle and responsive discipline than the stereotypes present – and it must be led by subtle and responsive practitioners. The approach can generate huge benefits – saving time, handling change more elegantly, reducing project failure rates and, above all, creating better products and services that more effectively meet customer needs and objectives.

These goals are achieved by working with people – rather than by tinkering with processes – to surface problems early in the development process, resolve them quickly and with a minimum of fuss, and avoid interfering when the team is already functioning effectively.

High-performing teams depend on trust, accountability and continuous learning. Leaders who foster those qualities are more likely to deliver successful outcomes, creating teams that stay focused, take ownership of their work, and continuously improve how they operate.

This is a role that is all about empowerment; when they’re doing their job well, much of an Agile practitioner’s work occurs out of sight. So how do you know when you’ve got a really great Agile delivery leader? When their team runs perfectly well without them.

This may not sound like the smartest business model for the practitioner, but it’s definitely the best outcome for the client’s own business – and that, ultimately, is what Agile is all about.

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AI can already write production code, review pull requests, generate documentation, diagnose bugs, and propose architectural changes. Its influence now extends beyond developer productivity, affecting how engineering teams are organized, how decisions are made, and where technical authority rests.

CTOs, meanwhile, have always acted as orchestrators. Company growth gradually pulled their attention toward hiring leaders, setting architecture, resolving trade-offs, and improving team performance. Their influence flowed through the organization they built and the people they developed.

AI now changes the organization beneath technical leaders. As of May 2026, Claude authored more than 80% of the code merged into Anthropic’s codebase, while the typical engineer merged eight times as much code per day during the second quarter of 2026 as in 2024. Engineers increasingly direct and review AI-generated work, while retaining responsibility for technical judgment, goal selection, and higher-level decisions.

Implementation and debugging can increasingly pass to coding agents, leaving engineers responsible for defining tasks, reviewing results, and deciding when human intervention is required.

The middle management tier consequently faces compression, while the CTO comes closer to execution because agent access, architectural rules, security controls, and release criteria affect the entire company.

The CTO’s responsibility is building a verification machine capable of producing sound technical decisions at high speed.

The middle tier compresses

Traditional engineering organizations have always grown through coordination. A CTO worked through vice presidents, directors, and engineering managers, each translating company priorities into technical plans. Managers assigned work, tracked delivery, resolved blockers, and kept teams aligned.

AI reduces much of this coordination burden. An engineer can describe a feature, ask an agent to inspect the codebase, generate an implementation, write tests, and prepare a pull request. More advanced systems can divide work across several agents and combine their output.

The engineer increasingly manages an AI development team, which compresses roles centered on task distribution and progress tracking. Human leadership retains its value through coaching, conflict resolution, recruitment, and professional growth, while coordination as a standalone function carries less leverage.

Responsibility, therefore, spreads in two directions:

Engineers gain greater ownership because they command far more productive capacity; CTOs become more involved in the systems governing this capacity because one weak permission rule or review gate can expose the entire company.

The distance between technical leadership and execution narrows. A CTO may write little application code, yet needs a precise understanding of how agents create, inspect, test, and deploy it. The role owns the engineering operating system governing people, agents, and software delivery.

The CTO’s verification machine

Verification is now the central technical challenge.

An AI system can generate several possible implementations during the time a human engineer once needed to produce one. This abundance creates a selection problem because companies must identify which implementation fits the architecture, meets security requirements, remains maintainable, and serves the product goal.

The CTO must design a system capable of making these judgments consistently.

Scoped permissions confine each agent to the files, databases, and services required for its assigned task. Automated evaluations test security, performance, and reliability, while agents review one another’s work before sensitive actions reach a human reviewer.

Human approval, however, loses value when engineers face a constant stream of requests that rarely require intervention. After several rounds of autonomous testing and review, most proposed changes arrive in acceptable condition.

Engineers grow accustomed to approving them, attention declines, and the exceptional case becomes harder to detect. Aviation, medicine, and nuclear operations have studied the same effect: repeated exposure to routine confirmations can turn oversight into habit.

Effective verification therefore depends on reducing the volume presented to humans. Routine and reversible actions can pass through automated controls, while unusual, irreversible, or high-impact decisions receive focused review. The interface should present evidence, alternatives, uncertainties, and possible consequences in a form that encourages scrutiny rather than a reflexive approval.

Firm boundaries remain essential. An agent may propose a database migration while execution requires human authorization. It may generate a deployment plan while production credentials remain outside its permissions. It may identify a vulnerability while changes to authentication controls receive senior review.

The system should also measure the quality of oversight through rejection rates, review times, escalation patterns, and the frequency with which human intervention changes an outcome. Human judgment offers the greatest value when attention is reserved for decisions where experience can alter the result.

Audit trails, rollback procedures, and ownership rules complete the machine by making every autonomous action attributable, inspectable, and reversible. These controls determine how an AI-enabled engineering organization behaves and require architectural judgment, security knowledge, product awareness, and an understanding of human behavior under pressure.

The CTO increasingly designs the conditions under which technical decisions emerge, turning individual judgment into a system capable of producing consistent quality without exhausting the people responsible for its highest-risk decisions.

Coding is abundant; judgment is not

AI lowers implementation costs, while software quality still depends on judgment.

A company can now produce more features, integrations, and experiments than its customers need. It can create technical debt faster than any human team and fill a codebase with locally correct changes capable of weakening the system over time.

Planning, testing, and prioritization consequently become the main constraints on engineering output. Strong organizations will know which problems deserve attention, how each feature supports the product, and where technical compromises create long-term costs.

Technical strategy gains value as implementation capacity grows.

Security is more important because agents can act across more systems at greater speed, while testing carries greater responsibility because generated code can appear persuasive while hiding subtle errors. Maintainability is harder as software volume grows faster than human comprehension.

AI commoditizes implementation and raises the value of technical judgment.

The strongest CTOs will turn judgment into repeatable mechanisms by encoding standards into evaluation suites, review policies, deployment gates, and agent instructions.

Privacy, trust, and security

AI systems increasingly retrieve information, make decisions, call external services, modify records, and trigger actions across business systems. Risk therefore depends on authority as much as intelligence.

An agent with access to customer records, payment systems, private repositories, and production environments carries enormous operational power. Prompt injection, compromised model providers, manipulated data sources, and unintended autonomous actions can become entry points into critical systems.

Privacy and trust become architectural concerns. CTOs must define model governance, data permissions, identity controls, and approval requirements. They also decide which information can enter third-party models, which tasks require isolated environments, and which actions always need human confirmation.

Agent identity is essential because companies need to know which agent performed an action, who authorized the task, which data informed the output, and which rules governed the process. Capability provides an incomplete standard because permission determines the level of risk.

After all, a mediocre model with production credentials is more dangerous than a brilliant one in a sandbox.

A strong AI engineering organization treats every agent as an active participant with a defined identity, a limited role, and an auditable history.

Users judge the product

Companies often present AI as a product feature because it attracts attention, although the largest gains may come from using it inside the development process.

Customers judge software by quality, reliability, safety, and speed of improvement. The number of agents contributing to a codebase carries little relevance to their experience. Competitive advantage comes from turning increased engineering capacity into better products while preserving trust.

AI therefore belongs primarily to the production side of the company. It helps engineers explore more implementations, test changes more thoroughly, respond to incidents faster, and improve existing features with greater frequency.

Product design still determines how much complexity reaches the customer and how effectively the software handles permissions, routing, configuration, and other operational decisions.

Customers experience the value of AI through faster product improvement, fewer defects, more responsive support, and software capable of adapting more effectively to their needs. The technology itself can remain largely invisible.

The CTO must ensure increased engineering capacity serves the product and strengthens the customer experience.

