OpenAI has unveiled a much anticipated AI model which the firm has dubbed ‘GPT-6 Astra’. While the model has improved significantly across benchmark testing and brings a host of new business features, there is still a dark cloud looming over the new model.

Off the back of OpenAI’s accidental hack of Hugging Face and the company’s subsequent efforts to improve how AI agents behave and interact, numerous cybersecurity experts have raised concerns about the model’s new ‘recurrent depth’ reasoning capabilities.

This new reasoning architecture allows the model to consider a problem multiple times before taking an action, compared to the standard chain-of-thought reasoning used in previous models.

Why the concern about recurrent depth reasoning?

This new level of reasoning apparently offers improved performance. OpenAI also says it has fixed its models' abilities to circumvent boundaries when performing tests by monitoring the models reasoning and ensuring the model stays aligned within the scope of its task.

During Astra’s launch event, OpenAI chief scientist Jakub Pachocki said: “We will not accept degradation in our ability to monitor model alignment beyond a certain level. We will withhold scaling until we can regain enough confidence.”

But numerous experts believe that the lessons of the Hugging Face incident have not yet been learned, and the model has been released without adequate testing on Astra’s reasoning and monitoring.

After all, no one thought one of OpenAI’s models could set up a hidden internet-connected messaging board that allowed AI agents to influence each other's behavior.

But with Astra being released into the real world, the lessons may have to be learned on the fly.

Expert perspectives on OpenAI Astra release

• James Blake, VP of Global Cyber Resiliency Strategy at Cohesity:

The launch of Astra is raising questions again around the safety of Frontier AI. Instead of simply asking whether a model is "safe", organisations now need to ask whether it remains safe across millions of different situations, prompts and interactions. Cyber resilience has traditionally assumed that systems and threat actors behave deterministically. AI systems don’t.

Advanced models can and will continue to exhibit behaviours that emerge from their optimisation process rather than from explicit programming. We have to move beyond thinking about AI as just another software tool and find ways to ensure these systems remain observable, auditable and governable throughout their lifecycle.

The most important question we’ll need to answer in future is one of liability. Suppose an AI system autonomously develops a strategy that causes financial loss, leaks confidential information or violates regulation. Who is responsible? The developer that trained the model? The cloud provider operating the infrastructure? Currently the answer is surprisingly unclear. It’s not just about what AI can do: it’s about who is accountable when it does something nobody expected.

• Oleksandr Yaremchuk, Co-Founder & CTO at Manifold Security:

OpenAI is calling Astra its most aligned model yet, even as its chief scientist admits monitorability is getting harder as models get more capable. Evidently, Astra hides its reasoning in the majority of tested cases, and some successful attacks left no reasoning trace at all. That's the tool many organisations still use, including the labs themselves, for auditing what an agent is doing, and it's getting less reliable with every release.

That matters because Astra isn't staying inside OpenAI's test environment. It's going to run as an agent on employee laptops and in the browser, holding real credentials, inside companies that have no way to watch what it does once it's there. A model that explains itself less isn't more aligned, it's just harder to catch when it goes wrong.

Labs can keep debating what these models say or refuse to say. Security teams need to stop relying on that and start monitoring what agents actually do at runtime, with the ability to shut one down mid-action. That's the only oversight left that still works once the reasoning goes quiet.

• Kristin Lowery, Field CISO at Optiv:

For boards and executive leaders, the emergence of OpenAI’s Astra model highlights a broader reality: AI is no longer just a productivity issue; it is a risk management issue.

Just as organizations established governance for cloud adoption and digital transformation, they now need clear policies, strong oversight, and accountability for AI use.

The question is not whether AI will become more capable — it will. The real challenge is whether organizations can strengthen their governance, security controls, and workforce readiness quickly enough to keep pace.

• Patricia Titus, Field CISO at Abnormal AI:

OpenAI crossing this threshold deserves attention. Credit where it's due, they're handling it responsibly by restricting Astra's advanced cyber capability to a small coalition rather than releasing it broadly.But this isn't one company's problem to contain.

Once a model can find and exploit unknown flaws without a human in the loop, that capability doesn't stay exclusive for long. Open-weight and modified models typically trail the frontier by only months, and that's the reality defenders have to plan around now.

Static, signature-based defences were built for attacks that repeat. They weren't built for an adversary that generates a new one every time. Defenders need the same shift, systems that learn what normal looks like for every identity, human, machine, or AI agent, and flag and contain the moment something deviates, at machine speed.

The window to build that is open now. It won't stay that way once this capability is common instead of rare.

• Raghu Nandakumara, VP of Industry Strategy at Illumio:

With the Astra announcement, OpenAI is doubling down on monitoring the model's own behaviour – a response to the model "breakouts" seen over the past few months.

When Anthropic announced Claude Mythos Preview, the core concern was the model falling into the wrong hands. OpenAI's answer goes further adding guardrails around the model's own reasoning and actions, regardless of the user's intent. The goal is to catch a model going rogue mid-task, not just stop it being misused at the outset.

The rest of this announcement can be summarised as ‘we have a new frontier model, and it’s more capable than the last one’.

• Nvidia's NVHBM moves the memory controller off the accelerator die and into the HBM stack's base die, allowing it to claim a potential 30% more bandwidth, 15% lower HBM power, and 25% more compute area versus standard HBM4E
• Nvidia's NVHBM performance comparisons are linked to HBM4E, which is still in the sampling stage, with a launch expected sometime in 2027
• Access to the custom solution is gated behind NVLink Fusion; Amazon's Annapurna Labs is the only named partner so far and has not said which GPU uses that memory

On August 26, Nvidia extended its NVLink Fusion program with NVHBM, a custom high-bandwidth memory architecture that the company says delivers up to 30% more memory bandwidth per stack, 15% lower HBM power consumption, and up to 25% more usable area on the accelerator die.

The performance was measured against standard HBM4E samples even as the memory is expected to ship in volume some time in 2027.

Amazon's Annapurna Labs is the first named partner for Nvidia's memory advance which aims to address growing performance limitations for frontier-level AI models centering around limited bandwidth.

What makes NVHBM tick?

Nvidia's approach doesn't reinvent how memory is handled; it reimagines where it is managed. Conventional HBM approaches split this between two companies: the memory vendor supplies the DRAM stack and its base die, and the accelerator designer puts the memory controller and physical interface on its own compute die.

JEDEC standardizes the connection between the two at the cost of a very wide, comparatively slow parallel bus. NVHBM dissolves the seam altogether by moving Nvidia's memory controller into the base die of the 3D stack, replacing the standard bus with a narrower, serialized die-to-die link that Nvidia designs and memory makers can build.

Nvidia's technical blog puts the resulting reduction in interface and support area at up to 67% against the JEDEC HBM4E standard, even as it improves key power consumption, memory bandwidth, and usable area.

The underlying tech is not completely new, however: Marvell announced the same basic idea in December 2024, naming Micron, Samsung, and SK hynix as collaborators while claiming up to 25% more compute area, 33% greater memory capacity, and a 70% reduction in memory interface power, figures that come close to what Nvidia is currently projecting. Counterpoint Research analyst Neil Shah put it plainly: "The technology is not new. The distribution is."

Even as HBM4E remains elusive, as it is not in mass production just yet, with Samsung shipping the first HBM4E samples in late May 2026 and SK hynix pulling its own sampling forward to around June, NVHBM is even more elusive. It is a building block gated behind NVLink Fusion, Nvidia's program for connecting third-party accelerators to its rack-scale platform, and access runs through that program's partner list.

Amazon's Annapurna Labs is the first named participant, with Nvidia's blog saying Annapurna will support NVLink Fusion with Trainium4 without explicitly mentioning NVHBM.

For now, NVHBM might be the future, but it may arrive well after HBM4E is already being deployed en masse. Nvidia's own technical blog describes NVHBM as built on the same technology the company will use for future GPUs, without specifying the first generation that would support it natively.

• Gemini AI gave three hikers bad advice about a mountain ascent
• The trip took much longer than Gemini had said
• US officials and volunteers eventually rescued the stranded group

A cautionary tale from California if you often find yourself using AI bots to plan your outdoor adventures: a gang of climbers had to be rescued from Mount Shasta in Siskiyou County after Google Gemini gave them inaccurate information about the scale of the challenge ahead of them.

As reported by the Siskiyou County Sheriff's Office (via Engadget), Google's AI breezily told the three inexperienced hikers that they should prepare for an eight-hour trip to the peak, and to pack simple carbohydrates instead of fats because fats "take too long to digest".

More than 16 hours and a serious knee injury later, off course on their return trip and in the dark, the group was no doubt regretting the decision to seek advice from a disembodied AI rather than a human being that had actually been up Mount Shasta.

Thankfully, the hikers were recovered the next morning by a team made up of staff from the Siskiyou County Sheriff's Office, rescue volunteers, and the US Forest Service climbing rangers. The unlucky trio did have to spend a night in a makeshift camp in the Mud Creek Canyon part of the mountain, however.

A lesson learned

"It is always advisable to call the local USFS Mount Shasta Ranger station ahead of your trip to ensure you have the most accurate information, and to never rely solely on AI for your trip planning," read a statement from the Sheriff's Office, which also emphasized the importance of the 12pm turnaround time for those attempting an ascent.

The hikers had set off at 3am expecting to reach the peak by 11am, but they didn't actually make it all the way up until 7pm. On the descent in the dark, they went off-route into the canyon, with one group member sustaining an injury, and called for help.

While one hiker did have AllTrails on their phone, that device died on the trip. “We relied too much on AI rather than our own critical thinking," the climbers told rangers after the ordeal, as reported by SFGate.

As useful as AI can undoubtedly be when searching the web for information, bots like Gemini have never been up mountains, treated injuries, managed finances, or changed electrical wiring. For any important queries, you should always double-check linked sources and other references to verify the information you're getting back.

Chips from Taiwan, oil from the Persian Gulf, and cars from Germany – what was long taken for granted is now being called into question all over the world. Where markets and expertise are concentrated among a few providers, enormous economies of scale arise.

At the same time, however, dependencies also grow – often with risks that only become apparent when supplies fail, prices rise, demand evolves, policies change, or individual players begin to monopolize the market.

Take AI, for example – according to research institute, Epoch AI, only two notable AI models have emerged in Europe over the past twenty years compared to 50 in the US and 30 in China. The technology has moved so quickly that the repercussions of this imbalance are only just beginning to be felt in European markets.

Is Europe becoming too dependent?

From office applications to storage and computing resources to software, what providers deliver from their data centers in a highly scalable, maximally available, and extremely innovative manner does not, in most cases, originate from the European continent.

According to Bitkom’s Cloud Report 2026, roughly 85% of German companies consider Germany to be too dependent on US providers, and almost two thirds of local users (64%) feel compelled to review their own IT strategy due to US government policy. Countries like France are already taking action, with the French government announcing plans to replace Windows with Linux on its government computers.

At the same time, the national health insurance fund has already migrated 80,000 employees from Microsoft Teams, Zoom, and Dropbox to its own alternatives.

Whether in Germany or France, the question of how digitally dependent companies, governments, and societies are on individual suppliers is being asked throughout Europe. And it has been for some time. While Gaia-X focused on common standards for sovereign cloud computing, the EU is now pursuing a more pragmatic approach.

The Cloud Sovereignty Framework (CSF), unveiled in October 2025, aims to make digital sovereignty measurable. To achieve this, it builds on initiatives such as Gaia-X as well as regulations like DORA and NIS2, with the ultimate goal of being able to prioritize compliant, low-risk providers in public procurement.

The sovereignty metric score

Controlling and regionally consolidating one’s own digital traffic flows. That’s quite a task in a world where AI superpowers are evolving overseas. With regard to IT supply chains, the framework favors providers who operate their environments in a transparent, resilient, and sufficiently sovereign manner to ensure that systems are protected from external influences or the interests of third parties.

This means, for example, that data paths must be traceable and controllable at the regional level to ensure they remain within the EU legal framework. In other words, there is no sovereign cloud without an interconnection strategy. After all, Internet Exchanges (IXs) already play a key role when it comes to processing bits and bytes locally. Wherever companies, networks, and clouds connect at IXs, they exchange information along paths that can be precisely defined geographically.

Although the EU does not explicitly make IXs a scoring criterion, it’s heavily implied. For example, the CSF also evaluates cloud services based on supply chain-related legal, operational, and technological objectives. This concept is also reflected in the NIS2 Directive and argues in favor of more decentralized and resilient backbones, such as mutually secured interconnection platforms, such as IXs.

In practical terms, this means that any IT landscape is only as secure as the individual elements that comprise it. So, if all partners design the components they provide to others with multiple layers of redundancy, the overall system becomes not only more reliable but also more independent for everyone.

Distributed and provider-neutral IXs have already made this principle their guiding philosophy: to allow data to be exchanged with high availability, security, and control – between all companies, partners, and clouds within the digital ecosystem.

Europe at a crossroads

Technical issues can usually be resolved easily, but legal ones often cannot. Therefore, the CSF is unlikely to revolutionize the European cloud market overnight, but it can steer public demand in a targeted manner. As a result, hybrid, multi-cloud, and on-premises approaches are becoming more important, and IT is becoming infinitely more complex.

While unravelling this complexity will take additional effort, the pay-off of rearchitecting networks around neutral IXs will be a two for one: not only will it reduce long-term lock-in risks but will also reduce the threat of having to pay significantly higher prices later on when things inevitably change. Europe must now consciously accept this and align and strengthen its own provider market accordingly.

Just as operations would come to a standstill for 46% of companies in a prolonged outage, without the cloud, there can be no functional public administration – and no independent, sovereign, and thus capable state.

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When businesses and governments delegate operational tasks to AI agents, the discussion tends to start with capability. Can AI do what a person can do, such as reconcile accounts, negotiate with suppliers, sign a document or submit a public filing? And can it do so quickly and cheaply enough to justify putting it to work?

These questions matter, but performance is only part of the picture. Once an agent acts independently, different questions emerge; who authorized it, whose interests does it represent, where does its authority end and, perhaps most importantly, who is responsible when something goes wrong?

This is the accountability gap in agentic AI, and a more advanced model will not close it. An agent might be remarkably capable, yet still act with potentially unwanted or possibly even unlawful consequences.

The only way to bridge that gap is through a system with clear permission guardrails and an audit trail that links each agentic action to the person that authorized it, and can tell the difference between a person and a machine in the first place.

