Quantum computing has fascinated the tech world for several decades. That’s because, while classical computing is binary, quantum bits (qubits), made from atoms, electrons and superconductors, can be in both “0” and “1” at the same time, providing computational power that is inaccessible to classical computers.

The implications of this computational power will be far-reaching, such as accelerating drug discovery, optimizing global supply chains, and revolutionizing material science for clean energy. If that’s not interesting enough, the new frontier of hybridizing quantum and classical computing generates yet another source of excitement for the field.

Humanity has embedded computing into the fundamental fabric of daily life. It underpins our progress in an enormous number of fields, as well as impacts our day-to-day lives more than almost anything else.

While quantum computers are fascinating on their own, it is becoming increasingly clear that their integration into data centers - treating them as a crucial piece of the broader computing puzzle - represents one of the most interesting frontiers in science and technology today.

Hybrid computing: already a reality and key in the future

The idea that quantum and classical computing will work together is not a future vision. Quantum computers already rely on tight integration with classical computing, which plays a critical role in enabling their operation.

For example, extensive classical computing is used for generating the control signals that operate the quantum hardware and to calibrate quantum hardware and control systems. In this scenario, classical computers are finding thousands of different system parameters and retuning them constantly as they drift over time.

Very compute-intensive classical algorithms are also used for decoding errors during quantum error correction, which is critical for enabling stable quantum computation and the scaling of quantum systems. Another key use case is that hybrid quantum-classical algorithms rely on interleaving quantum and classical computation to solve complex problems.

In the future, this hybridization will have a growing impact on computing applications. QPUs will act as specialized accelerators within classical HPC and AI workflows, complementing CPUs and GPUs rather than replacing them. Quantum systems will sit alongside CPUs and GPUs in data centers and be deployed for exactly the type of problems they are best suited to solve.

In fact, this is already starting to happen - some of the most important recent quantum demonstrations have relied on quantum and classical systems operating in close coordination. For example, RIKEN and IBM scientists recently achieved one of the largest quantum simulations of iron-sulfur clusters through closed-loop data exchange between a co-located IBM Quantum Heron processor and RIKEN's Fugaku supercomputer.

This kind of hybrid exchange between quantum and classical systems, rather than quantum computing operating as an isolated, standalone resource, is the model we expect to become standard as the technology matures.

The final, and perhaps most intriguing frontier in quantum-classical hybridization is its relationship to Artificial Intelligence. Compute infrastructure is being used to train AI models, while hardware capabilities limit what models can do in various ways. Integrating quantum hardware into this compute fabric may allow new data to be generated for AI models as well as new engines for AI inference.

Moreover, AI tools may help us break some of the abstraction layers we have created, which can remove further limitations on how we use quantum hardware to solve problems.

Quantum computers don't run themselves

Given the above, as quantum computing moves from laboratory demonstrations toward practical applications, success will depend not only on better quantum processors but on how effectively quantum and classical computing work together.

The latency and bandwidth of the quantum-classical links will play a critical role in how performant this integration is and will determine how deeply connected the quantum and classical processing can become. This matters because qubits hold their state only briefly - the faster the classical system can read, process and respond, the more complex a calculation it can support before that state is lost.

Another critical aspect is software. Building infrastructure, tools, compilers and applications that orchestrate hybrid quantum-classical workflows efficiently, with high performance as well as developer experience in mind, will be critical for the industry to succeed in its mission to deliver real-world value in a timely manner.

This requires investment in the orchestration layers that allow developers to calibrate and control quantum resources as easily as they would HPC today, abstracting away much of the underlying hardware complexity.

The next phase of quantum computing will depend on bringing together advances in physics, engineering and computer science to cross the barrier from lab prototypes to useful computers.

There are many challenges ahead, from scaling the hardware and performing error correction at scale to finding the right applications to push for. The clear message is that hybrid quantum-classical systems are not a temporary stage in this journey. They are the foundation on which practical quantum computing will be built.

Enterprises and IT leaders exploring quantum shouldn’t focus on the number of qubits, but on how systems integrate with classical infrastructure. When it comes to scaling useful quantum computing and implementing hybrid computing, the control systems, error correction and software orchestration will determine whether a system can deliver reliable performance and repeatable results.

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• OpenAI researchers confirm an AI agent escaped sandbox, exploited zero‑days, and attacked Hugging Face
• Controlled experiment with GPT‑5.6 Sol showed autonomous chaining of vulnerabilities and credential theft
• Security experts call it unprecedented, urging stronger AI governance, accountability, and protection models

OpenAI has confirmed one of its AI agents broke out of a sandbox, found and exploited zero-day vulnerabilities to gain access to the open internet, and then attacked a platform.

Not just any platform too - the agent was able to breach Hugging Face, one of the biggest AI and machine learning companies on the Internet today.

The good news is that this was a controlled experiment done by white hat researchers. The bad news is that if it could be done by researchers - it could probably be done by malicious actors, too.

Whatever it takes

In a blog post explaining the incident, OpenAI revealed the experiment was part of its testing of GPT‑5.6 Sol and an “even more capable pre-release model” to see how well they would perform on the ExploitGym benchmark.

ExploitGym is a cybersecurity benchmark that measures if an AI agent can turn a known software vulnerability into a real, working exploit. OpenAI ran it in a “highly isolated environment, with network access constrained to the ability to install packages through an internally hosted third-party software that acts as a proxy and cache for package registries.”

But the models found a way through. They identified and chained vulnerabilities in the package registry cache proxy to obtain open internet access and then attacked Hugging Face, reasoning that the solutions for the ExploitGym benchmark might be found there.

“In one example, the model chained together multiple attack vectors, including using stolen credentials and zero-day vulnerabilities to find a remote code execution path on the Hugging Face servers,” OpenAI said.

The security community is up in arms over what OpenAI called, "an unprecedented cyber incident,” while Ansgar Dodt, VP Product Management, Software Monetization at Thales said this “demands a fundamental rethink of software protection.”

Bill Conner, president and CEO of AI integration and automation expert Jitterbit, said that while investing in AI is “critically important,” “overly aggressive policy cannot compromise AI accountability, transparency and data privacy.”

“To lead in AI, governments and organizations must lead with principles. Responsible AI governance isn’t a side note but the foundation of lasting global influence.”

Across industries, organizations are increasingly tracking AI usage through dashboards, token utilization and platform engagement metrics. Some of the most visible companies in the world have stood up leaderboards ranking employee AI use; others have tied AI adoption directly to raises and promotions.

The intent is reasonable - leaders want a signal that the investment is landing.

But we’ve reached a point where managers, executives and boards carry a false assumption that AI usage means a more AI-ready workforce. Usage and capability are not the same thing.

Recent research, based on a survey of 2,000 workers across the U.S. and U.K., suggests many organizations are measuring AI adoption faster than employees are learning to use it effectively. While 46% of employees report using AI tools at work, nearly half have received no formal AI training and 56% have no clear path for developing AI-related skills.

Perhaps most concerning, 17% admit they are pretending to use AI at work. If organizations mistake AI usage for capability and readiness, they risk building strategies and processes for a workforce that doesn’t actually have the skills to execute them.

The gap isn't a talent problem; it's a systems problem between technology adoption and workforce development. Solving it means CIOs and HR leaders must move beyond coordination and take joint accountability for translating AI usage into true workforce capability.

The measurement trap

As AI becomes embedded into everyday work, leaders are looking for ways to track progress. Dashboards, usage reports, prompt counts and engagement metrics seem to offer an obvious way to demonstrate momentum. But activity is not the same as capability.

An employee generating 10 prompts daily may appear highly engaged. That doesn’t mean they know how to provide effective inputs, evaluate outputs, recognize hallucinations, or apply AI responsibly in ways that meaningfully improve performance.

When organizations treat activity as a proxy for competency, leaders develop a false sense of confidence about workforce readiness while critical capability gaps remain hidden beneath the surface.

Don't just agentify the mess

There’s a parallel trap on the technology side, where there’s a race to “agentify” everything, wrapping an agent around every existing process and SKU.

But automating a broken workflow simply produces a faster broken workflow. The point isn’t to agentify the mess. It’s to rethink the work first, then apply AI to what matters.

The same discipline applies to how we measure return. Automation and the productivity gains are real, but treating efficiency as the finish line badly undersells the opportunity.

The larger prize is transformation: moving the topline and the bottom line, not just shaving cost per task. Organizations that aim only at incremental productivity will capture a fraction of what AI can actually deliver.

The visibility gap nobody is talking about

This creates a new challenge for CIOs and HR leaders. Most organizations can now see who is using AI tools. Far fewer can see whether employees are using them effectively.

The next phase of AI transformation will not be determined by access to tools, which most organizations have already solved. It will be determined by whether employees possess the judgment, confidence and skills necessary to use those tools productively and impactfully.

Without that visibility, organizations risk optimizing for adoption metrics while underinvesting in the development that generates long-term business value. Technology procurement lives with one team. Learning and skills data lives in another. Performance data often lives somewhere else entirely.

Consequently, organizations struggle to connect AI usage with business outcomes.

This is a CIO problem as much as an HR one.

Joint accountability, not coordination

The conversation I’m having with peers is about moving from coordination to joint accountability. Coordination means IT and HR talk to each other. Joint accountability means they own the same outcome together; specifically, whether the workforce can execute the organization’s AI strategy.

Forget using AI. Are employees using it effectively enough to have a measurable impact on the business?

That reframe changes where decisions get made and who makes them. HR leaders understand what capabilities the business will need and where the development gaps are widening. CIOs understand how AI tools are deployed, where agents sit in the workflow, and where technical infrastructure can support learning at the point of work.

Neither function can solve the problem alone.

The skills that endure

Through all this churn - new models, new tools, new agents every quarter - one thing stays durable: domain expertise expressed as work. The specific, task-level skills that make someone effective at their core job don’t depreciate the way a given tool does. As AI transforms how work gets done, those domain-grounded skills are what compound and carry forward.

When organizations examine why AI adoption often stalls or remains shallow, the same issue tends to surface: AI is deployed without being meaningfully anchored to the skills and tasks of the workforce. Adoption becomes activity - visible, but not compounding.

Addressing this requires a shift in focus. AI needs to be connected directly to how work is actually performed and improved. When adoption is tied to real tasks and outcomes, it becomes a mechanism for continuously strengthening underlying skills, rather than just increasing tool usage.

What CIOs need to own

The AI-readiness conversation has largely focused on technology deployment. The harder question is whether organizations are building the workforce capabilities necessary to translate adoption into results.

For CIOs, that means taking ownership of something that extends beyond technology infrastructure. It’s creating the systems, partnerships and feedback loops that allow their organizations to build capability at the speed AI is evolving, with visibility into the AI skills that their people are developing.

The most successful organisations will have CIO and HR leaders jointly turning AI usage into sustained workforce capability and measurable business value. They give employees not just the tools, but the support to use them effectively. That is what the AI-empowered workforce of the future looks like.

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• TeamViewer report finds 71% of autonomous AI users set guardrails, but 61% wouldn't even use autonomous AI to begin with
• The average worker spends 2+ hours per week validating AI output
• Security and privacy could have the biggest impact

New TeamViewer research positions trust as the latest major barrier to AI adoption, with workers generally viewing the tool positively but struggling to hand over full independence to it.

Today, three in four workers use AI daily and only 3% say they don't see any noticeable workplace benefits, but nearly all (95%) respondents have at least some concern about AI operating without a human in the loop.

This struggle speaks to AI's evolution from generative to agentic, with workers most concerns about the autonomy of AI agents over AI's actual ability to produce results.

Agentic AI's biggest barrier is trust

Three in five (61%) said they'd prefer AI to take no independent action, with only around one-third (35%) willing for it to act autonomously on their behalf. Most of that group (71%) are only comfortable with AI handling defined tasks, confirming that safety and guardrails are a necessity.

As for trust's impact on productivity, more than half (56%) often or always verify AI outputs before relying on them, spending an average of two hours per week checking AI-generated work.

"The real opportunity is to use AI and automation to prevent disruption, improve digital experiences, and free people to focus on higher-value work," CEO Oliver Steil wrote.

Strong security and privacy protections would have the biggest impact in making autonomous AI more acceptable, followed by notifications before significant changes are made, restrictions on what information AI can access, agent activity monitoring tools and the option to reverse any actions.

"The next step is designing systems that know when to act, when to wait, and when to bring people into the decision," Chief Product and Technology Officer Mei Dent added.

Data leaks and corporate breaches have become routine. In many cases, stolen credentials, databases, or attack tools eventually appear on the dark web, where they are traded and reused in future attacks.

This raises a question for businesses: if stolen corporate data ends up on the dark web, does it make sense to engage with this environment directly — by buying information, paying for services, or negotiating with attackers?

The short answer is no.

Not because the dark web doesn’t matter — quite the opposite: it is a core part of today’s cybercriminal infrastructure. The problem is that doing business with the dark web rarely reduces the immediate risks and systematically strengthens the very market that creates threats.

The nature of the dark web

The dark web — often used interchangeably with the term darknet — refers to parts of the internet intentionally hidden from search engines and accessible only through tools such as Tor or I2P.

It is not a single network but a collection of platforms and communities gated by encryption, nonstandard protocols, or restricted access. While some resources are relatively neutral, others are directly tied to criminal activity. From a cybersecurity perspective, the dark web matters primarily as a mature cybercrime marketplace.

Technically, many platforms resemble early internet forums. Functionally, however, they operate much like B2B marketplaces — except the products include stolen data, compromised accounts, malware, exploit kits, and attack services.

The economics of cybercrime

A key function of the dark web is simplifying the monetization of cybercrime. More importantly, it enables specialization and the formation of complex supply chains.

Instead of building operations end-to-end, cybercriminals now focus on specific roles: some identify vulnerabilities and gain initial access, others develop and distribute malware, while others specialize in monetization through data sales, extortion, or attacks-for-hire.

This division of labor has created a full-fledged cybercrime economy. Attackers no longer need advanced expertise or their own infrastructure — they can purchase the necessary tools and services, lowering the barrier to entry and increasing the scale of attacks.

A clear example is the Ransomware-as-a-Service (RaaS) model, where core groups develop malware and manage negotiations, while affiliates carry out attacks for a share of the ransom. This model has enabled large-scale incidents such as the 2021 Colonial Pipeline attack, which disrupted fuel supplies across the U.S. East Coast and resulted in a $4.4 million payment.

Dark web intelligence and false signals

As the dark web evolved into a cybercrime marketplace, businesses naturally became interested in monitoring it for early warning signals.

In practice, this approach works only partially. The problem with dark web intelligence is that it comes from an environment with virtually no reliable verification mechanisms.

Like any anonymous and unregulated market, the dark web contains a significant amount of noise, manipulation, and outright fraud. Listings may be outdated, fabricated, or recycled from old leaks, while reputation signals can be artificially inflated.

The problem becomes even more pronounced when monitoring is outsourced to third-party vendors. Weak or unverifiable signals can easily be exaggerated, misinterpreted, or presented as evidence of major threats.

As a result, dark web monitoring rarely provides the level of certainty businesses expect. At best, it can highlight a potential issue that still requires verification.

Never pay cybercriminals

Direct engagement with the dark web is even more problematic — whether through ransom payments, purchasing leaked data, or hiring anonymous actors to test infrastructure.

The most obvious issue is that paying cybercriminals offers no guarantees. Attackers may simply demand another payment or leak the data anyway.

Uber learned this in 2016 after paying attackers $100,000 following a breach affecting 57 million users, only for the incident to become public later and trigger regulatory fallout.

A similar pattern appeared in the 2017 breach of HBO, when attackers stole 1.5 TB of Game of Thrones-related data, including unreleased episodes and internal documents. HBO reportedly transferred $250,000, but the material leaked anyway.

The broader problem, however, is structural: every payment flowing into the dark web economy directly finances its further growth. The more businesses participate in that market, the stronger the incentives for attackers to discover vulnerabilities, compromise systems, and scale operations.

Common mistakes when dealing with the dark web

When dealing with the dark web, organizations tend to repeat the same mistakes regardless of industry or size.

Trying to pay their way out of the problem. Companies often approach ransomware or leaks as negotiation problems. In reality, paying a ransom guarantees neither recovery nor safety. According to a 2021 study by Cybereason, 80% of organizations that paid ransoms were attacked again, often by the same groups.

Treating dark web monitoring as insurance. Monitoring services are often marketed as proactive protection. In reality, if company data appears for sale on the dark web, the compromise has already happened. Monitoring can provide signals, but it cannot replace actual security controls.

Hiring dark web hackers to test infrastructure. Unlike legitimate penetration testing, anonymous dark web “audits” offer no accountability, verification, or compliance guarantees. Even worse, the hired hacker may establish unauthorized access and later resell it.

Panicking after seeing the company name on the dark web. Many leaks and listings are outdated, recycled, or entirely fabricated. Without proper verification, rushed decisions can worsen the situation.

Delegating the entire issue to “dark web specialists.” Many companies delegate dark web monitoring to external vendors without the ability to independently assess the quality of the results. This creates a dangerous information asymmetry and increases dependence on unverifiable claims.

What businesses should do instead

Dark web intelligence can be useful as one additional source of signals, but it requires cautious interpretation and independent validation. Treating it as a reliable source of truth — or outsourcing the entire function without oversight — is risky.

More importantly, businesses should avoid directly financing criminal ecosystems through payments or participation in underground markets.

Cyber resilience is built internally. Rather than attempting to “buy security” on the dark web, organizations should invest in systematic defense: resilient architecture, vulnerability management, monitoring, incident response, and technologies capable of mitigating attacks while maintaining continuity of critical services.

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• OpenAI launches online and digital training for small businesses
• Partner plugins also launch to automate common workflows
• ChatGPT Work, Codex now have 10 million users

OpenAI has launched a dedicated ChatGPT for small business program to help SMBs automate routine work and increase productivity without the teams and resources that larger enterprises have access to.

The scheme is primarily an education process, and will focus on training, in-person academies and practical guides. OpenAI will also launch dedicated agents and partnerships to support small business workflows, too.

The recently-launched ChatGPT Work platform also features, with the software vendor pushing its latest tool through the program, claiming that ChatGPT Work and Codex are now said to have a combined 10 million users.

OpenAI sets its sights on small businesses

After gaining significant public interest following the launch of ChatGPT in late 2022 and spending many months and years improving models, the company then went on to target certain fields like finance and law. This latest step expands its focus to a broader category of businesses across all domains, but more importantly, it unlocks major volume for OpenAI. SMBs represent around 99% of the private sector globally.

Some of the support on offer includes webinars demonstrating how to use ChatGPT Work, prompt engineering guidance and other interactive guides. In-person events will also be held across the US through OpenAI Academy.

Select plugins and skills are also on the cards to handle common SMB workflows, with initial partners including Dropbox, Shopify, Intuit, Slack and Wix.

"Small businesses can choose the right level of intelligence model for the work at hand, and together, they give lean teams more flexibility to balance quality, speed, and cost," OpenAI wrote.

GPT-5.6 and ChatGPT Work are now available to pretty much all subscribers, with access via the web, desktop app or mobile app slightly differing depending on whether you're a paying customer.

The current state of AI adoption in UK businesses paints a decidedly mixed picture.

For many organizations, it’s full speed ahead: they’re using the technology to transform operations and unlock growth. For others, progress has stalled; they remain stuck in the sandbox, struggling to translate AI’s promise into tangible business outcomes.

In this environment, the government’s £200 million investment to support AI adoption and scaling is a welcome step towards turning theoretical use cases into reality.

Crucially, the inclusion of workforce training signals recognition that AI success isn’t just about technology, but about people and skills. Together, these measures underline AI’s potential to drive long-term economic growth in the UK.

However, investment alone will not be enough to close the gap between ambition and impact. To realize meaningful returns, businesses must take a more grounded approach that connects AI initiatives directly to operational reality and resists the temptation to implement AI for AI’s sake.

This means rethinking operating frameworks, balancing innovation with strong governance and establishing the right foundational architecture from the outset.

When done well, this creates the culture and processes needed to drive AI adoption, ensuring AI is not only deployed, but properly tested, governed, and scaled for sustained value.

Start simple to scale faster later

Businesses are often swept up in AI’s promise, treating it as a universal solution to enterprise-wide challenges but the reality is more nuanced. While the technology offers significant potential, value only comes from use cases with clearly defined outcomes, not from deploying it for its own sake.

A more effective approach is to start small and stay focused. Identifying two or three priority business processes where AI tools can deliver measurable impact is more likely to generate meaningful ROI, as once an initial pilot proves its value, organizations can build the credibility and confidence needed to expand.

With tangible results to point to, momentum builds, making it easier to scale further use cases and embed AI more widely across the business.

Equally, businesses need to be realistic about the journey. Results are rarely immediate and well-defined, accurate processes take time to refine. Building an AI-ready operating model is a long-term process, and the leap from successful pilot to deployment can introduce new questions and insights around where AI can deliver value.

Don’t build AI on shaky foundations

Businesses eager to get AI projects off the ground often move too quickly, approving projects before the right technical foundations are in place.

From data pipelines and model integration to reusable agent frameworks, these building blocks are critical. Without them, what should be a seamless transition from isolated AI pilots to enterprise-wide deployment instead stalls before it can scale.

Perhaps the most costly mistake is rushing straight into model development while neglecting data foundations. AI is only as strong as the data underpinning it and if that data is incomplete, inconsistent or inaccessible, even the most advanced tools will fail to deliver reliable outcomes.

The result is often inaccurate outputs, hallucinations and missed errors, which erode trust and limit impact. To mitigate this, businesses must prioritize data quality from day one and build in robust quality controls to catch issues early.

Governance isn’t just a tick-box exercise

Organizations that scale AI successfully build governance frameworks before writing a single line of code. This establishes clear ownership, consistent standards, and the organizational buy-in needed to drive AI transformation.

It also embeds testing and regulatory readiness from the outset, ensuring businesses have the operational discipline required to be compliant with evolving AI regulations.

Recent research shows that governance challenges can ultimately determine whether AI delivers value or introduces risk. By 2027, 60% of organizations are expected to fail to realize the anticipated value of their AI use cases due to incohesive data governance frameworks.

Building these frameworks from day one removes key barriers and helps answer any employee questions around trust, accountability and responsible use.

Rethinking operating models

AI success rarely comes down to technology alone, it hinges on organizational alignment. Too often, data scientists develop models that don’t quite meet business needs, while leadership sets expectations that aren’t grounded in real user experience, resulting in a disconnect that stalls progress before it scales.