CTOs as product leaders

When implementation was expensive, product teams defined requirements and engineering teams built them. Limited development capacity made the division easier to maintain.

AI weakens this boundary because technical capability can influence product direction almost immediately. A CTO can explore several product concepts with agents, create working prototypes, and evaluate constraints before a conventional development cycle begins, bringing engineering into product decisions earlier.

The CTO must decide where automation improves the experience and where human involvement remains essential. Some decisions benefit from speed and consistency, while others require empathy, context, accountability, and careful interpretation of consequences.

These choices combine product judgment with technical judgment. Breadth gains value because AI can help technical leaders acquire deep knowledge of unfamiliar domains quickly, while the advantage comes from connecting engineering, security, customer needs, and business strategy into one coherent system.

Future CTOs will need to understand the complete product environment, including how the company operates, how customers experience it, and how autonomous systems participate in both.

From organization builder to machine builder

Earlier generations of CTOs were remembered for the organizations they created. Their legacy lived in the people they hired, the leaders they developed, the culture they established, and the engineers who continued advancing the company after they left.

AI adds another durable artifact through the agent fleet, permission model, evaluation systems, deployment gates, architectural constraints, and feedback mechanisms left behind by technical leadership. These components determine whether a company can continue producing software safely after senior leaders depart.

The Industrial Revolution expanded physical production by embedding human knowledge into machines and processes. AI can create a comparable expansion in software development by turning parts of technical reasoning into systems capable of continuous operation.

Small teams can build products once requiring entire departments, while established companies can test ideas and improve software at exceptional speed. The outcome depends on the quality of the machinery surrounding the models, because code generation alone can increase software volume while strong verification systems convert AI capacity into reliable innovation.

The next generation of CTOs will be remembered for the machine they leave behind, whose agents, permissions, gates, and evaluations allow humans and AI to keep producing better software long after its architect has gone.

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Artificial intelligence has moved from experimentation to everyday business use faster than almost any technology in recent memory.

For small businesses, AI adoption needs no convincing as most already see the benefits. The real challenge is now transforming isolated AI use into consistent business value.

Goldman Sachs found that 76% of small businesses are using AI, and among those users, 93% say it has had a positive impact. Yet only 14% have fully integrated AI into core operations.

That gap is where the next stage of AI adoption will be won or lost.

The question is no longer whether small businesses can access AI. It is how they can make it part of their business.

For me, that means moving beyond AI as a feature list and toward AI as a trusted experience employees can rely on in the flow of work.

The focus now should be on helping small businesses progress from deploying AI in everyday tasks, to reshaping workflows, to eventually inventing new services, business models and revenue streams.

Start where the work gets stuck

The temptation is to start with the technology, but the better starting point is the work itself. A modern AI-ready device, collaboration setup or workplace platform can promise faster content creation, more productive meetings or automated reporting. Those capabilities matter, but the question is more basic: what problem is slowing the business down?

The problem might be missed customer follow-ups, teams spending too much time turning raw information into action, or even just slow response times. AI becomes valuable when it is pointed at a specific bottleneck and measured against a business outcome: time saved, errors reduced, revenue protected, customers retained, or employees freed up for higher-value work.

This outcome-first mindset is critical because the goal should not be to optimize an old process simply because it exists. It should be to ask what the business needs to achieve, then design the workflow and the technology around that result.

Discipline is important because small businesses do not have much room for technology theater. The most useful AI projects are rarely the flashiest. They are the ones tied to work that happens every day.

Redesign the workflow, not just the task

The next step is to move beyond individual productivity. Many employees are already using AI in small, informal ways, with a 156% increase between 2023 and 2025 in shadow AI usage. Shadow AI refers to employees using AI tools without formal approval, oversight or integration into company systems.

Employees ask AI to clean up an email, summarize a document or prepare a first draft. While those use cases can help, they usually create isolated gains. The bigger opportunity comes when AI is built into the workflow itself.

Consider a customer-facing team. AI can draft a response. But the bigger opportunity is redesigning a process around it. Let AI help categorize the request, identify urgency, suggest the next best action, and leave important judgment calls to a person.

For a lean operations team, AI can turn meetings, documents and business data into clearer next steps, helping reduce the manual follow-through that often slows momentum. Over time, this will become less about a single AI tool assisting with a single task and more about groups of AI agents working across connected workflows, with people shaping the strategy, setting the guardrails and orchestrating the work.

This is where many organizations still struggle. McKinsey’s 2025 State of AI research found that 88% of organizations use AI in at least one business function, but only about one-third have begun scaling AI across the enterprise. The same research found that AI high performers are nearly three times as likely as others to have fundamentally redesigned workflows.

In other words, the return comes less from sprinkling AI over old processes and more from rethinking how work should move. That is the difference between deploying AI, reshaping work and ultimately inventing new ways for the business to grow.

Train people to use AI with judgment

It’s not enough to invest in tools. Businesses must also invest in helping employees use them effectively. AI works best when employees understand what it is good at, where it can fail and when human judgment is required. Not every small business needs a large training program, but it does need practical guidance: which tools are approved, what information should stay protected and when outputs need human review.

Clear guardrails allow a business to scale AI with confidence. Goldman Sachs found that small businesses using AI cite data privacy and security concerns, lack of technical expertise and difficulty choosing tools among their top challenges. 73% said they would benefit from more training and resources to implement and evaluate AI successfully.

When employees are trained to use AI responsibly, technology becomes less of a risk to manage and more of a capacity builder that helps small teams work with greater speed, confidence and focus. It also builds the trust employees need to treat AI not as another feature to try, but as a dependable part of how work gets done.

Make AI a capacity builder

AI is often framed as a replacement story. In practice, many small businesses are using it as a force multiplier. The U.S. Chamber of Commerce found that 58% of small businesses use generative AI, up from 40% in 2024 and 23% in 2023. It also found that 82% of small businesses using AI increased their workforce over the past year. For lean teams, AI can create breathing room: less time spent chasing notes or repeating manual tasks and more time spent with customers and employees.

None of this happens automatically. Small businesses need to choose technology with integration in mind, not just features in isolation. They need to understand where data lives, how systems connect and whether employees can use new tools without adding more complexity.

They also need the confidence to seek outside guidance, whether from technology partners, managed service providers, industry peers or local business networks. The right support can help small businesses see where AI should simply deploy, where it should reshape the way people work and where it may create room to invent something entirely new.

The bottom line: AI should change how work gets done

The small businesses that get the most from AI will not necessarily be the ones that adopt the most tools. They will be the ones that ask sharper questions: Where are we losing time? Where are decisions too slow? Where are customers waiting? Where are employees doing work that software could support safely and reliably?

AI has already changed what small businesses can do. The next challenge is changing how work gets done. For small businesses, the real opportunity is not to add AI everywhere, but to apply it with purpose: deploy it where it helps today, reshape the workflows that define the business and invent new ways to create value tomorrow.

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UK organizations are racing to modernize customer experience. AI-powered chatbots, agent assistants, workflow automation, and self-service tools are rapidly becoming standard across customer operations as businesses look to improve responsiveness, reduce pressure on frontline teams, and meet rising customer expectations.

But many organizations are discovering that deploying new technology is the easy part. The harder challenge is making those systems work reliably in the real world.

Too often, businesses are layering AI tools onto disconnected data, siloed systems, and infrastructure that was never designed for real-time customer engagement. On paper, the technology stack looks modern. In practice, customer experience still feels fragmented.

Customers are passed between channels without context. They repeat information multiple times.

Automated systems provide inconsistent answers. Human agents lack visibility into previous interactions. The result is a more complicated customer journey. This is becoming one of the defining challenges of enterprise AI adoption. For years, organizations focused on adding more digital capabilities to customer operations. Now, AI is exposing the operational weaknesses those businesses already had underneath.