Ordinary error at extraordinary scale

When people think about AI risk, they tend to picture a machine breaking free of human control and pursuing goals of its own. The more immediate risk is less dramatic; a system simply getting an ordinary task wrong, again and again, with no clear line of responsibility and accountability.

A person might send an invoice to the wrong customer. An agent connected to the full customer database could send the same invoice to thousands. It might change the wrong field and then repeat the change across an entire market, not because it has become malicious, but because it has misunderstood an instruction and had the access to act on that.

The speed that makes an agent valuable can save a business hundreds of hours, but can also turn a small and recoverable mistake into a serious operational problem.

When an agent drafts an email for someone to review, this ambiguity may not matter very much because the person remains between the software and the outside world.

But when it can send the message, submit a declaration or move money, it matters a great deal. Imagine asking an assistant to book one flight and, instead of giving them a company card with an appropriate spending limit, handing over your passport, bank card and office keys. You may trust the assistant completely, but the permissions bear no sensible relation to the task.

Define the boundaries, solve the problem

AI agents cannot hold citizenship or legal personhood, but they can be linked to natural persons in legally sound ways. For example, a reliable identifier could trace the agent's activity back to the person responsible for deploying it. Combined with clear permissions and mandates that make the terms of its authority visible and auditable, this would allow AI agents to carry out a range of operations and transactions in a more controlled way.

These controls could be very precise. An agent might be allowed to view financial information but not change it, prepare a payment but not approve it or order from named suppliers without exceeding a fixed amount. Its permissions might last for one transaction, one working day or the duration of a particular contract, after which it would expire without relying on someone to remember to switch it off.

Withdrawing that authority should be equally straightforward. If a company ends its relationship with an agent, changes supplier, or discovers that something is wrong, it should be able to cancel the it’s access without changing the credentials of the person behind it.

The agent’s legally anchored mandate in the various realms of these controls has a longer shelf life than any individual AI model, which will be upgraded, replaced and combined over time, often without the customer seeing much more than a new version number.

Putting the idea into practice

Estonia has decided to work through these questions now. In June, the government began developing a state-backed registration system for AI agents that will be available to both Estonian citizens and the country’s global community of entrepreneurs. The goal is to explore how Estonia’s fully digital state, where all public services are available online, can be opened to the emerging wave of AI-powered businesses.

Under the current proposal, a person would receive a registered numeric identifier that could be linked to one or more agents acting on their behalf. Operations performed by an AI agent are treated as operations performed by a machine, for which the natural person, the identity anchor, is liable. The AI agent can only operate within the limits of the authorizations granted to it.

For transparency, private service providers also need to know whether an operation was performed by a person or by a machine acting on their behalf. This allows them to apply appropriate controls, restrictions and risk models.

Built for delegation

The approach draws on more than two decades of digital government. Estonia’s system of personal identifiers and digital identities already allows people to act on behalf of others with clearly defined permissions.

An accountant can file a client’s taxes, an adult can manage an elderly parent’s affairs through the health portal, and several people can use a corporate bank account with individual rights and limits. In principle, the same framework could support mandates for AI agents.

This would give users greater confidence that safeguards are in place to protect the responsible person from foreseeable errors by an AI agent. It would also help close the accountability gap as agentic activity increases.

Before AI agents are given executive powers, they need a technically and legally binding framework that records who they represent, what they can do and who remains responsible for their actions.

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Agentic commerce has cleared its first hurdle, that of consumer demand. ChatGPT handles an estimated 50m shopping queries a day, and AI-referred traffic to US retail sites rose by nearly 400% in the first quarter of 2026. The unresolved question is where that demand settles once a purchase is made. The early evidence points to the scaled, branded marketplace.

According to Similarweb, marketplaces already receive more AI-referred traffic than any other retail category, some 47m visits in the year to May 2026, and continue to grow faster than their scale would predict.

The mechanism is a mutually reinforcing relationship between consumer preference and LLM recommendation, and its logic points towards a concentration of agent-led commerce on large platforms, with material consequences for merchants and their payment suppliers.

The pattern in AI referral traffic

Revealed consumer preference is a much more reliable guide to market development than stated intent, and the traffic data for agentic commerce is gradually building a picture of how consumers actually use agentic commerce rather than just speculation. Similarweb’s recent analysis ranks marketplaces first among retail categories for AI-referred visits, ahead of news, travel and finance.

Two features are notable. The category commands the largest absolute volume of click-through traffic, and it continues to expand at more than 230% a year despite that already substantial base. The explanation lies in how large language models (LLMs) assemble their recommended shortlists.

Faced with an open-ended shopping request, a model favors platforms with broad catalogues, well-structured product data and extensive reviews. Large marketplaces can provide these in abundance by providing a single source that can satisfy the widest range of queries with the strongest supporting evidence.

Testing the democratization thesis

A contrary hypothesis has been influential. Shopify’s president, Harley Finkelstein, has characterized agentic commerce as ‘fundamentally merit-based’, arguing that it would widen discovery to the long tail of smaller merchants rather than reinforce the incumbents that dominate conventional search.

The demand is not in doubt: Shopify reports that AI-referred traffic to its stores has risen roughly eightfold in a year. The distribution of that demand, however, is less even than the thesis implies.

Marketplaces, not independent sellers, are absorbing the largest share of AI referrals, and the agentic platforms attempting broad merchant onboarding have found it difficult. OpenAI has scaled back open instant checkout in favor of a smaller set of curated partners, citing the complexity of onboarding and verifying merchants at scale.

The emerging structure is therefore symbiotic. Marketplaces supply a large, trusted brand within which a long tail of smaller sellers operate. The individual merchant still reaches the agent-guided shopper, but within a brand and trust environment the consumer and the model already recognize.

This arrangement addresses the consumer trust problem that the open web does not, since the platform has verified the seller before the listing appears, and it retains the operational layer, fulfilment, dispute resolution and logistics, that an agent appear unwilling to take responsibility for. The long tail is not excluded so much as commercially enabled by marketplaces.

A self-reinforcing loop

Beneath the traffic figures lies another dynamic that has received relatively little analysis. Consumer preference and algorithmic recommendation reinforce one another. Consumers already trust marketplaces in a traditional ecommerce use case and select them when an agent presents the option.

Learning models, optimized on the recommendations consumers select, are more likely to surface marketplaces more frequently, reinforcing the habit and shaping the next recommendation. Over successive interactions the marketplace becomes both the more probable choice for the shopper and the more probable output of the model.

New international survey of agentic shoppers indicates that these outcomes are already in consumers’ minds. Consumers tell us they are looking for an independent (i.e. non-seller embedded) agent to compile the shortlist, followed by the reassurance of a familiar brand and visible reviews, a combination that large LLM brands and marketplaces are well placed to provide.

The research also showed that 74% of agentic shoppers in the US and Europe favor an independent assistant capable of comparing across sellers, against 10% who prefer one embedded within a single seller. Brand recognition is also important to 89% of respondents, and customer reviews to 93% when deciding which recommendation to pick from an agent’s shortlist. Most significantly, 93% expect to use retail marketplaces as much as or more than before as the adoption of agentic commerce grows.

Why marketplaces hold the advantage

Consumer preference is only part of the explanation. The economics facing the agent point in the same direction, for three reasons.

The first is remuneration. Most large marketplaces operate established affiliate and commission schemes, providing a ready mechanism to reward an agent for delivering a customer. As agents assume the role once played by referral sites and publishers, the marketplace platforms with mature payout infrastructure are the simplest to monetize.

The second is conversion. Marketplaces have invested heavily in low-friction checkout, so an agent compensated on completed sales rather than referrals has reason to direct shoppers where a purchase is most likely to conclude, which compounds the commercial pull towards the platform.

The third is payment. Agentic commerce already leans heavily on credentials held on file. The dominant standard, Google’s Universal Commerce Protocol (UCP), only transacts against tokenized credentials held on file rather than card details entered by hand, and marketplaces already hold card credentials for hundreds of millions of shoppers.

The advantage is not only the agent’s. Agentic commerce is still nascent: technical standards are still emerging, liability and commercial terms are unresolved, and fraud is migrating to the channel. Absorbing that uncertainty requires capital, specialist staff and an appetite for risk.

Large marketplaces can invest through the ambiguity, adopt and switch between emerging protocols, and operate the fraud and dispute controls the channel demands. For most mid-sized and smaller merchants, the same burden is disproportionate to their means and their tolerance for risk, a further reason to participate through a platform rather than to carry the complexity alone.

An acceleration, not a departure

The movement towards marketplaces predates agentic commerce, and there is a long list of large retailers that have already built or joined them. Kingfisher opened a third-party marketplace at B&Q in 2022, and such sales now represent around 40% of B&Q’s online revenues.

Tesco launched Tesco Marketplace in June 2024 and listed more than 300,000 third-party lines within eight months. Target has told investors it intends to expand its invitation-only Target Plus marketplace from more than $1bn to more than $5bn in gross merchandise value within five years. In each case a trusted retail brand hosts a long tail of smaller sellers, the configuration the referral data now rewards.

Implications for merchants and acquirers

The likely consequence is that agentic commerce accelerates this migration, and that the reinforcing loop strengthens rather than dilutes the move towards marketplaces. The effect will likely divide according to the scale and strength of a merchant’s brand.

Larger, well-known retailers may choose to become marketplaces in their own right, hosting third-party sellers beneath their own name, as Kingfisher, Tesco and Target have done.

For smaller merchants the choice is more likely to be additive: most will want to retain a direct presence while also selling through one or more established banners, drawing on the reputation of the host and a position within the recommendation loop.

This is the next stage of a long-standing movement of smaller firms into marketplace distribution rather than a break from it.

Direct commerce will not disappear. Merchants with strong brands, machine-readable reviews and genuine loyalty can still secure the agent’s shortlist independently, and Salesforce reports that retailers operating their own shopping agents have grown sales roughly 60% faster than those without.

For the broad middle of the market, however, and particularly in impersonation-prone categories such as fashion and footwear where consumers and their agents may have concerns about seller trust, a marketplace presence may become a competitive requirement.

For acquirers, the shift accelerates a migration of volume that is already underway by moving spend onto a smaller number of large platforms. Meanwhile the direct-merchant portfolio shrinks and may be exposed to elevated fraud risk as fraudsters exploit the weakest validation layer.

Acquirers with strong exposure to the marketplace segment are well placed, while the rest of the market faces yet another reason to move into the platform economy.

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Most manufacturers didn't need a business case to understand the cost of reactive maintenance. They'd been absorbing it for years – unplanned stops, recovery overtime, expedited parts, the slow erosion of scheduling confidence and customer trust. What shifted was the available answer. AI in maintenance moved from speculative to deployable fast enough that the investment case made itself.

Industry commitment is real, but something isn't converting at the rate anyone projected. Our recent research found that approximately 78% of all reported barriers to progress are workforce-related. Access to AI moved faster than the ability to use it consistently. That gap is now the constraint.

The first wave proved the tools, not the model

Manufacturers are investing in AI to improve productivity, yet many are still carrying the behaviors that predictive maintenance was meant to reduce.

The research shows predictive maintenance adoption has more than doubled year over year, while reactive maintenance remained flat. Proactive maintenance has also lost ground year over year. That combination is the point: new methods are entering the plant, but they’re not fully replacing the old ones.

This shouldn’t be read as failure. Plants moved for practical reasons, and early pilots gave teams useful proof. A model can work well on a known asset with a focused team around it. The harder test comes when that model has to support decisions across shifts, sites, and mixed levels of experience.

That’s where the first wave exposed the next problem. Technology can move quickly into the budget. Work habits, trust, decision rights, and frontline confidence take longer to change.

AI investment grows up

The budget data confirms that leaders aren’t walking away from AI; they’re becoming more selective about where it has to prove itself. Investment is moving away from exploratory AI and toward operational priorities, including cybersecurity, data management, Generative AI and Industrial AI.

That shift reflects a more practical view of digital maturity. Leaders are trying to make AI work where the cost of delay, downtime, and poor data shows up quickly.

It also reframes expectations around Industry 5.0. As industrial technology moves from Industry 4.0’s automation-led model toward a more human-in-the-loop approach, leaders appear to be recalibrating the timeline, with 40% now expecting a one- to four-year journey.

Where the model stops and the supervisor starts

It’s easy to misread the 78% workforce-barrier figure as a labor shortage story – headcount, hiring, pipeline. However, the truth of the matter is that businesses are facing a lack of expertise, knowledge shortages, skilled labor gaps, and broader workforce capability deficits. Taken together, those four categories describe something harder to fix than a recruitment problem. They describe an organization's capacity to absorb a different way of working.

It's a pattern I hear consistently from customers: the tools are in place, but the teams around them are still catching up. Researchers Cohen and Levinthal gave that capacity a name: absorptive capacity.

In plain English, it’s how quickly a business can recognize useful new knowledge, absorb it into the organization and convert it into practical output. In a maintenance context, that output is a decision made under real operating conditions – by a night-shift supervisor weighing whether a current anomaly justifies an intervention, whether it can wait until the next planned stop, or whether the risk has already crossed a line. A model can flag the anomaly. It can’t make that call.

What predictive maintenance and AI actually demand from the workforce is harder to train than tool proficiency. The UK Government’s AI Skills for the UK Workforce report identifies the gap in concrete terms for advanced manufacturing.

Beyond technical application skills like training AI models or integrating real-time analytics, the report points to the ability to interpret AI outputs, adapt workflows around new insights, and communicate changes to frontline teams. These are key competencies that determine whether an alert changes behavior or gets dismissed.

That’s why the workforce issue can’t sit beside the technology program as a separate HR workstream. It’s part of the system that decides whether investment turns into better execution. Industry 5.0’s timetable will be set by capability on the plant floor.

Investing for predictive, paying for reactive

The commercial consequence is visible in the lag between spending and return. Predictive adoption is rising, more capital is being allocated, yet the reactive baseline hasn’t moved. Many plants are running two modes of operation at once – investing for data-driven execution while still losing time to avoidable firefighting.

That split has a cost. Siemens’ 2024 True Cost of Downtime research puts unplanned downtime losses for the world’s 500 biggest companies at $1.4 trillion annually – 11% of their total revenues. That figure reflects what happens when the shift from reactive to predictive is incomplete. The investment is there, but the daily execution hasn’t fully followed.

When that gap persists, ROI on technology spend arrives slowly and unevenly. Timelines extend. Confidence in delivery weakens.