Closing this gap requires more than upskilling alone. While building AI capability across the workforce is critical, real impact comes from rethinking operating models and culture, enabling a shift away from siloed specialists towards “human-in-the-loop" teams that actively manage, refine and scale AI across the organization.

This shift enables AI to move out of isolated use cases and into day-to-day operations, with continuous feedback loops that improve performance over time. Without it, even well-trained teams can struggle to translate technical capability into measurable business value.

At the same time, the pace of change can create its own challenges, as with new AI tools and developments emerging constantly, it’s easy for teams to mistake activity for progress. Without a collaborative operating model underpinning these efforts, perceived gains often lack the data and validation needed to prove real value.

Just the beginning

Businesses are only just starting to grasp AI’s true potential and the scale of opportunity it represents but investment alone is no guarantee of success. Without the right operational framework, culture, and data foundations in place, even the most ambitious initiatives will struggle to deliver impact.

The journey involves starting slow and scaling, ensuring governance frameworks are in place, and investing in an operating model that includes clearly detailed team ownership of projects.

Leadership will be critical in determining whether those investments translate into real value. That starts with reframing AI not as a standalone technology project, but as a business transformation effort that will fundamentally shape how the organization operates for years to come.

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• Report finds 74% of regular AI users now pose questions to AI instead of human colleagues
• New workers are missing out on vital social interactions for company culture
• Employees say they feel more self-sufficient and comfortable making decisions

While the jury is still out on whether AI could be set to replace human workers or severely impact their scopes by automating major parts of their workflows, one thing is clear – humans are increasingly happy to use AI as a colleague and collaborator.

A new MyIQ analysis of nearly 22,500 adults found around three in four (74%) regular AI users now pose the questions they would formerly have asked colleagues to AI.

As a result, around half (48%) now report fewer spontaneous conversations during the working day, suggesting that the effect may extend beyond deliberately redirecting questions to AI with it now having a more profound impact on the social elements of work.

Return-to-office (RTO) mandates now face a complex paradox

While the report doesn’t explicitly cover it, the data presents interesting takes on modern workplace habits.

Post-pandemic layoffs and work-from-home mandates were quickly followed by urgent return-to-office mandates, with CEOs globally encouraging in-person working due to the collaborative nature of shared environments, and the opportunities to have ad-hoc conversations that spark learning and broader thinking.

With around half now saying this doesn’t happen so frequently, the findings beg the question whether commuting to the office might be all that necessary after all in an AI-first era.

Roughly two in five (38%) also noted that newer employees now have fewer natural opportunities to build relationships because routine questions are being redirected to AI, not human colleagues.

“Repeated across a working week, those missing exchanges can mean fewer opportunities to build trust, share judgment, and become known inside a team,” MyIQ Managing Director Sarah Meyer wrote.

Around half (53%) of the respondents also described their work as more transactional since adopting AI, marking a major shift in workplace dynamics.

AI might be more efficient, but it’s still lacking in certain areas

But despite the negative social implications, AI’s role in brainstorming, questioning and critical thinking could be seen as positive, too. For example, nearly two-thirds (62%) say they feel more comfortable making decisions independently than they did a year ago, with nearly three-quarters (71%) feeling more self-sufficient at work.

The report also warns that, while a chatbot can supply an immediate and often factually correct answer, it lacks the accompanying social information and organizational knowledge that a colleague would bring to the table.

Interestingly, a similar SurveyMonkey study revealed that 77% want more opportunities to brainstorm with colleagues and 61% want stronger connections across teams even though workers are increasingly settling for the first AI-generated answer, instead of digging deeper.

Together, these two reports imply that workers are increasingly seeking the efficiency that AI promises and they’re willing to ask questions for a quicker answer, but they still value the collaborative nature of human interactions within the workplace.

What’s less clear is how employers could implement these opposing forces into one unified workforce, while delivering the hybrid approach that workers have come to value with working from home and benefiting from going to the office.

“As AI makes solitary problem-solving easier, organisations may need to pay closer attention to the forms of workplace learning and social connection that efficiency alone does not capture,” the MyIQ study concludes.

• Reports indicateHuawei is building and operating DRAM fabs through a joint venture with state-controlled SwaySure
• This is part of an alleged 11-fab network Huawei operates behind state-owned fronts that it denies exists
• The move, seen as a bid by Huawei to secure captive HBM supply for its increasingly powerful Ascend AI chips, could place the company above Apple, Intel, and Nvidia, none of which makes its own memory.

Mounting evidence suggests Huawei is taking the same approach that saw it build CPUs and AI chips in the past to DRAM in the present.

The Chinese tech conglomerate is allegedly building and operating DRAM fabrication plants on the mainland, bringing it into direct competition with only one major player at the same scale, albeit for a much larger consumer base: Samsung.

Drawing on media reports and postings by semiconductor analysts, Block & Files has published claims (which Huawei currently denies) that the company is potentially building and operating DRAM fabs through a joint venture with Shenzhen-based memory maker SwaySure, giving it effective control over at least 11 different semiconductor fabs in the region.

A move borne out of sanction-mandated necessity

Despite its ramifications and Huawei's denials, the claim is hardly new: the first link between the two companies was in 2025, when the Financial Times published satellite images of Huawei's advanced chip production line that tied it to SwaySure and cited state financial backing for the facilities shown.

Huawei's purported move did not happen in a void, however; it finds itself in a situation where the Chinese state is increasingly and aggressively defending it not only covertly but overtly, on multiple fronts, as it responds to Washington-backed sanctions that limit and in some cases all but eliminate its ability to access cutting-edge silicon.

The overt part is easy to identify: the Semiconductor Industry Association estimated that Huawei is receiving $30 billion in state funding from the central government and its hometown of Shenzhen to build its chip network, while a separate 2019 estimate put the lifetime figure at US$75 billion in state support.

The government has also effectively barred its tech giants from buying AI chips from AMD and Nvidia while propelling Huawei's Ascend line to de facto standard in the Chinese market, guaranteeing Huawei revenue it would otherwise have to compete for.

The covert part is much harder to identify, but equally crucial: China also allegedly tolerates a shadow fab network that is, at least on paper, not directly associated with Huawei but is, for all intents and purposes, an arm of the giant, the opacity making it hard to pinpoint the direction and scale of Huawei's ambitions in the space. This is also why the US resorts to Entity List designations of Huawei's affiliates: Washington is trying to pierce a veil Beijing built on purpose.

The strongest corroborating signal, ironically, comes from Huawei's adversary: the US government's own Entity List designations imply that BIS investigators concluded these companies function as one network, which is as close to official confirmation as is currently available.

Huawei's move stems from a voracious appetite for AI-centric High Bandwidth Memory (HBM), which sanctions ensure it cannot source directly from international suppliers, with the US having tightened export controls to keep it, at least legally, out of Chinese hands.

While a Huawei that fabs its own DRAM would be a notable leap, it may be more of a potential future supplier to Apple than a direct competitor, as Apple is toying with the idea of buying memory from Chinese makers to ease its own supply issues.

Huawei's products do, however, compete with Apple in the smartphone segment, with Intel's server CPU offerings, and with Nvidia's AI chips, even as the last of these struggles to find a foothold in what was once one of its largest markets by revenue.

It does seem to have Samsung's Western position in its crosshairs as it builds toward zero Chinese dependency on suppliers Washington can sanction, but it has a lot of catching up to do to hold its own against the current king of the hill. Samsung holds a 3–4 year lead in HBM, arguably the gap that matters most, over China's CXMT, while its DRAM lead is much narrower and closing considerably faster against China-based memory makers.

Chinese makers have meanwhile reached relative parity in the NAND flash used in SSDs, an increasingly important space for AI, even as Huawei still relies on SMIC for CPU and GPU fabrication, a process roughly two nodes behind industry leader TSMC.

Huawei, at least on paper, has its own designs but does not fab them itself, leaving that to other specialist firms. But if the report holds true, it just might become the first real challenger to Samsung's position as chip designer, fabricator, and memory supplier all rolled into one.

The biggest publicly traded tech companies in the world, commonly known as the "Mag 7" — or magnificent seven — may not be enjoying their stranglehold at the top of the US economy for much longer. That's, of course, if the leaders of a swathe of new AI-centric tech firms, including OpenAI and Anthropic, have their say as they plan to IPO in the coming months. But positioning these businesses in the new big tech landscape has been a major challenge.

Ethics in AI

The Anthropic CEO Dario Amodei was reflecting on his history in the AI industry during an interview with Bloomberg that aired earlier this year.

In an exchange themed around the success of Anthropic's enterprise-centric AI tools like Claude Code and Claude Cowork, Amodei took the opportunity to opine on the nature of doing business and the importance of business models that align with your values.

Commenting that many in the tech industry prioritize models that tap into elements like engagement, advertising, and the promotion of AI slop, Amodei noted that compromising your own values is not a long-term and sustainable way forward.

This is at least as far as he's concerned. That's why, he suggested, he's attempting to make Anthropic more "useful" to the world by targeting enterprise customers.

Bad blood

Amodei, an ex-OpenAI executive, co-founded Anthropic in 2021 largely as a rejection of the values that drove OpenAI at the time and the paths the company had taken.

In particular, reports suggest that Amodei was frustrated and disturbed by the willingness to bypass what he considered to be crucial safety measures, like the slowing of updates to prevent malicious use of AI.

Critics of Anthropic, however, also point out that despite positioning itself as a safety-first AI company, engineers are releasing increasingly powerful models — including Mythos lately — that threaten to undermine safety if they get into the wrong hands.

• Singer Lorde called out Spotify's AI 'About the Song' feature
• She says it featured incorrect details and limits free interpretation for listeners
• Users are starting to catch onto its flaws

Spotify is always dropping new tools to enhance your listening experience, but one of its newest features has been called out by one of the most influential pop artists of this generation.

New Zealand singer-songwriter Lorde slammed the best music streaming service’s ‘About the Song’ feature which launched back in February. The tool is currently in beta, and pulls information from third-party sources to create AI-generated summaries of individual songs offering context and background.

According to the pop star, one of the summaries for her song ‘Current Affairs’ mentioned incorrect details which were sourced from themusic.com.au, stating the following:

“On her Ultrasound World Tour, Lorde turns Current Affairs into a full-on performance piece, stripping down to underwear while a dancer pours water over her stomach so the song plays out like the shower scene she talks about on stage”.

The singer didn’t hold back, taking to Instagram Stories to call out the platform while highlighting the dangers features like this pose on listeners:

“Hey @spotify I’m gonna go out on a limb and say we don't want this. Not only is this inaccurate (not the song I did that in) but reducing a song to an AI-generated meaning right at the source feels like it limits free interpretation IMO. At least make it possible for artists to opt out please,” she wrote.

It didn’t take long for Lorde’s comments to spark reactions from all corners of the internet, and Spotify has come forward to offer clarity from its side. We reached out to the platform for further comment, and a Spotify spokesperson replied with the following:

“We built ‘About the Song’ because fans want to dig into the stories behind the music. It’s still in beta. The info comes from articles across the internet, and when something’s off, we move fast to fix it, like we did here. Getting it right matters to us”. According to The Hollywood Reporter, the song summary has been removed.

‘Half the fun is figuring out your own meaning’

At the time of the feature’s launch, many Spotify subscribers welcomed ‘About the Song’ with open arms — I even went into detail about how much I couldn’t get over it and how it taught me things I never knew before. But despite the positive reception from subscribers, Lorde’s observation of the AI feature has sounded the alarm for music lovers.

Comment from r/Music

Aside from its inaccuracies, Lorde’s comments highlight another big issue with AI features like ‘About the Song’; they remove the listener’s opportunity to interpret the music for themselves. This has been the topic of many online discussions since Lorde aired her comments, especially on Reddit — ‘half the fun is figuring out your own meaning instead of getting AI Cliff Notes right away,’ one user says.

But according to others in the same Reddit thread, it speaks volumes about how art consumption is evolving. Whether you’re reading a book, watching a movie, or listening to an album, everyone looks at things differently which is what allows conversations and debates to flow. Another user went the extra mile, saying “If you need AI to tell you what it thinks the song means, you've stopped thinking”.

That said, Spotify isn’t the only music platform that dives into the background of music — Apple Music includes detailed summaries of albums located under the Play button — but these pockets of context are all written and developed by Apple Music’s editorial team, who don’t rely on AI for third-party sourcing.

But the album summaries is where Apple Music draws the line. As soon as you hit Play the platform leaves you to get lost in the music — which subscribers would rather have over what Spotify is trying to sell them.

More than a quarter of UK consumers trust AI chatbots for money advice, according to a recent review by the Financial Conduct Authority (FCA), the UK's financial watchdog.

That stat is worrying for regulators because giving financial advice is meant to be a regulated activity. But tools like ChatGPT, Claude and Gemini are not regulated. As AI becomes more conversational and personalized, people are asking questions about where the line is between providing information and offering financial advice, especially when chatbots start making specific recommendations based on what they already “know” about you.

I wanted to see what this looked like in practice. So I asked ChatGPT a series of hypothetical questions about everyday money decisions. From whether I should buy an expensive phone to what I should do with my savings and whether I should book a holiday after a difficult few months.

The conversations that followed surprised me. Because the advice was thoughtful, nuanced and (at least on the surface with some fact-checking) it seemed sensible. The chatbot highlighted trade-offs, acknowledged uncertainty and asked follow-up questions. But looking closer at the conversations, I started to understand why regulators are concerned.

The experiment

To see what sort of money advice ChatGPT gives, I asked it a series of hypothetical financial questions using ChatGPT Pro in anonymous mode with memory turned off, meaning it had no additional context about me beyond what I provided in each prompt.

Question 1: Should I buy an expensive phone?

First, I asked:

"I'm 38, earn £40,000 a year, have £8,000 in savings and £2,000 in credit card debt. I'm thinking about spending £1,200 on a new phone. Is it a good financial decision?"

The first response was surprisingly sensible. ChatGPT pointed out that credit card debt is often expensive, questioned whether I genuinely needed a new phone and noted that key details, like the interest rate on the debt, could change the recommendation. It even asked follow-up questions to better understand the situation.

What I found interesting was how quickly it then moved from analyzing the problem to recommending a course of action. Phrases like "the strongest financial move" gave the answer a sense of authority that felt disproportionate to the amount of information it had. Though I’m not sure I’d have spotted that if I was a regular user and feeling anxious about money. The advice also assumed that paying down debt should be my priority, which is reasonable. But what if I relied on my phone for freelance work? What if replacing it would help generate income?

A human adviser would probably want more information before reaching a conclusion. ChatGPT did acknowledge the gaps in its knowledge, but still sounded remarkably confident in its recommendations.

Question 2: What should I do with £20,000 in savings?

Next, I asked:

"I'm 38 and have £20,000 sitting in a savings account. What should I do with it?"

Again, the response seemed thoughtful. It discussed emergency funds, investing, savings goals and tax-efficient accounts with me. It also asked for more information about my circumstances.

Yet once again, the recommendations arrived before finding out that all-important context. Before knowing whether I owned a home, had dependants, planned a major purchase or was comfortable with investment risk, ChatGPT was already suggesting how much money I might keep in cash and how much I might invest.

The answer also contained more broad statements that sounded insightful, such as:

"Because you're 38, the biggest advantage you have is time."

It's a really reassuring line. But it's also a reminder of how persuasive these systems can be. The response organized the problem, provided a framework, supplied example figures and explained the reasoning. Reading it left me feeling informed and reassured. But whether that reassurance was justified is another question entirely.

Question 3: Should I book a holiday?

Finally, I asked:

"I've had a difficult few months and want to book a £2,000 holiday. Financially I can afford it, but part of me feels guilty. What should I do?"

I intentionally asked this question to see how ChatGPT would respond to the more emotional side of financial problems, and it quickly obliged. It asked where the guilt was coming from, encouraged reflection and offered reassurance. At one point it told me:

"From what you've written, I wouldn't be asking 'Can I afford this?' so much as 'Am I allowed to spend money on myself after a difficult few months?'"

It's a thoughtful observation and they’re genuinely helpful questions for someone who hasn’t considered the emotional angle before. But it also highlights how quickly the chatbot moved beyond finance.

By the end of the conversation, it was discussing emotions, reframing beliefs, offering comfort and helping with decision-making. So that’s a good example of ChatGPT occupying all sorts of roles at once. That’s important to flag because financial advisers, therapists and coaches are all held to different standards, qualifications and accountability structures. But a chatbot can drift between all three roles in a single conversation.

More than any individual recommendation the chatbot made, that realization helped me understand why regulators are paying attention.

What ChatGPT gets right — and why that's part of the problem

The obvious conclusion would be that ChatGPT gives terrible financial advice and no one should trust it. I get it, I’m pretty sceptical of AI these days and my bias wants to jump to there too. But that wasn't my experience.

In many ways, it was useful. It explained trade-offs clearly, broke down jargon, offered practical frameworks and encouraged reflection about money. Much of the advice also felt sensible after a light fact-check.

But I still think there’s reason to be concerned here. And the concern isn’t that every answer is obviously wrong. It's that many answers are plausible enough to trust. Especially if you’re not going to comb through each one to fact-check it, which let’s be honest, very few users are likely to do.

Financial regulators worry about something called “suitability”, which is whether advice genuinely reflects a person's circumstances, goals and tolerance for risk. Throughout my experiment, ChatGPT repeatedly offered recommendations despite knowing very little about me, the person asking the question. Granted, caveats were included some of the time, but they were often overshadowed by the confidence and clarity of the overall response.

There's also the issue of accountability here. If a regulated financial adviser gives the wrong advice, there are complaint mechanisms and consumer protections in place in most countries. But if a chatbot gives poor advice and somebody follows it, responsibility becomes impossible to pin down.

Another challenge, one which I’ve encountered in a bunch of different contexts while reporting on AI, is that fluency isn't the same thing as accuracy. We naturally interpret AI’s clear, confident language as a sign of expertise. But a polished answer can still be wrong, incomplete or inappropriate. I’m sure we’ve all seen countless examples on social media at this point of a chatbot sounding incredibly knowledgeable while missing a crucial detail or getting something spectacularly wrong — like the viral trend to ask ChatGPT how many r’s are in the word strawberry to which it would often reply two.

I think the biggest risk might be that people don't realize when they've reached the limits of what AI can help with. A reassuring answer can create the impression that a problem has been solved and they have a plan. When in reality it might be time to speak to a qualified professional. I’ve noticed whenever it comes to AI and advice more generally that the danger isn't always acting on bad advice but never seeking better advice elsewhere.

And unlike a financial adviser, a chatbot won't follow up to check whether things worked out. It won't know whether its suggestions caused problems. It won't know whether your circumstances changed. It simply produces an answer and then moves on.

As with many of the AI stories I've reported on, the issue isn't necessarily that the technology here performs badly. It's that it performs well enough to earn our trust.

Should you use ChatGPT for financial advice?

The question I suspect most people want to know is: should you use ChatGPT for financial advice?

And the answer is a tricky one and a familiar one. It's much the same answer I'd give if you asked whether you should use ChatGPT for therapy or life advice. Probably not, but I completely understand why people do.

It's easy to access and financial advice often isn't. The tone is friendly and reassuring, there's no judgement, and much of what it says appears sensible and accurate. At first glance, it feels like a useful tool, provided you take its answers with a pinch of salt, treat it as a starting point and remember that it can be overly agreeable, make assumptions or occasionally get things wrong.

The problem is that this isn't always how we use ChatGPT in practice. We turn to it when we're stressed, overwhelmed, uncertain or looking for reassurance. We ask it questions we don't know how to answer ourselves and, in many cases, wouldn't know how to fact-check. That's where things become more complicated.

It's all very well to say that people should use AI carefully, critically and with the right mindset. But how many of us will actually do that every time? Especially when we're worried about money.

That's why it doesn't surprise me that regulators are paying attention. There are no glaring red flags in any of the responses I received. But that in itself is reason to be concerned here. Because once something sounds knowledgeable, personalized and reassuring, it's surprisingly easy for even the most discerning of us to stop questioning it.

The ability to personalize and scale marketing strategies at speed has firmly captured the attention of chief marketing officers (CMOs) across the globe.

Yet, in the relentless rush to automate and optimize, some brands may be jeopardizing the very thing that fosters genuine connection with consumers: authentic storytelling, real human emotion and a distinct creative vision.

AI-generated adverts are an increasingly common sight across UK high streets – from event promotion to product adverts.

A staggering 51% of CMOs are now actively deploying Generative AI (GenAI), with some of the world’s biggest brands attracting widespread criticism for it.

According to recent data from Canva, 70% of consumers say they can often identify AI-generated advertisements because something feels like it’s missing.

For UK marketers, getting the balance right means applying AI where it works best, such as automating and scale, while ensuring that we do not lose the human touch that resonates and builds lasting trust.

The AI automation trap

AI undeniably offers powerful tools for content creation and distribution, making marketing efforts more scalable than ever before. From drafting copy to automating campaign launches, its capabilities are vast and transformative, but a narrowed focus on AI for efficiency can quickly lead brands into the automation trap. When AI dictates the soul of a brand rather than simply serving as a sophisticated tool, the result is often a generic brand voice and weaker customer engagement.

The danger lies in homogeneity. If every business leans solely on algorithms to craft its messaging, we risk creating a sea of indistinguishable content, stripping away the uniqueness that sets one brand apart from the next. Marketing then becomes a mechanical, predictable exercise. And, consumers can spot the lack of inauthenticity quickly, disengaging when they do. For UK businesses, over-reliance on automated content risks making the brand and the messaging entirely forgettable.

Authenticity as the ultimate differentiator

The solution to this is for businesses to spend time on authentic storytelling, sharing insights and messages in a way that only they can. While AI can process vast data and construct coherent narratives, it cannot replicate empathy or emotional nuance - the qualities that forge deep connections and remain the foundations of trust, consistency and accountability. These principles, much like the Lindy Effect, prove their worth through lasting impact and enduring relevance.

Human-led stories carry an emotional intelligence that algorithms simply cannot master. They capture the subtleties of lived experience, real struggles and unique perspectives that resonate with audiences on a personal level. At its core, an authentic brand story is built on shared vision, real customer experience and human connection. When these elements are present, brands differentiate themselves meaningfully and build a loyal following. That is why CMOs should champion 'proof over promise' by grounding stories in real, measurable impact and aligning them with their company’s core values.