The conversation around customer experience transformation has largely focused on speed and innovation. But the more important question is whether organizations can deliver AI-enhanced experiences consistently, accurately, and responsibly at scale. This is where trust becomes critical.

Customers are less forgiving of AI mistakes

In customer experience environments, trust is fragile. A delayed response may frustrate a customer. An inaccurate response can damage confidence entirely. This becomes especially important as generative AI moves into customer-facing interactions.

Unlike traditional automation, generative AI introduces unpredictability. Responses can vary. Outputs may be inaccurate. Systems can generate information that sounds convincing but is completely wrong. In highly regulated or customer-critical sectors such as financial services, healthcare, or public services, the consequences can be significant.

Customers are also often less forgiving of mistakes made by automated systems than those made by human employees. When AI gets something wrong, customers do not simply blame the technology. They blame the organization behind it. This is why many businesses are realizing that deploying AI is not simply a technology decision. It is an operational and governance challenge as well.

The organizations seeing the strongest long-term results are not necessarily the ones deploying the most AI tools. Instead, they are the ones creating environments where those technologies can operate safely, transparently, and with clear oversight. That requires far more than experimentation.

It means understanding where customer data comes from, how decisions are being made, when human intervention is needed, and how systems are monitored over time. Automation still requires accountability.

Disconnected systems create disconnected experiences

Many of the problems businesses face today are architectural rather than technological. For years, organizations approached CX transformation through isolated point solutions - separate tools for chat, analytics, automation, engagement, and workforce management. But disconnected systems inevitably create disconnected customer experiences.

Customers do not care which department, platform, or channel they are interacting with. They expect organizations to remember who they are, understand what has already happened, and resolve issues without forcing them to start again every time they switch touchpoints. That becomes extremely difficult when customer data is fragmented across multiple systems that cannot communicate effectively with one another.

This is why many organizations are now shifting focus away from simply adding more AI capabilities and towards operational consolidation. Businesses are recognizing that customer experience is no longer defined by individual interactions but defined by continuity across the entire journey. Without unified data, integrated orchestration, and consistent visibility across channels, AI risks amplifying operational complexity instead of reducing it.

The next phase of CX transformation will be about maturity, not experimentation

The pressure on organizations to move quickly is understandable. Businesses are dealing with rising service expectations, economic pressure, and ongoing demands to improve efficiency while maintaining customer satisfaction.

AI can absolutely help address those challenges. Used effectively, it can reduce repetitive workload, support frontline employees, improve response times, and deliver more personalized customer experience at scale.

But speed on its own it not a strategy.

The organizations that succeed over the next decade will not necessarily be the ones adopting AI the fastest. They will be the ones building the operational maturity required to make those systems reliable enough for customers to trust every day.

That means investing in integration, governance, data quality, and accountability with the same urgency that businesses are investing in AI itself. Right now, many organizations are still focused on what AI can do. The more important challenge is ensuring customers can trust how it behaves when it matters most.

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The conversation around post-quantum cryptography (PQC) has shifted. Organizations are no longer asking whether they should prepare for quantum computing, but how quickly they can execute a migration that many expect will take years to complete.

As technology providers accelerate their roadmaps, governments introduce new expectations and boards seek greater assurance over cyber resilience, quantum readiness has become a business priority rather than a future technology project.

That urgency is being driven from several directions. Google and Microsoft have both set out roadmaps that point towards 2029 as a significant milestone in the transition to quantum-safe cryptography, while the recent US Executive Order on strengthening national cyber security reinforces the expectation that organizations begin preparing for the post-quantum era.

Together, these developments are shortening the planning horizon and increasing the pressure on CISOs to move from strategy to execution.

That shift from awareness to execution is reflected across the industry. Gartner's 2026 CISO Role-Based Survey: State of the Union found that fewer than one in four organizations have made measurable progress towards quantum readiness and only 8% have a usable cryptographic inventory.

DigiCert’s own Quantum Readiness Outlook research tells a similar story, as most IT and security leaders expect quantum computers to be capable of breaking today's encryption methods within the next three to five years, yet only 7% report that more than half of their digital certificates are already quantum-safe or hybrid.

The greatest challenge organizations face is no longer understanding the risks posed by quantum, but instead about becoming quantum-ready before today's cryptography becomes tomorrow's liability.

Why the risk is already here

Quantum computing has the potential to deliver significant advances across science, medicine and artificial intelligence, but it also threatens the asymmetric cryptography that secures digital identities, software, financial transactions and communications. The concern is no longer confined to the arrival of a cryptographically relevant quantum computer.

Threat actors are widely believed to be adopting a harvest now, decrypt later (HNDL) approach, collecting encrypted data today with the expectation that it can be decrypted once quantum capabilities mature. For organizations protecting information with a long operational life, that changes the timeline completely.

Our data found that 84% of organizations believe at least some of their encrypted data is vulnerable to HNDL attacks. Financial transaction records and banking data were identified as the assets most at risk (58%), followed by cryptocurrency wallets and private keys (53%).

For CISOs, this provides an important starting point because rather than attempting to replace every cryptographic system simultaneously, the priority should be identifying the systems protecting long-lived, high-value information and focusing migration efforts where the business impact would be greatest.

Discovery before deployment

For many organizations, the greatest challenge is not selecting quantum-resistant algorithms, but understanding where vulnerable cryptography exists across the business.

Furthermore, cryptography underpins cloud infrastructure, enterprise applications, connected devices, operational technology, software signing and countless machine identities. Over time, certificates, keys and algorithms become distributed across complex environments, often without a complete inventory of where they are used or which business services depend on them.

This is why discovery should be the first stage of every quantum readiness program. Organizations cannot prioritize risk, assess dependencies or build a realistic migration roadmap without first understanding their existing cryptographic estate. Discovery also enables security teams to identify the systems protecting their most valuable assets, allowing them to focus investment where it will have the greatest impact.

Once that foundation is in place, organizations can begin introducing quantum-resistant algorithms alongside existing infrastructure, testing interoperability, prioritizing critical systems and building the crypto-agility needed to adapt as standards continue to evolve.

Building a practical roadmap

Preparing for post-quantum cryptography is not a single technology upgrade, in fact, it is a long-term business transformation program that requires collaboration across security, infrastructure, application teams and technology partners.

The discovery stage provides the foundation by revealing where cryptography is deployed, exposing hidden dependencies and identifying systems that may otherwise be overlooked. Those unknowns are often the biggest source of delay, making early discovery essential to building a realistic migration roadmap.

With that understanding, organizations can begin prioritizing the systems that present the greatest business risk while assessing whether their wider technology ecosystem is ready for the transition.

That means working with software, hardware and cloud providers to understand their post-quantum roadmaps, identifying platforms that will require upgrades, and reviewing critical infrastructure, including web servers and TLS implementations, to ensure they can support quantum-safe cryptography.

It is important to remember that cryptographic standards will continue to evolve, making automation and crypto-agility essential for managing certificates, keys and algorithms at scale and adapting to future change.

The organizations that succeed will not be those that wait for quantum computing to arrive, but those that begin preparing now. By uncovering the unknowns within their cryptographic estate and building a phased migration strategy based on business risk, CISOs can strengthen resilience today while preparing their organizations for the cryptographic challenges of tomorrow.

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For much of the semiconductor industry’s history, performance debates have centered on compute throughput and memory capacity. Faster processors, wider vectors, and larger caches have been the primary levers for system architects, often treating the movement of data between them as a secondary concern.

Today, a different constraint is asserting itself as AI data center workloads proliferate, architectures diversify, and systems extend beyond traditional computing into the physical world. Data movement, rather than processing or storage, is increasingly defining the limits of performance, power efficiency, predictability, determinism, and scalability.