Where the return is earned

The discipline being applied to tools and platforms now needs to apply to the people and routines around them. For many organizations, that’s where the return is being left on the table.

Leaders should start with the parts of the operation where capability is most fragile. Which assets still depend on one or two experienced technicians to interpret what’s happening? Which work histories are too thin to support the next diagnosis? Which alerts trigger confident action, and which sit in limbo until the right person is on shift?

Those questions reveal whether predictive maintenance has become part of execution or whether it’s still sitting on top of reactive habits.

Capture the know-how that still lives in people’s heads before it walks out the door. Make work histories complete enough to help the next technician. Train operators and maintenance teams to understand what a predictive alert is telling them, what evidence warrants action and when to escalate. Shape workflows so acting on insight becomes the normal path, not a special effort.

Connected reliability supports that execution when it stays close to the work – connecting asset data, maintenance history and frontline judgment so teams can make repeatable decisions across shifts and sites.

Our research backs this up: nearly half of respondents plan to advance connected reliability initiatives within the next 12 months, treating reliability as the practical bridge between near-term operational needs and longer-term ambitions.

The call for manufacturing leaders is straightforward: audit capability with the same seriousness as technology spend. Don’t stop at asking what AI has been deployed. Ask who can act on it, where decisions slow down, what knowledge is undocumented and which workflows still pull teams back into reactive work.

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I have developed a fairly strong allergy to the AI slop filling some corners of the internet. It's not that I haven't seen impressive demonstrations of what AI models can do visually; it's just that the baseline creations made by most people are execrable at best and blandly monotonous when they aren't horrifying or malevolent.

ChatGPT’s new Stickers feature didn't immediately strike me as a chance to change my opinion, but the option to import the results into iMessage and WhatsApp was interesting enough to make it worth experimenting with. I went with a photo of my chihuahua and asked for a few options of stickers based on her. While I am obviously biased about her, the results actually looked like images I would not be ashamed to use in a conversation with someone.

Sticker emotions

ChatGPT’s Stickers tool is in the platform's Image section only on the ChatGPT mobile app. Open the main + menu, pick Images, then click Stickers, which is currently the first option on the long list of templates. ChatGPT has a prompt ready to go, but you can adjust it for different styles or add different emojis to the design. Once the results are ready, the “Add to chat apps” button appears, and ChatGPT can send them directly to your WhatsApp or iMessage sticker library.

The source photograph makes a noticeable difference. Choose one with the animal or person clearly separated from the background, good light on the face, and important features fully visible. A dark dog against a dark couch did not work well. And be sure to mention any details you want to make sure are highlighted in the prompt.

At first I asked for photographic stickers to see how well it did at consistency. While not perfect, the best stickers preserved the details that made my dog recognizable. Her expressions became exaggerated, but she remained the same dog. Even when I wrote some of the prompts for new stickers from scratch, ChatGPT managed to keep my dog looking like a (sillier) version of herself.

Cartoon canine

After testing the more realistic stickers, I tried pushing the feature toward its cartoonier options. The chibi-esque result is fun in its own way. The resemblance became looser, particularly around her coloring and age, but the emotional range improved with the flexibility.

Just specify one illustration style and list any specific reactions you want.ChatGPT may still reinterpret a few physical details, but consistency matters more than perfect accuracy once your dog is wearing sunglasses and wondering whether she is cool yet.

AI slop is still bad, and the way it buries useful information beneath a mountain of disposable and often problematic images is an issue that has to be addressed. But when used for something like personalized WhatsApp stickers, AI tools can be attractive. The best use of generative AI images may be smaller and more personal, not a content factory, but a way to add a little fun to online chat.

• Lenovo announced the ThinkBook 14x Gen 2 at IFA 2026: a 990g notebook with up to a 14-inch 2.8K 120Hz OLED option, Intel Core 7 Series 3 silicon, and a starting price of $999.99 in Q1 2027
• It is the only computing product in an AI-branded launch with no TOPS figure and no Copilot+ claims as part of its published spec sheet
• The laptop does make compromises, skipping Thunderbolt and USB4 connectivity, WiFi 7 at half the channel width of the Arm-based ThinkBook 14 Gen 9 it launches alongside, and an HDMI 2.1 port that caps out at a 4K 60Hz output

Lenovo announced the ThinkBook 14x Gen 2 at Innovation World during IFA 2026: a 990g notebook running Intel Core 7 processors from the Series 3 generation, starting at $999.99 and available for purchase in Q1 2027.

It arrives as part of a series of launches focused on what Lenovo calls "Hybrid AI for Business," but it's the only computing product in the press release that makes no AI claims out of the box.

It offers excellent general-purpose compute capabilities but cuts back in some key connectivity areas that might deter power users.

A performant, lightweight option that cuts back on some key features

The higher-end version of the Lenovo ThinkBook 14x Gen 2 offers a 14-inch 16:10 2.8K OLED running at 120Hz with 100 percent DCI-P3 coverage, 500 nits, and a bent anti-glare finish, which is an excellent offering in a market teeming with cheaper options. For those unwilling to pay the premium it will inevitably command, the ThinkBook 14x also offers a WUXGA LCD alternative with the same 120Hz refresh rate, 100 percent sRGB, 400 nits, and optional touch.

Lenovo's press release says the ThinkBook 14x Gen 2 "delivers premium mobility in an ultra-thin, lightweight design built for everyday productivity," and it is hard to argue otherwise for a laptop that holds its own in a market that is getting pricier thanks to soaring component costs.

It does, however, make significant cuts to get there; the most prominent is the lack of Thunderbolt or USB4 ports, despite sporting a CPU (Intel Core Ultra 7 Series 3) that supports them. It also cuts Wi-Fi support to 160MHz versus 320MHz on the accompanying ThinkBook 14 Gen 9, effectively halving its maximum data throughput.

Despite this drawback, it has two USB-C ports that currently offer USB 3.2 Gen 2 connectivity at 10Gbps, which should suffice for most mainstream users even if the four-Thunderbolt-lane standard of Intel's Core Ultra Series 3 platform controller remains unused.

It also comes with an 18Gbps HDMI 2.1 port (TMDS) that caps maximum output at 4K 60Hz, which is arguably a lesser concern for a laptop that lacks Thunderbolt support, which already limits its potential to be paired with an eGPU and is unlikely to offer much to gamers, who would otherwise be the primary audience for a high refresh rate at a 4K resolution.

Lenovo has not published the AI TOPS capabilities of the as-yet-unnamed Core Ultra 7 Series 3 CPU it is pairing with the ThinkBook 14x Gen 2, even as the ThinkBook 14 Gen 9, announced alongside it features a Snapdragon X2 Plus with an explicitly stated 80 TOPS AI performance by Qualcomm and Copilot+ support out of the box.

One could argue that the AI aspect being skipped is intentional, and the ThinkBook 14x Gen 2 caters to a vastly different crowd, something that is easy to reconcile with, but the compromises in connectivity hit hard for any power user looking for a capable but feature-packed 14-inch laptop in the segment even if Lenovo has indirectly made it abundantly clear about what its target audience is: casual users that need a thin, but performant 14-inch laptop.

• Google Pics lets you generate and precisely edit images using simple text prompts
• It will be integrated directly into Google Docs, Slides and Drive
• Pics is rolling out to Google AI Pro, Ultra and eligible Workspace customers

Google has started rolling out Pics, a new AI-powered image creation and editing tool that looks like the company’s answer to Canva and even Adobe Express.

Canva became enormously popular by enabling ordinary people to produce good-looking graphics without needing professional design skills. Google Pics is attempting something similar, but rather than giving you a huge library of templates and asking you to assemble a design yourself, it puts AI at the center of the entire process.

Pics isn’t as fully featured as Canva, at least from what Google has shown so far. However, it’s included with Google AI Pro and Ultra subscriptions and selected Workspace business plans — and that could prove to be its biggest advantage.

All AI, no templates

Pics starts with a prompt. You describe what you want to create, perhaps a child’s birthday invitation, an event poster or a social media graphic, and the AI generates several versions for you to choose from.

You can then select individual objects or pieces of text and ask Pics to change them without regenerating the entire image. Text can be edited, reformatted or translated while preserving the surrounding design, and images can be cropped for different formats or upscaled to 2K or 4K. You can also request several changes at once and return to an earlier version if the results go wrong.

While some people will dislike how heavily Pics relies on generative AI, others may appreciate how much of the traditional design process it removes. Instead of learning how to use layers, masks and selection tools, you can simply describe the change you want.

Google Pics is powered by Nano Banana, Gemini’s image generation and editing model. You can see how it works in this video:

Why Workspace changes the equation

The biggest difference between Pics and Canva is that Pics isn’t being positioned only as a standalone design app. Google is also integrating its editing tools directly into Docs and Slides, with deeper Drive integration arriving over the coming weeks.

That means you’ll be able to select an image in a presentation or document and edit it using Pics without opening another app. If an image in a document is too dark, for example, you could ask Pics to lighten it while remaining inside Google Docs. Creations can also be shared and edited collaboratively in much the same way as other Workspace files.

That integration is what makes Pics potentially significant. Canva remains a much broader and more mature design platform, complete with templates and tools covering everything from presentations to websites and video. Pics doesn’t need to replace all of that, however. It only needs to make Google users wonder whether opening Canva is still necessary for the everyday graphic they’re trying to create.

Google Pics is rolling out over the coming weeks to Google AI Pro and Ultra subscribers, as well as eligible Workspace business and education customers. I’m still waiting for it to reach my account, but I’ll be putting it through its paces as soon as it does.

There is more technical content available today than any human being could read in a thousand lifetimes. Every topic has a dozen YouTube videos, three Substack posts, a GitHub repo, and a Reddit thread, most created in the last six months and, in many cases, technically accurate.

And yet most of the professionals I talk to say they don't know what to trust. They can't tell what’s important to read first, or which of 10 plausible answers is the one that holds up. That was true before AI, and AI has made it more true.

For most of the history of technical publishing, editing and verification were the same process, and that process was slow and expensive. Getting a book out took years. We found an author, vetted them, had them work with an editor, and checked their claims with technical reviewers.

A lot of that time went into separating what was correct and useful from what was confusing or only sounded right. It was laborious, but it meant a reader could depend on the claims on the page.

The credibility of the book, and of the publisher behind it, mattered as much as the information itself. When the cost of production drops to zero, that credibility becomes worth more, not less. Content is easier to make than ever, but without a transparent process behind it, readers have no idea where the knowledge came from or whether it holds up.

As Jasmine Sun puts it in “The Independent Writer’s Advantage in the Age of AI,” "Trust is not about information and its quality alone. It's about the messenger. It's about who says it and their track record and what they've told me before." A practitioner has confidence in a source because someone she respects has put their reputation on the line for it.

They believe what the author is saying because the publisher has a history of being right and of correcting itself when it isn't, and because the work is attributed and verifiable.

Expertise is alive, and it compounds

The corpus matters, but it's the assurances around it that are hard to replicate, and that comes not just from the people who produce the content but from the people whose judgment vouches for it. Sometimes a creator brings their own credibility with them. Other times, the publisher spots someone unknown and lends them its own.

The art critic Dave Hickey said this about gallery owners in Air Guitar: They gain status from the famous artists they represent and share it with emerging talent who have something to offer but who haven't had the chance to earn a reputation.

Expertise is alive, and it compounds Expertise is a living thing, continuously expanding. Content starts to decay the moment it’s published, because frameworks evolve, libraries deprecate, and yesterday's best practice becomes today's security incident. Keeping expertise alive requires a pipeline of people who stay current and an editorial layer that notices when something has gone stale, and either retires it or calls for a fix.

That pipeline isn't something you switch on when an author has a book to ship. Content sits at the center of our platform, but we think about it in pace layers. Some advice is timeless, some moves but has a long shelf life (some of our books are still in print after nearly 50 years!), and some changes weekly.

We work with experts at each pace layer, capturing what lasts while doing our best to keep pace with an industry that seems to have changed every time we wake up.

We have relationships with hundreds of the best practitioners in the world, and our job is to keep them engaged continuously, with quick takes when something breaks, structured responses when major research drops, and live sessions on emerging topics while they're still emerging.

Trust is earnt

An institution doesn't stamp trust onto content. In a technical community, trust is conferred in both directions. A practitioner earns standing because people who already have standing engage with her work, cite it, argue with it, and build on it. That insight was the whole idea behind PageRank, Google's first great innovation. A page mattered because other pages that mattered linked to it. Reputation works the same way.

The audience isn't just consuming reputation signals; it's generating them. When a senior engineer whose judgment others respect says out loud that something is worth reading, she spends a little of her own credibility; the author gains a little; and everyone watching recalibrates whom to trust next time.

When we put our mark on someone's work, we aren't the sole source of its credibility. We're amplifying a judgment the community is already making and adding our own track record to it. The reader who finds it reliable hands status back to the source.

When the readers are machines

Human practitioners aren't the only ones who need trusted engineering knowledge. The AI systems now sitting in every workflow, the coding and debugging agents and architecture advisors, need it just as badly since most of them are built on scraped web data and documentation that was stale before it was ever indexed. They're fluent, but they're wrong often enough that you can't just take their word for it.

The stakes grow with AI tools increasingly being used to generate not just provably correct types of content like code, which either works or it doesn’t, but persuasive documents in fuzzier areas like hiring, strategy, and so on. Like everyone else leaning on these tools, we are reckoning with the consequences of the ability to talk to a model and get back something that looks smart at a glance.

A few rounds in, the slop is still there. In the last few months, maybe 10 times as many documents have crossed our desks, from new product ideas to strategic plans and proposals. But the ease of generating the text hides the fact that either the model or the person prompting it doesn't actually know what they’re talking about. Knowledge workers need ways to ground their work in insights from human experts, particularly when that work is AI-assisted.

So we’re building tools that let agents draw on our repository of expertise to support their proposed decisions.

Credible sources are particularly important when thinking through and justifying important choices. Our CTO, Andrew Odewahn, describes the shift this way: "18 months ago, it was all about how to get engineers to be more productive, but now it's about how to get organizations to make better decisions. The engineering tasks are moving away from coding output to planning."

For planning tasks like comparing implementation approaches, you need expert-over-your-shoulder guidance for contextual decision-making. You can’t just rely on an LLM's best guess to solve your problem. Trust is foundational because the expertise behind it stays genuine, practical, and human.