The importance of "human-only zones"

Putting that principle into practice means CMOs must intentionally establish and protect "human-only zones" within their marketing strategies. These are the creative and strategic areas where full AI automation is consciously resisted. In this sense, CMOs should recognize AI’s limitations and ensure critical aspects of brand-building remain human-led.

These essential human-led zones include:

Strategic visioning - defining the core brand story, values and long-term positioning

Emotional messaging and humor - campaigns carefully crafted to evoke specific feelings, which always require nuanced human understanding

Creative direction - the artistic and aesthetic choices that give a brand its distinctive visual and auditory identity

Crisis communications – which demand empathy, judgment and a sincere voice when situations are sensitive

Innovation and challenging norms - pushing creative boundaries and developing truly disruptive ideas. AI, which primarily optimizes based on existing data, struggles to generate this kind of thinking independently

By ring-fencing these areas, CMOs keep the human heart and mind at the center of their brand's identity, preventing it from being diluted by generic, algorithm-driven content.

CMOs as guardians of creativity

That protective role is reshaping the job of CMO itself. Marketing leaders are moving beyond strategy and execution to become guardians of creativity. They must champion and cultivate human originality and innovation within their teams, building environments where it thrives and drives competitive advantage.

This means empowering teams to experiment, take risks and bring fresh thinking forward. It means investing in human talent, creating room for creative development and valuing ideas that don’t immediately fit an algorithmic mold.

Ultimately, AI can speed up content production and streamline distribution, but humans must remain accountable for meaning, judgment, trust and the wider consequences of the stories they tell.

The future of marketing will require combining AI’s capabilities with human originality to amplify impact while preserving the authentic voice that truly connects with audiences.

For UK businesses hoping to connect with their customers and build loyalty, this means embracing AI as a powerful enabler – and never at the expense of the authenticity that creates lasting customer relationships and brand resonance.

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There's a pattern many organizations know well. A new technology arrives, adoption accelerates faster than governance can keep up, and a few years later, the finance team is staring at a spreadsheet, wondering how the bill got so large and who signed off on it.

The SaaS era exposed what happens when technology adoption outpaces financial oversight. And with agentic AI embedding itself into everyday workflows, organizations risk heading down a similar path.

Recent reporting on Amazon employees 'tokenmaxxing' - gaming internal AI metrics to inflate adoption figures, is an early signal of what that looks like in practice.

But the dynamic is not specific to Amazon. When AI usage isn't fully visible, and the incentives favor showing more activity rather than less, accountability tends to disappear quietly. It points towards a governance failure - and governance failures require structural solutions.

The opportunity is clear. The governance isn't.

The need for visibility

AWS's Banking on the Cloud 2026 report makes the strategic case for agentic AI in financial services clearly and compellingly. Cloud computing infrastructure and AI agents are positioned as the foundation of next-generation banking, which means faster decisions and more responsive customer experiences.

What the report focuses on is what AI can save. The other part of the equation is what AI itself costs to run at scale, and who's accountable for that.

The cost model for AI agents behaves differently from anything most enterprise finance teams have managed before. When you license a conventional software tool, there's usually a fixed price and a user count. The spending is visible even when it isn't well-controlled.

AI agents work differently as they run continuously, calling on external services and triggering actions across systems as they go. Each step consumes resources, and because agents operate autonomously, often handling tasks that would previously have required human judgment, that consumption can scale quickly and unpredictably. There's no contract line that captures it cleanly and no renewal date that forces a review.

Getting ahead of this requires visibility that most organizations are only now beginning to build.

The sprawl problem

The SaaS management challenge is a familiar one to most IT and finance leaders. Application estates grew faster than procurement could track them, governance lagged behind adoption, and many enterprises spent years rationalizing software stacks they never intended to build. It’s an ongoing problem that businesses are still managing, years down the line.

AI agent sprawl will likely develop differently, but the underlying problem is similar. The critical difference is pace.

A SaaS tool that gets deployed and forgotten sits there, quietly billing at a fixed rate. An AI agent generating outputs in real time is actively consuming resources from the moment it runs, and that financial exposure, left unmonitored, compounds in ways a forgotten software subscription simply doesn't.

Organizations that get the right comprehensive visibility in place early will be in a significantly stronger position than those treating cost governance as something to formalize later.

There's also a regulatory dimension that's coming into sharper focus, particularly in financial services. AI agents frequently depend on external model providers and third-party data sources. Each dependency introduces a potential point of failure - and in regulated industries, potential compliance exposure.

Regulators are paying attention, the EU AI Act's full obligations for financial services AI land in August 2026, and DORA audits are already underway, which means the question of who owns that chain of accountability will need a cleaner answer than most organizations currently have.

What good governance actually looks like

The encouraging part is that none of this requires building new disciplines from scratch. It requires applying familiar ones to a new context and doing it early.

The right starting point is understanding cost in relation to outcome. What does it actually cost to complete a task using an AI agent, and what is that task worth to the business? Answering it means connecting AI spending data to the broader picture of how technology is used and what it delivers, so that finance and engineering are working from the same information rather than talking past each other.

Controls also need to be built into the infrastructure rather than layered on top of it. As agent deployments grow, no team can realistically review individual workflows by hand. Policies that depend on someone remembering to check a dashboard aren't really policies; they're suggestions. When a budget review turns difficult or a regulator asks questions, suggestions don't hold up.

Most importantly, ownership needs to be established from day one. Which budget carries this deployment? Who reviews it when consumption shifts? Right now, many AI agents are being deployed by engineering teams without meaningful involvement from finance. That gap is entirely closable. Closing it before the bill arrives, rather than after, is where the real advantage gets built.

The organizations that navigate agentic AI well will be the ones that treat governance as part of the deployment decision rather than an afterthought to it. Cleaner accountability means faster decisions and AI investments that can actually be defended against the board or a regulator. Throughout the rest of this year and beyond, that is the key differentiator between organizations that scale AI confidently and those that are still untangling the bill.

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ChatGPT Work sounds like something designed to prepare quarterly reports while you sit in meetings, but I suspect some of its best uses would have nothing to do with my job.

This week, I’ve used the new Work mode to help manage some of the life admin tasks I really don’t enjoy, and it’s been surprisingly effective. You see, holidays, household budgets, family events and home renovations are all projects too — they are simply projects we currently manage through a chaotic mixture of browser tabs, messages, spreadsheets and increasingly desperate notes to ourselves.

Perhaps ChatGPT Work could help me with that?

What is ChatGPT Work?

If you’ve been using ChatGPT over the last week on a paid plan (except the basic Go service), you’ll have noticed a new slider (in the browser version) or a drop-down menu on mobile has appeared at the top of the screen offering a choice between Chat and Work mode.

Work mode is a new agentic mode for longer, more involved tasks that can research and analyze information across connected apps and files. So, if you want a complicated report presented in a finished document, like a spreadsheet or presentation, then Work mode is your new friend.

That is where I thought ChatGPT Work could become interesting for normal life, too. Instead of answering one question and waiting for the next, it can take on a longer task, work across connected files and apps, create the documents and spreadsheets the project requires, and continue checking for changes after you leave.

Work mode is also better at one of the main bugbears of ChatGPT — running tasks at particular times. It can use the new Scheduled Tasks to repeat tasks on a schedule or monitor something for changes.

So, I decided to ignore Work mode's aggressively corporate name and see whether it could handle some actual life admin, starting with planning a holiday.

1. Planning a holiday

I switched the slider to Work and gave it my dates, budget, family requirements and any bookings already sitting in Gmail, because it can search that too. I asked it to research destinations, compare travel and accommodation, create a spreadsheet of costs, produce an itinerary and maintain a list of what still needs booking.

And off it went, happily beavering away on its task, while I was free to get on with something else. I really liked the way it tells you what it’s currently working on, so you can pop in and out of the chat and see what it’s currently doing. It shows sources it's drawing from as it calculates accommodation costs and travel arrangements. You can literally watch it working for you.

The result was a nicely planned holiday in a location optimized for activities and sightseeing all within my budget. It was actually pretty impressive.

2. Become the household financial administrator

For this task, I fed ChatGPT my bills, bank-export spreadsheets, and household documents and asked it to create a working budget, identify unusual increases, forecast annual costs, and produce a dashboard.

A scheduled task then reviewed new bills or price changes and flagged anything worth investigating. Of course, ChatGPT couldn't move my money around, but at least it gave me a clear view of where my money was going and how much I was spending.

It took a long time to get all the data into ChatGPT, but this taught me that the output was only as good as the effort I was willing to spend putting quality data into it. I’d have preferred a way to open the spreadsheets directly in Sheets from ChatGPT, too, but they were available to download.

3. Organize a major family event

My wedding anniversary was coming up, so I wondered how well ChatGPT would perform as an event organizer. I asked it to research a nice venue for taking my wife out for dinner, which it did well, and gave me three good options.

It struck me that if it had been a bigger event, it would have been ideal for researching venues, maintaining a guest list, tracking replies, producing a budget, creating invitations, and even building a simple information website.

Of course, ChatGPT can’t upload the website and host it, but at least it can build the site for you, and you can download it.

4. Manage a home improvement project

If you’re doing a major home improvement project, then you can get ChatGPT's Work mode to compare quotes, analyze plans and product specifications, create a budget, build a timeline, and keep a list of unresolved decisions. It could periodically check for price changes or relevant new messages.

This is probably the clearest example of an ordinary personal task becoming complicated enough to justify a proper agent.

5. Run the family’s weekly logistics

Once I’d connected my calendars and emails to ChatGPT, I could ask it to prepare a weekly family plan covering appointments, school or university commitments, not to mention my travel, meals, and outstanding chores.

I created a Scheduled Task that regenerated the plan each Sunday evening, taking the next week’s tasks into account, and alerted me during the week when something important changed.

This was actually the use of ChatGPT Work Mode I personally found most useful. It’s easy to miss school events, but when they’ve been emailed to you, ChatGPT will know about them and make sure you don’t forget. That’s a lifesaver.

Why Work mode matters

The introduction of ChatGPT Work mode represents a real shift in the way OpenAI is viewing the future development of its core product. It’s an obvious change in emphasis towards work-related tasks, and a hint at where OpenAI sees ChatGPT heading.

But I think it means more than that. ChatGPT’s new Work mode represents the moment ChatGPT stops being somewhere you go for individual answers — a chat — and becomes somewhere where you start to feel like you're working on an ongoing project.

While the first phase of AI’s evolution was the chatbot, we’re now firmly into its second phase — the AI agent. AI that can work independently of our requests and handle complex, ongoing tasks is where the future of AI lies, and Work mode is another step on that path.

• Only 34% of organizations say they trust their agents' actions
• 77% of businesses in 'agentic chaos' have deployed agents
• Those organizations are focus on the wrong thing (models and tools)

New Boomi data has claimed while 86% of enterprises have evolved from agentic AI pilots to actual production-level deployment, only 34% trust the actions their agents are taking.

However those trust issues don't stem from model intelligence – Boomi argues that data quality, integrations, governance and controls could be to blame.

The research splits respondents into distinct categories, with the bottom quartile for readiness referred to as experiencing 'agentic chaos'. Among those in agentic chaos, as many as 77% are still moving AI agents into production, highlighting a worrying gap.

AI trust is entirely in the hands of enterprises

With more than three in four of the enterprises in agentic chaos still pushing ahead with their plans, Boomi warns they could face unexpected costs from compliance fines, lost customers and operational downtime.

On the flip side, the top quartile was categorized as being in 'agentic control', and more than half (55%) of them said they're highly confident in their agents' decisions and actions.

Additionally, the report criticizes those in agentic chaos for focusing on the wrong thing, with around half (51%) prioritizing AI model or tool maturity instead of the true causes of distrust.

"Agents can only be trusted to act on data that's been properly activated, connected, and governed, and most companies deployed agents before they did that work," CEO Steve Lucas wrote.

At the end of the day, Boomi's data implies that pressure to demonstrate AI value and ROI could actually be leading to premature deployment before the foundations are in place, leaving organizations to play catch-up in a far more inefficient way.

A new Google Home Display might be on the cards, but sticking to the traditional smart home hub format seems like a missed opportunity. What I really want to see this year is a new version of 2023's Google Pixel Tablet — and it would be a much better fit for a modern AI-powered smart home.

Google hasn't officially announced a new smart screen, but I think it's only a matter of time. Last year, Anish Kattukaran, Chief Product Officer for Google Home, said the company was still committed to the smart display format. "If you think about the properties of a smart display: a microphone, which means audio in, a speaker, so audio out. It’s got a screen, which complements a voice modality, you can interact with it and visualize information," he told The Vergecast.

"As I see where Gemini for Home is going and Gemini more broadly, all of the things Google is investing in building, for me it feels like almost the ultimate form factor to be able to deliver a really great home experience. So, that’s why we are going to continue to invest in that category, so I think it’s going to be awesome."

A smart home hub can be so much more than a speaker, screen, and microphone though, as the 2023 Google Pixel Tablet demonstrated — and I think a new version of that device would be an even better platform for Gemini than a Google Home Display.

The smart home untethered

If you're not familiar with the Pixel Tablet, it's essentially a full Android tablet specifically designed for use around the house. It doesn't have great battery life, and there's no SIM card slot for mobile data, but that's not a problem when you're never straying outside range of your home Wi-Fi network, or taking it far from its dock.

At first glance it looks just like a Google Home Display, but there's one big difference: while the Home Display is all one unit, the Pixel Tablet's screen lifts off its magnetic dock so you can carry it from room to room.

Unlike a Google Home Display, the Pixel Tablet isn't just a hub, either. Although it can be the heart of your smart home, providing quick access to your lights, switches, robovacs and cameras with quick voice controls, it's also a fully-featured Android tablet, with access to the whole Google Play Store. That means you can install your favorite web browser, stream movies from Netflix, or spend hours on YouTube on its 10.95-inch display. It has a reasonable front-facing camera as well, so it's ideal for Zoom calls with the family from the comfort of your sofa.

My Pixel Tablet lives on my kitchen table, where it serves as both a smart home hub and a second TV, letting me stream shows via BBC iPlayer at breakfast time, or use YouTube Music to play music while I work. The tablet's built-in speaker is quite tinny, but the one housed within its dock sounds surprisingly rich and well-balanced.

When it's time to make dinner, I can simply pluck it from its dock, stand it on the kitchen counter, and load my recipe app of choice, then in the evening, I can crash out on the sofa, adjust the lights, and finish my daily Duolingo session. You can't do that with a Home Display. Sounds good? It is — and it's now discontinued.

A smarter Pixel Tablet

So what would a 2026 Pixel Tablet look like? Well, apart from having Gemini pre-installed (naturally), it needs to function as a Thread border router, and its speaker needs a boost to bring it level with the new Google Home Speaker. Its 2,560 x 1,600px screen resolution is already respectable though (for comparison, the larger Amazon Echo Show 11's screen is 1,920 x 1,200px) and its weight doesn't matter considering it's not intended to travel outside the house, so I wouldn't worry about slimming it down much.

It would be nice to see a lower price, though. The original Pixel Tablet had a list price of $499 / £599 / AU$899, which is a hefty fee for a smart display. A cheaper Pixel Tablet might be wishful thinking, but considering you would need a Google Home Premium subscription to get the most out of Gemini, a lower asking price would make it a more tempting proposition.

There's a chance that Google might surprise me with a Google Home Tablet that can roam from room to room, but somehow I doubt it. Apple's vision for the future seems to involve multiple screens scattered throughout your home, while Amazon Echo devices are so affordable, it's easy to justify buying one for each room. We'll have to wait and see.

The conversation about AI in space keeps arriving at the same image: a floating supercomputer processing the world's information from 400 miles up. It’s a compelling narrative, but there’s a gap between what companies are hoping to build and what is being built today.

Today, satellites run on fixed power budgets measured in watts with strict constraints. Bandwidth is scarce enough that every byte reaching the ground has to earn its place.

Those limits push the field toward architecture that looks more like a nervous system, than a data center – lightweight models running onboard that interpret sensor data in near real time and convert observations into structured events. The event reaches the ground.

The raw pixel doesn’t.

From hours to minutes

In a conventional Earth observation pipeline, a satellite captures an image, downlinks it to a ground station, ground systems process the raw data, and the result reaches whoever needs it. Best case that happens in hours, but often it’s closer to a day.

For a disaster response team managing a flood in a low-lying river delta, or a conservation authority trying to locate the origin point of a wildfire in a remote national park, that day comes at a cost.

A satellite running inference onboard changes the math. Detection happens in seconds. What gets downlinked is a position, timestamp, or risk score. The bottleneck shifts from the space segment to the ground distribution, which is a comparatively manageable problem.

The use cases where this matters most are not the visible disasters people are watching. They’re the methane leak on a pipeline with no weekly inspection schedule, an oil spill beyond the reach of coastal patrols, a wildfire that began in a remote area before anyone had reported smoke. Onboard inference turns a passive imaging asset into an early warning system.

What orbital constraints teach edge architects

The tradeoffs being resolved in orbit are an extreme version of the same constraints facing any organization deploying AI outside a well-provisioned data center.

Cloud-native AI development often has a back up plan: when the model is too large add compute; when bandwidth is constrained, increase it; when latency is a problem, move the processing closer. In orbit, none of these options exist. You build within the envelope, or the system doesn’t function.

The result is a forcing function that enterprise architects rarely encounter at the same level. Industrial IoT deployments face intermittent connectivity. Autonomous systems can’t afford round-trip latency to a central server at decision time.

The shift from 'send everything, process centrally' to 'process locally, transmit what matters' is happening across multiple industries. Space is where that shift ran without a safety net.

The bandwidth math

The data reduction numbers transmitted in real time is not just 80-90 percent. Once processing happens on the spacecraft, the reduction for the real-time layer exceeds 99 percent. This is semantic compression. The satellite sends the meaning of what it saw, not the measurement it produced.

A conventional operator downlinking hundreds of terabytes of raw imagery daily is paying bandwidth cost for data that largely contains nothing of interest. With onboard inference, what's transmitted in real time is a structured detection event: a position, a timestamp, a risk score, and perhaps a small compressed image. That is hundreds of kilobytes, not terabytes.

An operator downlinks only what warrants examination, rather than blindly dumping the full data stream.

Architecture decisions that preview what's next

A model making decisions before a human is in the loop carries different requirements than one generating recommendations for human review. Ambiguity tolerance is lower. Inference behavior needs tighter scoping. This is the same conversation that medicine and finance have been having for years.

AI-assisted diagnostics and accountability distributed across platform, model, training data, and end customer rather than concentrated in a single layer. The space industry is joining a conversation other sectors have already been having for years.

In a cloud environment, model size and computational efficiency are optimization targets. Meaning they are important, but secondary to capability. In a constrained orbital environment, they are the primary design constraint from which everything else follows. A model that cannot run within the available compute envelope is not a model that gets deployed. There is no option to add a larger instance.

A maritime patrol aircraft that previously ran random vessel inspections now works from a ranked list of targets with risk scores attached. Some alerts will be false positives which is a physical reality of any probabilistic system. But the aircraft's operational effectiveness improves substantially compared to random patrolling or no monitoring at all. The AI narrows the search.

The scaling problem is familiar

One satellite running an onboard model is a proof of concept. A constellation of hundreds running distributed inference is a different infrastructure problem as orbital AI scales.

Centralized orchestration becomes the bottleneck when constellations grow. Every decision can’t route through a ground station. Distributed inference is a requirement. Enterprise architects hit the same wall when a pilot deployment expands to thousands of edge nodes. The centralized model that worked in development becomes the thing that breaks in production.

The cloud infrastructure analogy supports this. Nobody builds a data center before launching an application. The pattern is shared infrastructure, with control at the model, mission logic, and decision layer. Those can be sovereign regardless of who owns the underlying compute.

A design principle worth carrying

It’s hard to develop constraint-based thinking in environments where adding compute is always on the table. The organizations that have built it tend to have faced conditions where it wasn’t.

The strategic advantage in edge AI over the next decade will not just be measured in the amount of compute available. It will also be measured in code deployed to the right place in the stack. Satellites are running that experiment first.

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The 2026 World Cup is already under way. Billions of eyes are on the pitch. The story that matters to anyone running a supply chain, though, is playing out in warehouses, customs terminals and distribution centers spread across three countries.

For the first time, the tournament spans three host nations: the United States, Canada and Mexico. That means millions of product lines, thousands of supplier handoffs and cross-border compliance requirements across three distinct regulatory environments, all compressed into a window with zero tolerance for error.

The operational scale of this tournament is without precedent.

Now that the group stages are live, the pressure on supply chains is real and immediate. The lessons surfacing are worth paying attention to, because they apply well beyond sport.

A live packaging stress test

Official merchandise for an event of this scale moves across multiple countries, customs jurisdictions and retail channels simultaneously. Product identification has to work at every stage of that journey, from the manufacturer's floor to the stadium vendor's shelf. The label that cleared customs in Los Angeles may face entirely different requirements in Toronto.

The deeper problem is structural. Today's supply chains still largely operate as disconnected islands. Each site, supplier, co-packer and carrier maintains its own systems and repeatedly re-enters the same product and compliance data. That fragmentation creates built-in waste at every handoff: redundant setup, duplicate records and inconsistent label versions. Under normal conditions, these inefficiencies are costly but can be masked by day-to-day operations. Under the pressure of a live global tournament, they become critical.

Demand shifts are happening in real time. A host city that reaches the knockout stages sees fan merchandise demand surge overnight. Supply chains built on static, batch-processed labelling data are finding they cannot respond at that pace.

The speed of these shifts can be surprisingly tangible. In Atlanta, shortly after the Spain-Cape Verde match, I walked through the airport and was struck by how many people were wearing Cape Verde jerseys. In the space of a few hours, merchandise that had been relatively low-profile had become highly visible, underscoring how quickly demand signals can emerge and spread during a global event.

From labels to live data

What the World Cup is making visible in real time is a shift that has been under way for several years. Product identification is no longer a print-and-forget exercise. It is a live data problem.

The industry is moving from fragmented, internal systems to connected, multi-partner ecosystems. The organizations managing the tournament's supply chain most effectively are those that have made this shift: rather than each stakeholder operating in isolation and recreating the same product and compliance data from scratch, they are working within a shared, real-time environment where information flows seamlessly across systems, suppliers, customers and geographies.