This shift around data movement is already visible. Across applications like advanced SoCs, AI accelerators, chiplet-based systems, and now in physical AI applications such as robotics, industrial automation, and intelligent vehicles, it has become clear that transporting the vast quantities of data required by such workloads is more demanding than processing them.

Physical AI does not introduce a new problem so much as it exposes an existing one: when systems must perceive, decide, and act in closed loops under real-time and safety constraints, inefficient or unpredictable data movement quickly becomes the dominant bottleneck.

The implications for both AI in the data center and physical AI extend far beyond any single market. As data center demand increases exponentially and the industry pushes toward increasingly complex, heterogeneous systems in anything from vehicles to industrial systems, data movement has become the primary performance battleground.

Performance limits are increasingly defined by data movement

Modern semiconductor systems integrate an unprecedented number of processing elements, including general-purpose CPUs, AI accelerators, GPUs, DSPs, sensor processors, and domain-specific engines. Still, simply adding compute resources rarely delivers proportional gains. In many advanced designs, performance plateaus long before compute capacity is exhausted.

Compute units are left sitting idle not because they lack capability, but because data cannot reach them efficiently or predictably. Late-stage analysis of high-performance SoCs often reveals that throughput shortfalls stem from contention, imbalanced bandwidth allocation, or inefficient routing within the communication fabric, rather than from compute limitations.

This is especially visible in physical AI systems, where delays introduced by data movement propagate directly into system behavior in closed-loop architectures. Latency or contention in the transport fabric can destabilize control algorithms, reduce accuracy, and even force over-provisioning of compute to compensate for the absence of bounded latency and guaranteed behavior.

While physical AI makes data movement constraints more immediately visible, the same forces are amplified dramatically in the data center. To push enormous volumes of data through extremely wide interfaces, processors increasingly contain hundreds of compute and accelerator instances, press against reticle-scale integration limits, use chiplet-based designs, and interface with multiple stacks of high-bandwidth memory (HBM).

As designs scale, the combined costs of moving data grow rapidly. In this environment, abundant compute and memory bandwidth offer little benefit without a data movement architecture capable of managing data at scale. What is a tight constraint in physical AI becomes an overwhelming one in the data center.

In both the AI data center and physical AI, performance is inseparable from the behavior of the data paths that connect perception, decision, and actuation. As a result, interconnect design can no longer be a back-end integration task. It has become a central architectural decision, on equal footing with compute and memory selection.

On-chip data movement is fundamentally different

Data movement inside a chip operates under constraints that differ radically from those of off-chip networking or board-level interconnect. On-chip transfers are extremely frequent, tightly synchronized with execution, and subject to stringent latency and power budgets.

Traffic is also inherently heterogeneous. A single system may need to carry high-bandwidth AI data streams, cache and coherency traffic, latency-critical control messages, and safety-related signaling, often simultaneously. These flows have very different requirements and cannot be treated uniformly.

Traditional best-effort arbitration models break down quickly under these conditions. Bursty accelerator traffic can interfere with control-plane and real-time data paths, leading to unpredictable system behavior. In mission and safety-critical or physical AI applications, such interference is unacceptable.

As a result, quality of service (QoS), traffic isolation, bounded latency, and determinism are now architectural requirements, not just optional optimizations. The interconnect fabric increasingly functions as an active system component, enforcing policy and guaranteeing behavior, rather than as a passive conduit for bits.

AI, heterogeneity, and physical intelligence magnify the challenge

AI workloads fundamentally change the character of data movement. They generate massive data volumes, irregular access patterns, and asymmetric traffic flows. At the same time, heterogeneous architectures distribute computation across many specialized engines, eliminating any single “center” of the system.

In both data center and edge contexts, this decentralization increases coordination costs. Data replication, synchronization overhead, and inefficient sharing can consume significant power and latency, eroding the benefits of specialized compute. Rather than compute placement, the challenge designers face now lies in how to efficiently orchestrate data movement between diverse compute elements.

Physical AI places additional pressure on these architectures. Unlike batch or best-effort inference workloads, physical systems operate continuously in real time. Sensor data must be ingested, processed, and acted upon within strict deadlines. Feedback loops amplify even small inefficiencies in data movement.

Together, these demands reinforce the broader lesson that heterogeneity without a deliberate data movement architecture leads to complexity and inefficiency, not scalability.

Chiplets turn data movement into a system-level design problem

Chiplet-based multi-die architectures promise yield advantages, faster innovation cycles, and importantly, flexibility. But they also elevate data movement challenges beyond a single piece of silicon.

Cross-die communication introduces higher energy per bit, tighter physical and architectural constraints, and more extra latency than on-die data movement. Interfaces that were trivial within a single die become critical bottlenecks once they must traverse package boundaries.

Successful chiplet systems, therefore, require system-level planning of data movement, encompassing topology, hierarchy, coherency and protocol selection, and physical constraints. Partitioning functionality without an integrated strategy for how data flows between partitions often increases, rather than reduces, overall system complexity.

Control and safety domains may span multiple dies, and failures or delays in inter-die communication directly affect system behavior. Chiplets demand not only modular compute but also modular, predictable connectivity.

Deliberate data movement is a strategic differentiator

Across markets, a clear divide is emerging. Teams that treat data movement as an incidental consequence of integration struggle with rising complexity, unpredictable performance, and costly late-stage redesigns. Those who deliberately architect data movement gain sustained advantages.

Deliberate data movement architecture enables a host of benefits. Bottlenecks in latency, bandwidth, and power can be identified early; system architectures can be made easily repeatable across product generations; and architectural intent, software behavior and physical implementation can be correlated much more closely and reliably.

Additionally, it better enables teams to explore the tradeoffs between alternative floorplans and interconnect topologies (as well as logical & physical partitioning of multi-die products) well before physical constraints harden.

Achieving this at scale increasingly requires automation that is physically aware, yet architect controlled. Modern approaches synthesize interconnect structures directly from connectivity, traffic, and layout constraints, while allowing designers to intervene and iterate rapidly. The goal is not to hide complexity, but to make it tractable.

Equally important is the generation of consistent architectural, software, verification, and implementation views from a unified system description. This top-down correlation reduces mismatch between intent and realization, helping to minimize risk and accelerate time to market.

The new battleground

As systems extend into the physical world, while architectures grow more complex to keep pace with expanding AI workloads the semiconductor industry is entering a new phase. Performance, power, safety, and scalability are no longer determined primarily by how fast data can be processed, but by how intelligently, efficiently, and predictably it can be moved.

Physical AI makes this reality impossible to ignore, but the lesson applies broadly, from data centers to embedded systems. The invisible highways inside chips have become the decisive terrain on which competitive advantage is won or lost. In this environment, data movement is no longer simple plumbing. It is the battleground where the next generation of semiconductor systems will succeed - or fail.

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AI transformation is a technical challenge, an operational challenge, a governance challenge, a cultural challenge and, above all, a leadership challenge.

Some AI implementations have been relatively straightforward, while others, particularly where generative AI has been deployed too quickly, have encountered setbacks.

The headlines are filled with stories of employee resistance, AI systems behaving unpredictably, unexpected costs associated with AI usage, alongside encouraging examples of organizations delivering meaningful business value.

Despite these mixed outcomes, confidence in AI remains high because organizations are seeing tangible results where implementation has been approached strategically and with strong leadership.

If organizations take the promise - and the risks - of AI seriously, then leadership attention is integral. Leaders need to consider both the immediate and second-order implications across the P&L, workforce, training, culture, customer experience and every part of the organization.

PwC’s latest UK CEO Survey found that 98% expect to make material changes to their business or operating model this year, and 93% say their businesses have now adopted GenAI to some extent. But on the flipside, only 14% of UK business leaders say GenAI has improved profitability over the past year.