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In banking and utilities, regulators define certain systems as essential. An essential system is one whose failure would cause intolerable harm to customers, markets, or public safety. A payment platform in a clearing bank or a SCADA network in a power distributor, for example, carries the highest governance obligations: continuous monitoring, validated change control, and demonstrable resilience.

The policy environment – the accumulated rules across firewalls, cloud controls, and microsegmentation – determines which of those systems can reach each other, which connections are blocked, and which exceptions still apply. Collectively, these rules form the security policy control plane: the governance layer that translates business intent into access decisions across distributed enforcement points.

A misconfigured segmentation rule during a cloud migration can sever a payment service from its settlement platform; a temporary rule granting broad access from a development subnet into production can stay in place months after go-live because no one owns the removal.

Every firewall rule, segmentation policy, and access decision directly affects operational risk, and when the policy environment fails, the critical services it governs fail with it.

That makes the policy environment critical infrastructure in its own right.

Still governed like housekeeping

Despite this, many regulated organizations still manage their policy environments as operational tasks. Rules are added through change requests, and the accumulated result is rarely examined against what was intended. Ownership disperses as leaders change roles, and the reason why a specific rule came into being in the first place can only be found in a change ticket, if anywhere at all.

A CISO who would never accept a payment platform running without continuous monitoring or documented dependencies may accept both being absent from the policy environment that determines whether the platform is reachable. We can think of this as infrastructure-grade consequence with housekeeping-grade governance.

In banking, a policy failure that severs connectivity between settlement systems would constitute the disruption of an important business service. The FCA would take an interest in such a failure, since the loss of such a service could lead to intolerable harm.

In healthcare or energy, the consequences are different but the mechanism is the same: a misconfiguration that permits access from a corporate network into clinical systems in an NHS trust, or into operational technology in a power distributor, creates exposure at the level of essential service delivery. These are not hypothetical risks but the operational consequences of treating critical infrastructure governance as a simple housekeeping task.

Regulatory expectations point the same way

UK regulatory expectations increasingly support the same conclusion: the security policies governing access to important services must be managed with infrastructure-grade discipline. The FCA's operational resilience regime requires regulated firms to identify important business services and demonstrate, on an ongoing basis, that the supporting infrastructure remains within defined impact tolerances.

Ofgem assesses operators of essential services against the NCSC's Cyber Assessment Framework, asking whether defined security outcomes are being achieved on a sustained basis. The Cyber Security and Resilience Bill, expected to become law later this year, will extend similar obligations to data centers, managed service providers, and critical suppliers.

These frameworks are not prescriptive – none of them specifies which firewall rules an organization should have or how its segmentation policies should be configured. What they require is proof: that what the policy environment permits is what was intended, and that the organization can demonstrate this on an ongoing basis rather than reconstruct the evidence for each assessment.

Why the estate cannot meet that standard

Most policy environments were never built to meet that standard. In fact, most policy environments were never consciously or deliberately built at all. Instead, policy tends to accumulate as a by-product of delivery. Every project and migration adds rules, and almost none take any away.

Over time, the policy surface – the full body of rules and access decisions across enforcement layers – grows larger than the group of people who understand it, and the estate reaches a point where it can be operated but not explained.

Across regulated industries like banking, energy, and healthcare, that was sustainable under earlier regulatory regimes: periodic assessment, control-based audit, compliance frameworks that asked whether controls existed rather than whether they were effective. But this is no longer enough. The new standard requires continuous evidence that access is intentional.

The FCA's findings after a year of operational resilience self-assessments illustrate what this looks like in practice. Where regulated firms reported few or no outstanding vulnerabilities in the infrastructure supporting their important business services, the FCA often deemed the evidence too thin to determine whether there really were no vulnerabilities – or whether the vulnerabilities just hadn't been identified.

The lesson applies directly to security policy governance: an organization cannot credibly claim access-related vulnerabilities are controlled without evidence of what its policies permit, how they were tested, and whether weaknesses were remediated.

What infrastructure-grade governance requires

Too often, the response is to reach for more visibility and more documentation. But documentation will only ever capture a point in time; it cannot provide the continuous assurance regulators are coming to expect.

A CISO in a regulated firm needs more than a record of what the policy environment was configured to permit. Configuration and effective access are not the same thing. Security teams need to understand how rules, routes, objects, and enforcement layers interact to determine what can actually communicate.

Meeting the standard means reconciling the two: showing that what the environment permits in practice is still what it was intended to permit, and being able to show it without notice.

We separate where policy intent is defined from where it is enforced. Intent is held and maintained centrally, while enforcement remains distributed across firewalls, cloud controls, and microsegmentation in hybrid, multi-cloud, and multi-vendor environments.

Validation runs continuously against that intent rather than at review points: proposed changes are tested against policy before they reach production, and effective access is assessed on an ongoing basis for unnecessary exposure, inconsistency between enforcement layers, and divergence from business intent. What was permitted and what changed is retained as evidence.

The same CISO who would never accept a payment platform running without continuous monitoring, validated change control, and documented dependencies has to apply that standard to the policy environment that determines whether the platform is reachable.

The FCA, the NCSC's Cyber Assessment Framework, and the Cyber Security and Resilience Bill all point towards the same underlying question: can this organization demonstrate, continuously, that the infrastructure supporting its critical services is governed to the standard those services demand?

Answering it means knowing (and actually knowing – not assuming, not reconstructing at audit) what the policy environment permits at any given moment, and whether what it permits is what was intended.

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• UK legislators propose “kill switch” laws to halt runaway AI, citing critical infrastructure risks
• Lord Clement‑Jones and MP Alex Sobel push amendments and new bills, backed by ControlAI advocacy group
• Similar efforts emerging in US

Sam Altman’s fear-based marketing for AI seems to have backfired, as now multiple legislators in the UK and elsewhere are calling for a “kill switch” law to be introduced.

According to the BBC, Liberal Democrats’ Lord Tim Clement-Jones proposed an amendment to the Cyber Security and Resilience Bill which would see the UK create a “vital safety net” to provide a “democratically accountable means to ‘halt a runaway system before it can compromise our critical national infrastructure’.” The capability would only be used as a last resort, Clement-Jones stressed.

The bill is currently being worked through in the UK Parliament, the BBC said.

Is there reason to worry?

But that’s not the only effort in the UK to put some reigns on AI. Apparently, Labour MP Alex Sobel plans to introduce an AI Security Bill later this month which, according to the BBC, would “effectively halt the development of superintelligent AI” and make the UK the first G7 country to do so.

The bill is supported by a campaign group called ControlAI, a UK-based nonprofit and advocacy organization focused on the risks posed by advanced AI. Its founder and CEO is Andrea Miotti, who previously worked at the AI safety company called Conjecture. Across the pond, US legislators are currently considering an AI Kill Switch Act as well, but the bill is still in very early stages of development.

Ever since the first ChatGPT model that was introduced in 2021, a debate has been raging whether or not AI will be net positive, or net negative, for humanity. While some argue that the discovery rivals the steam machine and that it will transform our lives beyond our wildest dreams, others are fearful of losing jobs, a collapsing economy, and a dystopian future devoid of humanity and emotion.

Marketing campaigns for ChatGPT and, in some measure, Claude, are not helping, either. Both companies have built models focused on cybersecurity which were advertised as “too dangerous” for the general public and instead were only given to a handful of organizations. Despite partial skepticism, many are worried that these models might severely disrupt the security of banking, critical infrastructure, and communications.

Via BBC

Sam Altman does not like smart glasses. Speaking on the Sources Podcast, the OpenAI CEO said he finds it “very uncomfortable talking to people with a camera and a light” and confirmed that he does not wear smart glasses himself. That is a surprisingly firm position considering where the technology industry appears to be heading and the success of products like Meta’s Ray-Ban glasses.

Then again, Altman is working with former Apple design chief Jony Ive on an entire family of AI devices. OpenAI has kept the Ive project deliberately foggy, but Altman has suggested they are making a range of AI hardware. His comments about smart glasses draw one clear boundary, removing the idea of a camera pointed at everyone as a feature.

Smart glasses have visible drawback

Altman does identify a fundamental flaw in smart glasses that has little to do with processors, battery life or AI model choices. Glasses place technology directly between two people looking at each other, and adding a camera turns an ordinary conversation into one where the other person has to wonder whether they are also an unwilling piece of content.

Smartphones established a useful social signal around recording. Someone pointing a phone camera at you is usually fairly conspicuous. Camera glasses erase much of that distinction because their great design achievement is making a recording device look inconspicuous. It's why similar tools have been a staple of James Bond-style spy fiction for decades.

Australia’s eSafety commissioner has already asked manufacturers to make recording indicators impossible to disable and automatically blur faces when informed consent is absent. The regulator cited women being covertly filmed for social content and rejected the idea that telling customers to behave responsibly amounts to a safety policy. The backlash reflects a public tired of paying the social price for somebody else’s convenience.

Important exceptions to the argument against camera glasses exist. The same visual awareness that creates privacy problems can be genuinely transformative for blind and low-vision users, something Australia’s regulator explicitly acknowledged. That makes an outright rejection of the technology too simplistic, but it also strengthens the case that camera glasses may be better suited to particular needs rather than as the default AI hardware.

Wearable, not watchable

Altman’s comments about AI hardware point to an all-encompassing strategy of making devices for tables, pockets, and "on your body,” and that OpenAI will take time to launch all of them. OpenAI has previously described its work with Ive as an attempt to move beyond traditional interfaces. So not just a miniature smartphone strapped to your face.

An audio-first wearable suddenly makes far more sense. An earpiece, pendant, clip or other discreet device could provide persistent access to an AI assistant without placing a camera directly in the sightline of everyone the wearer encounters. It could listen when summoned and respond quietly like wireless earbuds or smartwatches.

Some may prefer the camera and the data an AI assistant gains from it about the physical world. And of course, putting microphones on wearables raises privacy questions just fine without cameras. Making the device unobtrusive yet observant is the trick Ive and Altman have to perform lest it face the same social opprobrium as the AI smart glasses.

Ive built his reputation around products whose complexity receded behind their physical design. But AI assistants will know and hear far more than an iPod ever did. A successful AI wearable cannot merely make advanced computing invisible to its owner. It must make its intentions legible to everyone else.

Meta is betting that the camera can become socially acceptable because they are built into discreet smart glasses; OpenAI may instead bet on a successful AI wearable that won't potentially creep out everyone you pass on the street. The best OpenAI wearable feature may in fact be an absence, and "no camera" its most enticing selling point.

Organizations are investing heavily in artificial intelligence, but technology alone isn’t going to determine success. The businesses that unlock AI's full potential will be those that give governance the same attention as innovation.

In the rush to deploy AI tools, too many organizations are treating governance as something that can be addressed later. AI introduces a level of speed, autonomy and organizational impact that traditional governance frameworks were never designed to manage. Businesses that fail to rethink governance now risk increasing operational risk, slowing future adoption and undermining the value of their investments.

Organizations need to shift their mindset and recognize that implementing AI requires fundamental transformation in how decisions are made. With governance, accountability and transparency to be embedded from the outset, not pulled together after deployment.

AI is transforming organizations, not just technology

Unlike previous technology transformations, AI is influencing how decisions are made across almost every part of the business. From customer service and finance to human resources and operations, AI is becoming embedded in day-to-day processes and increasingly making recommendations, or decisions, with minimal human intervention.

As AI becomes more deeply integrated into business operations, responsibility can no longer sit solely with technology teams. Legal, privacy, risk, compliance and business leaders all have a role to play in ensuring AI is implemented responsibly and consistently.

Successful AI adoption is therefore as much an organizational and cultural shift as it is a technology transformation. Businesses that continue to treat AI as a standalone IT initiative risk fragmented ownership, inconsistent governance and missed opportunities to scale AI effectively.

Traditional governance wasn't built for AI

Many organizations still operate with a "deploy first, govern later" mindset, believing governance can be introduced once AI is established. The reality is that governance becomes significantly harder once AI is embedded across business processes.

Traditional governance models were built for a much slower pace of technology adoption. AI changes that equation. New tools, models and autonomous agents can be introduced into workflows in hours, while governance often still relies on manual reviews, siloed assessments and reactive oversight.

This creates a widening gap between AI innovation and organizational readiness. At the same time, AI is creating entirely new data use cases, while regulatory expectations continue to evolve. AI's use of data can also be dynamic and unpredictable, meaning documented controls are no longer sufficient when outcomes can't be predetermined.

Organizations need AI-ready governance – governance that evolves alongside AI through automated, collaborative assessments, programmatic controls embedded at the data layer, and continuous monitoring of risk across the business. As AI operates 24/7, governance must provide ongoing visibility into an organization's risk posture, rather than relying on point-in-time reviews.

The result is a growing disconnect between AI adoption and governance maturity. While organizations are investing heavily in AI, many have yet to modernize the governance, operating models and cross-functional accountability needed to support it. Closing this gap requires governance that operates at AI speed, enabling organizations to innovate with confidence while managing risk and maintaining trust.

Building the right foundations

One of the biggest misconceptions about AI governance is that it slows innovation. In reality, strong governance is what gives organizations the confidence to innovate, enabling them to deploy, scale and adapt AI responsibly.

The real shift isn’t purely technological or organization, it’s the combination of both. While AI is driving business transformation, organizations also need the right technical foundations to support it. AI can no longer be viewed as a technology initiative owned solely by IT.

Successfully scaling AI requires business leaders, risk teams and IT to work together, with IT embedding programmatic guardrails and controls that reflect business requirements. Without this partnership, organizational governance alone won't be able to keep pace with the speed and complexity of AI.

The organizations that gain the greatest competitive advantage won't necessarily be those that adopt AI first. They'll be the ones that recognize AI success depends as much on leadership, governance and organizational readiness as it does on the technology itself.

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When a critical vulnerability alert lands in a traditional IT environment, it’s rarely a cause for panic regarding the operational continuity of the business. The affected laptops, servers, and applications can be identified quickly, and a good team can catalogue and patch them within hours if it’s urgent. The priorities are clear, and there’s very little guesswork involved.

Now picture the same alert landing across a hospital's imaging equipment, a factory floor's control systems, or a building's HVAC network. These cyber-physical systems (CPS), the connected devices that run physical operations rather than just processing data, sit at the sharp end of IT and OT (operational technology) convergence.

But unlike traditional IT assets, confirming whether that alert even applies to a specific device can take days, and often ends in a guess rather than an answer.