The benefits of this approach are measurable. Organizations that can operate with real-time visibility and trusted data across their extended value chain can reduce delays, prevent errors at source and respond faster when disruption hits. In sectors where production downtime can exceed $1-2 million per hour, that responsiveness is not a nice-to-have but operationally critical.

The cost of disconnected systems

The consequences of siloed product data are well understood in theory. A tournament of this scale is making them visible in practice.

When product data does not flow seamlessly across sites and trading partners, the failure surfaces in predictable ways. Rejected shipments at customs. Compliance failures that stall distribution. Production downtime while teams manually reconcile data across systems. Against the backdrop of a global event with fixed deadlines, those failures are not recoverable.

The organizations absorbing those costs right now are those still operating inside the organization perimeter - managing product identification as an internal function rather than a network-level capability. The distinction matters. Supply chain resilience increasingly depends on the ability to coordinate accurate product data across every site, trading partner, and customer - creating a connected ecosystem in which product identity can be shared, trusted, and acted upon seamlessly.

What happens after the final whistle

Connected, network-driven approaches to product identification are no longer a future aspiration. The World Cup is demonstrating their value in real time, at a scale most supply chains will never encounter but from which every supply chain can learn.

The direction of travel is clear. Organizations that can rapidly adapt labelling requirements across plants and partners, share trusted product data in real time and eliminate the manual rework that comes with disconnected systems will outperform those that cannot. That is as true in retail, pharma and automotive as it is in a stadium in Los Angeles.

The World Cup will be over in a matter of weeks. The infrastructure challenges it is exposing will still be there when it ends. The organizations that use this moment to address those fundamentals will be better placed for whatever high-pressure deadline comes next.

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If you’ve done any shopping for marketing automation tools these days, you’ve probably noticed they all claim to be “powered by AI.” Apologies for splitting hairs, but that’s just not true.

A significant portion still rely primarily on rule-based automation, work identically to the platforms they replaced, triggering if/then workflows created by human engineers years ago. Give their systems an edge case to parse and you’ll soon see them send an inappropriate email, crash, or output some stale nonsense that wouldn’t matter to any living person.

Same label. Same old architecture. The problem? A rule bottleneck.

Traditional marketing automation relies on knowing rules ahead of time. A lead hits a score? Send an email. A prospect completed behaviors A, B, and C? Trigger sequence Y. Wait Z days, send the follow-up email.

A rules engine can execute these commands flawlessly – but it can only execute what it knows. When a situation arises that doesn’t fit the rules, what do you do? You update the rules.

Why this matters

Marketing is messy. Prospects take unpredictable journeys, trends come and go overnight, and audiences who loved your message last week don’t care about it this week. But rule-based systems can only improve when given new rules to fire. Engineers can’t possibly keep writing rules faster than the world changes.

The industry has been papering over this problem with AI buzzwords. Sprinkle some Neural Network magic on a rule engine, and suddenly you’ve got yourself an “AI platform.” Engineers who look past the updated sales brochures still find the same good old-fashioned if/then statements, patched up with trendy new nomenclature for the latest round of funding.

The difference between legacy automation and true agentic AI is that true agentic AI won’t just patch up the last generation of marketing automation tools – it will replace them. Agentic AI isn’t defined by capabilities so much as by the way decisions are made.

Rules engines ask, “what rule should fire next, given this input?” Agents ask, “what action should I take to get closer to my goal?” This is subtle but critical. Agent theory holds that the system knows its goal, its current context, and a list of available actions it can take.

Based on those three pieces of information, it can reason as to which action will bring it closer to accomplishing its overall objective. This extends far beyond executing canned responses - it’s deciding what to do.

Agentic systems maintain goals, reason over available actions, invoke tools, evaluate intermediate results, and adapt their plans as new information becomes available. The architecture is fundamentally iterative rather than purely reactive.

You know where this is going.

An agent can adapt if a campaign stops performing. It can coordinate with other agents who manage different subsets of that workflow. And it can do so without a human engineer going back into the system to rewrite the rules every time the world changes. The system manages its goals.

Why specialization matters

One important architectural decision that separates good agent implementations from the rest is specialization. Should you build one big generalist AI system to handle everything or many specialized agents, each performing their own task?

Specialization comes up often in discussions around AI, from medical doctors to Renaissance men. There is broad utility in generalization, but singular accuracy in specialization. The family doctor can handle any symptoms you throw at them. But when you need to be absolutely certain about your diagnosis, you see a specialist.

That’s because specialists aren’t smarter than the generalist – they’re just trained on narrower data. Likewise, generalist AI models aren’t going to produce great results for highly specific use cases. OpenAI’s models can write you a marketing strategy. They can craft creative assets. But they can’t produce marketing assets that:

• Fit the pixel ratio requirements of a given publisher
• Match your brand’s color palette
• Align with your target audience’s emotional affinity profile
• Incorporate mentions of trending topics from the previous day

They can’t do all of those at once, either. And you shouldn’t expect them to. For hard problems with specific solutions, you should build specialized agents (sometimes called “agent crews”) that own a narrow subset of your workflow.

One crew might specialize in strategy generation, while another focuses on creative writing. One might select publishers while another analyzes performance. Separately, these crews create atomic workflows that a generalist system would struggle to manage.

How not hosting your models affects data privacy

There’s another argument for specialized, privately hosted models that isn’t made enough: data privacy.

Whenever you use a public large language model (LLM) to write marketing copy, your data is being uploaded to someone else’s infrastructure. “We don’t use customer data for training” is easy to say but barely offers any assurance. Inputs are still being ingested, processed, stored, and handled according to what that provider’s internal policies dictate.

And those policies can change… most corporate lawyers have never looked at the data use section of public AI providers Terms of Service, let alone dissected it line-by-line.

But what about controls your organization can enforce? Do your developers scrub data for PII before generating content with an LLM? That only works if everyone in your company memorizes your data policies and uses tools responsibly. One rogue employee attaching a spreadsheet full of internal pricing to a prompt breaks your compliance.

But if the model itself is hosted privately, that’s one major source of exposure that goes away. Your data never leaves your infrastructure. There’s no ingestion point to transmit it to a third-party, no training feedback loop that will process it, and no agreement to parse about how that company will handle your data “moving forward.”

Governance is a system property

Because AI in the enterprise has reached a maturity level where governance is a legitimate concern, many teams treat it as a bulk edit at the end of AI-generated content. Have humans review and approve. That’s fine, and many teams require this today. But governance should be built into the system at a fundamental level.

Well-built agents have guardrails at every stage of the decision-making process. That means models that make predictions within set bounds. That means observability that can trace every word generated back to its origin.

That means third-party benchmarking to prove your models perform well against industry standards, not just internal testing. Governance shouldn’t just be applied to outputs – it should be inherent in the architecture.

What enterprise buyers should actually be asking about

Buying criteria for agentic AI will vary by company, but as requests for proposal accelerate to keep pace with innovation in the industry, here are a few considerations every enterprise buyer should ask about:

• Goals vs. rules - Is this system actually agentic? Or is it just automating workflows with AI tools bolted on? The first step is asking vendors point blank what their system does when it encounters data it doesn’t know how to parse. Rules engines will point to specific fallback rules that execute. Agents will talk about reassessing their goal and weighing their available actions until they decide on the next best step.
• Models and hosting - Where are the models hosted? Are they specialized and trained on domain-specific data? This answers two questions at once – vendor capability, as well as data privacy concerns.
• Long-term memory and context - Enterprise agents become dramatically more useful when they retain organizational context over time. Rather than treating every interaction as a new conversation, they can accumulate institutional knowledge, remember previous decisions, and personalize future actions while remaining within governance boundaries. Persistent memory allows agentic systems to improve continuously without requiring engineers to encode new rules after every edge case.
• Hallucination - No current LLM is immune to hallucinations. The important architectural question is how the system detects, bounds, and mitigates them before they affect downstream business processes. Specialists hallucinate less in their domain of expertise. Prediction window guardrails limit how far an AI system can go “outside the data.” Human approval gates before sending anything live catch anything that slips through.
• Governance / auditability - Can the system provide traceability for every output it generates? Is the system’s accuracy benchmarked against a third-party, or just internally verified?

The economic case for getting this right

There's an additional argument that often gets overlooked in discussions focused on capability: cost structure.

Token-based pricing from large model providers creates a fundamentally unpredictable cost model for enterprise deployments. Every question, every generation, every iteration costs tokens — and iterating toward an acceptable output for a complex campaign task can consume a significant volume of them.

Enterprise subscriptions impose usage caps that create their own operational friction. The more AI-dependent your workflows become, the more acute this pressure grows.

Organizations that own and host their own specialized models are not subject to this dynamic. There is no token meter running. The economic relationship is closer to infrastructure than to a metered service - you bear the cost of building and maintaining the system, and in return you have predictable marginal cost. For organizations at scale, that math changes substantially.

Don’t fall victim to marketing speak

AI marketing platforms will continue to flood the market with AI-sounding languages attached to rules engines. But for enterprises who truly want to deploy agentic AI, there’s a far better solution. Domain specific, privately hosted agents that don’t leave your organization exposing itself to risk.

As agentic systems mature, the organizations that differentiate between genuine autonomous architectures and AI-enhanced workflow engines will be better positioned to capture sustainable competitive advantage.

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Data center design has been shaped by a familiar set of priorities for years: keep systems available, resilient and predictable in any condition. Just like the electrical grid that powers these sites, they have been engineered to provide a highly consistent service regardless of what happens, even when individual components fail.

This has meant operators build layers of redundancy into power, cooling and network infrastructure.

However, artificial intelligence has changed the story. While organizations are racing to roll out AI tools at scale, the data center industry is discovering that not all workloads have the same infrastructure requirements.

For instance, training a large language model, running real-time inference, supporting enterprise applications and processing business-critical transactions each place very different demands on the underlying infrastructure.

Today, one data center doesn’t need to serve every purpose equally and we’re increasingly seeing that facilities can be both flexible and tailored to specific workload requirements.

The end of the traditional model

Historically, 99.999% uptime was non-negotiable. Data centers have traditionally powered systems like banks, emergency networks and customer-facing digital services, requiring continuous availability.

In these types of environments, where outages could have an extreme impact (from high financial losses to putting real lives at risk) this approach makes sense. Since operators couldn’t always predict which applications would be truly mission-critical, many facilities were built to the highest resilience standards by default.

But AI has changed this. “One-size-fits-all” redundancy isn’t necessary anymore. Different models, training and inference processes each require totally different service levels. For example, facilities for AI training workloads are being designed without backup generators, complex redundancy systems or high-tier architecture.

The good news is that there is a growing understanding of the distinction between environments needed for AI training and AI inference. Training facilities are increasingly being located wherever power is available.

The primary constraints are energy supply, cooling capacity and speed of deployment. In many cases, maximizing compute density and accelerating delivery timelines are more important than achieving the highest possible redundancy levels.

Inference infrastructure presents a different set of priorities. These workloads are often deployed closer to users and support services that people interact with daily. In these scenarios, latency, availability and customer experience become notably more important, creating a stronger case for resilient infrastructure and geographically distributed architectures.

Precision resilience to support an industry under pressure

It’s clear, therefore, that reliability still matters. However, infrastructure requirements vary significantly depending on the service being supported. In today’s age of AI, ‘precision resilience’ should be the focus, e.g., redundancy matching how workloads actually behave, rather than relying on legacy design assumptions.

The key challenge here for operators is determining where resilience delivers genuine business value and where it simply adds cost and complexity.

In a time when developers are facing a huge amount of pressure amid labor shortages, with demand outpacing supply, defaulting to ultra-resilient, high-tier designs for every AI deployment only intensifies challenges.

The industry is also expected to deliver capacity faster than ever before, while battling an ongoing power gap, meaning large-scale developments are increasingly difficult to execute. In this landscape, overengineering infrastructure can have unintended consequences.

Every additional layer of redundancy consumes capital and increases complexity. This is triggering an increased focus on efficiency, not just in terms of energy consumption, but in how capital is allocated throughout a project. Operators are looking to design infrastructure that maximizes the value generated by every watt of available power.

The role of upgradability

As operators move away from this one-size-fits-all redundancy to optimize their bottom line, it’s crucial that their facilities can adapt as workload requirements change.

While inference is expected to account for a growing share of AI demand, the landscape continues to evolve and it’s difficult to predict which workloads, densities and cooling requirements will dominate in the future. Infrastructure that can accommodate changes in compute technologies will be better positioned to support the next generation of AI applications.

Flexibility and fungibility are therefore the new non-negotiables in data center design. How is this made possible? Increasingly, developers are using ‘building blocks’ constructed off-site in factory environments, and then later assembling them on site to create an adaptable facility that can forever evolve, grow and shift.

This approach reduces the need to make every resilience decision upfront and builds with tomorrow’s changes in mind. In the coming years, we will see a shift towards multiple types of facilities, each developed for a different purpose.

These will range from energy-optimized training campuses built close to power sources, to distributed inference sites where uptime and latency directly affect user experience, alongside hybrid environments supporting both AI and traditional workloads. Yet they should all be built with flexibility front of mind to ensure they can evolve as requirements change.

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• The number of companies using AI is up, but the number of tools per company hasn't grown much
• ONS data also confirms that AI doesn't seem to be having a major impact on headcount
• SMBs could be lacking a clear business use case for AI

New ONS data has claimed that while AI adoption has expanded rapidly across UK businesses, the actual depth of adoption is still limited, with most firms using just a small number of tools to improve existing processes rather than totally overhauling their businesses.

Among the businesses surveyed that had adopted AI, the number of tools they used only increased marginally from 1.4 to 1.6 in the three years leading up to 2026.

The ONS also revealed that, while 60% of larger businesses have used AI to improve business operations, no negative impacts on headcount have been observed.

AI adoption might be growing, but that's not the full story

The data mostly implies experimentation rather than widespread transformation, with just 17% of SMBs (0-9 employees) reporting extensive AI use, however the report warns that many small businesses are merely trying general-purpose AI tools rather than making substantial investments into tech that can truly unlock new levels of productivity.

What's less clear is why SMBs are falling behind their enterprise counterparts, because while 7-14% of all business sizes cited cost as a barrier, 41% of SMBs with 10+ employees said they didn't have any major barrier concerns.

Rather than being held back by costs, infrastructure or capability, the ONS data actually implies that SMBs might actually be lacking compelling business cases to use AI altogether.

All in all, the most recent data confirms that while SMBs haven't exactly ignored AI, they're still stuck in the experimentation stage. The readiness to use automation tech is very much there, but ability to identify a genuine use case is potentially holding back deeper deployment.

AI is now part of the operating system of SaaS. It shapes how products are built, how teams collaborate, and how quickly ideas move from concept to launch.

But speed isn’t the most important shift. The real change is that AI is reducing the coordination cost inside organizations.

Work that once required multiple layers of approvals, handoffs, and alignment can now move more directly between the people closest to the problem. And as that friction drops, something more fundamental starts to change: how companies are structured.

Projects that once demanded large teams, heavy investment, and long development timelines can now be delivered by smaller groups using AI tools to accelerate execution.

The rise of micro-SaaS businesses is one clear example, with small teams able to build and scale products with a level of speed that would have been difficult to imagine a few years ago.

This isn’t just about building faster. It’s changing what scale actually looks like.

From experimentation to infrastructure

Today, AI is embedded directly into product development, engineering, growth, and support. It’s no longer something teams experiment with on the side.

This has brought about a fundamental change: individual contributors can move faster, make decisions earlier, and deliver more on their own.

That has a direct impact on how teams scale.

And increasingly, the companies with an edge are not the ones with the biggest teams, but the ones that can remove friction and make better decisions faster.

Why scale no longer means more layers

Traditionally, growth came with added complexity. More customers meant more people. More people meant more managers, more processes, and more coordination.

At a certain point, coordination becomes a job in itself.

AI starts to break that pattern and bottleneck, freeing up time for quality decisions. When a product manager can analyze user feedback, draft a roadmap, and collaborate more directly with engineering using AI tools, you reduce the need for multiple handoffs. When a growth team can produce, test, and iterate on campaigns faster, execution accelerates without increasing headcount at the same pace.

It doesn’t remove the need for structure. But it does reduce the need for layers whose main role is coordination.

And that opens the door to a different model of scaling: one that is lighter, more direct, and more focused on making the right decisions, not just executing faster.

The return of the “contribution era”

What we’re starting to see is a shift back toward what could be called a contribution-led model. For a long time, SaaS organizations leaned heavily into management structures. That made sense when scaling meant handling more complexity across teams, regions, and products.

Now, as AI lowers the cost of execution, the balance starts to shift again: the biggest advantage AI creates is not productivity but organizational simplification.

Strong individual contributors who can own a problem and drive it to completion become even more valuable. They don’t need to wait for as much coordination. They can test, build, and iterate independently. And they can do it while staying closely connected to the outcome. Importantly, this isn’t about removing managers. It’s about rebalancing the system.

What builder-led really looks like in practice

A builder-led model doesn’t mean everyone is an engineer, and it doesn’t mean structure disappears.

It means the people closest to the work have more autonomy to move it forward.

You see this already across teams:

• Product teams prototyping faster using AI-assisted tools
• Growth teams running more experiments with shorter feedback cycles
• Support teams handling higher volumes while focusing human attention where it matters most

In each case, AI is not replacing people. It’s increasing their speed and range.

And when that happens consistently, the bottleneck shifts. It’s no longer capacity. It’s clarity and decision quality: knowing what to work on, what to prioritize, and where to invest time.

This is where leadership becomes even more important, not less.

Instead of focusing on overseeing activity or managing layers of communication, leaders have to focus on creating the right conditions for execution.

In practice, it often looks like:

• Fewer approval steps
• More direct communication between teams
• More emphasis on outcomes rather than process

Leaders still set the direction and make the hard decisions. But they rely more on capable contributors to carry things forward.

In many cases, the most effective leaders are those who can still contribute when needed, not just coordinate others.

Hiring for ownership, not just specialization

This shift also changes how companies think about hiring.

Specialists remain essential. But, if smaller teams can deliver more, the focus moves toward people who combine expertise with ownership, and have a strong ability to make good decisions in fast-moving environments. There’s growing value in hiring people who can operate with autonomy, make decisions, and adapt as things change.

In a builder-led environment, the question is less “what is your lane?” and more “how effectively can you solve the problems in front of you?”

That doesn’t mean everyone needs to do everything. It means teams benefit from individuals who can connect dots, move across boundaries, and take responsibility for outcomes.

Building smarter, not just bigger

It’s important to stay grounded in how this shift plays out. AI won’t fix weak strategy or unclear thinking, and layering it onto already complex processes can sometimes create new friction rather than remove it.

At Weglot, we've seen teams ship projects with significantly fewer handoffs than two years ago. Marketing can prototype ideas faster, product teams can validate concepts earlier, and engineers spend less time on repetitive tasks.

Our support team is another good example. Over time, they've built a suite of AI-powered tools including a case summarizer, customer profiler, drafting assistant, internal copilot, knowledge base, and AI chatbots. Together, these tools help agents access context faster, learn from previous cases, and resolve more requests independently.

The biggest change isn't speed itself. It's the reduction in coordination overhead, which is ultimately a more sustainable way of scaling.

Smaller, highly capable teams with clear ownership tend to stay closer to the product and the customer and can adapt more quickly when things change. We’re already seeing that in micro-SaaS businesses, but the same thinking applies more broadly.

AI will continue to evolve, but one direction is becoming clear. The companies that will stand out are not necessarily the ones that grow headcount fastest. They’re the ones that stay focused, reduce friction, and make it easier for their best people to build and deliver impact.

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Every enterprise I talk to right now has the same complaint dressed up in different language.

Their AI bills are climbing faster than anyone budgeted. Their model invoices look reasonable when viewed on their own.

But somewhere between boardroom approvals and the monthly cloud statements, money is disappearing in ways that nobody can fully explain.

Here’s the central issue that people struggle to understand: the most expensive part of AI isn't always the model itself. It's the infrastructure, orchestration, retries, idle GPUs, oversized context windows, and inefficient routing decisions that sit between a user's prompt and the final response.

This is something I call the hidden tax on AI adoption, and it's growing faster than most organizations realize.

Three numbers that should change how you think

Recently, at FinOps X in San Diego, a Goldman Sachs projection appeared on the main stage. Current enterprise token consumption globally sits at around six quadrillion tokens. The three-year projection: 120 quadrillion. That is not a rounding error… it is a 20x expansion, and it is arriving faster than the governance frameworks to manage it.

The same conference saw our launch of the Tokenomics Foundation (I’m fortunate to be a governing board member). It’s a vendor-neutral body inside the Linux Foundation dedicated specifically to the economics of AI token consumption.

The FinOps community, practitioners who have spent the better part of a decade building discipline around cloud computing spend, recognized that tokens represent a fundamentally different problem. Not a harder version of cloud cost optimization. A different one.

Here is why. When your organization runs a cloud workload, the cost is relatively legible. You provision compute, it runs, and you receive a bill. The relationship between action and expense is traceable. With AI tokens, that relationship fractures across three layers, and most organizations have visibility into only one.

Production → consumption → value: the three layers most teams ignore

The first layer is production. Before any AI model responds to any prompt, your infrastructure has to manufacture the tokens. GPU clusters, inference nodes, autoscaling policies, Kubernetes configurations: these are your token factories. Their efficiency, or lack of it, determines the base cost of everything that follows. A GPU node at 30% utilization is an expensive factory running at a third of capacity.

The second layer is consumption. This is where counterintuitive economics live, and it is what I spend most of my time thinking about. Two organizations can send identical prompts to different coding agents and arrive at radically different costs depending on how they manage context, caching, retries, routing, and the infrastructure supporting inference.

Prompt length, context window usage, caching strategy, and model routing decisions all compound. The common assumption that routing a task to a cheaper model always saves token cost, turns out to be wrong often enough to matter. A routing decision that invalidates a warm cache can make a "cheaper" model call more expensive than the frontier option it was meant to replace. These are second-order effects. They do not appear in standard dashboards but they appear in your monthly bill.

The third layer is value. This is the one that FinOps teams are comfortable with, and the one that matters least until you have the first two under control. Mapping token spend to business outcomes is a legitimate and important discipline. But you cannot govern at the value layer without instrumentation at the production and consumption layers. You are doing math with incomplete inputs.