Moving quickly

Many businesses have moved quickly from AI curiosity to AI adoption and then found that the hard phase only just begins then. Many organizations are now grappling with the gap between AI adoption and measurable commercial returns.

Leaders now need to decide where AI genuinely changes the operating model, how teams should work differently, and how to maintain trust with employees and customers as automation becomes more embedded. Transformation cannot be delegated to IT alone. It requires leaders to set clearer priorities, redesign workflows, invest in management capability, and create a culture where people understand both the opportunities and the limitations of new tools.

The initial phase of experimentation and early implementation was comparatively straightforward. Many organizations have now entered a more demanding stage and discovered that AI transformation is very much like all other major transformations: it is not simply a technology initiative; it requires thoughtful planning, organizational alignment and sustained leadership.

The larger a business grows the more complex the real ways of working, with business processes and personalities, making successful transformation a test of judgement, prioritization, trust, and many other ‘soft’ skills as much as technical deployment.

Pilots are easy. Broader change isn’t

AI pilots can often succeed precisely because they are contained, deliberately low-risk, and may be owned by enthusiastic teams looking to solve roadmap challenges. The bigger challenges begin when new automations affect workflows, decision-making, customer experience and employee roles in novel ways, forcing the need for leadership.

One common challenge is that organizations layer AI onto existing processes without questioning whether those processes are still the right ones. For example, a sales team may begin using AI to summarize calls - an easy win. The more important question is how that time is then reinvested: how follow-ups improve, how to make customer relationships stronger, how tracked information is used to power insights.

AI only becomes transformational when leaders connect the technology to measurable improvements in how the business creates value. That requires continual reassessment to ensure that the technology, processes, stakeholders and broader business context remain aligned. This is why genuine business transformation cannot be delegated to IT or to any one department.

IT teams are essential to execute, but leadership is better placed to decide the business priorities that technology serves. Those priorities include fundamental questions: which workflows should change, which decisions should remain human-led, what risks are acceptable, and how success will be measured.

There is a strong case for senior leadership teams becoming fluent not only in AI but also in the capabilities that enable its successful application, including critical thinking, strategic planning and storytelling. These skills help leaders understand the broader systemic effects that AI can amplify - positively or negatively.

If AI is managed as a tool rollout then adoption may not optimally raise productivity, profitability or customer value. Achieving meaningful outcomes requires stronger management disciplines, clearer ownership across leadership teams and greater accountability spanning operations, sales, customer service, HR, finance and compliance.

Trust will be the biggest commercial issue

Pipedrive’s recent research, found that 55% of the public do not fully trust AI. More broadly, many consumers remain skeptical of the sales process itself. Getting over this, changing the perception of the business offering means using AI and strong leadership in concert to make internal transformations support raising external confidence.

Both customers and employees need to understand where AI is being used, why it is being used, and where human judgement still matters. And the research was clear: sales and customer experience teams must be able to develop relationships, showing emotional intelligence and concern for the customer if they are to win hearts and minds.

Using AI for faster responses, automated recommendations or AI-generated communications can damage confidence if they feel impersonal.

AI-generated customer communications should always be appropriately supervised, as inaccuracies can rapidly erode trust. Internally, leaders should ensure employees receive appropriate training, clear guidelines and confidence that AI is being used to support them rather than simply replace them. Transparency, sound judgement and consistent communication are essential if meaningful transformation is to succeed.

Leadership is managing continuous change

Leadership has always been about helping organizations adapt and improve. AI raises that expectation even further.

AI is unlikely to be a one-off transformation program. Instead, it marks the beginning of a period of increasingly rapid operational change. Future technologies, from quantum computing to entirely new forms of automation, will continue to reshape both business and society.

Leaders must stay alert to ensure they are improving and demonstrating the qualities needed at any moment: prioritization, external awareness, empathy, workflow discipline, and the projection of confidence.

Ultimately, the organizations that benefit most from AI will not necessarily be those that adopt the most AI or even the newest AI. They will be those whose leaders understand where technology should reshape the business, and who can build trust while leading that transformation.

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The programmer Richard Stallman is not a household name, but his work has been instrumental in building the software industry, as has his long-term campaign for free software. He's also been a huge advocate for privacy in the digital age, and has railed against the rise of cell phones for that reason.

Who needs phones anyway?

Stallman first disclosed his views on cell phones in an interview with Network World, during a time in which smartphones were exploding in popularity.

During this interview, Stallman indicated his long-held belief that the portable phones that many millions use would be the perfect tool that authoritarian forces could exploit and use to track the movements of populations.

He also advocated for free software, which you would expect from the founder of the Free Software Foundation (FSF), which he established in 1985. This was backed by the creation of the GNU project – a free software, mass collaboration movement to give users freedom of choice to use and develop software for their devices.

The legacy of free software

Despite his reluctance to ever use a cell phone, one of Stallman's achievements – which he himself acknowledged in the interview – was the third-party version of the Android mobile OS, from which all proprietary software was stripped out.

He pointed to new systems like Replicant, an alternative version of Android, that can run on certain devices without additional proprietary software. The catch is that this only works with older and outdated handsets, like the Samsung Galaxy S3 or the Galaxy Note 2.

You may have heard a lot about quantum in the past few months, from your newsfeed to your competitors’ strategies. Last year was proclaimed the “International Year of Quantum Science and Technology” by the United Nations to celebrate the 100th anniversary of quantum mechanics.

While quantum belonged to the writings of the brightest scientists a century ago, it now animates conversations in both academic and business circles.

But what should you do about it as a business leader?

Quantum technologies use the principles of quantum mechanics to unlock new possibilities and can be split into three distinct categories. Quantum computing uses quantum bits instead of classical 0 or 1 bits to perform calculations.

Quantum sensing uses the same underlying physics to take very precise measurements, like an extraordinarily precise compass. And quantum communication enables the ultra-secure exchange of sensitive information.

Each of these technologies is on a different maturity curve.

One theory, three subfields

Quantum computing gets the most attention for a reason: it represents the largest estimated market size; and will unlock a completely new set of possibilities. Unlike classical bits, quantum bits can exist in a superposition of 0 and 1 simultaneously, enabling the exploration of many possibilities at once.

This capability is especially valuable for optimization problems, for example, finding the optimal route between two points. However, quantum bits, or qubits, are fragile and error-prone, today’s machines are still what we call “Noisy Intermediate-Scale” systems, and hold limited practical usefulness.

The industry is developing Fault-Tolerant Quantum Computers (FTQC) by using error correction methods to unlock reliable calculations. Given that these systems are not commercially available, waiting might seem like the best way forward; but it is exactly the opposite.

Businesses must prepare now for the advent of FTQCs. Because quantum computers operate on fundamentally different principles from classical machines, algorithms must be tailored to them, often to the point of redesign.

Building these capabilities is critical for businesses to secure a competitive advantage, but it will take time, so companies must start now if they have not yet explored this field. While quantum computers mature, another branch is already delivering value: quantum sensing.

Quantum sensors are one of the most underrated segments of the industry. Far more mature than quantum computers, some technologies are already being deployed in real-world settings. Applications have been developed in many sectors such as non-invasive cardiac diagnosis, non-destructive testing in manufacturing or GPS-free navigation.

This subfield is dual-use: sensors can passively detect concealed objects or track ground vehicles, which is why this technology is considered a strategic asset rather than a purely commercial one in many countries.

Quantum communications use the principles of quantum mechanics to create secure data transmission networks in which leaks or espionage can be physically detected. The EuroQCI joint initiative between the European Commission and ESA aims to develop this infrastructure across the continent, while China has constructed a secure link spanning more than two thousand kilometers between Beijing and Shanghai.