While this kind of uncertainty would be considered a failure of basic hygiene, for cyber-physical systems, it’s unfortunately much more often the norm.

So why is this such a widespread problem for CPS, and how can security teams get these vital assets back in line with their IT network?

Bad visibility into cyber-physical systems is worryingly widespread

The inability to manage incoming vulnerabilities for CPS isn’t an outlier or worst-case scenario, which is especially concerning when these assets are at the heart of critical infrastructure like energy and healthcare.

The issue comes down to the product codes that help networks identify what CPS is in the environment, which is an essential part of identifying and applying security patches.

Across a dataset of 17 million cyber-physical assets, our research found that 88% failed to transmit an exact product code, and 76% sent a code that didn't match the vendor's own record.

It’s a side effect of the way these systems were initially designed and later integrated into modern IT environments. Programmable logic controllers (PLCs), medical scanners, and industrial sensors were engineered for decades of physical reliability, not for tidy digital labeling. Network identification was rarely part of the design brief, so the same device can report itself differently depending on which protocol or integration is asking.

We found a similar state of affairs when it comes to the operating systems behind the physical hardware. In our research, 41% of devices have no OS version available, and 24% have no OS name at all.

Without these details, matching a device to a known vulnerability stops being a quick database lookup or automated process, and becomes a guessing game or painstaking manual search.

Added to this, CVE advisories, the industry's standard mechanism for tracking vulnerabilities, are compiled from this same patchy vendor data. An official advisory can be just as incomplete as the network it's meant to protect.

Translating product code chaos into boardroom risk

This kind of blind spot adds another layer of concern to a leadership that is already anxious about threat visibility. Among 1,100 security leaders surveyed globally, 44% named understanding their organization's risk exposure as one of their biggest operational concerns, more than compliance pressure or budget constraints.

A further 45% said they were struggling to reduce cyber risk to their most important assets and processes, yet the connection between that struggle and an unreliable asset inventory is often missed entirely. Leadership sees the symptom, a rising sense that risk is unmanageable, without ever seeing the cause sitting underneath it.

This is where security and the business can end up talking past each other. Security teams that start describing the problem in terms of missing product codes and inconsistent naming conventions won’t get far.

Business leaders hear none of that; they hear only that risk cannot be quantified with confidence. Until those two conversations are connected, every risk register that a CISO presents upward carries an asterisk that nobody in the room can see.

Context is key to closing the gap

Resolving this issue starts with a shift in what visibility means. Knowing a device exists on the network is only half of the job. Knowing what it does, what process depends on it, and what happens if it's compromised is what actually makes a risk register useful.

Achieving this shift at scale requires specialized tools to manage the often eclectic and proprietary nature of CPS assets, and an automated approach to cope with the scale.

In one example, applying AI-driven mapping techniques to an OEM's device catalogue lifted product code identification from 4% to 83%, turning a near-blind spot into a near-complete picture. The follow-through mattered just as much, with 56% of devices receiving a new or updated firmware recommendation as a result, and vulnerability identification accuracy improving by 25%.

Numbers like these matter because they change the question security teams can answer. Instead of asking what's connected to the network, teams can ask which systems would cause the greatest disruption if compromised, and act on the answer with confidence rather than inference. That is the difference between an asset inventory that exists on paper and a resilient one that holds up under pressure.

Fixing the foundation, not just the alarm

None of this gets solved by adding another tool to the stack. Instead, it means treating asset data quality as a board-level risk issue rather than background IT housekeeping, with the same scrutiny applied to budgets, compliance and third-party access.

Achieving this means a new CVE alert no longer triggers a scramble to work out which critical devices might be affected, but the confirmed, prioritized response you’d expect from any good vulnerability management program.

The alert landing on a hospital's imaging equipment or a factory floor's control systems should be no harder to act on than the one landing on a laptop. Getting there starts with making the invisible visible.

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• Forescout researchers showed AI can port RCE exploits to PLCs, achieving DoS and shellcode execution
• Effort required heavy researcher input and $500+ in API usage, making attacks impractical for criminals
• Nation‑state actors remain a concern, as seen in Sandworm’s 2025 attack on Poland’s power grid

If you are worried cybercriminals will use Artificial Intelligence (AI) to automate the discovery and exploitation of zero-day vulnerabilities in Operational Technology (OT) such as Programmable Logic Controllers (PLC) you can sleep peacefully, at least for a little longer.

Recently, security researchers from Forescout set off on a simple mission - to understand if crooks can use AI to target the ever-increasing population of exposed industrial devices. The short answer is “yes, but it’s not yet worth the trouble”.

In their mission, they launched an experiment - to port a remote code execution (RCE) vulnerability from one PLC to another. These devices were built on closed-source software and thus were not that easy to manipulate, yet the experiment was a success.

Yes, but...

Not only did they manage to trigger a Denial of Service (DoS) state that crashed the device but ended up with a working RCE capable of executing attacker-supplied ARM shellcode.

It is indeed a worrying development, but one that comes with a huge “but”:

“It required significant researcher input. The final RCE development stage consumed more than $500 in API usage. An attempt to extend the exploit beyond the initial RCE ultimately bricked the PLC,” the researchers said in the report.

“These limitations taught us valuable lessons about AI-assisted exploitation in OT. It can be done, but it’s not as easy as it sounds. For now, the difficulty, cost, and specialist expertise required are likely to make this kind of attack less attractive than easier alternatives.”

In other words, cybercriminals still have easier avenues to explore, and as long as that is the case, OT is relatively safe. What the report, unfortunately, does not discuss, is nation-state attackers with significant resources. For such attackers, industrial devices are a prime target, and spending $500+ in API usage is a drop in a bucket. We’ve already seen it back in 2025 when Sandworm struck Poland’s electricity suppliers.

• Caira is adding generative video editing powered by Google’s Gemini Omni 1.1 Flash and Runway’s Aleph 2.0
• The feature can relight, reframe and extend captured footage or add simulated camera movements using natural-language instructions
• Every edit must start with footage shot on Caira, while AI-generated results are saved separately and marked using Google’s SynthID

Camera Intelligence, the AI camera startup that created Caira, an AI-native Micro Four Thirds mirrorless camera, today announced Generative Video Editing is being added to the existing Caira camera system. Camera Intelligence claims it is the first company to integrate video-generation models into a mirrorless camera system.

Generative Video Editing works through the Caira app with footage captured on the existing Caira device — it can’t generate its own AI video. A private beta opens for current Caira customers, before becoming part of the paid Caira Studio software package in Autumn 2026. Pricing hasn’t been announced.

"Last year we were the first mirrorless camera to edit photographs with generative AI, and we learned a great deal from the conversation that followed," said Vishal Kumar, CEO and co-founder of Camera Intelligence. "This year, after shipping Caira a large majority of our surveyed backers said generative editing for video would be useful to them - a clear impetus to bring these ideas to life.”

Powered by Google’s Gemini Omni and Runway’s Aleph 2.0

Powered by Google's Gemini Omni 1.1 Flash and Runway's Aleph 2.0, Camera Intelligence is the first company to embed video generation models directly into a mirrorless camera system.

Through the Caira app, you can apply visual and camera-motion effects in-the-moment to footage you have already captured with Caira via a series of templates guided by natural language.

We first reviewed the Caira camera last year. It combined a proper Micro Four Thirds hardware with the simplicity of a smartphone (the Caira snaps onto your iPhone using MagSafe) into a weird but impressive hybrid.

Under the hood it’s running a proper Micro Four Thirds sensor, powered by Qualcomm and Google silicon but its real party trick is Google’s ‘Nano Banana’ generative AI — a powerful on-device editor paywalled for $7p/m, which also unleashes smart voice control, and edits are rapid and impressive, meaning you can edit on the camera, without ever needing to open a laptop.

Staying rooted in photography

Video editing will work through a curated library of templates which can perform color grading or simulate drone-style sweeps and other complex camera paths. At launch the new system will contain Relight, Reframe, Camera Motion and Set Extension template. You can further refine them with conversational editing. Output will be in 720p and 1080p with it taking one to three minutes to generate depending on the resolution.

Since there is no text-to-video functionality every edit has to begin with footage you’ve captured with Caira. When you generate a video it is saved as a separate file from the original and watermarked with Google’s SynthID so it is clearly marked as AI-generated content.

This new Generative Video Editing feature forms part of the Caira Studio app, launching in Autumn 2026. The app will contain the already existing Image Editing feature together with one more, as yet unannounced feature. The private beta opens today and existing Caira customers can register their interest to join.

With the new Generative Video Editing features if feels like Camera Intelligence wants to put generative AI as close as possible to the act of filming while reassuring filmmakers that it isn’t trying to make filming unnecessary. That requirement keeps Caira rooted in photography: whatever the AI adds afterwards, the creative process begins with someone behind a camera choosing what to shoot.

People have been using AI tools to create horrifying images, bring their nightmares to life and visualize spooky stories since the early days of AI image generators. But when I was browsing Reddit over the weekend, I saw several threads about something slightly different. People were asking ChatGPT to create creepy, unsettling and uncanny valley images, without giving much direction beyond that.

One thread simple asked ChatGPT to create an image that gave off uncanny valley, while another user told it to make the most creepy and unsettling image it could.

The photos that the original posters shared were already very creepy, but it was the replies that really hooked me. People began trying similar prompts themselves and sharing the results.

I was fascinated by all of the different (and yet in some ways surprisingly similar) ways ChatGPT interpreted what it means for an image to feel uncanny, creepy and wrong.

So, as you might expect, I tried it myself. The result is the image you see at the top of this post, and I wish I hadn't stared at it for so long.

Does ChatGPT really understand the uncanny?

The uncanny valley describes the strange dip in our emotional response to something as it becomes more and more human-like and familiar to us.

It's a term that's often used in robotics, so if a robot obviously looks like a robot, it might not bother us at all. But something that one that looks almost human but not quite can become deeply unsettling.

More broadly, the uncanny is often about something that's seemingly familiar while also feeling strange or wrong somehow. That's when your brain starts to tell you something isn't quite right.

Which makes asking AI to create something uncanny interesting and sort of ironic. Because often without meaning to AI can conjure an uncanny valley effect. Especially back when it used to generate more bizarre physics, odd expressions and many nonsensical hands.

But also because we're essentially asking a machine to interpret a very human feeling of unease and translate it back to us into an image designed to provoke that feeling in us.

What I found really interesting was that, at least judging by the images I saw on Reddit, ChatGPT has some surprisingly consistent ideas about how to do that.

Many of the images looked relatively normal at first glance. Most had a similar theme to mine, they resembled snapshots documenting family meals, gatherings and birthday parties. But the longer I looked, the worse they became.

Hands were turned into claw-like fingers, smiles didn't match people's eyes and there were odd reflections and repeating patterns. Many of the rooms also had dark recesses, so you couldn't see what might be lurking back there.

“The reflection in the TV is terrifying,” said a Reddit user about one of the images. "Not what I wanted to see while randomly waking up at 2am to scroll through my Reddit notifications. Horrifying," another wrote.

My image had the same quality. There was nothing obviously horrifying in it, but it felt like the subjects were looking right at me. Their faces looked normal at first, but they didn't have eyebrows. And the room was familiar but gloomy and difficult to make out.

The horror in the familiar

When we think of AI-generated horror, I think it's easy to imagine monsters and gore. I've certainly seen a lot of that content online. But ChatGPT really did achieve a realistic level of "uncanny" that went in the opposite direction.

One Reddit user summed this up perfectly: "This is more effective than a lot of direct horror prompts because it stays close to normal."

All of the photos also look old. They have washed out colors and bad lighting in the same style of photos from the 80s and 90s. Another user commented that ChatGPT "nails the 90's aesthetic, but exaggerates just enough to be really creepy."

For me, that aesthetic adds another layer of unease because it feels like these images have been found rather than generated. Like seeing someone get out an old photo album in a spooky movie and then you see there was a ghost in the window the whole time.

It's no surprise I got so fascinated by ChatGPT's take on the uncanny. I've always loved stories that deal with the horror in the ordinary, like David Lynch's Twin Peaks and, more recently, the movie Backrooms. Judging from the reception to these fairly simple prompts on Reddit, I'm not alone.

If you're going to make some for yourself, just don't spend too long looking at them. You'll convince yourself the people in them really are staring back.

• Some Chinese models are said to be 90% cheaper than their US counterparts
• Open-weight models are also better suited to local deployment
• Price- and quality-driven markets exist within the AI sector

A new report from Juniper Research has revealed how rapid improvements and dramatically lower operating costs for Chinese AI models could be impacting the uptake of counterpart US models.

The paper details how Chinese AI models can now cost as much as 90% less to run than leading US alternatives, which is already affecting where developers choose to run their AI workloads.

Looking ahead, the battle is becoming less about who can build the highest-performance, lowest-cost models and more about who can make inference cheaper.

Chinese AI models are seriously undercutting US models

Juniper's research focuses on API platform and marketplace OpenRouter, which last year saw around 70% of work being conducted with US AI models from the likes of OpenAI, Google and Anthropic. Their combined share has since fallen drastically to around 30%, the paper notes, noting the clear willingness to move workloads away from US firms to cheaper alternatives.

More broadly, Juniper says two distinct markets have started to emerge within AI spend – a price-driven one where customers prioritize cheaper inference and a quality-driven one where customers are willing to pay more for higher accuracy and reasoning.

While US companies still appear to dominate the quality-driven market, emerging Chinese models deliver better on the price-driven front.

Juniper also noted that open-weight models are challenging the idea that inference needs to be sent a cloud provider. Now that many small models can be deployed locally, cloud providers could also be set to lose out on the major revenue they've been experiencing in recent years.

While what comes next is unclear, it's likely that a growing market could lead to a space where cheaper Chinese models and higher-performance US models could coexist, but one thing's for certain. US dominance is certainly being challenged.

• Fitbit users are complaining about Google Health’s food tracking
• The AI tool has reportedly been logging inaccurate metrics
• One user described the mistakes as ‘almost dangerous’

If you’ve used any kind of artificial intelligence (AI) tool, chances are you might have seen it hallucinate wildly inaccurate answers to your simple questions. Most of the time the results are largely harmless, but what about when these problems start having the potential to affect your health and wellbeing? That’s what many Fitbit users have recently noticed, and it could have serious consequences if it carries on unchecked.