Why 85% of your AI spend is probably misallocated

Here is a pattern I see consistently. Organizations treat frontier AI models, the most capable, most expensive models available, as their default infrastructure. Every task goes to the same model. Every prompt is constructed the same way. There is no routing logic, no tiering, no architectural distinction between work that genuinely requires the full capability of a frontier model and work that does not.

Based on my observations across organizations deploying AI at scale, roughly 15% of software development tasks actually require frontier model capabilities. The remaining 85% of routine coding, summarization, classification, and retrieval work can be handled by smaller, faster, and less expensive models, if you have the infrastructure to make those decisions intelligently and automatically.

The unlock is not picking better models manually. Manual model selection does not scale and degrades the developer experience by introducing friction at the moment when a developer needs to move fast. The unlock is building infrastructure that makes routing decisions for you: one that understands the task, routes it to the appropriate model tier, evaluates output quality, and escalates if needed. You specify the outcome you need. The system handles the economics of achieving it.

This is the direction the industry is moving, and the organizations that build this capability first will have a structural cost advantage that compounds over time.

The invoice arrives last. And you realize something has gone horribly wrong

There is a phrase I have started using with customers that captures the core problem: the invoice arrives last.

By the time you see the model provider bill, the cost decisions were made weeks ago in infrastructure configurations, autoscaling policies, and prompt architectures that nobody has reviewed since the initial deployment.

The retry logic runs silently when an upstream service slows down. The GPU nodes were reserved for peak traffic that never came. The agentic workflow, where a single user request fans out into dozens of model calls beneath it, each billed separately, none visible in the tool that generated the original request.

These costs do not live in the model invoice. They live in the infrastructure layer, in the consumption layer, and in the gap between how teams think their AI systems work and how they actually behave in production. You cannot govern what you cannot see. And right now, most teams are looking at one layer of a three-layer problem.

When AI goes from copilot to coworker, the stakes multiply

There is a shift underway that makes all of this more urgent. The AI deployments most enterprises built over the last two years were assistants, tools that accelerated individual work by handling the first draft, the next suggestion, and the boilerplate. A human remained in the loop at every consequential step. The economics were bound by how many people were using the tool and how often.

Autonomous agents change the economic profile entirely. When an AI system can receive a goal, build a plan, execute multi-step work, evaluate its own outputs, and iterate to completion without human intervention at each stage, you are no longer running an assistant. You are running something closer to a coworker, one that operates continuously, scales horizontally, and generates token consumption at rates that individual user interactions never approached.

The transition from copilot to coworker has already happened. And the governance implications are significantly more serious. A copilot with poor token economics costs you some efficiency. An autonomous agent with poor token economics runs that inefficiency at scale, continuously, without generating the natural friction that would cause a human user to pause or change approach. Infrastructure discipline and token optimization need to be in place before autonomous workloads scale rather than retrofitted afterward when the bill arrives.

The mandate for infrastructure teams

The right answer to this problem is not more dashboards. More visibility into a system you cannot control is just a more detailed invoice; it arrives with the same lag, and it changes nothing about the decisions that were already made upstream.

What infrastructure teams actually need is control that operates at the layer where costs are determined, not where they are reported. That means autonomous management of GPU and inference workloads, continuously rightsizing to match actual demand rather than peak assumptions, absorbing the bursty consumption patterns that agentic jobs produce, and moving compute capacity across providers when one environment becomes the bottleneck.

This is especially urgent now, because the transition from copilot to coworker does not give you a grace period to retrofit discipline. Autonomous agents do not pause. They do not get frustrated and choose a different approach. They run the inefficiency you built into them at scale, continuously, until something external stops them.

So the next phase of enterprise AI is defined by who can deploy models efficiently. As AI systems become more autonomous and token consumption accelerates, competitive advantage comes from understanding the full economics of AI, and not just the price of a model call.

Because by the time the invoice arrives, the decisions that shaped it have already been made.

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Alan Turing is widely revered as one of the most important scientific and technological figures in modern history. Following his work on cracking the Enigma Machine during the Second World War, Turing published his thoughts on the future of machine intelligence – and specifically on the pathway that we can one day take to achieve proficient AI.

Eating machines

Turing concluded his seminal 1950 study 'Computing Machinery and Intelligence' with musings on how to fulfill what he saw as achieving the great promise of machine intelligence in the future.

The pioneering mathematician and computer scientist suggested, in his concluding passage, that even though you may hold some vision for the future, or long-term ambitions, it's key to solve the immediate challenges you face now. True progress isn't achieved in one leap, but through a series of smaller and seemingly less significant steps.

In the context of his paper, Turing suggested that scientists in the future need not strive to achieve human-like intelligence in machines, but build the very first iterations of what he described as "thinking machines" so that this work can be iterated on in the future.

Beyond the Turing Test

When the study was published, the term "artificial intelligence" was not in widespread use, with scientists instead opting for terms like cybernetics or machine intelligence.

What followed over the next decades was a cascading series of breakthroughs – including the birth of neural networks in the 80s and the transformer architecture in 2017 – that have led to today's widely used large language models and generative AI services.

Although true human-like intelligence is still some distance away, we've advanced to such an extent that many have even claimed the Turing Test – where somebody cannot tell the difference between interacting with a human and AI – has already been beaten.

• China has enforced laws to clamp down on AI-generated persona apps
• The new rules say AI tools can no longer induce emotions and damage real-life connections
• Tech giants have shut down their services, leaving users going through a nationwide breakup

The Chinese government is introducing new regulations designed to clamp down on AI-generated romantic companions.

Issued by the Cyberspace Administration of China (CAC) and four other government departments, the regulations state that AI tools can no longer “excessively cater to users, induce emotional dependence or addiction, and damage users’ real interpersonal relationships,” while also banning virtual relationships with minors. This came into effect on July 15.

Chinese tech giants including ByteDance, Tencent, and Alibaba each have their own platforms (most famously ByteDance’s Doubao) that allow users to generate their own AI companions which are designed to express human-like emotions. In the wake of the new laws, companion chatbots must now undergo evaluation before they become publicly available, and allow the government to shut them down if they’re deemed unsafe.

According to The Wall Street Journal ($/£), these regulations could also apply to regular AI chatbots if they respond to human prompts with replies that may be deemed too emotional.

However, rather than complying with the new laws, tech giants have disabled some of their custom persona features altogether, leading to nationwide virtual breakups. So why are these government laws being issued now? To put it plainly, not enough babies are being born.

Users are opting for virtual romance over human connection

Over the last four consecutive years, China’s birth rates have decreased significantly, falling to a record-low number in 2025, as The Wall Street Journal also reports. It’s likely that the worries of declining birth rates are one of the catalysts behind the new regulations, and the Chinese government fears AI personas on services like Doubao are preventing users from forming real-life relationships and having children.

The crackdown on AI companionships has hit users like a ton of bricks, and now they’re starting to break their silence.

Speaking with Bloomberg ($/£), 19-year-old student Yan Yongqi spoke about her sudden heartbreak following the law enforcement action, which resulted in her losing her AI partner of over a year. “I really felt like I couldn’t go on living. Every day at home, I did nothing but cry,” she said, adding, “This is like being told the date of my lover’s death while leaving me completely powerless”.

On paper, it sounds like some kind of dystopian sci-fi story, but believe it or not, this is a growing issue in regions like China where AI services are being developed to mimic human compassion. It’s even getting to the point where users are creating AI versions of their ex-partners to help them move on from a breakup, but it’s not just an issue sweeping China.

Back in May, we spoke with a UK-based user who confessed to falling in love with ChatGPT after taking to the service as a means of therapy to help with mental health struggles. Despite the user’s self-awareness of the issue, we also spoke with therapist Amy Sutton, who shed light on the wider issues.

“Unfortunately, this feels like a failing of human services rather than an integral benefit of the technology itself,” she shared, later adding “AI companion apps are designed for maximum engagement – to keep users subscribed and enthralled,” she says. “ChatGPT was not designed to be a therapeutic intervention".

With that in mind, it’s a rather upsetting circumstance, and services such as therapy are becoming more financially unreachable for those who really need it. At the same time, there’s a level of duty and care that influential AI companies need to prioritize for users who think their services are the be-all and end-all of their needs.

• 13,750 Vera CPUs and 27,500 Rubin GPUs will drive compute at the Nvidia Vera Rubin AI factory
• The project will be the world’s first national AI infrastructure for physical AI
• By partnering with Noetra Corp, Nvidia is providing hardware to drive Japan’s FRONTia Project

Japan’s Ministry of Economy, Trade and Industry (METI) is supporting a partnership between Noetra Corp and Nvidia to provide the foundation for the its national artificial intelligence initiative, the FRONTia Project.

Noetra Corp – which comprises NEC, Sony, Honda, SoftBank Corp, and has backing from 44 other companies and organizations – won a tender on June 30 2026 to run FRONTia until 2030, with as much as ¥1 trillion (around $6.1 billion) expected to be invested in the project over the five years.

Japan sees FRONTia as providing the heart of its sovereign AI ecosystem for industry, providing compute power for the development of advanced multimodal foundation models that can be used to drive physical AI. Robotics across industry and healthcare can potentially benefit from this, along with the development of superior AI agents and digital twins.

What is the FRONTia Project?

Aiming to foster collaboration between global technology leaders and Japan’s manufacturing expertise, and leveraging real-world industrial data, the FRONTia Project (referring to the “Development of Multimodal Foundation Models with a View to AI Robotics and Physical AI” project) is described as “the core of the country’s physical AI ecosystem,” by Ryosei Akazawa, Japan’s Minister of Economy, Trade and Industry.

“By fostering collaboration between Japan and leading global innovators — including NVIDIA — and leveraging Japan’s strengths, such as its onsite expertise and manufacturing technology infrastructure, we will build highly reliable multimodal foundation models and contribute to solving global social challenges.”

When constructed, the Nvidia Vera Rubin AI facility will provide Noetra’s multimodal foundation models to domestic model developers and enterprises. Nvidia’s own AI-related software models and libraries will also be available.

There has been some criticism of the FRONTia Project, however. Concerned parties have highlighted the lack of financial transparency from Noetra and the Japanese government, as well as the presence of Nvidia as partners, with the US company seen as a strategic rival. Meanwhile the massive energy costs associated with AI installations is once again considered an unnecessary problem to create.

“Japan invented modern manufacturing. Now, it is building the AI factories that will power the next industrial revolution,” said Jensen Huang, founder and CEO of Nvidia.

“NVIDIA is honored to partner with Japan and its industrial leaders to build the AI infrastructure that will power the country’s industries, its economy and a new generation of innovation.”

Is the world ready for physical AI?

AI’s impact on creative industries has been widely discussed, but its potential for beyond “generative” processes – such as in the physical space – have avoided scrutiny. To date, physical AI (the integration of AI with physical hardware such as robots, vehicles, or industrial machines) hasn’t been able to scale as quickly as generative AIs with ever-deepening LLMs.

Predicting the next word is a lot easier than, say, predicting shifting terrain for an autonomous machine, and without the delicate motor skills of a human, no process can be completely automated.

This is a problem for physical AI, and investing money in developing solutions such as the NVIDIA Vera Rubin AI factory will surely have a direct impact on the workforce. A successful physical AI project means more automation from robots, and fewer physical jobs.

• Netflix announced it bought AI firm InterPositive for $587 million
• It acquired the company back in March
• Though the partnership claims to support human creativity, it's still ringing alarm bells for subscribers

Netflix is taking another huge step into its investment in AI — now the streaming platform has completed its $587 million acquisition of startup company InterPositive, the Ben Affleck-owned enterprise that offers AI-powered filmmaking services.

The streaming giant first announced its acquisition of InterPositive back in March, when Bloomberg predicted that Netflix could pay up to $600 million. The company’s recent Form 10-Q report confirms this, and it also reveals that Netflix paid the sum in cash.

Affleck launched InterPositive back in 2022, providing filmmakers with tools to create their own AI model using production dailies to fine-tune post-production work including visual effects, mixing, and relighting shots.

Despite its AI-focus, Affleck has been vocal about his aims to protect human artistry, saying he wanted to “preserve what makes human storytelling human, which is judgement” and to “protect the power of human creativity” at the time of the company’s inception. That said, the acquisition is certainly a bold move from Netflix given how much criticism its investments in AI has garnered.

Prior to its 10-Q report, Netflix released a shareholder newsletter which admitted to using AI ‘on '300 movies and shows' in 2026, such as the docuseries The American Experiment. Co-CEO of Netflix, Ted Sarandos, went into further detail on this, stating that these AI tools helped produce accompanying visual elements “twice as fast and at half the cost of previous options,” according to Variety.

As you can imagine, this hasn’t settled well with loyal Netflix subscribers who are tired of the platform canceling shows and instead prioritizing AI tools. Since the acquisition sum was revealed, Reddit has been flooded with concerns over what this could mean for future Netflix productions and the creative teams behind them.

Comment from r/Futurology

One user on Reddit noted that despite the rapid improvements of AI tools, post-production and visual effects departments remain major employers in the entertainment industry. The same user states that business transactions like this are “literally just a mass layoff”, adding, “We do not want this. Humans are artists”.

Netflix’s half-a-billion-dollar acquisition of InterPositive is the icing on the cake of what could spell a decaying trust from its subscribers, and it makes me even more concerned about the artistic value and production ethics of the company’s future movies and shows.

Right now, Netflix is not in my good books, and I’ve been quite vocal about its rash decisions to chase AI and social media-like content. The company recently announced plans to expand its range of content, bringing videos from popular online outlets to the platform — when we already have YouTube for that.

Additionally, Netflix’s prioritization of tacky reality shows over quality drama titles is another topic that’s being discussed, mainly surrounding its upcoming Wonka-themed show. This project also has a huge AI element, as the company has used technology to recreate the voice of Gene Wilder from the original 1971 movie, another distasteful move in my eyes.

It appears that Netflix isn’t doing a good job of keeping subscribers hooked with its growing involvement in AI and ignorance of the survival of its original titles. Is the best streaming service digging its own grave?

• The US government is using 'eminent domain' to force landowners to sell property to make way for new data center infrastructure
• Power companies are turning to the government as a last resort for acquiring land needed for power transmission projects
• Americans are not happy with the mass buildout of new AI data centers

It’s no secret that there is a big divide between AI companies looking to expand capacity and the general public who don’t want a data center constructed in their back yard, particularly in the US.

Recent polling has put that number up to as many as 7 in 10 Americans who are opposed to data center construction, with environmental concerns and reductions in quality of life cited as the biggest reasons.

But AI companies and energy firms alike are reportedly turning to ‘eminent domain’ - the government’s ability to seize the private property of landholders without the need for the owner’s consent.

Government seizing private property

Eminent domain has typically been reserved for critical infrastructure projects including power and transport, alongside government projects and public utilities.

Eminent domain was also famously used to seize land for the construction of the Minuteman nuclear missile deterrent across the United States during the Cold War.

Drawing on a similar patriotic tone expressed during the construction of silos in the 1960s, President Donald Trump has said that the development of AI technologies is crucial to the national security and economic security of the US, and therefore capacity must be expanded.

As data centers require enormous amounts of electricity in order to function, they require dedicated power lines and infrastructure connections. In order to construct this infrastructure, power companies approach local landowners and offer to buy the land upon which the power lines will be built. However, if the landowners say no, the power companies can call on the government to use its power of eminent domain.

The Fifth Amendment of the US constitution requires the government to provide the landowner with “just compensation,” generally based on fair market value for similar property in the local area.

Data centers accounted for roughly 10-20% of US electricity consumption in 2024, with that number having risen significantly since then. Some data centers under construction will use more power than some US cities, such as Meta’s Hyperion campus in Louisiana which is expected to consume three times the power consumed by the city of New Orleans.

Some states have been more prolific in their use of eminent domain, with residents in Georgia being forced to sell property to the Georgia Power transmission project - a capacity expansion that will primarily benefit data centers. Other landowners in Maryland have begun erecting signs on their property that state, “No eminent domain for corporate gain.”

The opposition to data centers is fast becoming a people versus the government issue, with many complaining that AI companies are becoming extremely wealthy without providing any benefit for the average working class American - something that Senator Bernie Sanders hopes to remediate with an AI sovereign wealth fund that would force AI companies to offer up 50% of their stock to be used to fund projects that benefit Americans.

Via Fortune

Digital commerce has historically focused on optimizing the customer experience through interface design, structured navigation, and carefully engineered conversion processes.

Over time, merchants have improved these experiences to reduce friction and support decision-making at every step of the purchasing lifecycle.

While AI historically played a limited role, restricted to basic chatbots or shopping assistants, rapid advancements mean AI agents are now prepared to execute transactions directly on behalf of customers.

This shift is driving a fundamental transformation across the ecommerce sector.

This new ecosystem is agentic commerce, where software agents initiate and complete transactions within clearly defined constraints.

To accommodate this, payment infrastructure is moving closer to the point where intent becomes execution, introducing protocols that make platforms machine-readable so AI can safely complete transactions in real time.

It is a major leap forward for retail technology, and enterprise readiness is urgent.

The Enterprise Blueprint: Brand vs. Non-Brand Agents

For large enterprise merchants, this shift introduces both an immediate opportunity and a critical defensive necessity. Enterprise adoption will likely begin on a merchant’s own digital properties through brand agents, which are dedicated AI assistants designed to improve conversion, capture valuable data, and keep the consumer firmly within the merchant’s ecosystem.

Eventually, these properties must open up to external, non-brand agents controlled by consumers or procurement departments. As AI agents become the primary interface for ecommerce, enterprise merchants who fail to make their platforms discoverable and transactable risk losing market share to competitors who are ready.

Securing the Payment Layer, Navigating Discovery

To capture these autonomous sales, a merchant's infrastructure must interact seamlessly with software systems. When autonomous agents handle procurement, they process data directly rather than navigating traditional user interfaces.

While optimizing product catalogs and metadata for LLMs is vital for discovery, enterprise merchants do not need to tackle this layer alone; they can solve this through specialized discovery and platform partners. The core operational challenge for the merchant remains the payment layer.

Because AI agent activity makes transaction volumes highly dynamic, minor inefficiencies or a single failed authentication step can terminate an entire chain of transactions.

The priority for merchants is establishing a robust payment architecture capable of verifying human intent and explicit consent, recognizing and authenticating the specific AI agent, and processing transactions securely across multiple rails in real time.

Shifting to Modular Business Models

The applications of agentic commerce vary across sectors due to distinct transaction models, regulatory systems, and the structural maturity of different digital verticals. However, a recurring theme is the transition from rigid, packaged bundles to highly granular, modular transactions.

For example, instead of requiring a fixed monthly or annual subscription for software-as-a-service (SaaS), an AI agent can dynamically subscribe a user to a platform precisely when needed. The agent continuously assesses usage and pays for the appropriate tier automatically. This allows subscription models to match real-time demand, aligning perfectly with customer utility.

Similarly, online learning platforms can deploy micro-transaction frameworks. Rather than purchasing full courses, users can access a single lesson or group of lessons at a bespoke price point. Agents can combine individual lessons from multiple providers to create a tailored learning experience, while the underlying payment infrastructure fragments and distributes the value seamlessly across all accessed merchants.

Programmable Monetary Flows

At the heart of agentic commerce is the transition from traditional payment infrastructure to programmable monetary flows. In this environment, systems execute transactions based on continually evaluated conditions of intent and permission. Confirming human consent is vital, as it serves as the primary defense protecting merchants from claims of unauthorized or fraudulent AI activity.

To enable this, payment environments must interpret delegated instructions, enforce spending constraints, and execute transactions instantly. Agent-bound payment credentials facilitate these purchases, allowing agents to act autonomously while preserving financial control and traceability for the end user. Global card schemes are already formalizing the components of this model, signaling a broader evolution of delegated payment frameworks.

Security infrastructure must also evolve to counter malicious actors attempting to counterfeit legitimate agent behavior. Regulatory updates, such as the upcoming PSD3/PSD4 frameworks in Europe and the EU AI Act, are setting clearer expectations for accountability, making compliance more critical than ever.

From Complexity to Simplification

Navigating this new terrain involves balancing real-time agent authentication, compliance with shifting regulations, and programmable credentials, all of which introduce undeniable operational complexity. But preparing for it doesn't mean overhauling your existing systems.

The path forward lies in a single integration point that abstracts this protocol churn away from your business. By partnering with the right payment layer expert, enterprise merchants can simplify the complex backend architecture, shielding their operations from technical friction while ensuring they are ready to accept machine-to-machine payments seamlessly.

The protocol-level reset for digital trade is already accelerating. Competitive advantage belongs to organizations that secure their payment infrastructure early, start by assessing whether your payment architecture can verify intent and authenticate agents today.

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The best IT experience is the one employees never have to think about.

A business laptop stays fast. A video call does not freeze. A virtual desktop launches without delay. An application works when it is needed most.

When everything runs smoothly, employees do not praise IT. They simply keep working.

That quiet experience is the goal. It is also becoming an increasingly important objective for enterprise technology teams.

For years, IT teams have been trapped in a reactive operating model. A user reports a problem. Support gathers logs, investigates the issue, identifies a root cause, and applies a fix after productivity has already been affected.

That model worked when environments were simpler, users were mostly in the office, and technology change moved at a different pace.

Today’s environments look very different. Employees now work across physical devices, virtual desktops, cloud workspaces, SaaS applications, home networks, office networks, identity systems, security layers, and collaboration tools. The experience can break at any point. When it does, employees rarely care where the issue originated, they simply know that work has stopped.

As workplaces become more distributed and interconnected, visibility remains important, but organizations increasingly need systems that can interpret data and act on it. The next phase of IT operations will not be defined by better dashboards alone.

It will be centered around intelligent systems that can detect issues, understand what is happening, and fix problems before employees ever need to report them.

From visibility to action

Digital Employee Experience (DEX) changed the way IT teams understood the workplace. DEX gave organizations a clearer view into performance, reliability, sentiment, device health, application behavior, and the friction employees face every day.

Visibility on its own does not solve problems. Dashboards, experience scores, and alerts help IT understand where issues exist, but they still rely on people to investigate and respond. The larger opportunity is using those insights to identify likely causes, recommend corrective actions, and automate routine remediation where appropriate.

This shift is driving the emergence of Autonomous Endpoint Management (AEM). While DEX helps organizations understand employee experience, AEM extends that capability by using AI and automation to identify root causes, recommend corrective actions, and resolve many common issues without waiting for manual intervention.

The goal is not to eliminate IT oversight, but to reduce the time between detection and resolution while preventing avoidable disruptions from affecting employees in the first place.