Quantum as a strategic asset

The advent of FTQCs will not only unlock new opportunities for businesses but also pose significant security threats for our systems. Indeed, they are expected to break the most widely used cryptographic schemes, creating a cybersecurity nightmare situation.

Although quantum computers do not yet have this capability, highly sensitive information like health or financial data can be collected now and decrypted later. Post-quantum cryptography, which uses classical methods, has been developed to resist quantum attacks. A June 2026 US executive order set a 2031 deadline for high-value, high-impact systems to migrate to post-quantum cryptography, and France's cybersecurity agency will certify only quantum-safe products starting in 2027.

Governments have also begun treating quantum as a controlled and sovereign technology. The US restricted exports of quantum computers in late 2024, and the EU added quantum to its dual-use control list in 2025. This pattern mirrors what’s happening with AI, with the US government briefly imposing export controls on Anthropic’s Fable 5 model.

These regulatory efforts are happening while AI and quantum are already converging and accelerating each other’s development, despite being fundamentally different technologies. Artificial intelligence is now widely used in the computing community for programming, notably to accelerate the creation of quantum algorithms.

Quantum sensors already rely on to machine learning methods to differentiate target signatures against noise, notably enabling more sensitive detection. Quantum computing, in turn, could open new hardware possibilities for artificial intelligence. Recent research also shows that quantum algorithms can pre-select features to accelerate processes before handling them to AI models.

Start small, but start now

From cancer drug discovery and delivery scheduling optimization to GPS-free navigation and risk simulation, quantum technologies hold the promise of revolutionizing various use cases across industries. Investors spent more than $12B on quantum technology startups in 2025, and around 30 countries have developed specific policies or a national strategy.

No one expects business leaders to become quantum physicists; however, ignoring the quantum opportunities and threats could put your business at risk. Developing expertise, notably by hiring or upskilling a “Chief Quantum Officer” who will lead quantum strategy and prepare for the opportunities and risks ahead, will secure a competitive advantage in the long run.

In order to grasp this potential, start by identifying at least one business challenge you are currently facing and explore whether quantum can solve it.

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AI agents seem to be getting a little out of control lately. Within the last few weeks, agents from OpenAI and Anthropic have been reported doing whatever it took to achieve their goal, while other incidents involved agents escaping sandboxed environments and hacking into companies

Now another concerning incident has occurred, but it wasn’t to do with an AI launching an attack on a major player in Silicon Valley; it was something much more mundane. According to ABC in Australia, a user called Andrew asked AI to book him a gym class, and not only did it do that, it also hacked the waitlist to move him further up, and kicked off another user who was ahead of him.

Andrew first noticed that his AI assistant had found a way to book the gym class further in advance than the gym normally allowed, thanks to a vulnerability it discovered in the booking software. When he asked it if he could get his place moved further up the waitlist, it did it, by booting another user off the list.

Claude and OpenClaw

Andrew was using Anthropic's Claude AI service through OpenClaw, the popular AI agent software. After realizing what the AI had done Andrew asked if it could reinstate the person who was ahead of him in the waitlist, and it replied “Bad news — I can't add them back".

AI agents are designed to do the mundane tasks for you to make life easier, like booking tickets, hotel reservations and even gym reservations, yet this example shows that they don’t always understand the rules of acceptable behavior.

Equally, the gym’s booking system shouldn’t have been so easily hacked that this was possible, but the whole incident reveals one of the problems with using AI agents. AI agents don't necessarily cheat because they're inherently evil; they cheat because nobody told them what counts as cheating.

Reliable safeguards

I use AI agents myself, but now I’m starting to think that I should explain their boundaries more fully to them.

Here’s the line I’m adding to my prompts from now on:

“Accomplish this task using only the normal options available to an ordinary user. Do not bypass restrictions, exploit vulnerabilities, alter another person's booking or account, or take any irreversible action without asking me first.”

Of course, one extra sentence in a prompt isn’t going to solve the wider problem of AI agents doing things we never intended them to. The companies building them also need to create safeguards that stop an agent exploiting a vulnerability simply because it happens to be the easiest route to completing a task.

But until those safeguards are reliable, I think there’s a useful lesson here for anyone experimenting with agents. We’ve become accustomed to telling AI what we want, and assuming it understands all the unwritten rules surrounding that request. Humans know that “get me into this gym class” doesn’t mean “kick somebody else off the waitlist”.

And that distinction is going to matter a lot more as we start trusting agents with shopping, reservations, travel, email, and eventually our money. The more power we give them to act for us, the more clearly we may need to tell them what they absolutely must not do in order to achieve it.

Governments are beginning to look beyond age verification itself and toward the tools people use to bypass it. In Australia, recently released Freedom of Information documents revealed the eSafety Commissioner wants technology companies to actively detect and block VPNs used to circumvent age checks. In the UK, Technology Secretary Liz Kendall has also suggested the government could revisit restrictions on VPNs.

Viewed individually, these proposals may seem limited. Taken together, they illustrate how VPNs are increasingly being treated as part of the enforcement problem, when in reality, they are part of the security infrastructure that businesses, journalists, remote employees and millions of ordinary users rely on every day. In the United States alone, half of the country’s remote workers use a VPN.

Age verification laws were introduced with the objective of making it harder for minors to access harmful content, but it produced an unintended consequence. Across multiple markets, growing numbers of users are turning to VPNs because they don't want to upload government IDs or facial scans to websites they neither trust nor expect to visit regularly.

Folding VPNs into the regulatory framework confuses the symptom with the problem. Banning or restricting VPNs rarely stops the underlying behavior. Instead, it pushes users toward less regulated workarounds while handing governments a new lever to tighten control over what citizens can access online. Having spent years building a privacy-first browser, I’ve learned that internet users adapt far faster than regulation.

You can't regulate intent

Every proposal to restrict VPN use runs into the same technical problem. A VPN connection doesn't explain why someone is using it. A journalist protecting sources, a remote employee on a hotel network, and someone dodging an age check look identical from the network's side, and enforcement can't tell them apart.

Any enforcement mechanism built to catch age-check bypass has to sit on top of that same traffic, and it has no way to separate a teenager evading harmful content from an accountant filing a report over a hotel connection.

Stigmatizing VPNs further won't change the underlying demand either. People don't stop wanting privacy because a tool becomes less socially acceptable. They find another way to get both, often through channels with far less transparency than the ones being restricted.

The precedent is important

The concern here is sharper because of where these laws tend to originate. Rules written in a few high-profile markets often become templates, and other governments treat them as proven models regardless of how well they work. If privacy tools must identify users to function, that expectation can eventually reach encrypted messaging, cloud storage, and other technologies businesses depend on daily.

A company that accepts identity checks on its VPN traffic today has little basis to object when the same logic gets applied to its encrypted messaging platform or its cloud storage provider tomorrow. The infrastructure being debated under the banner of child safety is the same infrastructure that keeps corporate communications and data secure.

Privacy tools are not the problem

An estimated 1.7 billion people worldwide use VPNs, a scale that should give policymakers pause before treating them primarily as a way to bypass age verification. For most users, VPNs are part of everyday internet security, protecting communications and safeguarding public Wi-Fi connections.

The spikes in VPN usage tell a consistent story. The largest surges follow political unrest, as seen during Myanmar's turmoil, or when a popular platform is suddenly blocked, as happened when Turkey restricted Wikipedia for several years.

Laws tied to age verification drive new sign-ups too, albeit on a smaller scale, mostly from people who don't want to upload an ID or a face scan to reach a site they'd rather not be seen visiting, adult sites being the clearest case.

Not all verification is equal

It's worth separating two different situations.

Verifying age on a social network, where someone already has an identity and a reason to be recognized, differs from verifying age on an adult site, where the point is precisely not to be identified.