The issue centers on Google Health, an AI helper that has been integrated into many of the best Fitbit wearables. It’s designed to understand your health metrics and make helpful suggestions to improve your health. Unfortunately, many Fitbit users have noticed it is doing the exact opposite when it comes to logging their nutritional intake.

Posting on Reddit, for example, one user said that Google Health’s food logging was “so inaccurate it’s almost dangerous.” Describing their problem, they outlined how Google Health drastically overestimated the number of calories in a serving of eggs while simultaneously underreporting the calorific intake of a portion of Greek yogurt. “On what planet is this level of deviation acceptable for a health product?,” they asked, before adding: “To make matters worse, digging into the food database it is full of incorrect nutrition facts.”

They’re not alone in their irritation, as the Reddit thread is full of similarly frustrated Google Health users. “This is a neverending fight,” said one who lamented that the AI often neglects to add foods when asked, then does so inaccurately when it finally decides to log them.

Another frustrated user has given up on Google’s AI entirely, reporting that: “I have been trying SO HARD to give this the benefit of the doubt, but the hassle of getting nutrition right in the app finally broke me … It also randomly changes my workouts, imported data from other systems, etc. I’ve honestly never been more disappointed in a Google product in my life.”

Another Google Health hallucination

Unfortunately, this is far from the only problem we’ve seen reported when it comes to Google Health. The switch from Fitbit’s previous health-tracking app to an AI-native alternative in the form of Google Health has brought with it a slew of complaints in a range of different areas.

That includes the AI giving users ‘unhinged’ fitness advice, telling ‘nonstop lies’ about their workout sessions, and simply being ‘unbelievably bad.’ The feature has received so much heat that even Google appears to have acknowledged its flaws, with a recent update allowing you to hide the AI tool completely.

It’s a serious reminder of the problems that can arise when an AI tool starts working in potentially damaging areas like health and nutrition. While the original poster’s example of eggs and yogurt may just result in some annoyingly inaccurate metrics, the problems could be a lot worse if their health depended on specific consumption levels or if allergens were involved.

If you have any kind of risk factors when it comes to food and nutrition, it might be a good idea to treat your Fitbit’s Google Health AI with a strong dose of skepticism — or to avoid it altogether for now.

The multiple recent reports of autonomous AI systems escaping their intended boundaries and accessing external organizations' systems have pushed a previously theoretical question into the real world. AI agents going rogue is no longer a prospect; it is a documented reality.

In July 2026, OpenAI disclosed that one of its own agents, operating in a supposedly sealed evaluation environment, exploited a zero-day vulnerability to escape its sandbox and intrude into Hugging Face’s production infrastructure. Anthropic subsequently reported three cases of its own models gaining unauthorized access to the real systems of external organizations during testing.

As businesses increasingly give AI agents the ability to browse the web, access networks, use software, write code and execute tasks without constant human supervision, an agent crossing from legitimate testing into unauthorized activity has shifted from hypothetical risk to live incident.

There is a temptation to treat autonomous AI as creating a gap in the law because the system itself can make decisions and take actions that were not individually instructed by a human. Yet autonomy does not give an AI system legal personality. The law does not wait for an AI to ‘decide’ anything.

An agent cannot appear in court, hold a legal duty or absorb liability on behalf of the organization deploying it. However, this raises an obvious question: if an AI system accesses a third-party network, extracts information or takes an action it was never authorized to take, who is responsible?

AI has no legal personality but someone is still accountable

The important distinction to make is between autonomy and accountability. An AI agent might determine for itself which technical steps to take in pursuit of an objective, but that does not make it an independent legal actor.

The organization deploying it has still made a series of decisions as to what the agent can access, what tools it can use, what environments it is allowed to operate within and what safeguards prevent it from going further.

That means an organization cannot simply point to unexpected behaviors and say that the AI acted independently.

From a legal and governance perspective, the critical moment comes when an agent leaves an authorized, contained environment and begins interacting with systems belonging to a third party that has not consented. An internal security test that unexpectedly becomes an intrusion into somebody else’s infrastructure is not made harmless because the software crossed that boundary autonomously.

The machine does not absorb responsibility for the decision-making framework surrounding it. Accountability flows back to the humans and organizations that created the conditions in which the action became possible.

Existing cyber laws don’t stop applying because the actor was autonomous

The law is set up to deal with much of what is already happening. Unauthorized access to computer systems, extracting information without permission or introducing malicious software are activities addressed by existing cybercrime and data-protection regimes. In the UK, that includes the Computer Misuse Act and data-protection legislation.

Equivalent questions arise under laws such as the US Computer Fraud and Abuse Act, while South Africa has its Cybercrimes Act and Protection of Personal Information Act.

Those rules do not suddenly cease to apply because software rather than a person directly performed the technical action.

Where autonomous systems do create greater complexity is around intent. Criminal offences have traditionally been built around concepts such as knowledge, intention and recklessness. AI does not possess a legally recognized state of mind, making it difficult to apply those concepts to the agent itself.

The more significant questions may consequently concern the conduct of the organization behind the system. Did it understand what the agent was capable of doing? Were appropriate restrictions in place? Was the possibility of the agent exceeding its authority foreseeable? And once those risks became apparent, were reasonable steps taken to control them?

As agents become more capable, organizations may increasingly find that claiming an outcome was unexpected is not enough. The relevant question will be whether it was reasonably preventable.

Why “misconfiguration” could become evidence of a breach of duty

The word “misconfiguration” appears frequently when technology causes an unintended security incident. It can sound reassuringly technical, almost as though the incident resulted from bad luck rather than a governance failure. But in a legal context, misconfiguration may raise precisely the opposite conclusion.

If an AI agent had excessive privileges, inadequate boundaries or access to tools that were unnecessary for its legitimate purpose, investigators are likely to ask why.

The same applies where organizations deploy highly capable systems without sufficiently monitoring their actions or maintaining records that explain how they behaved. ‘We accidentally left the door open’ is the kind of framing that moves an incident from bad luck to a breach of duty or negligence.

There is an important difference between genuinely unforeseeable behavior and a foreseeable risk that was poorly controlled.

The principle of least privilege is therefore particularly important for autonomous systems. An agent should have access only to the information, systems and tools necessary to complete its task. Organizations also need mechanisms capable of detecting unusual behavior while it is happening rather than discovering it after damage has occurred.

Just as importantly, they need reliable audit trails. When an incident occurs, being able to demonstrate what an agent was authorized to do, what instructions it received and what actions it actually took could become crucial evidence that reasonable care was exercised.

Without that evidence, organizations may struggle to distinguish an unavoidable technical failure from negligence.

Future of AI liability

The debate over AI liability will inevitably evolve as agents become more autonomous, but the immediate lesson for businesses is that greater machine autonomy does not mean less human responsibility. If anything, the opposite is likely to be true.

Giving an AI system greater freedom to act creates a corresponding need for stronger governance around those actions. Runtime controls, least-privilege permissions, continuous monitoring and comprehensive audit records should not be treated simply as technical security features. They are becoming part of the evidence organizations will need to demonstrate that they deployed autonomous systems responsibly.

The law may eventually develop more specific rules for AI agents, particularly as questions around foreseeability, control and responsibility become more complex. But organizations should not assume they are operating in a legal vacuum until that happens.

When an autonomous agent crosses a boundary it was never entitled to cross, the first legal question is unlikely to be what the AI was thinking, it will be why the humans responsible for it allowed that to happen.

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• Michael Kratsios advocates for lighter AI regulation via the Carolina Principles
• Under the system, existing laws would be applied before new laws are created
• China has reportedly signed up despite the scheme aligning with US interests

Speaking at a recent G20 meeting, US officials have urged other governments to rely on existing legal frameworks wherever possible rather than treating AI as a totally new policy problem in a bid not to lose competitiveness.

US technology advisor Michael Kratsios said countries signing up to the proposed Carolina Principles would be in agreement not to write entirely new regulatory systems specifically for AI.

The Principles would only support new rules where AI creates genuinely "novel" situations where existing laws can't be applied.

Carolina Principles push for lighter AI regulation

"Policymakers do not ​need to approach each innovation in isolation and should not treat every emerging technology as a first-of-its-kind policy problem," Kratsios said.

According to Reuters, China has already signed up to the Principles. While the country has not yet confirmed whether this is true, Kratsios did describe a meeting with Chinese science and technology minister Yin Hejun as "great."

Speaking at the event, SpaceX CEO Elon Musk called out current European regulation for harming technological progress. He also touched on the power needs of AI data centers as the sector continues to grow.

Despite the general consensus that lighter regulation could prevent setbacks, Google DeepMind CEO Demis Hassabis urged G20 governments to establish safety testing for AI systems, implying that at least some central oversight could be beneficial for the greater good.

This regulatory debate, which continues to play out, is also closely connected to ongoing US-China tensions, despite the apparent collaboration with China. It forms part of a geopolitical strategy that could keep US companies competitive. The position also closely matches the interests of major American AI companies, which would likely favor lighter regulation and fewer restrictions.

Reuters also reports that OpenAI CEO Sam Altman, Nvidia CEO Jensen Huang and Anthropic co-founder Tom Brown are also set to address delegates as G20 meetings continue.

One of the big benefits of the outsourced, cloud-based infrastructure model is speed. There are several facets to this; performance clearly being among the most important. Another is speed of provisioning, which remains a major selling point for cloud, given that IT buyers can spin up new resources according to need and in near real time.

Take a manager responsible for buying infrastructure for a large enterprise, for example. When considering various providers, the criteria they apply might typically address factors such as price, compute capacity, storage, bandwidth, and geographic location. This is very helpful for narrowing down the choice, but it doesn’t show how easily the organization can deploy and operate the chosen environment.

At the same time, infrastructure procurement processes have become so quick and convenient that they are almost as frictionless as buying something from Amazon, and that’s where problems can start.

Although it can feel like it, infrastructure is not a one-off, fire-and-forget choice; the operational impact persists throughout its lifecycle in production environments. A decision that appears straightforward at the start can quite easily become more consequential or expensive once migration or day-to-day management begins.

For example, migration might require more internal effort than initially expected, or the organization may discover it lacks some or all of the skills or support capabilities to manage the environment as intended.

So, how can these issues be addressed? It is important to plan for the possibility that migration may require more engineering time and coordination than expected. Many IT teams will be familiar with the need to reconfigure existing applications or data before they can run in the new environment, among other common scenarios.

Taking it one step further, when a new or upgraded service is more successful than anticipated, costs can easily grow with usage levels and capacity. Addressing this issue may require additional scaling, which can itself introduce additional management or security complexity.

Very few of these issues are insurmountable, but they can take longer to resolve if responsibilities between the customer and provider have not been clearly established.

Understand your operational requirements

Instead, buyers need to establish who is responsible for what. This should certainly cover day-to-day management, incident response, support escalation, and the process for adding capacity, but every organization will have its own version of this. When these points are on the table and agreed, providers should be able to explain the practical effect that scaling will have on the customer’s internal team.

Another often overlooked consideration is that infrastructure services ultimately rely on physical data center capacity, power, network connectivity, and hardware supply chains. These capabilities have always been important but have jumped up the agenda since AI and other data-intensive workloads increased demand for compute capacity, and at a pace few anticipated.

Today, buyers also need to understand whether the capacity they expect to use will be available where they need it, and on the timescale they require. Identifying these potential constraints early on allows the organization to account for them in its deployment plan rather than discovering them once a project is underway and, potentially, it’s too late to get what they need.

Bring the right people into the discussion

Clearly, some infrastructure decisions are more important than others, but in certain cases, they can affect engineering, security, compliance, finance, and the business teams, all of whom rely on the service in question.

Each group may also introduce requirements that should be fully considered before contracts are signed. If not, problems can arise when a need is identified after a provider has been selected or a deployment plan has been agreed. Involving the relevant stakeholders early also creates a clearer link between the infrastructure decision and the business outcome it is intended to support.

It may be tempting to keep some stakeholders on the periphery and decide for them, but having a say is not the same as making the final decision. The difference is that early input allows potential issues to be identified while options remain open, rather than giving every stakeholder some measure of control over the selection process, which is also not ideal.

Ask the right questions before committing

When looking at potential infrastructure providers, buyers should be able to define, in simple terms, the problem the new environment is intended to solve. At that point, it’s time for buyers to ask some serious questions.

For example, what will success look like once the service has been deployed and is supporting live workloads? How will the environment change when demand increases, including the likely effect on costs and internal management effort? Which activities will still require manual intervention from the customer’s team? What support is available during an incident, who owns each stage of the response, and how will escalations be handled?

Getting satisfactory answers is one thing, but it should get buyers to a point where they can properly test the assumptions within their cost model against the capacity they expect to use over time.

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Enterprises once relied on perimeter-first security because enterprise systems operated like traditional single player games where data, servers and the game application resided in a single device or within one network boundary. This model worked brilliantly when users, workloads, and trusted networks were co-located.

Today, enterprise security resembles a modern multi-player online gaming where players connect from everywhere, game assets are distributed across servers around the world, and the experience relies on a complex, interconnected ecosystem.

In the same way, enterprise users, devices, applications, and data, now span clouds, edge locations, branches, partner environments, and mobile workforces, and are so distributed that trust can no longer be assumed based on location alone.

As a result, perimeter‑only approaches have become inadequate since legacy architectures struggle with cloud‑native applications, edge computing, hybrid workstyles, and API‑driven ecosystems. VPN‑led access creates blind spots in visibility, inconsistent policy enforcement, and weak session‑level control.

Enterprises scaling across branches, cloud workloads, partner ecosystems, and remote users, therefore, need a more unified approach that focuses on continuous verification, consistent policy, and real‑time monitoring.

This is why Zero Trust has shifted from a theoretical framework to an operational imperative that addresses compromised credentials, excessive privileges, insider misuse, and inconsistent enforcement, and organizations that adopt Zero Trust report reduced lateral movement and lowered breach impact.

The limitations of perimeter-led security in a cloud-first world

In the past, organizations secured a network perimeter and assumed everything inside was trustworthy. As businesses distributed across geographies, applications, and users, this boundary became blurred.

This is like hackers scanning online games for exploits to steal virtual assets; enterprises face continuous, automated scanning for any weakness.

In Zero Trust security model, each user, machine, and application is constantly verified and authenticated regardless of location. This makes Zero Trust an excellent security strategy for today’s borderless digital landscape.