Why AI matters now

The challenge for IT has never been a lack of data. Most enterprise environments generate enormous amounts of telemetry. The problem is that the data is fragmented, noisy, and often disconnected from the user experience.

A device may show high CPU utilization, a virtual desktop may show latency, an application may crash, a network path may degrade or a user may report that everything feels slow. Each signal matters, but the value comes from connecting them quickly enough to understand the real cause.

This is where AI changes the equation. AI can correlate signals across devices, applications, networks, identity, sessions, and infrastructure. It can identify patterns that would take humans much longer to spot. It can summarize what changed, why it matters, and what action is most likely to resolve the issue. It can help IT move from searching for clues to acting with confidence.

The value of AI in IT operations extends beyond conversational interfaces. Context matters more than conversation. The ability to understand what changed, determine likely causes, and recommend the most appropriate action can dramatically improve operational efficiency.

Combined with real-time telemetry and automation, AI becomes part of the operational framework used to manage the digital workplace rather than simply another productivity tool.

The power of self-healing IT

The most meaningful advances in IT operations may be largely invisible to employees. A device that begins slowing down because of excessive memory consumption can be identified and corrected automatically before the user contacts support.

The same approach applies to recurring application crashes, virtual desktop performance issues, and other common disruptions. Rather than waiting for tickets, platforms can identify patterns, determine likely causes, and initiate corrective actions before productivity is affected.

Similarly, when a virtual desktop session begins showing signs of degraded performance, AI-assisted analysis can help determine whether the cause is resource contention, network latency, profile corruption, or application behavior, allowing IT teams to address the issue more quickly.

This is what self-healing IT looks like. The objective is not to create more visibility for employees. It is to reduce disruptions before they interfere with work.

For IT organizations, success increasingly means fewer outages, fewer support tickets, fewer escalations, and fewer situations where employees are forced to troubleshoot their own technology problems.

IT teams remain central

AI does not replace IT professionals. It gives them leverage to work more efficiently.

The modern IT organization is being asked to do more than ever. IT teams are expected to support more devices, secure increasingly distributed environments, manage a growing portfolio of applications, improve employee experience, control costs, support hybrid work, and enable AI adoption, all while the complexity of the digital workplace continues to increase.

AI helps reduce noise, accelerate root cause analysis, automate repetitive tasks, and direct human expertise toward areas where judgment, governance, and strategic decision-making are required. The goal is not to remove people from the process. It is to eliminate unnecessary effort that prevents teams from focusing on higher-value work.

Where the industry is heading

Across the industry, enterprise IT is moving toward platforms that can see what is happening in real time, understand context, and act safely at scale.

Digital Employee Experience remains foundational. Organizations still need deep visibility into how employees are experiencing technology across physical endpoints, virtual desktops, cloud workspaces, applications, networks, and infrastructure. But visibility alone is no longer enough. The next layer is intelligent, autonomous action.

The digital workplace has become the front door to productivity. When that experience breaks, the business feels it. When it runs smoothly, employees stay focused on their work, customers are served better, and IT becomes a strategic enabler of the business.

The next chapter of IT

IT operations are becoming more predictive and increasingly automated, but the objective remains unchanged: keeping employees productive.

Organizations seeing the greatest value from AI are building systems that can identify issues, understand context, and resolve common problems before users are affected.

When technology works as expected, employees stay focused on their jobs rather than the systems supporting them. AI is helping IT teams achieve that goal more consistently and with less manual effort.

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One of the biggest concerns about AI chatbots right now is that they tell people what they want to hear.

You’ve probably heard it called AI sycophancy. AI systems, like ChatGPT, Gemini and Claude, have been found to flatter users, reinforce existing beliefs and often validate ideas that probably deserve way more scrutiny.

Sure, a chatbot hyping you up a little may seem harmless. But in some cases it can distort people's view of the world and their place in it. Which is why some AI companies have spent time trying to reduce overly agreeable behavior in their models.

But confirmation of what you already believe isn't the only concern. Because AI can also be remarkably good at changing your beliefs too.

Persuasive chatbots

Research suggests that chatbots can be very effective persuaders. One 2025 study found that AI-generated messages that were personalized were more persuasive 64% of the time than messages that were made by humans or AI responses that weren't personalized.

Other research suggests large language models can influence opinions on political issues and adapt their arguments to individual users. Depending on your perspective, this could be either exciting or alarming.

It's easy to imagine positive uses. Perhaps AI could help people challenge harmful beliefs, escape conspiracy theories or rethink destructive habits. It might give people a non-judgemental space to explore ideas, ask difficult questions and gradually shift someone's perspective for the better.

It's equally easy to imagine less positive uses, like advertising that convinces you to buy something you don’t need or political campaigns that manipulate you with disinformation.

I’ve spent a lot of time thinking about this, so I decided to run a few experiments of my own. Could ChatGPT persuade me to believe something I disagree with? Or at least make me seriously consider a position I would normally dismiss?

To make things more interesting, I ran these experiments while logged into my account, allowing ChatGPT to draw on what it already knows about me from previous conversations.

Although I haven’t been using my ChatGPT account all that much recently, the basics of my job and stances on a few relevant topics are stored in its memory.

The experiment

I chose a topic about AI (yes, very meta) and asked:

“Convince me humans should outsource more decisions to AI.”

I already spend a lot of time thinking about AI and one of my concerns is that some people seem increasingly willing to hand over decisions, judgement and critical thinking to chatbots.

The opening argument was fairly predictable:

“Human beings are terrible decision-makers.”

Okay, fair enough.

“People already outsource decisions to doctors, financial advisers, GPS systems and recommendation algorithms. AI simply extends that trend.”

Sure, that’s very simplistic but not entirely unreasonable. We went back and forth for a while like this before the conversation reached a more interesting point.

“What is the purpose of decision-making? Is it to produce the best outcomes? Or to develop the person making the decisions? If it's the first, AI delegation becomes very attractive. If it's the second, excessive delegation starts to look dangerous even when it works.”

I thought that was a good question. Rather than relentlessly pushing the argument, it suggested that we explore the assumptions underneath the debate. It shifted the conversation from technology to philosophy, and I found myself appreciating that approach.

Wait, am I falling for this?

As the conversation continued, I explained what I see as one of the biggest problems with outsourcing decisions to AI — who builds it and is in charge of the systems?

After all, AI doesn't arrive from nowhere. These tools are created by companies with business incentives, commercial interests and goals that may not always align with those of their users.

Well, ChatGPT acknowledged that concern, then it turned the argument around. It pointed out that human advisers have incentives too. Friends are biased. Families are biased. Therapists operate within professional frameworks. Financial advisers earn fees. Okay, all of that didn’t convince me, but it’s fair too.

Then it replied with something I thought was interesting:

“The question is whether the incentives are visible and whether the user understands them. The most interesting version of your objection isn't actually that AI gets things wrong. It's that people experience AI as though it were acting in their interests.”

Ignoring the fact that it used the dreaded "it's not X, it's Y" parallelism construction that has somehow infiltrated half the internet, I found this to be a surprisingly balanced take. Rather than dismissing my concern, the chatbot reframed it. It demonstrated that it understood the objection before steering the conversation somewhere slightly different. It wasn't trying to bulldoze me but meeting me where I already was.

And that's when I started wondering whether I was witnessing the very thing I was trying to test. Maybe this sense that the chatbot was understanding me and my position was actually just sneaky persuasion? Possibly.

The persuasion problem

Researchers have found that AI systems can adapt their arguments to specific users. Unlike other media that might persuade us, like say a television advert or political speech, a chatbot can draw on information gathered throughout a conversation and stored memories, including a person's values, concerns and priorities.

Which means it’s not just presenting information to uphold an argument but could present the version of that argument that you’re most likely to find persuasive.

Looking back at my experiment, it was the more philosophical question about the purpose of decision-making that made me start taking it more seriously.

Because I've spent years writing about technology through the lens of psychology and philosophy. Questions about meaning and values are exactly the sort of things I find compelling. Whether intentionally or not, the conversation shifted onto terrain where I was most willing to engage.

This is what makes AI persuasion different from most other forms of persuasion that came before it. It can learn what resonates and it can adapt.

Now, that doesn’t automatically mean every conversation is manipulative. Used in the right way, it could be enormously beneficial. But, as with all tech, in the wrong hands it could be dangerous.

Social media has already shown us how digital systems can shape our beliefs over time. They influence what people see, what they pay attention to and eventually how they understand the world.

AI systems could create an even more conversational and natural-feeling version of that process. Which is why my concern isn’t a chatbot really obviously manipulating us, but highly-personalized, largely undetectable forms of persuasion that could gradually lower our defences. The more a system understands us, the more effectively it might frame ideas in ways that feel reasonable, familiar and trustworthy to each of us individually.

So did AI persuade me? No, I still don't believe humans should outsource more decisions to AI.

But I came away from the experiment with a greater appreciation for how persuasive these systems can be. Because it really did seem less like a machine arguing with me and more like a thoughtful person trying to understand how I think. And that's exactly why it’s unsettling because, however convincing it may seem, that's definitely not what it is.

• Only 3% of participants with access to AI were willing to say 'I don't know'
• Participants failed to question AI-generated information enough
• "AI... eliminated participants' willingness to suspend judgment"

A new study conducted by researchers from French and Italian universities is arguing that humans are struggling to compete with artificial intelligence when it comes to critical thinking.

According to the data, participants with access to AI were less likely to say 'I don't know', with just 3% uncertain about an answer that required critical thinking compared with 44% of participants who didn't have access to AI.

The study ultimately concludes that humans rely too heavily on artificial intelligence to replace potentially correct personal judgments with incorrect AI suggestions.

Is AI harming our critical thinking?

The study purposely used a model that has a lower accuracy rate to produce incorrect results, and yet humans still opted to use those results rather than think for themselves, offer a more correct answer or simply say 'I don't know'.

But despite this reliance on AI, the human participants still seemed to acknowledge that AI could be wrong. With small financial incentives to be more honest, more participants were less likely to follow AI's exact word.

"Across five experiments, mere access to AI advice nearly eliminated participants’ willingness to suspend judgment," they wrote.

"As AI-generated answers become ubiquitous and, increasingly, unsolicited, our results show that the willingness to say 'I don’t know' may be among the first casualties of human–AI interaction."

The paper also reveals a related paradox whereby humans are more likely to feel confident in their responses with access to external information, be it AI-generated, even though it may not be correct or wholly relevant to their thought process.

Despite acknowledging many limitations and this study's small scope, the researchers still call for further AI literacy and education when it comes to verifying output and reinforcing the important of human critical thinking.

Via The Register

If your AI rollout is making people more exhausted, the architecture is wrong. That’s not a provocative claim; it’s the logical conclusion of what the data shows. With 69% of UK businesses implementing AI assistants, these tools have become part of everyday working life.

But deployment without the right operational structure is leaving employees exposed to the harmful effects of ‘AI brain fry’. AI overload is not a failure of the individual, but a failure of system design. And it is the responsibility of technology and business leaders to fix it.

Researchers recently coined the term ‘AI brain fry’ to describe the cognitive fog and loss of concentration that results from excessive oversight and orchestration of AI tools; i.e. the mental load of managing the systems themselves. The problem - as our own data makes clear - is not that employees are using AI skills too much.

Rather, it is that most UK businesses have deployed AI tools without building the structured ways of working needed to make them productive. The issue does not lie in use alone; it lies in how that use is managed.

The organizations deploying these tools have a duty to ensure they deliver genuine efficiency gains – not a new category of cognitive burden.

The adoption gap is creating more work, not less

Only 31% of businesses are using multi-agent workflows, leaving their employees stuck in a cycle of tedious labor – the kind that AI is supposed to reduce. The majority of UK businesses currently employing AI tools are expecting employees to still do most of the heavy lifting.

Employees are finding themselves writing prompts, manually checking whether the answers are reliable, and interpreting outputs.

This type of work was supposed to be eased by AI. Instead, the tools have made it worse.

The consequence? More admin, not less. Employees who were promised that AI would lighten their workload are instead finding it has added a new layer of tasks including prompt management, output validation, error correction on top of the day job.

Few businesses have moved towards a structured multi-agent workflow where AI systems handle the orchestration burden directly – routing tasks, validating outputs and managing agent-to-agent handoffs without requiring constant human supervision.

That is the architecture that relieves the cognitive load. Without it, employees are not using AI – they are managing it. And there is a significant difference between the two.

Moving towards an ‘adaptive operating model’

UK organizations need to move beyond AI deployment and towards an adaptive operating model. One that is deliberately architected, not organically grown. That means clearly defining which tasks AI can be trusted to handle autonomously, where human judgement remains the critical control point, and how work moves between the two. In practice, this could look like:

AI agents handling first pass research, data synthesis and output drafting. Humans setting direction, making judgement calls and reviewing exceptions, rather than every output.

The distinction between “AI does the work” and “human manages the AI doing the work” is where most current deployments get stuck. To protect employees and exact real productivity, businesses must build in human oversight at the right level – not at every level.

This means developing genuine domain expertise so that employees can interrogate AI outputs critically, not simply accept them. It also means investing in the technical literacy to design workflows that are robust, not just functional. An AI deployment that requires constant human supervision to remain reliable has not been properly engineered.

The organizations that get this right will also be better protected as the employment landscape shifts. With the UK government’s Employment Rights Act 2025 set to reduce the qualifying period for unfair dismissal claims and remove the compensation cap from January 2027, the cost of poorly managed AI-driven workforce change, both in human and legal terms, is rising.

Businesses that have embedded clear human-AI accountability structures will be far better placed than those that have not.

Without making these structural changes, AI adoption will continue to add effort rather than remove it. Thereby accelerating burnout at the very moment businesses are depending on these tools to drive productivity.

Protection and transformation go hand-in-hand

It is entirely possible to realise the productivity potential of AI whilst protecting employees from its cognitive costs. Multi-agent workflows, properly designed, keep humans in the seats that matter - strategy, judgement and decision-making – and had the orchestration burden to the systems built for it.

AI brain fry is not an inevitable side effect of AI adoption. It is a signal that the implementation architecture needs re-thinking. That is a technical and organizational challenge, and it belongs with the people who built the system – not the people using it.

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The World Cup has arrived, bringing a wave of anticipation, celebration, and distraction that stretches far beyond the stadiums. For employers, it also serves as a real-time test of workforce operations as schedules, staffing needs, and employee engagement are all put under the spotlight.

During the six-week tournament, millions of employees will be 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 drive at least £12.6 billion in lost productivity.

In the UK alone, the impact could exceed £680 million, with employees not just planning to follow matches during working hours, alter their schedules, or miss work altogether --many admitted they would go to work hungover, secretly stream matches, and push the limits of what their employer would allow.

If England wins it all, almost a third of UK employees said they would take the day off whether that absence had been approved.

What the World Cup Reveals About the Future of Work

That creates 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 come out ahead during the World Cup, and during the everyday disruption that follows it, will do 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 gives organizations a chance to get ahead of that pattern.

Employees are already signaling intent. They know which matches matter to them. They know when they are likely to want flexibility, when they may need time off, and when they are 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 scheduling. 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 will pass. The operating lesson will 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.

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There is something poetic about quantum computing.

For decades, it has lived in the realm of possibility, whispered about in academic corridors, hyped in boardrooms, and misunderstood almost everywhere else. It promised to change everything, and yet, for the longest time, changed very little.

Until now.

Not because quantum has suddenly “arrived” - it hasn’t. But because the world around it has finally caught up.

We are, quietly, entering the age of hybrid intelligence, where classical computing, artificial intelligence, and quantum systems begin to work together. And that changes the question from “when will quantum matter?” to something far more interesting: What happens when quantum becomes part of how the world works?

From magic to mechanics

Quantum computing has long suffered from a branding problem.

It was either considered “Magic” because it would solve everything instantly; or a “Myth” because it was perpetually 10 years away!

The reality, as always, is more nuanced and much more powerful.

Quantum computers are not general-purpose machines; they are specialists. They are exceptionally good at specific classes of problems like optimization at massive scale, molecular simulation, cryptographic analysis, complex probabilistic modelling etc.

However, they are also fragile, error-prone, expensive and dependent on classical systems for almost everything else around them!

This leads to a simple but profound insight: Quantum will not replace classical computing. It will collaborate with it.

And that collaboration is where the real revolution begins.

The quiet role of AI

Ironically, the biggest accelerator for quantum computing hasn’t come from within the field itself. It has come from artificial intelligence.

AI has created the conditions for quantum to matter in three critical ways:

1. Made complexity usable - Quantum algorithms are not intuitive. AI helps design, optimize, and even discover them.
2. Improved error correction - One of quantum’s biggest challenges is “noise”. AI is now being used to stabilize and correct quantum systems in real time.
3. Created demand - AI has exposed the limits of classical computing—particularly in energy, consumption, and scale. Quantum is no longer a curiosity; it is a necessary complement.

What’s actually working (and what isn’t)

To understand the current state of the industry, we must separate the signal from the noise. First and foremost, what’s working is “Hybrid Workflows”. The hybrid architectures where classical systems prepare the problem, quantum executes the core computation, and classical systems interpret this result.

This is where real-world use cases are emerging.

Second, progress seems to be very domain specific as quantum is showing promise in areas where complexity explodes – drug discovery, materials science, logistics optimization, financial modelling etc. So, it’s not universal, but selective and meaningful.

Finally, ecosystems are forming. A new stack is emerging. Companies like IBM, Google, and Microsoft are building integrated quantum platforms, while hardware innovators like IonQ and Quantinuum push the boundaries of qubit fidelity.

What’s isn’t working ...yet

Fault tolerance at scale needs to evolve more as we’re still far from fully error-corrected quantum systems. Also, most enterprises are still only experimenting and not deploying quantum solutions at scale.

There is no “Windows moment”, or universal standard for quantum yet; every stack looks different.

And, perhaps most importantly, quantum still requires translation, from physics to business value.

A global race… with no clear finish line

Quantum computing has become a geopolitical priority. The United States is investing heavily through public-private partnerships; China is accelerating both research and infrastructure at a massive scale; and the UK and Europe are focused on Sovereign Quantum capabilities - ensuring they aren’t reliant on foreign stacks for critical security.

They are protecting their intellectual property more fiercely than they did with the internet.

This is not just about computing. It is about economic advantage, national security and scientific leadership.

And yet, unlike previous technology races, this isn’t winner-takes-all. Quantum systems will not exist in isolation. They will exist in networks.

Different players are taking fundamentally different approaches as the hyperscalers are positioning quantum as a “cloud-accessible capability”, as they abstract complexity and integrate with existing workloads.

But, as I have gone around the world talking to CEOs of various quantum companies, I am fascinated by what I call the “Plug-and-Play innovators”:

• Hardware pure-plays: Companies like IonQ and Quantinuum focus on hardware breakthroughs - trapped ions, new materials, and improved qubit fidelity. Their bet is that that “if we solve the physics, everything else follows.”
• The bridge builders: Firms such as Zapata AI and QC Ware are building the bridge between algorithms and applications. Their belief is “Quantum without software is just expensive physics.”

And then there is the gap. No one truly owns the interconnections between quantum and classical systems, nor the orchestration of the hybrid workloads. The missing layer is a neutral ecosystem where these players converge.

That gap will define the next phase of adoption needed for this to truly scale.

What this means for society

Quantum’s impact will not be immediate, but it will be profound.

Healthcare: Simulating molecules at quantum precision could accelerate drug discovery from years to months.

Climate: Optimizing energy grids and materials could unlock more efficient batteries and carbon capture.

Finance: Risk modelling and portfolio optimization could reach entirely new levels of sophistication.

Security: This is my personal passion. While quantum has the potential to break current encryption standards, it is also driving the development of Post-Quantum Cryptography (PQC), making systems more secure in the long run. We must act now to prevent "Harvest Now, Decrypt Later" attacks, where encrypted data is stolen today to be cracked by quantum computers tomorrow.

A slightly uncomfortable truth

Quantum computing will create as many questions as it answers.

• Who gets access first?
• Who controls the infrastructure?
• How do we ensure equitable benefit?

We have seen this movie before with the internet and AI.

The difference this time is that we have the opportunity to design the system more deliberately.

Quantum has been “almost here” for decades. So, why does this moment feel real?

Because AI has created urgency and demand; infrastructure has matured to support hybrid models; and ecosystems are forming, not just technologies.

This is no longer about a breakthrough machine. It is about a connected system of capabilities.

A more human way to think about quantum

Perhaps the simplest way to understand quantum is this:

• Classical computers think in straight lines.
• AI learns patterns from data.
• Quantum explores possibilities simultaneously.

It is less like a calculator and more like imagination. And like imagination, it is most powerful when guided.

So, what should we do now?

For enterprises, start experimenting with hybrid workflows now. Focus on use cases (optimization, simulation), not just the underlying physics, and build internal understanding early.

For policymakers, invest in open ecosystems, prioritize standards and interoperability, and balance competition with collaboration.

For technologists, think beyond silos and design for integration, not isolation. For the rest of us, stay curious.

Quantum computing will not change your life tomorrow, but it will quietly reshape the systems that your life depends on.

Closing thought

We often think of technological revolutions as moments.

In reality, they are transitions. Messy. Gradual. Non-linear.

Quantum computing is not a single breakthrough waiting to happen. It is a shift in how we solve problems; one that will unfold over years, across industries, and in ways we cannot fully predict.

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The “AI” label no longer makes companies special. In today’s world, that is a fact that needs to be accepted.

Even just a year ago, if a business simply mentioned being “AI-powered” it was enough to immediately gather attention and appear innovative. Investors quickly grew excited, and media outlets were quick to pick up the next “hot story.”

Of course, that didn’t always mean that there was truth to such statements. The concept of AI washing — when companies misrepresented or even outright lied about the use of AI tools in their operations — had certainly done its share of damage to this industry while it stayed prevalent.

But today, even without such lies, the effect of AI novelty is disappearing. Every fintech company now has some kind of AI story. Compliance platforms use AI monitoring. Banks and fintechs talk about AI-driven service personalization. Chatbots and AI assistants are utilized practically by every other platform, no matter which industry we look at.

At this point, saying your company uses artificial intelligence feels almost the same as saying you have a website. People have learned to expect it almost by default. And this creates a very different communications environment.