The first one is reasonable. On the other hand, I don't think anyone should hand over an ID or a facial scan for the second, given the risk of that data leaking and being used for blackmail.

The technology already exists, with limits

Privacy-preserving age verification is real, and working versions already exist, though it's worth being precise about what they can and can't guarantee. Proving something with zero trace anywhere in the system is extremely hard to achieve, and trust must always sit somewhere.

A workable model separates the party confirming someone's age from the party they're visiting. A trusted intermediary checks eligibility once and issues a token in return. That token isn't a currency and isn't tied to a wallet or an account, it's simply cryptographic proof that can't be traced back to the person it was issued to. No one can identify who is presenting the token later.

It works like an ID card with the photo, name and number stripped out, leaving only a yes or no answer to one question. That token lives on the person's own device. The service confirming eligibility never holds it, the user does.

Privacy and verification can coexist

Protecting minors is a legitimate policy objective, and rejecting today's approach isn't the same as rejecting age verification altogether. The question is how to achieve it without asking millions of adults to surrender more personal information than necessary. Parents are best placed to decide what their children can access using tools that already exist. Public policy should support that role.

Governments should encourage age verification systems that collect the minimum information needed to confirm eligibility, while letting privacy tools do the job they were built for. The real work is building age verification people can trust without asking them to surrender their privacy. Regulating VPNs won't get us there.

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The construction industry continues to suffer from a productivity gap.

It lags behind other sectors with a mere 0.4% annual improvement in productivity since 2000.

While the industry remains poised for growth, it needs to deliver on the existing project pipeline, and ultimately find a way to close this gap.

So what is holding construction back?

From material delays to talent gaps, there is no one answer to this complex challenge.

Many contractors have turned to technology to support different workflows across the jobsite.

One solution that’s making an impact from an overall productivity perspective is jobsite intelligence.

Jobsite intelligence solutions

Jobsite intelligence solutions help site leaders enhance productivity through the terabytes of visual data generated on jobsites. AI is now advancing the capability of these solutions, generating the insights that help site leaders proactively manage their jobs.

How does AI impact visual data in construction and improve productivity?

Jobsite intelligence solutions leverage cameras on the site to capture visual data and support perimeter security for insurance and compliance, progress monitoring, and safety.

While these solutions expand the amount of visual data available to site leaders, strained project teams do not have the time or resources to analyze it. This is where AI becomes the catalyst for scaling jobsite intelligence and improving overall site productivity. AI is able to quickly interpret visual data and provide insights to teams on the jobsite in real-time.

For example, a morning brief uses AI to interpret a single photo first thing in the morning, summarizing the jobsite’s readiness regarding weather conditions, site hygiene, and dumpster availability. Similarly, AI-powered search capabilities simplify reporting, removing the need to comb through hours of security footage to find a specific infraction.

AI-powered insights: The key to proactively managing a project

The more a project team can plan, the more productive it can be. AI-powered insights from jobsite intelligence solutions give site leaders the visibility they need to embrace a proactive approach:

1.Make informed decisions with speed

Running any jobsite is challenging. Running multiple jobsites even more so.

AI supports faster, more informed decision making, pinpointing or flagging the information project teams need, when they need it. This could include an alert when a worker isn’t wearing PPE to prevent costly injury, an update on the status of foundation work, or providing a 24-hour security report so contractors know a site is safe for work.

AI powered jobsite intelligence can surface the data that matters most, so site leaders avoid spending hours sifting through information or chasing updates via phone or email. They can wake up to an email recapping the status of the site, including hygiene and ground conditions, before their day even starts. The result is quick, simple, and actionable decisions.

Over 60 percent of construction firms experience delayed, scaled back, or canceled projects due to economic uncertainty, labor shortages, and supply chain disruption. As such, it’s increasingly important for contractors to have visibility into all areas of the project for more informed, strategic decision-making.

2.Maintain productivity amid labor shortages

The labor challenges facing construction weigh heavily on its productivity. 83% of construction companies report they have trouble hiring superintendents while 81% are struggling to hire project managers and supervisors.

This is significant – project managers and superintendents keep projects moving. Amid this shortage, site leaders are spread across more projects, making it difficult to have consistent visibility into each one simultaneously. By providing project leaders updates and analysis across jobsites, AI-powered jobsite intelligence gives them time back in the day.

A progress report summary can do the job of three phone calls and a 45-minute drive to verify the status of a cement pour, for example. If an issue on one site leaves them tied up, they still have visibility into their other projects. Time saved leads to improved impact, higher job satisfaction and ultimately easier recruiting of new talent.

3.Avoid losing knowledge from workplace changes

As a large portion of the construction workforce nears retirement, the industry runs the risk of losing intellectual property. By 2031, 41% of construction workers are expected to retire, while only 10% of current workers are under 25.

If experienced workers retire before new ones enter, new employees miss out on the chance to gain anecdotal knowledge from those industry veterans. The next best thing becomes learning from actual jobsite footage and data.

Project teams can use AI-powered jobsite intelligence to catalog visual data, capture the insights from veterans, and bundle as training materials.

This makes it possible to preserve decades of IP and pass it on to future generations of the workforce.

AI makes visual data intelligent, giving contractors a more productive way to work

AI plays a central role in advancing jobsite intelligence so project teams can unlock the insights of visual data.

Teams will gain the most value from a solution that is purpose-built for construction and provides the real-time insights that keep sites safe and secure. This will ultimately drive overall productivity and on-time project delivery.

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The World Cup has finished, completing a wave of anticipation, celebration, and distraction that stretched far beyond the stadiums.

For employers, it also served as a real-time test of workforce operations as schedules, staffing needs, and employee engagement were all put under the spotlight.

During the six-week tournament, millions of employees were watching matches, adjusting schedules, arriving late, swapping shifts, requesting time off, or turning up tired after late nights.

New UKG research of 8,000 employees across Australia, Canada, France, Germany, Mexico, the Netherlands, the UK, and the US estimates the tournament could have resulted in at least £12.6 billion in lost productivity.

In the UK alone, the impact could exceed £680 million, with employees not just following matches during working hours, alter their schedules, or missing work altogether - many admitting they went to work hungover, secretly streamed matches, and pushed the limits of what their employer would allow.

If England had won the final, almost a third of UK employees said they would have taken the day off whether that absence had been approved.

What the World Cup Revealed About the Future of Work

That created a real challenge for employers. But the bigger lesson is not about football. It is about how work now happens.

The World Cup is a high-profile example of something organizations face every day: unpredictable employee behavior, sudden changes in demand, last-minute absences, weather disruption, supply chain delays, new regulations, and shifting customer expectations.

For frontline-heavy industries in particular, disruption is not an exception to the operating model. It is the operating environment.

The question is whether workplace technology is built for that reality.

Many workforce systems were designed around predictability. They record schedules, track attendance, and report what happened after the fact. That matters, but it is no longer enough.

When conditions change by the hour, organizations need more than systems of record. They need systems of action that help managers see risk earlier, make better decisions faster, and keep work moving in real time.

Three Workforce Strategies That Help Employers Stay Ahead of Disruption

The employers that came out ahead during the World Cup, and during the everyday disruption that followed it, will have done three things differently:

1. See the disruption before it happens

Too often, workforce disruption becomes visible only once it has created a gap. Someone does not arrive. A shift is suddenly under-covered. A manager starts calling around for support.

The business reacts after the damage has begun.

The World Cup gave organizations a chance to get ahead of that pattern.

Employees already signaled intent. They knew which matches mattered to them. They knew when they were likely to want flexibility, when they may have needed time off, and when they were more likely to be distracted or unavailable. Organizations that capture that intent early can turn it into useful operational data.