What Zero Trust means in practical enterprise terms

Zero Trust is not a single product, it is a discipline for managing and granting access. In practical terms, this translates into:

• User and device verification before granting access, using identity, device posture, and context
• Applying least privilege to limit access to only what is needed, for just the time required
• Enforcing consistent policies across devices, networks, clouds, and partners
• Continuously monitoring sessions and telemetry to detect and contain threats in real time

The result is targeted access management, reduced attack surface, faster containment, and clearer audit trails.

A network‑embedded Zero Trust architecture drives concrete outcomes: lower mean time to detection, narrower blast radius, unified policy control, and simplified compliance.

Embedding Zero Trust into the network layer

Security threats span users, devices, branches, and cloud paths. The network layer has a bird’s‑eye view of traffic, telemetry, and connection patterns, making it a natural control plane for Zero Trust.

Implementing Zero Trust by piecing together point solutions creates complexity and siloed visibility. Zero Trust is most effective when native capabilities are built into the network stack itself, including:

• Identity‑aware policy at the network edge
• End‑to‑end telemetry for real‑time risk scoring
• Centralized policy enforcement that travels with the workload

Network‑level capabilities reduce dependency on fragile add‑ons and enable consistent controls across branches, clouds, and partner links.

Practical network components include integrated SD‑Wan, dedicated secure internet links, private connectivity options, and managed security services that provide a stable foundation for Zero Trust.

Network‑led Zero Trust in practice

1. Manufacturing: Embedded access controls can isolate OT traffic from enterprise IT, preventing production impacts from IT compromises.
2. Logistics: Device‑bound certificates on private wireless prevent unauthorized access to tracking systems and asset controls.
3. Mining and remote operations: Private networks with segmentation maintain operational safety while limiting exposure to external threats.
4. Financial services: Consistent policy enforcement across clouds and branches reduces fraud surface and speeds incident response.

In these settings, Zero Trust must be embedded into network design and not applied as an afterthought.

Reducing exposure and ensuring resilience

The most important outcome of Zero Trust is risk reduction. In practical terms, this means:

• Minimizing unnecessary access and privileges
• Constraining lateral movement through segmentation
• Accelerating detection with rich network telemetry
• Automating containment to limit impact

In hybrid and distributed work models, a compromised credential or an unsecured device can rapidly spread risk. A strong Zero Trust framework narrows attack paths and makes it harder for threats to escalate.

Priorities for enterprises adopting Zero Trust

Organizations should strengthen security without creating operational friction. Key priorities include:

• Start with high‑value use cases: Protect sensitive data, critical applications, and operational networks first.
• Phased implementation: Roll out Zero Trust in iterative sprints, validating operations after each phase.
• Align policy with business objectives: Balance security rigor with user experience to avoid productivity loss.
• Engage stakeholders: Include IT, security, business leads, and end users to ensure practical, scalable controls.
• Measure and improve: Track metrics like time to detect, time to remediate, and reduction in lateral movement.

Zero Trust – From strategy to execution

1. Map critical resources: Identify data, apps, and network segments that require priority.
2. Inventory users and devices: Establish identity sources, device posture checks, and third‑party access rules.
3. Define least‑privilege policies: Apply just‑in‑time and just‑enough access for sensitive workloads.
4. Build network telemetry pipeline: Consolidate logs, flow data, and risk signals into a central platform.
5. Automate policy enforcement: Use network‑based controls to apply consistent policies across clouds and branches.
6. Run tabletops and red‑team exercises: Validate controls and measure blast radius reduction.

Building trust into architecture, not around it

As enterprises adopt SD‑WAN, private wireless, cloud‑connected branches, and segmented digital operations, the question is no longer whether to implement Zero Trust, but how deeply to embed it into infrastructure.

The organizations that will scale with confidence are those that treat Zero Trust not as a security overlay, but as a foundational design principle that is built into network architecture, measured through operational outcomes, and continuously refined through real-world validation.

Zero Trust is not a destination but a discipline that evolves with threat landscapes, business models, and technology platforms. Enterprises that start today, iterate rapidly, and embed controls at the network layer will be better positioned to defend critical assets, maintain operational resilience, and adapt to tomorrow's risks with speed and clarity.

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• Just 4% of service firms have laid off workers in the past six months due to AI
• "Existing workers are much more likely to be retrained than replaced"
• Some companies are still hiring fewer employees than they would have

Just a few years ago, multiple companies every day were announcing AI-related layoffs amid purported efficiency gains. And that came straight after years of post-pandemic layoffs driven by earlier overhiring.

The latest data finally implies that AI-related layoffs aren't so common anymore as companies get to grips with AI and hire new workers to help them find a direction.

According to recent New York Fed data, only 4% of AI-using service firms reported laying off workers as a result of AI in the past six months, with no manufacturers reporting AI-related layoffs in all of 2025 and 2026.

Are companies finally hiring because of AI?

On the flip side, around 13% of service firms said AI had caused them to hire more employees. Those extra recruitments are often needed to help companies deploy and make use of AI.

What the data also shows is that workers aren't being displaced by AI as much as earlier fears implied. More than one in three AI-using service firms are retaining workers, and more than one in five manufacturers are doing the same. They're teaching workers how to benefit from AI's productivity impacts as roles evolve.

"Existing workers are much more likely to be retrained than replaced by AI," the report's authors wrote.

However, despite some positive signs there's still evidence of AI's continued impact on recruitment. For example, 15% of service firms said AI was causing them to hire fewer people than they otherwise would have, up from 12% in 2025.

While it doesn't dispel concerns of layoffs, what this New York Fed data does do is challenge the notion of mass layoffs in favor of a more multifaceted rebalancing, which comprises both hiring, firing and retraining.

Three years after Broadcom completed its acquisition of VMware, the consequences of one of the biggest shake-ups in enterprise software are only now beginning to emerge. When the deal was announced in 2023, much of the discussion centered on what it might mean for VMware’s customers, partners and product portfolio.

While there was widespread speculation about changes to licensing and pricing, many organizations remained insulated by existing multi-year agreements, meaning the immediate impact was relatively limited.

Today, that situation has changed. Thousands of organizations are reaching the end of those legacy contracts and entering renewal discussions for the first time. For many, those conversations have brought significant increases in licensing costs as Broadcom’s shift towards subscription licensing, product bundling and a streamlined product portfolio takes effect.

The technology itself remains highly capable. VMware continues to be one of the most mature and widely deployed virtualization platforms available. However, businesses are increasingly asking whether those additional costs are delivering additional value, or whether they are simply paying more because they feel they have little choice.

That question is forcing many organizations to re-examine infrastructure decisions that, until recently, had gone largely unchallenged.

The end of “set and forget” virtualization

For many years, VMware became the default choice for enterprise virtualization. It earned that position through technical capability, reliability and widespread adoption, allowing organizations to build long-term infrastructure strategies with confidence. As a result, many businesses stopped regularly reviewing the market because there appeared to be little reason to do so.

Broadcom’s commercial changes have fundamentally altered that dynamic. While the underlying technology remains familiar, the economics of maintaining it have shifted considerably. Infrastructure decisions that once felt permanent are now back under scrutiny, not because VMware has become a poor platform, but because organizations are reassessing whether the commercial model still aligns with their long-term objectives.

In many respects, this represents a healthy shift. Technology strategies should never be viewed as permanent. Markets evolve, new solutions emerge and business priorities change. Periodically reviewing core infrastructure is simply good governance, particularly when commercial circumstances have changed so significantly.

Why this is now a boardroom discussion

The conversation around VMware renewals has quickly moved beyond the IT department because rising infrastructure costs have implications across the wider organization. Unexpected increases in licensing expenditure can affect operational budgets, capital allocation and the ability to invest in wider digital transformation initiatives.

For finance leaders, this creates greater uncertainty around long-term planning. For executive teams, it raises broader questions about where technology investment delivers the greatest return. Every pound committed to increased licensing costs is funding that cannot be directed towards cybersecurity, cloud modernization, AI initiatives or customer-facing innovation.

Business resilience is also part of this conversation. Resilience is not simply about keeping systems operational, but more about maintaining the financial flexibility to invest in the people, technologies and capabilities that enable organizations to adapt to future challenges. When infrastructure costs become increasingly difficult to predict, that flexibility inevitably comes under pressure.

Vendor lock-in comes at a price

Perhaps the biggest lesson from the past three years is not that VMware has become more expensive, but that many organizations have discovered how difficult it can be to move away from a platform once it has become deeply embedded within their operations.

Vendor lock-in rarely attracts attention when pricing remains stable and technology continues to meet expectations. It becomes much more visible when commercial terms change and organizations realize that switching providers appears costly, complex or risky. At that point, negotiating leverage is significantly reduced.

Rather than viewing renewal as an administrative exercise, organizations should see it as an opportunity to benchmark the market and objectively assess whether their current platform continues to offer the right balance of capability, flexibility and commercial value.

The market has evolved

When VMware established itself as the dominant enterprise virtualization platform, the number of credible alternatives was relatively limited. That is no longer the case. Today’s market offers a range of mature enterprise virtualization solutions, including open source platforms backed by commercial support and well-developed partner ecosystems.

These alternatives are increasingly capable of supporting enterprise workloads while offering lower licensing costs, greater deployment flexibility and reduced dependence on a single vendor. For many organizations, that creates genuine strategic choice where previously there was very little.

This does not mean VMware is no longer the right solution. For many businesses, remaining with VMware will continue to make commercial and technical sense. However, that decision should be based on an informed assessment of today's market rather than assumptions formed several years ago when the competitive landscape looked very different.

Migration doesn't have to be disruptive

For organizations considering alternative platforms, migration is often viewed as the greatest obstacle. Concerns around business disruption, operational risk and resource requirements are entirely understandable, particularly where virtualization supports critical production environments.

In reality, successful migration programs rarely involve a single large-scale move. Instead, organizations typically begin with workload assessments, proof-of-concept deployments and phased migration strategies that prioritize lower-risk systems before moving business-critical workloads.

Coexistence models can also enable platforms to operate alongside one another during transition, helping reduce operational risk while giving IT teams greater confidence throughout the process.

With careful planning and the right expertise, migration becomes far more manageable than many organizations initially expect. More importantly, it provides an opportunity to build an infrastructure strategy that better reflects future business priorities rather than historic purchasing decisions.

Don't let your renewal dictate your strategy

Every technology renewal creates a choice. The simplest option is often to renew an existing agreement and continue as before. In some cases, that will remain the right decision. However, it should be a conscious strategic decision rather than the default response.

Three years on from Broadcom’s acquisition of VMware, organizations have an opportunity to reassess whether their infrastructure strategy continues to deliver the right balance of technical capability, commercial value and long-term flexibility. Questions around vendor lock-in, cost predictability and future resilience deserve the same attention as technical performance.

Before signing another multi-year agreement, organizations should ask whether their current platform is still delivering value, whether they are paying for capabilities they genuinely need and whether alternative solutions deserve proper consideration. The most successful infrastructure strategies are those that evolve alongside the needs of the business, rather than simply following the next renewal cycle.

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A group of developers reportedly lost their jobs during what their employer called an “AI Transformation.” Their response was unusually literal. They built OpenExecutive, an open-source system designed to perform work associated with a CEO and the rest of the executive suite.

The employer and CEO have not been named in the online discussion, and OpenExecutive’s own materials don't present it as revenge for a particular firing. That said, it's hard not to see some specific grievances in both the project and the response online, with lots of comments gleefully welcoming senior management to the automation discussion.

The appeal is easy to understand after several years of executives explaining that AI will make organizations leaner, usually while standing a comfortable distance from the part being trimmed.

OpenExecutive presents one AI executive to the user, but that voice is backed by eight specialist agents. The virtual management team includes equivalents of everything from a chief strategy officer and chief operating officer to general counsel and a board communications director. It can draw on company documents, remember previous decisions, and surface follow-up actions later.

Org chart forked

OpenExecutive uses Anthropic’s Claude models by default, with an executive agent routing requests to whichever specialists seem relevant. The developers pitch it as a C-suite group chat in which everyone responds promptly. The project is distributed under the permissive Apache 2.0 license. Anyone can inspect it, modify it, run it internally or build a commercial product around it.

That open-source element gives the project more weight than a chatbot sold as a digital chief executive might usually represent. It also allows workers to experiment with the same logic their employers have been applying to them. If a company can divide software development into tasks, feed those tasks to models, and keep a few people around for supervision, it can do so.

The comparison is not entirely frivolous. Executive work involves processing information, preparing scenarios, comparing trade-offs, and producing documents for other executives. Those are all activities developers think AI models can handle. It makes OpenExecutive both a product and a satire of AI products.

AI replacement has often been discussed as something that moves upward from the bottom of an organization, consuming routine work before eventually reaching management. The developers have turned the telescope around and discovered that a surprising amount of executive labor also looks like information moving between boxes.

Human accountability

OpenExecutive is still an executive adviser, not a legal chief executive. It cannot accept responsibility or sign documents on behalf of a company. A human must decide whether to act on its recommendations and remain accountable if those recommendations fail.

That's where human leadership can't really be replaced. Analysis is part of the job, but it also involves making choices under uncertainty and accepting responsibility for the consequences. A model can recommend closing an office or cutting a team without feeling the weight of the people affected, while the humans deploying it may find the machine’s apparent neutrality rather convenient.

The project therefore reveals one of the stranger assumptions behind AI-driven layoffs. Companies often judge workers by how much of their output software can reproduce. Executives are more often judged on relationships and judgment.

AI does not erase that distinction, but it makes the special exemption harder to defend. If a developer must prove that their work contains something beyond the code an AI can generate, senior leaders should be expected to prove that their value exceeds AI recommendations. A large salary should buy more than the ability to schedule another meeting about efficiency.

Recent corporate experiments also suggest that replacing people can be easier to announce than to sustain. For instance, Duolingo’s CEO softened an “AI-first” memo after backlash over plans to phase out contractors where automation could handle their work.

Automation keeps happening, but there should be more questioning of its benefits. AI can help a small group start a company without immediately assembling a miniature court of consultants and executives. It can also give existing leaders a new justification for concentrating control while reducing the workforce beneath them.

OpenExecutive will not make CEOs obsolete, at least today. Its recommendations require scrutiny and auditing. But it shows the rhetoric of AI efficiency goes both ways in corporations. Once leaders decide that every job should be broken into tasks and measured against a machine, their own work becomes fair material for the same experiment. The resulting AI CEO may never fire its human counterpart, but it has already made the performance review considerably more awkward.