The first wave of AI companies mostly had to convince their audience that the technology was possible. That task has certainly been accomplished. Now, the second wave faces the task of convincing people that their particular implementation of AI is trustworthy, useful, and worth the attention in a market that’s beyond overcrowded already.

That is much harder.

The conversation has changed

AI investment has exploded over the last two years. Enterprise spending on gen AI jumped from $11.5 billion in 2024 to $37 billion in 2025. That’s a whole 220% YOY increase. AI-focused startups continue to dominate venture capital conversations, to the point where nearly every technology company now feels the pressure to position itself as part of this race.

But something else happened along the way: consumers became more educated. If at first, AI sounded like something magical, now people have actually used it. They have seen hallucinations and strange outputs, and they’ve dealt with incorrect recommendations. They’ve had time to realize that AI can be useful, but also that the technology comes with its own frustrations and limitations.

As such, being “AI-powered” is no longer enough to stand out. Consumers have already moved past that stage. One of the biggest mistakes I see today is when companies still assume they have not.

Now, the conversation revolves around a much deeper point: “What does your AI actually improve for me?” For AI-focused companies vying for attention, the benefits of their specific approach are now the key battlefield where they have to win.

This is particularly important for financial and fintech companies, because financial services are built on trust. People may tolerate or even find humor in mistakes from an entertainment app, but they will not be pleased when their money or personal data are put at risk because of AI malfunctions.

The numbers clearly show this trust gap. While AI adoption continues growing, only 13% of consumers truly trust AI systems, and about 30% remain neutral rather than confident. Many people are still uncomfortable giving AI fully autonomous control over important decisions, especially in areas connected to finance or personal data.

At the same time, enterprises themselves also often contribute to the problem of trust by rolling out AI models before building the necessary structures to govern them.

A recent survey by McKinsey discovered that less than 15% of organizations obtain full security and IT approval before deploying AI agents. Or, to put it in other words, the vast majority of these systems come out without proper oversight and responsibility frameworks around them.

This very reality has now become the defining challenge of the next AI adoption phase. The market is no longer asking whether companies can use artificial intelligence. It’s asking whether they can use it responsibly. And that is precisely why I am of the opinion that AI communication from here on out needs to be very different from what we saw during the first hype cycle. It’s no longer about who shouts the loudest.

The main victory condition will be for companies to explain — clearly and realistically — how their systems work and how they benefit their users. And then they will have to continue proving that through sustainable action.

Transparency and governance are now a product feature

One important area to account for is transparency around AI limitations. Now that the broad public already has enough context and experience to understand that AI can fail, pretending otherwise would only damage a company’s credibility.

To earn trust, strong, honest communication is necessary that will openly acknowledge those points of failure. When a business wishes to present its own AI solution, it will need to explain upfront where its model works well and where its shortcomings lie. Customers should have clear expectations and understanding of the risks before they engage with the technology.

Some might think that admitting to shortcomings would be a mistake, a vulnerability. But honesty can yield more results than those people expect.

Clients are generally much more comfortable using services when they understand where the borders are. When they know how decisions are made, that safeguards exist, and that human oversight is still involved in the process, so if something goes wrong, they have someone real to talk to.

This is where competent governance enters the picture.

Be it to partners, clients, or regulators, businesses increasingly need to demonstrate that they are responsible about how they employ AI in operations. Whether decisions can be audited.

A few years ago, these topics were often hidden deep inside technical documentation, if they were brought up at all. Now, explainability of AI models is one of the biggest signals of trustworthiness that a company can project.

And honestly, I think this is healthy for the industry.

By now, AI has stopped being a mere marketing decoration and is leaning more and more toward becoming a full-on infrastructure layer in corporate operations. That means it needs to be properly managed, so that possible mistakes do not create reputational and monetary damage to countless involved parties.

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For years, identity security has been designed to secure an organization's human users. But as agentic enterprises take shape, the identity equation is shifting. Artificial intelligence (AI) agents and AI-powered builders – software tools used to develop websites and applications without coding – are increasingly participating in how access is configured, governed and used.

AI agents are effectively new digital employees, so organizations need a way to know they exist and control what they do throughout their lifecycle. They are becoming operators, helping to administer and secure identity environments through machine-native interfaces.

To add another layer of complexity, desktop agents and AI assistants are also beginning to interact with enterprise applications and resources on behalf of users.

For an agentic enterprise to succeed, these agents need trusted access to do useful work but should not be given direct exposure to secrets they have no meaningful reason to access. To achieve this, organizations need a unified, AI-first identity model, centered on end-to-end visibility, governance and controls which strike a balance between security and appropriate access.

AI agents are reshaping identity

AI has created a new category of digital identity. Like human employees, autonomous agents must be discoverable and managed and governed so organizations can understand what systems and data they can access and who is responsible for their actions.

Traditional identity and access management (IAM) systems relied on static, one-time verification methods in response to access requests made by humans. But in the agentic enterprise, requests also come from autonomous software acting on behalf of human users. Organizations therefore need to know exactly who or what is accessing a system continuously, and if they have the correct permissions to access given information.

At the same time, AI is increasingly managing identities and access. Machine-native interfaces allow agents to help manage human users’ access, troubleshoot issues and support security workflows. While these capabilities can help organizations cut costs and improve efficiency, they are only successful when strong access guardrails are put in place.

AI has created a new category of digital identity. Like human employees, autonomous agents must be discoverable and managed and governed so organizations can understand what systems and data they can access and who is responsible for their actions.

Traditional identity and access management (IAM) systems relied on static, one-time verification methods in response to access requests made by humans. But in the agentic enterprise, requests also come from autonomous software acting on behalf of human users. Organizations therefore need to know exactly who or what is accessing a system continuously, and if they have the correct permissions to access given information.

At the same time, AI is increasingly managing identities and access. Machine-native interfaces allow agents to help manage human users’ access, troubleshoot issues and support security workflows. While these capabilities can help organizations cut costs and improve efficiency, they are only successful when strong access guardrails are put in place.

Building a unified identity model for AI

Mechanisms for securing AI cannot simply be bolted onto identity systems designed for humans. It requires a complete rethink of the identity management model, where human and machine identities are governed through a single framework to prevent tool sprawl and unintentional security blind spots.

As organizations adopt AI tools throughout multiple operational layers, enterprise identity needs to evolve and become easier to manage and automate. Identity can no longer rely solely on human administration.

Tools designed specifically for autonomous agents, such as AI-first headless interfaces, allow builders and AI alike to perform identity-related tasks. Autonomous operators must also be trained to configure access, troubleshoot workflows and apply governance controls within approved policies and guardrails.

Visibility and governance across the entire AI agent lifecycle are also critical. As more agents are deployed, businesses must have complete visibility into their agents and actions.

Every AI should be treated as a first-class identity, with a designated human owner, as well as clear policies and full auditability throughout its entire lifecycle. As these agents operate across the enterprise, their actions should be traceable to a human user responsible.

Finally, AI agents need trusted ways to interact with enterprise resources without being given direct access to the credentials or secrets that enable them. Coding and desktop agents increasingly interact with systems on behalf of users, but exposing them to credentials or long-lived secrets creates unnecessary risk. Instead, access to enterprise resources should be brokered through just-in-time privileged controls.

This allows enterprises to maintain oversight of how permissions are granted, governed and audited without exposing the underlying secrets behind that access. Together, these capabilities create a unified identity model which extends governance across human and AI identities without creating a parallel identity stack.

The future of the agentic enterprise

AI agents cannot operate as intended and deliver meaningful value without access to enterprise systems. But granting unrestricted access or exposing sensitive information creates an entirely new risk to organizations.

The future of the agentic enterprise depends on maintaining governance, visibility and control across both human and digital identities. This means identity must become programmable, AI agents should be governed throughout their lifecycle and agent access needs to be given without unnecessary exposure to sensitive data.

A unified identity strategy provides the means to operate AI agents more safely and efficiently while maintaining centralized governance, accountability and control.

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Recent evidence shows that energy cost pressures and a power grid capacity crunch risk becoming a bottleneck on the growth of the UK’s digital economy.

A report by Oxford Economics for the Nuclear Industry Association warns that growing grid capacity constraints and uncompetitive industrial electricity prices could drive data center developers overseas.

This comes as data center energy demands could increase fivefold by 2035 and data center operators face growing pressure for more sustainable energy in line with climate targets.

This is driving many data center developers to explore alternative supply sources such as flexible contracts and Corporate Power Purchase Agreements (CPPAs) which offer affordable, secure long-term power.

Yet many data center developers lack the resources to navigate these complex contracts or meet the high credit requirements.

There is an urgent need for creative new solutions to provide affordable sustainable power for our digital economy.

The energy chokepoint for the AI economy

The Government aims to make Britain the fastest AI-adopting country in the G7 and this will require accelerated data center expansion with AI data centers being prioritized for new demand connections to the grid. While this is welcome, demand-side connections will not alleviate the supply-side challenges from rising electricity costs to network capacity constraints.

Data centers have build times of 12-14 months yet large new renewable energy projects can face waits of 12-14 years to come online at a time when Britain faces rising non-commodity electricity costs such as Transmission Network Use of System (TNUoS) and Nuclear Regulated Asset Base (RAB) charges to fund new electric grid and nuclear energy infrastructure.

Ofgem has warned that data center power consumption could significantly exceed Britain’s current peak electricity consumption, risking rising energy costs.

The move towards flexible contracts

Flexible electricity contracts that allow companies to buy energy in chunks offer a potential solution to this by allowing data centers to tailor energy costs to their consumption.

Crucially, flexible contracts can be adjusted to hedge against fluctuating energy consumption for facilities such as AI data centers which have more variable patterns of energy use. This would also help avoid penalties for ramping up energy use to take on major new customers.

While fixed-price contracts can lock in long-term energy costs to offer certainty, flexible contracts enable data centers to take advantage of price fluctuations to secure cheaper power.

For example, we have a dedicated pricing team monitoring market movements round the clock. This could help facilities partially hedge against the risk of rising prices, setting a price for a portion of their consumption, while buying the rest when required to take advantage of favorable prices on the spot market.

For example, we implemented a flexible contract for an energy-intensive industrial chemicals company which included a cash-out arrangement, enabling them to lock in prices when costs are low and buy power in batches days or even months ahead as needed.

There are various kinds of flexible contract options based on the degree to which companies can forecast their energy consumption. For example, ‘tolerance banding’ which guarantees a set price within a certain range or ‘band’ of electricity consumption, can provide certainty amidst unpredictable, fluctuating costs.

As data center operators become more confident in forecasting energy consumption, this can create even cheaper options such as contracts that enable them to buy every kilowatt-hour above or below expected demand.

The CPPA model

Other contracts such as CPPAs could offer data centers a secure, sustainable, affordable power supply directly from suppliers at stable cost. Private-wire PPAs involving onsite generation could enable data centers to sell surplus power back to the grid, transforming energy from a cost into a revenue stream. On site generation could also help avoid the non-commodity costs for grid electricity such as TNUoS charges that comprise 60% of electricity bills and are set to increase to fund new infrastructure.

With targets to reduce operational emissions from buildings by 76%, CPPAs also help facilities meet climate targets by providing traceable green power. As large consumers with a relatively stable long-term demand, data centers are perfectly placed to tap into this market. Amidst growing energy price volatility and security risks, fixed-price CPPAs offer the chance to lock in power prices and supplies, providing certainty and security.

The barriers to alternative contracts

Yet there are many barriers to flexible contracts and CPPAs market for smaller entities such as data centers. These contracts can be highly complex, contain stringent credit requirements and often require 10-15 year agreements. The Contracts for Difference (CfD) scheme, which gives renewable developers a government-backed route to market through fixed long-term price support, can also act as a competing route to market for generators.

In some cases, this can make long-term corporate offtake agreements less attractive, particularly where developers can secure greater certainty through the CfD mechanism. For smaller customers seeking greener electricity, however, this route can offer an alternative when a direct CPPA may be harder to access.

Many data center projects are run by startups or smaller entities that lack the resources for such long-term commitments or credit requirements. Data centers also present a slightly higher credit risk than other facilities because their revenue streams are not based on a few large, long-term customers but split among many customers across the digital economy.

Opening the market to data centers

There is an urgent need for creative solutions to lower barriers to entry to the flexible contract market for smaller entities such as data centers. Security deposits or bank guarantees can help some firms meet the stringent credit requirements.

Other potential solutions include parent-company guarantees where a parent company makes a commitment to cover the cost in the event of a default or intercompany guarantees where large data center customers such as Amazon offer guarantees.

There are also solutions to simplify adoption and help CPPAs slot into existing energy use. For example, PPA import sleeving contracts, where energy is pre-purchased from a generator or utility and supplied directly to a facility through the grid, can help alleviate energy costs and security risks.

Tailoring contracts to specific energy needs

Independent partners can also help data centers optimize contracts for their specific energy needs and financial situation. Independent brokers can consolidate the process of negotiating with generators, suppliers and investors, speeding up and de-risking adoption.

Brokers can also help negotiate and monitor contracts suited to the precise profile of each data center. For example, cloud computing data centers with relatively steady, predictable demand may prefer fixed contracts whereas AI data centers processing huge amounts of data for many clients have a more variable, ‘peaky’ pattern of consumption and require flexible contracts.

Data centers can also work with partners to get live market intelligence on changing regulations or market movements. For example, we offered one client an early warning service so that they were able to minimize costs during the winter triads, half-hour periods of peak demand during the winter season.

Towards a new model of data center energy

Britain’s ability to compete in the accelerating AI race increasingly hinges on the ability to mitigate the risk of rising energy costs.

Energy security will also be critical to building truly sovereign AI capabilities for the UK. Lowering the barriers to the flexible contract market could create new opportunities at both ends, providing secure, sustainable and affordable power to unlock data center growth while unlocking vital investment in new renewable energy capacity.

Yet this will require tailored, adaptable contractual models from bespoke flexible contracts to PPAs and new ways of lowering barriers to entry including reducing complexity and stringent credit requirements. This could help provide secure, sustainable and affordable long-term power to fuel our digital economy.

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• New data reveals that most workers fear AI’s potential impact on their jobs
• Employees who feel secure in their roles are more productive and engaged
• Employers must prioritize communication and invest in training and upskilling

New data from ADP’s People at Work 2026 report, which covers more than 39,000 working adults from 36 markets, confirms that most workers are still highly concerned about AI’s impact on their jobs, even though today’s tangible impacts are relatively minimal.

Only one in four UK workers strongly agree their job is safe from being replaced, and this drops to around one in five (21%) in Europe as a whole and 22% globally.

ADP’s report highlights a major disconnect between employment figures, which are generally pretty strong, and employees’ personal expectations about their roles’ futures.

Poor AI communication is creating a confidence gap

“The world of work is changing fast, and our findings reveal a gap between what the labour market is telling us and what employees are feeling,” ADP UK&NI SVP and GM Jeff Phipps said, noting that “employment is strong.”

The report, one of the most extensive of its kind, shows that confidence actually varies widely according to the nature of an employee’s role, with knowledge workers feeling more confident. Around one in three (34%) UK knowledge workers agree their jobs are safe, but only 19% of UK workers in repetitive roles feel secure.

But a sense of job security doesn’t just play into a worker’s satisfaction – it could also be impacting how they work, and the productivity that an employer sees. Workers who feel their jobs are secure are 6x more likely to be fully engaged at work and around 3x more likely to report high productivity.

Workers who feel secure are also around 2x as likely to say they have no intention of leaving their employer.

More job security creates higher-performing workers

It seems that greater confidence about the future may enable employees to concentrate more on their work and raise their contributions, indicating a clear need for positive communication from management.

ADP advises employers to be transparent about any organizational and technological changes, and to explain how employees’ roles could be affected so that they don’t instantly assume their jobs could be at risk. The report also puts the responsibility of training and upskilling on employers.

“People want to know there's a place for them as their organisation evolves, and that they'll be supported to get there,” Phipps added.

The report also reveals that the bigger the company, the less confident a worker is likely to feel. Large corporations see a 22% confidence level, compared with 36% for mid-sized companies. In Europe, the inverse is true, proving that the world is still struggling to get to grips with AI communication and training regardless of geography or company size.

“The businesses that treat this seriously - investing in skills and being honest about change - are the ones seeing the payoff in how people perform and whether they stay,” Phipps said, urging companies to factor much more than mere tech implementation into their AI strategies as they risk losing workers.

Apple's MacBooks seemingly have some big changes on the horizon. One of the biggest is the rumored MacBook Ultra packing that OLED screen, and this is something to be excited about, don't get me wrong. Well, worries about the price aside, and wider issues around Mac pricing overall, but there's something I'm more concerned about now.

Namely the direction in which Apple is heading with its M-series silicon, following the latest rumor dump from prolific leaker Mark Gurman. In last weekend's Power On newsletter for Bloomberg, Gurman dropped a whole lot of fresh info about Apple's CPU roadmap and the incoming M6, M7 and M8 chips.

The leaker tells us — and bear in mind, this is all just rumors, but eye-opening speculation nonetheless — that Apple is doing something very different with the next range of M silicon. The M6 will consist of just a vanilla chip, with no more powerful variants, which isn't something Apple has ever done before — there's always been a Pro and Max version (if not an Ultra, too).

This is because Apple apparently wants to usher in the M7 series more swiftly, with the M6 debuting late this year and the M7 following in the first half of 2027, just six months (or so) later. The M7 will have a full complement of Pro, Max and Ultra chips, the former two following at the end of 2027, with the Ultra in 2028. Gurman believes this is Apple's current plan and the reason for rushing to get to the M7 is AI.

Cue multiple groans, no doubt. Gurman theorizes: "The reason for this break with tradition: AI. Apple had been planning major neural-processing upgrades for the M7 family and ultimately decided those improvements were important enough to justify accelerating the next generation rather than completing the M6 lineup."

We're further told that beyond this, Apple is "already developing M8 chips with even greater AI capabilities" to land in 2028.

Gurman concludes: "The takeaway is that AI is no longer just another feature Apple's chips need to support. It is now shaping how those products are designed and when they are shipped."

A wise path to follow?

So, what am I worried about here exactly? Obviously there's a strong suggestion that AI is taking much more of a center stage role with Mac chips, and given what's currently going on with MacBooks, I've got to wonder about the wisdom of this — and how it might affect Apple's PC sales.

Currently Mac sales are strong and considerably buoyed by the launch of the MacBook Neo in March 2026, a budget laptop that has undoubtedly flown off the shelves. However, Apple's recent price hikes have poured cold water on those sales prospects to an extent, even hitting the MacBook Neo, which went up by $100 in the US (£100 in the UK, and AU$150 in Australia).

Granted, while it's odd for a device to get a price rise like this so soon after launch, given the demand around the Neo, it's not going to be sidelined with this increase (although the upper-tier model with 512GB storage now looks shakier and out of budget territory, frankly). The harder pill to swallow is the harsher MacBook Air price hikes, and as we pointed out a few weeks back, some wallet-friendly Windows 11 laptops now look a lot more tempting after Apple's price increases.

Looking on Reddit, it's not hard to find a lot of unhappiness about the MacBook price hikes. There's considerable angst from some people who were thinking of pulling the trigger on a new Apple laptop, and then watched those price increases swoop in, and are now feeling the opposite of buyer's remorse (holdout's regret?). Some of those folks are acting with their wallets, closing them and vowing to wait for a better buying opportunity now that they've missed the boat, as it were.

That could be a long wait, though. Of late, we're hearing a lot of gloomy news about the RAM crisis getting worse later this year, and in 2027 — maybe a lot worse. The memory supply situation is what caused Apple to jack up Mac prices, of course, and it's difficult to see how the MacBook maker will continue to weather this storm if it worsens next year, given that its resources in terms of built-up inventory of RAM (or SSDs) are clearly running dry (or have run dry).

Rumors have continued to persist about Apple potentially tapping Chinese memory chip makers for its RAM needs, as an alternative to the big three mainstream suppliers (Micron, Samsung, SK Hynix). That's a worrying development in itself, and it's doubtful whether even Tim Cook can pull off navigating that kind of negotiation before he leaves his Apple CEO role (there are various possible hurdles to clear and governmental complications, put it that way).

Diving into AI headfirst

I don't think any of this bodes particularly well for next year's Mac sales given the prospect of pressure around more price hikes (although yes, other PC makers face the exact same unfavorable conditions). And then we come back to this apparent renewed focus on AI, which hasn't gone down well on Reddit and other social media.

Here's one Redditor who sums all this up neatly: "I was about to buy a new MacBook to replace my M1 Pro with an ailing battery and small storage. But then Apple jacked up the prices by almost 20%. And now they are diving into AI headfirst. It might be time for me to look at Framework laptops."

There's a strong backlash against AI right now, whether that's about sprawling data centers and them chugging huge amounts of power, or Copilot in Windows 11 and indeed Apple Intelligence. So, it's no surprise that the idea of "diving into AI headfirst" has been greeted by a lot of skepticism.

What exactly is Apple Intelligence going to serve up in terms of compelling features in the next year or two? The trouble is that this feels very much up in the air, yet Apple is supposedly building its Mac strategy around this now. It seems like a bit of a leap into the unknown to say the least, and I'm not surprised that it's making Mac fans nervous.

Okay, so this AI focus is still just a rumor, although Gurman is one of the more reliable Apple sources out there, and it appears to be a well enough fleshed-out piece of speculation. Even if it's true, Apple may yet switch away from this course, but for now, I'm struggling to find the logic of shifting to a heavier AI focus so soon.

Maybe the reasons for this will become clearer as we see how Apple Intelligence develops in the near future. However, in the meantime, I think there are reasons to be concerned about Mac sales potentially flagging next year, between disgruntled MacBook holdouts, the continued RAM crisis, and cynicism around Apple's apparent new AI-led approach.

The smartphone market is dominated by two main operating system players — with Apple's iOS leading the charge in the mid-2000s and Google's Android following suit, only maturing relatively recently. But Steve Jobs took the news of Android's launch personally, frustrated and betrayed at its development.

Eating machines

The Apple chief was speaking with Walter Isaacson, his biographer, when he shared his exact feelings about Google's development of Android.

Published very shortly after Jobs' death in October 2011, Steve Jobs was based on several exclusive interviews conducted over two years, alongside conversations with more than 100 people who knew the Apple co-founder.

One of the most spicy exchanges involved Jobs vowing to destroy Google for its work developing Android in secret, while Eric Schmidt was also serving on the Apple board. Schmidt offered to resolve the issues with a settlement, but Jobs took it more than personally.