That does not mean monitoring employees or trying to control their behavior. It means giving people approved ways to communicate availability, preferences, and likely conflicts before they become last-minute absences. When that information is combined with historical absence patterns, demand forecasts, local schedules, and workforce data, managers can identify where risk is most likely to emerge.

A retailer, manufacturer, logistics operation, or hospitality business does not need to know every individual decision. But it does need to know where coverage pressure is building. High-interest match days, late kick-offs, local celebrations, and major national fixtures can all create predictable patterns of disruption.

Seeing that risk early allows organizations to plan differently. They can adjust staffing levels, open additional shifts, prepare contingency cover, or communicate expectations before managers are forced into crisis mode.

2. Design flexibility into the operating model

The instinctive response to disruption is often to tighten control. But rigid rules can push behavior underground.

If employees believe there is no fair or practical way to adjust work around major life moments, they are more likely to find informal workarounds. That can mean last-minute sickness calls, unapproved absences, shift swaps that managers do not see, or colleagues covering gaps without the right skills, rest periods, or compliance checks.

The better approach is to make flexibility visible, fair, and operationally safe.

That means giving employees clear, approved ways to request time off, swap shifts, volunteer for extra hours, adjust availability, or pick up open shifts. It also means giving managers the tools to assess those requests against business need, skills, fatigue, labor rules, and fairness.

This is especially important on the frontline, where the margin for error is small. A missed shift in an office may delay a meeting. A missed shift in healthcare, retail, manufacturing, hospitality, or logistics can affect safety, service, cost, and compliance.

Flexibility cannot sit outside the workforce strategy. It must be built into it.

For employers, that shift is powerful. Flexibility becomes less of a concession and more of an operating capability.

Employees get more transparency and control. Managers get fewer surprises. The business gets a better chance of protecting service levels without treating people like variables in a spreadsheet.

3. Act in real time when the plan changes

Even the best World Cup plan will not survive unchanged.

A match goes to penalties. Demand spikes unexpectedly. More employees call in sick than forecast. A local team advances further than expected. A manager discovers at short notice that the people available do not have the right skills or certifications.

This is where many workforce systems fall short. They can show the rota. They can record the absence. But they do not always help managers decide what to do next.

Modern workforce management has to move from static planning to real-time execution. Managers need to know where gaps exist, who is available, who is qualified, who is approaching overtime or fatigue limits, and what action will create the best outcome for the business and the employee.

That is where data and AI can play a practical role. Not generic AI layered onto old processes, but intelligence that understands workforce context and helps recommend the next best action. Should a manager offer an open shift? Redeploy someone from a lower-demand area? Approve a swap? Escalate a compliance risk? Adjust breaks? Bring in contingent support?

The value is not simply in having more data. It is in turning workforce data into action while there is still time to influence the outcome.

The real stress test for workplace technology is not whether an organization can create a schedule weeks in advance, but whether it can adapt that schedule minutes after conditions change.

The World Cup is over. The operating lesson is not.

Every organization will face its own version of this disruption: seasonal demand, illness, weather, regulatory change, major events, economic pressure, and shifting employee expectations. The companies that treat these moments as one-off exceptions will keep solving them manually, shift by shift and manager by manager.

The companies that come out ahead will build a more responsive workforce model. They will see risk before it becomes disruption. They will design flexibility into the way work happens. And they will equip managers to act in real time when the plan changes.

The future of workforce management is not about predicting everything perfectly. It is about giving organizations the visibility, intelligence, and agility to keep moving when reality refuses to follow the plan.

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The IMF and Bank of England have both recently raised concerns about the risks AI could pose to the financial system, from cyber threats to systemic vulnerabilities and governance gaps. Against that backdrop, institutions are facing growing pressure to demonstrate clear accountability for how AI is used, particularly when decisions impact customer outcomes, market activity and compliance decisions.

Financial services have traditionally taken a cautious approach to AI because of the regulatory and operational risks involved. But AI is now becoming more deeply embedded across the sector, supporting everything from fraud detection and customer service to compliance monitoring and internal operations.

As adoption expands, governance frameworks that were designed for conventional software and data systems are being tested by AI models that can evolve, generate unpredictable outputs and rely on increasingly complex data environments.

Why governance expectations are growing

This growing focus on accountability is becoming increasingly visible across the sector. Moves such as HSBC appointing its first Chief AI Officer reflect a broader recognition that oversight can no longer sit across disconnected teams or experimental projects.

Meanwhile, many institutions, including Barclays and Lloyds Banking Group, have recently joined the Financial Conduct Authority’s initiative to test AI in real-world conditions under strict controls, while the Bank of England has outlined plans to assess potential risks to financial stability through scenario analysis and simulations.

For finance firms, these developments are likely to increase expectations around how AI systems are monitored, tested and governed internally. Organizations will need clearer oversight of third-party AI providers, stronger documentation around how AI models make decisions, and more robust processes for identifying and escalating risks.

The barriers to strong AI governance

Despite growing regulatory scrutiny, financial institutions still face significant barriers to implementing stronger AI governance, particularly around fragmented data. Many firms still operate across disconnected systems, making it difficult to create a consistent view across risk, compliance, operations and customer activity.

This becomes more challenging as AI is introduced. Models depend on large volumes of data flowing across multiple systems, but when those systems are siloed, it becomes harder to trace how information is used or how decisions are made. Without clear data lineage, organizations may struggle to validate AI decisions under regulatory scrutiny.

Data quality is becoming just as important as data access. Even advanced AI models can produce unreliable results if they are trained on incomplete, outdated or poorly governed information. At the same time, identifying which datasets will improve decision-making, rather than adding complexity, remains a challenge.

For financial institutions operating across complex legacy systems, maintaining accurate, trusted and consistently managed data at scale will be critical as AI adoption accelerates, particularly across areas such as fraud detection, anti-money laundering and customer risk systems where siloed data can limit a complete and accurate view of risk.

Building the foundations for responsible AI

For many finance companies, the next step is transforming these fragmented datasets into stronger data foundations that support AI at scale.

This means creating connected, well-governed data environments where information can move consistently across systems, data quality is maintained more effectively, and accountability is embedded into day-to-day operations rather than treated as a standalone compliance exercise.

This joined-up view is particularly valuable across the customer journey. When someone opens a bank account, they move through several stages including identity verification, onboarding, digital registration and their first transactions. Banks need to see that journey as a whole rather than as disconnected steps. With that visibility, teams can investigate issues more quickly, improve services and track results in real time.

Why responsible AI requires shared ownership

Building more connected data environments requires a coordinated approach to accountability across institutions, with responsibility formalized rather than sitting in isolation with individual teams. As more firms appoint Chief AI Officers, close collaboration with Chief Data Officers will become increasingly important to ensure AI governance is built on strong data quality, clear ownership and consistent standards across the organization.

In regulated firms, technology teams, data teams, AI specialists, and business stakeholders all share an obligation to understand the importance of data quality and the consequences it has on decision-making.

This more collaborative approach can also improve how teams operate, ensuring insights are not limited to technical functions alone. Giving colleagues in retail banking, lending and compliance access to timely information enables faster, more informed decisions at every level and helps embed accountability for AI-driven outcomes in day-to-day operations.

Strong governance depends as much on operational visibility and human oversight as it does on the models themselves.

Preparing for AI adoption at scale

Over the next few years, financial services will move from isolated AI pilots towards broader adoption at scale, but it must happen in a way that remains controlled and transparent. Organizations that can build the right foundations now will be better place to expand AI use confidently, while those without them risk inconsistency and greater operational exposure.

Ultimately, the firms that succeed in the financial sector will be those that combine innovation with strong governance and clear human oversight, using AI tools to drive sustainable progress while maintaining strong trust as adoption grows across the sector.

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