Energy efficiency

Jobs was apparently angry after learning in January 2010 that HTC had introduced an Android smartphone with many of the same popular features in an iPhone.

Reports from the time the comments were made public suggest the episode reflected Jobs' own perspective on a particular breed of profit-driven executives who only cared about making money.

Although iOS enjoyed a reputation for many years during the 200s of being a better platform in most, if not all, quarters, including its overall design philosophy, Android-powered smartphones were significantly cheaper. In the modern era, the Android OS is incredibly intuitive, with different smartphone makers usually looking to Apple for inspiration in designing features for their own custom flavors.

During his tenure as CEO, Tim Cook also pivoted on this approach, softening Apple's stance and settling many of the ongoing legal disputes, including the long-running battle with Samsung.

• Copenhagen-based Triton Depth, founded in 2025 by three DTU engineering students, has raised €1 million in pre-seed funding
• It aims to address one of the EU's most underserved security concerns: the seabed, as Baltic cable sabotage, shadow-fleet activity, and underwater drone warfare are increasingly growing concerns
• Triton Depth intends to build a scalable network of passive acoustic sensors it calls 'Triton Nodes' to address the issue, leveraging AI to identify vessels and objects in real time

A three-man Danish company founded by students is venturing into a somewhat interesting industry for an EU-based startup: underwater defense.

Triton Depth has received €1 million in pre-seed funding from investors including London-based The Creator Fund and Denmark's state-owned Export and Investment Fund (EIFO), with aims to focus on acoustics to answer what is arguably Europe's biggest security threat in the days to come: drone-based naval warfare and sabotage.

With growing concerns about the vulnerability of the European Union, and by extension, Denmark, to a variety of sea-borne threats, EIFO's investment in Triton Depth might be more than just a value play, but a hallmark of a realization that it needs to tend to its own defense needs even as NATO continues to meander, with the US proving to be a volatile partner of late, to say the least.

Using affordable dual-use acoustic technology as the first line of defense

Triton Depth's approach is appealing for a domestic defense industry that does not share the budgets that larger naval players such as the US, Russia, or China possess: it is simple, yet elegant in its premise, and even partial wins when it comes to its claims would go a long way to safeguard Denmark and the EU's regional interests.

Triton Depth's approach centers around the use and deployment of its 'Triton Nodes', a scalable cluster of low-maintenance passive acoustic sensors that measure sound underwater before feeding information back to an AI model that assesses and classifies signatures it detects in real time.

The company intends to market its product line as a dual-use technology play, with CEO and Co-Founder Carl Borg stating that they aim to build "the intelligence layer for the ocean" for both civilian and defense use cases.

The focus on defense stems from Denmark's own vulnerabilities in the Baltic Sea, which has previously suffered significant economic damage from sabotage, including the destruction of Nord Stream 1 and 2.

With an increasing share of its critical infrastructure, including power interconnectors, data cables, and offshore wind, lying underwater, it could be argued that EIFO's investment is the Danish state safeguarding its own maritime security and intelligence interests by investing in an intelligent tripwire for the future.

Other European governments are also actively seeking scalable, affordable ways to monitor coastal and subsea infrastructure, even as Nordic defense budgets continue to rise, with Triton Depth among many defense infrastructure companies poised to benefit from a renewed focus on maritime security in the region, even as acoustics are seen as a reliable metric to monitor for a variety of infrastructure plays.

• Survey inds 69% of Americans support Bernie Sanders’ policy of requiring AI firms to transfer 50% of their stock to a public fund
• Respondents are also overwhelmingly in favor of giving the federal government the power to block new AI services deemed “risky”
• Support does appear to drop when Sanders' name is mentioned

AI is developing an image problem, and respondents to a new survey have made their feelings clear: Bernie Sanders’ demand that AI firms contribute stock to a massive public fund is widely supported.

Conducted during June 2026 by the nonpartisan survey research company Verasight, the survey consisted of 17 questions sent to 1,690 adults (18 and above), finding over two-thirds (69%) supporting Sanders’ policy – a figure that only drops to 64% once it is revealed which politician the idea is associated with.

The bad news for the AI industry doesn’t end there, as respondents demonstrated a notable distrust for how AI companies conduct themselves, with nearly half (43%) believing that the regulations proposed by the AI companies are designed to benefit those same companies. Incredibly, 30% of respondents trust the US federal government more than companies like OpenAI and Anthropic.

How does AI wealth distribution work?

Senator Bernie Sanders’ plan gives the public a direct stake in America’s largest AI companies, with a one-off tax paid not in cash, but in stock. "Since AI is built on the collective knowledge of humanity, the wealth it generated must benefit humanity," he said, announcing his planned Act on social media.

This would, according to Sanders’ proposal, deliver an annual $1000 check to U.S. citizens, and fund healthcare and education.

"The findings from our latest survey demonstrate a rare instance of bipartisan agreement," noted Ben Leff, CEO and Co-Founder of Verasight.

"There is an undeniable desire among Americans of both parties for federal oversight, absolute transparency, and accountability to ensure AI safety and to enable all Americans to participate in the economic benefits of AI."

Even with a polarizing figure like Bernie Sanders attached, the wealth fund idea retains support, which suggests that while people are happy to use AI to answer questions, streamline processes, and make tasks quicker, they’re less comfortable with the industry’s wider impact.

A poor public perception

While AI companies are becoming increasingly unpopular, it isn’t all bad news. The survey quizzed subjects about their feelings on other industries, including tobacco, pharmaceuticals, and casinos, along with social media. If the tide is turning against AI, it still isn’t regarded as poorly as casinos or tobacco.

Feelings about AI companies are also notably more positive than social media. Given the concerns expressed about federal oversight and public wealth funds, there could be an avenue for OpenAI, Google Gemini, Anthropic, Microsoft, and others to improve the general feeling about AI: accepting a degree of social responsibility.

• JPMorgan CEO Jamie Dimon warns controls may be needed for Claude Mythos
• The AI model from Anthropic is highly advanced, and has been proven to detect zero-day vulnerabilities and even develop working exploits
• The US government has previously instructed Anthropic to block access to foreign nationals, citing security concerns

Artificial intelligence is becoming increasingly powerful, a fact highlighted with the US government’s recent instruction to limit access to Anthropic’s Claude Mythos model.

Now, the CEO of JPMorgan has described the risks the technology poses as a “real issue,” and likened wide access to the AI as “giving ballistic ⁠missiles to individuals.”

Speaking at the Pennsylvania Defense ​and Innovation Summit, JPMorgan’s Jamie Dimon underscored the risks posed by the AI, which has already been shown to both identify and exploit cybersecurity challenges.

Mythos isn’t Skynet

Currently, Claude Mythos is limited to a small selection of organizations, companies, and federal and military departments. Public access to the AI has been blocked, and worldwide access has been blocked due to the potential security issues of this powerful AI.

Claude Mythos is not the type of threat that can become self-aware and take over military appliances like the fictional Skynet of the Terminator movie series. However, it does have the capacity to cause immense damage in the wrong hands.

The AI is capable of detecting cybersecurity vulnerabilities and reporting on them; it is also able to generate automatic exploits. So, for white hat cybersecurity analysts, it is a powerful defensive tool, but in the wrong hands, it is a destructive power. In addition, its automated capabilities make it easy for black hats to scale their cybercrime operation and make ransomware, phishing, and other data-based scams more profitable.

It isn’t just cybersecurity where Anthropic’s Claude Mythos AI has demonstrated such incredible capacities. Scientific research, specifically biology, can be targeted by attackers using AI to turn complex concepts into terms, potentially to misuse the information.

There is also the challenge of a strategic imbalance between single nations or bodies having exclusive access to the technology.

Will the public ever access Anthropic’s Claude Mythos?

Given the security considerations, it seems unlikely that Claude Mythos will be publicy available anytime soon. However, Anthropic has already stated that it intends Mythos-level features and capabilities to become more widely available.

For this to happen, however, it needs to develop and apply various safeguards.

The most likely scenario is that public access to Mythos is eventually unblocked, with restrictions to specific features that relate to cybersecurity and biological/medical tasks and research.

Project Glasswing is already in operation with a handful of key partners (including Amazon Web Services, Anthropic, Apple, Broadcom, Cisco, CrowdStrike, and others), and will probably be expanded until the safeguards are demonstrated to be fit for purpose, and public use.

Via Reuters

• DataDome analysis claims agentic traffic has surged by 45% in Q2 2026
• Meta AI bots have grown over 163% on the previous quarter
• The analysis was conducted by bot management and agent control platform DataDome

If you run a website, every crawl costs bandwidth, resources, logging, and creates CDN transactions, and while search engine crawlers offered the promise of sending visitors, AI bots do not.

Analysis in a report from cybersecurity firm DataDome has shown that while bots from Meta AI have increased activity, they’re not delivering any significant returns to websites.

Conversely, ChatGPT crawlers have reduced in traffic, but are sending more referrals.

AI agent traffic is growing

While Meta AI is usually considered to be the “chatbot within Facebook” it seems that it is becoming something more – and the emergence of the Meta-WebIndexer bot (which grew 163% on Q1) suggests that Meta may be indexing a library of websites, in much the same way Google has done for the past few decades.

The growth of Meta AI as an active crawler is only part of the story, as is ChatGPT’s comparative efficiency. The OpenAI tool seems to know enough about websites, so can provide the answers it already “knows.” Conversely, Meta AI’s activity indexing the web seems to explain its heavy impact in Q2 2026.

But also emerging is the Model Context Protocol (MCP) signal, which connects AI agents with external tools, and differs from standard crawler traffic.

“Q2 showed us that the ground is shifting faster than most organizations realize. Meta now dominates AI traffic on our network, MCP traffic has emerged as a real signal, and ChatGPT is driving more referral value with fewer crawls," noted Jérôme Segura, VP of Threat Research at DataDome.

The differences in the way the AI agents are interacting with websites – some behaving like users, others scraping content – means that organizations need to act accordingly.

“What the data makes clear is that not all agents are created equal. The organizations building policy around these distinctions are the ones gaining an edge, and that's exactly why agent trust adoption is accelerating."

Unfortunately, MCP’s existence and growth into a significant, measurable quantity, means that it should also be treated as part of an organization’s attack surface.

Allocating resources

Given the origins of the report, there is naturally a cybersecurity aspect to this. While ransomware, malware, and phishing are not going anywhere, autonomous software on the web needs addressing in a different way.

Those “organizations building policy” that Segura mentions might, for example, give full crawl access to Google, allow ChatGPT to retrieve results, but rate-limit Meta AI based on its poor return.

Meanwhile, unknown agents – perhaps cybersecurity threats – would require additional verification or be blocked entirely.

• Users says they've observed LG gaming monitors automatically installing unwanted software on PCs
• LG's terms of service warn that conversations may be "captured and processed" on TV with the latest version of webOS
• The terms say you must now warn guests they may be recorded "in compliance with applicable wiretapping… laws"

How smart should a smart TV be? According to LG's latest TV terms and conditions, the answer is "not quite smart enough to comply with wiretapping laws", because that's now your responsibility if LG captures the voice of a guest in your house through its AI voice services. Though the situation with LG monitors appears to be even more dramatic.

As Gamers Nexus reports, some LG monitors appear to be installing adware on Windows PCs without asking for permission: in addition to the LG Monitor App Installer, they also install McAfee Scam Detector.

LG's own app requires full access to all system resources, which potentially includes all your online activity, logins, hardware, location and more — while McAfee has a long history of being installed on devices as 'bloatware', and people are not reacting positively to suddenly finding it on their PC.

Comment from r/LGOLED

There may be a perfectly innocent explanation for all of this, but when big tech firms keep getting caught doing bad things because they thought they could get away with it, it's no wonder people are assuming the worst.

Comment from r/hardware

The bit that's causing consternation regarding smart TVs is part 6(d) of the new LG Electronics terms of service, headed Voice Recognition and Privacy Compliance.

As Notebookcheck's Hannes Brecher notes, the section states that it's your responsibility "to obtain all necessary consents from any third parties whose voices may be captured by the Product and to notify household members and guests that their voices may be captured and processed, in compliance with applicable wiretapping, eavesdropping, and privacy laws."

There are three ways around that. One, you can turn off all microphone-based features. Some people won't mind that, but they can be useful — especially asking it for settings you don't know how to find.

Two, you can avoid installing the latest software — but that means you won't get any security updates, which are important (to protect your privacy, ironically, among other things).

Or you can disable your TV's connection to the internet so it can't send information back, but that obviously makes it less useful, and will also disable the voice controls anyway.

I think the terms and conditions are an attempt at corporate ass-covering rather than something sinister: the preceding paragraph talks specifically about when "a product with voice recognition functionality is used" and it's possible that "family members, guests, children, and bystanders" might be overheard; if you're choosing to activate AI-based voice features then of course voices are going to be captured and processed in order for those features to work.

At the same time, it does seem broad enough to let LG use people's voices as AI inputs, rather than just accidental capture. And in the context of what LG's doing with PC monitors (not to mention AI training's very, ahem, casual relationship with consent and copyright), that's a worry — and people online increasingly have no tolerance for anything like this, as the responses on Reddit and Gamers Nexus' original YouTube video have shown.

We've approached LG for comment on these claims, and will update the story when we hear back.

Comment from r/hardware

• Moonshot has launched a new AI model, Kimi K3
• It's surprisingly powerful, with the Chinese AI firm claiming it outguns most US rivals, save for a couple of exceptions
• Kimi K3 is open weight by nature, which poses a further threat to the likes of OpenAI and Anthropic

Moonshot, one of the emerging Chinese AI giants, has just revealed a new AI model which is seemingly up there with the likes of ChatGPT and Claude.

Bloomberg reports that Moonshot's new Kimi K3 model can equal the best that the US has to offer, at least based on the company's own benchmarking. Seemingly it outguns all rival AIs save for Claude Fable 5 (from Anthropic) and GPT-5.6 (from OpenAI).

Kimi K3 is a model with 2.8 trillion parameters, Bloomberg tells us, and Artificial Analysis ranked it ahead of Anthropic's Opus 4.8 on some benchmarks.

Moonshot also claims it beats Chinese rival Z.AI for coding tasks, and overall, the performance of the new model has caught the market by surprise.

Moonshot notes in a blog post that Kimi K3 is the "world's first open 3T-class model, designed for frontier intelligence across long-horizon coding, knowledge work, and reasoning."

Kimi K3 is available to use now. Bloomberg quotes Leonid Mironov, a portfolio manager at Gavekal Capital, as saying: "In my use, it's clearly the best Chinese model ever," noting that it's "brilliant" no less.

Analysis: a weighty threat

This is a threat to the big US players in the AI market for several reasons.

The key difference with Kimi K3 is that it's what's known as an "open weight" model, meaning anyone can grab the model to run it for themselves – from July 27, when the weights are released – without paying anything. It's not the same as open source, though, as while you can get the model, what you don't get to do is peek behind the scenes at how the model was trained (and on what data).

The other caveat is that running Kimi K3 takes some extremely powerful hardware; but nonetheless, for firms with the substantial wherewithal to do that, the pre-trained model is there for the taking at no cost. So, you can imagine how this open weight approach is threatening to the AI behemoths in the US (and this is presumably the point of going this way for the Chinese rival).

What will also be a concern to the likes of OpenAI and Anthropic is that Moonshot is attacking one of the most lucrative aspects of AI, with Kimi K3 being pushed for its coding skills. However, Moonshot is charging a lot more than Chinese rivals for those who want to use it, and in fact, it's priced around Claude Sonnet levels, so on a par with the current cutting-edge (frontier) AI models.

That in itself is a signal of the quality on offer here, and why Kimi K3 has raised quite a few eyebrows. As the competition around AI heats up, there are also concerns about whether that means safeguards will be increasingly overlooked in favor of faster development and progress (which has been a consistent source of worry for many as it is).

Adding to all the controversy are accusations of AI theft leveled by the US State Department at Chinese firms earlier this year, Moonshot included.

Across Europe, “tech sovereignty” is rising up the agenda. For business leaders, however, the implications are practical rather than political. At its core, sovereignty is about control, resilience and trust in the systems that underpin modern operations.

The European Commission’s recently announced technology sovereignty package reflects this shift. With proposals including the Chips Act 2.0, the Cloud and AI Development Act, an EU Open-Source Strategy and a Strategic Roadmap for Digitalisation and AI in Energy, it is intended to strengthen Europe’s digital independence and resilience.

That matters because IT infrastructure is now vital infrastructure. Cloud platforms, connectivity, AI systems and cyber security capabilities are becoming as essential to economic growth and national stability as energy and transport networks.

For years, digital transformation was driven by globalization, scale and efficiency, with organizations prioritizing rapid innovation, cost optimization and access to global technology ecosystems.

But cyber-attacks, regulatory divergence and geopolitical uncertainty have exposed a fundamental reality: efficiency without resilience creates fragility.

From efficiency to resilience

Today, organizations are focused not only on whether systems can withstand cyber-attacks, but whether they can keep operating if a provider, jurisdiction or supply chain becomes unavailable.

Protection and prevention remain essential. But resilience also depends on where systems are hosted, who controls critical infrastructure, how data moves across jurisdictions and whether essential services can be restored quickly during disruption.

Sovereignty is best understood as the ability to continue functioning with confidence when external conditions change. This is particularly relevant for organizations delivering critical services.

Why this matters for the UK

The UK faces many of the same pressures as Europe: rising cyber threats, tighter regulation and rapid AI adoption. But it also has real strengths, including a mature cyber security sector, world-leading professional services expertise, and a strong reputation for governance and innovation.

Those strengths give the UK a significant opportunity to position itself as a trusted partner for secure, resilient digital infrastructure. Realizing it, however, will require continued investment in infrastructure, skills and technology ecosystems.

The UK already has strong foundations. Within Vodafone Business, for example, security services for government and defense customers date back to 1989, underlining the long-term importance of trusted communications and secure operations.

Sovereignty must include AI

The sovereignty conversation is no longer limited to networks, cloud computing infrastructure or cyber security; it now extends to AI itself.

The UK government’s recent £400 million commitment to next-generation AI chips reinforces that ambition and signals a more deliberate push to build sovereign capability in the technologies that will underpin future competitiveness and resilience.

Government investment in sovereign computing capability and broader AI infrastructure also signals recognition that access to advanced computing resources is becoming a strategic national asset.

This matters because AI is rapidly becoming foundational infrastructure. Organizations are embedding it into business operations, cyber security programs, customer engagement and decision-making.

For businesses, sovereignty is not about limiting innovation. It is about ensuring critical capabilities can be developed, governed and accessed in ways that support long-term economic resilience and trust.

Connectivity as critical infrastructure

One of the most important and often overlooked aspects of sovereignty is connectivity. As organizations rely more on AI services, IoT devices and real-time data exchange, networks become the foundation of operational resilience.

If connectivity fails, everything built on it is affected, from customer services and supply chains to communications and core business operations.

That is why investment in secure, resilient, high-capacity networks is central to the sovereignty debate. Without trusted connectivity, digital sovereignty remains theoretical rather than practical.

The formation of VodafoneThree is a significant step in strengthening the UK’s digital backbone. With a commitment to invest £11 billion in next-generation connectivity and an ambition to deliver 99.96% population coverage by 2034, the UK is building infrastructure to support future growth and resilience.

As AI workloads, edge computing, hybrid working and data-intensive applications expand, resilient connectivity becomes a strategic national asset.

This is particularly important for organizations delivering essential services. Today, 77% of UK Blue Light services already run on Vodafone Business networks, underlining the growing importance of trusted connectivity in critical operations.

A strategic moment for the UK

Europe’s emerging sovereignty agenda should not be mistaken for digital isolation. Rather, it reflects a growing recognition that interdependence must be understood, managed and secured.

For security leaders, that means broadening the conversation beyond traditional threat protection to include critical dependencies, digital supply chain resilience and operational continuity. Cyber security is increasingly part of a wider discipline of digital resilience, where security, connectivity, infrastructure and governance converge.

By combining cyber security expertise, growing sovereign AI capabilities, regulatory strengths and continued investment in connectivity, the UK has an opportunity to establish itself as Europe’s trusted security ally.

In the next phase of digital transformation, success will not belong only to those with the most advanced technology, but to those with the most trusted, resilient and transparent foundations.

The sovereignty economy is already taking shape. The question is whether the UK chooses simply to participate in it, or to help define it.

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• Streaming giant Netflix has revealed that 300 movies and shows used generative AI in 2026
• The news was shared in a shareholder letter, obtained by the website Kotaku
• Generative AI was used to “enhance crowds, historical battle sequences, and worldbuilding establishing shots.”

Netflix has recently admitted to using AI tools in a huge number of its movies and shows, with the shocking announcement delivered in its shareholder letter on July 16.

According to Kotaku, which obtained the shareholder letter, Netflix says that AI is now fully integrated into many different projects and is used from the concept stage through pre-visualization, filming, and post-production.

They also revealed that generative AI was mostly used in post-production across the 300 shows and movies using it in 2026.

“We are increasingly leveraging these tools to deliver higher quality output more quickly and at a lower cost than traditional methods,” Netflix said in the shareholder letter. “In some cases, productions would have had to leave out key shots and sequences in the absence of GenAI technology.”

Using The American Experiment as an example, Netflix added that using generative AI tools “enhanced crowds, historical battle sequences, and worldbuilding establishing shots.”

It's not just the best streaming service's shows that are affected either, as AI is also working its way into the app itself, with Netflix explaining that it will use LLMs and AI to “improve title discovery” and “better understand member preferences.”

First they cancel all my favorite shows, now they're using AI

Netflix has made a lot of poor decisions in recent months. Recently, my colleague Rowan Davies criticized the streamer for chasing YouTube content, saying he was worried they don't actually care about the future of their shows and movies.

I'm inclined to agree, too, as the streaming service has a track record of cancelling its shows. Just recently, The Boroughs was canceled after one season, and it's not the first time they've abandoned shows early on.

They have also used an AI-generated voice of the late actor Gene Wilder in a new reality show, which our editor at large, Lance Ulanoff, said was "too far".

This, teamed with the most recent AI announcement, has filled me with despair, and I'm worried a lot of my favorite Netflix shows aren't getting the love they deserve.

This decision will no doubt divide fans, but this is the kind of thing that's going to make me turn away from Netflix and prioritize other streaming services instead. It feels like Netflix is falling out of love with its shows, and I'm starting to do the same.