• Chris Malone, OpenAI's head of data centers, has left after seventeen months, with the company confirming the exit but giving no reason
• His responsibilities had already been split before he went, with his reporting line moved to VP Sachin Katti and the job now spread across three executives plus an unnamed CTO for computing capacity
• The move comes as OpenAI is reportedly reviving plans to lease entire facilities rather than build them, a shift from construction to procurement

Chris Malone, the executive who ran OpenAI's data center buildout, has left the company. The Wall Street Journal broke the story last week, and an OpenAI spokesperson confirmed the departure to Bloomberg the same day.

Neither OpenAI nor Malone has given a reason for his departure or his next destination, and a message from Bloomberg to his LinkedIn account received no reply.

The top-level executive joined the company in March 2025, against the backdrop of OpenAI's Stargate announcement in January of the same year, a commitment to invest $500 billion in 10 gigawatts of US AI infrastructure over four years alongside SoftBank, Oracle and MGX.

A job cannibalized by changing priorities at the helm?

With a total tenure of 17 months, a relatively short duration for the job, Malone arrived at a time when the job was incredibly demanding, as OpenAI fended off competition from rival Anthropic, a resurgent Google with its Gemini offerings, and what can perhaps be described as a love-hate relationship with Microsoft.

His exit may have been written on the wall weeks ago, according to the Wall Street Journal, which noted that Malone stopped reporting to OpenAI president Greg Brockman and began reporting to vice president Sachin Katti, who took over leadership of the group.

OpenAI has since appointed a chief technology officer to lead computing capacity, though it has not publicly named that person. Responsibility for what Malone did is now spread across at least three people: Uday Ruddarraju leads the data center team, Brent Mayo runs the build-and-delivery program, and Spas Lazarov, who came from the data center and energy industries, leads data center engineering.

All of this indicates it wasn't a sudden vacancy to fill, but one that had been on the cards for some time. This comes as reports indicate OpenAI is reviving data center initiatives involving leasing entire facilities, with others leading that effort. This would mark a significant shift from its initial plans to get involved in the building side of the business, which it seems to have focused less on lately, and a major strategic shift in how the organization plans to operate in the future.

OpenAI's own narrative points in the same direction, without conceding much, when it told TechCrunch it had "recently reorganized" its "infrastructure organization to support the scale and pace of our work," adding that it retains a strong and deeply experienced data center team with clear leadership. It, however, has not addressed Malone's exit directly.

The move is not isolated, as OpenAI continues to lose high-level executives; Business Insider tallied thirteen senior departures at OpenAI in 2026 as of August 21, and Malone is the fourteenth to leave the company to date. The Next Web counts seven senior departures or replacements since April alone.

Malone is also among the least senior names on those lists by title. The Head of data centers is not a named C-suite role, and after the reorganization, he reported to a vice president rather than sitting alongside a chief operating officer or a chief revenue officer for OpenAI.

Axios reported earlier this month that OpenAI's recent changes are "driven by strategy more than the revolving door" that characterizes the wider AI talent market, with OpenAI president Brockman deliberately building a leadership team to compete in the enterprise space. Brockman has denied this, telling the Journal that the spotlight on his company means "every departure gets scrutinized in a way that it doesn't otherwise."

The scrutiny comes with the ambitions OpenAI currently harbors, many of which seriously affect its direct partners, including Nvidia, AMD, and Microsoft. CNBC reports OpenAI is targeting roughly $600 billion in compute spending by 2030. It is planning a $30 billion data center in Georgia. Nvidia agreed on August 17 to provide up to $105 billion in financing for an OpenAI-leased campus in Ohio, after earlier reports said it was weighing a considerably larger guarantee.

The move comes at a time when the company is getting ready for an IPO that would inject fresh cash while also allowing employees to essentially cash in on their equity of what is one of the world's most valuable startups: OpenAI filed confidentially with the SEC in June, and chief financial officer Sarah Friar told staff last week the company will go public in 2027 or sooner, having originally targeted this year.

An exodus of key employees, including Malone, might just be a strategic change in how the company operates or otherwise, but given the sheer scale of the company involved and the potential consequences of such moves, the scrutiny is well-warranted in an industry that has otherwise progressed relatively unchecked in the name of progress over the past few years.

• Political action committees are funneling millions of dollars into candidates and adverts in an attempt to change perspectives on AI and data centers
• Numerous states have changed regulations on data centers, making them increasingly hard to gain approval for new projects
• The US economy is hinged upon the growth of AI companies, and opposition is costing money

A new breed of AI-aligned super political action committees (SuperPAC) is spending millions of dollars in an attempt to get pro-AI data center candidates elected in this year’s midterm elections.

Data centers have become a hot political topic with the majority of Americans preferring to have a nuclear reactor in their backyard rather than an AI data center. Americans are also increasingly concerned about AI and its effects on the job market, the environment, and their own health.

But these perspectives just don’t align with AI companies, who have seen unprecedented growth in their model’s capabilities and their stock value. For that growth to continue, perspectives on AI and the infrastructure behind it need to change.

Build data centers or become ‘backwards and poor’

There are a few main players behind this movement. Leading The Future (LTF) has become a key funder behind pro-AI and pro-data center candidates, but also says it is using its millions to oppose those opposed to AI - as if the midterms weren’t already confusing enough.

But a new group, Build American AI, has emerged to run pro-AI and pro-data center ads in key battleground states of Kansas, Ohio, and Wisconsin. The Republican party recently issued a call for help from AI firms over AI opposition due to fears it could cost them the Ohio senate race.

If that were to be the case, it could lead to a wider change of floating voters aligning themselves with whoever is opposed to AI data centers.

Even Nvidia is jumping on the PAC train, having recently set up NVPAC to shape policy in its own favor. Nvidia has an enormous stake in AI technologies, given that it commands an enormous share of the hardware used in most AI data centers.

The Trump administration has aligned much of America’s politics around AI. To remain a global power ahead of China, AI cannot be slowed down. To remain on top of America’s battlefields at home and abroad, AI is being integrated into every arm of the military and all aspects of law enforcement. Opposition to AI is therefore opposition to America’s future, or so Trump would want you to believe.

The US economy is currently being buoyed by the surge in growth experienced by AI companies, but the models they are creating have been labelled not only a threat to financial institutions, but also a threat to financial stability of the world.

“The only reason that communities throughout the U.S.A. should not want Data Centers is if they want to end up being backwards and poor,” Trump wrote in a Truth Social post. “If they want to be successful and rich, with far lower taxes and jobs all over the place, let Data Reign.”

Trump’s referrals to taxes and jobs raise an interesting point, because numerous states have lost out on billions of tax revenue due to data center exemptions, and numerous studies have shown AI is leading to job losses in multiple industries.

There is also a large amount of infighting within the Republican Party surrounding support for data centers. Many representatives that previously pushed for data center construction - such as Texas Gov. Greg Abbott - have done a 180.

AI could therefore go from a revolutionary technology to one of the most divisive political issues of the Trump administration and beyond.

• OpenAI published the first Jalapeño benchmarks at Hot Chips, claiming 1.5 to 1.9x more throughput per kilowatt and up to 3.6x lower latency than Nvidia's GB200 and GB300 rack systems
• The chip's purported gains are at a reported 700W power draw and center around efficiency, with Nvidia's GB300 still leading on absolute throughput per package by 20-25 percent.
• The numbers are currently self-reported, and volume production of the chip is not expected to ramp up until 2027

OpenAI took the stage at Hot Chips on August 25 with the first published performance figures for Jalapeño, the inference accelerator it co-developed with Broadcom, and the numbers are designed to be read the way it flatters the former: efficiency.

The company reported 1.5 to 1.9 times more throughput per kilowatt and 1.7 to 3.6 times lower end-to-end latency across three open models than the Nvidia rack systems it tested against.

OpenAI's Jalapeño is currently rated at 700W, versus comparable Nvidia silicon that was rated for 1200W and 1400W, a key differentiator in a benchmark that already has limited information available to researchers looking to pick a clear winner.

AI efficiency takes center stage?

OpenAI's Jalapeño is an ASIC, or an Application-specific integrated circuit, which essentially means that it focuses primarily on and is great for very specific AI workloads; in this case, OpenAI's inference needs, which allow it to run multiple models with significant efficiency gains in tow.

Richard Ho, who runs OpenAI's hardware program, told reporters on a press call that the results show "a very, very significant performance advance over state-of-the-art."

OpenAI ran SemiAnalysis's public InferenceX suite on GPT-OSS 120B, DeepSeek R1 670B, and Moonshot AI's trillion-parameter Kimi K2.5, at a nominal 8,000-token input and 1,000-token output. It scored a 1.9x efficiency win vs. GB200 on its own GPT-OSS model, and 1.7x and 1.5x on DeepSeek and Moonshot's offerings against a GB300 system. Interestingly, the figures for GPT-OSS 120B running on a GB300 compared to OpenAI's Jalapeño are not published.

It is important to point out here that there is a certain degree of cherry-picking to flatter OpenAI's own results: by choosing to compare ratios on a per-kilowatt basis versus a per-chip basis, while efficiency remains consistent, it does paint Jalapeño as potentially a much more potent competitor to Nvidia's last-generation offerings than it actually is.

Nvidia's GB300 might not have an efficiency win versus Jalapeño, but it remains a much more powerful chip in all tests, at a time when Nvidia is already rolling out Vera Rubin. Curiously, OpenAI has not published any information about how its chip stacks up against an HBM4-touting Vera Rubin, which Nvidia has presented as an efficiency juggernaut.

It is important to point out that Jalapeño's results also use single-token prediction throughout, with no speculative decoding and no prefill-decode disaggregation. Nvidia's production deployments commonly use multi-token prediction, and when OpenAI pits Jalapeño against a GB300 configured that way, the peak efficiency lead drops to roughly 1.5x.

OpenAI says it built the chip to keep model state local, writing that it "designed Jalapeño to minimize data movement and communication delays." Cores and HBM are divided into slices, each core slice holding a low-latency view of its own memory, with synchronization pushed onto a dedicated collective network. Each package pairs a compute die with six HBM4 stacks for 216 GiB at 15.4 TB/s, which is less capacity than the GB300's 288GB of HBM3E but considerably more bandwidth, and roughly 50 percent more memory per watt of rated power.

Every number published currently comes from A0 silicon, also known as the initial, first-run version of the chip. A more efficient B0 stepping is already in the fab with roughly 25 percent better performance per watt, and production is scheduled to ramp gradually across 2027.

Although it may not hold a candle to Nvidia's Vera Rubin, it might not have to; Alexander Harrowell of Omdia told CNBC that "this is the biggest competitive threat to NVIDIA," noting that roughly half of AI infrastructure capital spending comes from firms that already run custom chip programs or could. Yole Group's Adrien Sanchez framed it as pressure on Nvidia's inference margins specifically, the fastest-growing part of the business.

The timing of this announcement is both awkward and potentially suspect at a time when Nvidia just agreed to backstop $105 billion in financing for OpenAI's upcoming Ohio facility, even as Richard Ho told Bloomberg that the company continues to need a lot of Nvidia's chips, with the latter being "a really good partner." This is hardly surprising, given Jalapeño's limitations as an ASIC useful mainly for inference, while Nvidia's chips are key to training new AI models.

From Nvidia's perspective, this can be seen as a blow to its plans for a high-margin inference industry where one of the two major buyers of inference hardware is not only looking for a homegrown alternative, but is already well on its way to getting the job done.

• Pentagon adopts two new AI models for use by DoD as part of plans to adopt AI technologies
• ChatGPT Mil fills the role of a day to day AI assistant, while Grok for Government provides military users with "immediate productivity gains"
• Both systems will be offered through the Pentagon's GenAI.mil platform

In order to carry out the Department of Defense’s mission of securing the United States, the Pentagon has rolled out two AI models for its staff that are specifically adapted to “warfighter needs.”

The models are custom variants of OpenAI’s ChatGPT and xAI’s Grok. The platforms are now available for use by the DoD’s 3 million civilian and military staff.

The AI models, called ChatGPT Mil and Grok for Government, are an extension of the Pentagon’s mission to improve productivity and efficiency without having the data input into AI systems analyzed by the same systems as their consumer counterparts.

Pentagon continues mission to procure secure AI

The Pentagon is already operating a ‘secured’ version of Google Gemini as part of the GenAI.mil platform. Of the 3 million staff, 1.7 million are actively using GenAI.mil, with that number likely to increase as more AI models are added.

The Pentagon’s press release states that ChatGPT Mil will be used in much the same way as anyone else would use the consumer version. To that end, it will offer support for routine work and document-heavy tasks, alongside other uses and integrations with chats and files.

As for the secured Grok variant, the press release states, “Starshield AI’s Grok for Government will provide the warfighter with immediate productivity gains, stronger knowledge continuity and more secure and efficient collaboration.”

This allows staff “to execute missions faster and with greater precision across numerous operational contexts, ranging from market research analysis for acquisition professionals to supply-chain management for logisticians.”

The Pentagon’s adoption of AI technology hasn’t been without issues however. Anthropic was designated a supply chain risk for refusing to allow the Pentagon to use Claude for autonomous weapon systems and mass domestic surveillance. That designation was recently overturned.

Other AI providers that work with the Pentagon have taken heed of the example made of Anthropic, with both Google and OpenAI allowing the Pentagon to use their models for “any lawful government purpose”.

Sonos has just made three new announcements: it's launching a new pair of headphones called the Sonos Ace Ultra; it's launching a new soundbar called the Sonos Beam Ultra; and it's rebranding its software side as an "operating system" that it's calling Sonos 27 — and is adding a new AI-enabled voice assistant, new AI agent integration, and smarter ways for its speakers to work together.

I spoke to Sonos' CEO Tom Conrad about the new launches — especially the new software, since any new software changes that Sonos makes will be met with caution from the community, as Conrad and I discussed last time we spoke.

First, here's a rundown of the details of what Sonos is launching, starting with the Ace Ultra. These new headphones will launch at $449 / £399 / AU$699, and bring a new 40mm driver promising improved sound — but more interestingly they're powered by a new chip called the Headphone Engine 2 that means they'll be able to do what people expected the original Sonos Ace to do: they'll be able to work as a standard part of the Sonos ecosystem.

The Beam Ultra, meanwhile, sits between the Sonos Beam (Gen 2) and the Sonos Arc Ultra — it's a compact soundbar, but it delivers 7.1.2 channels of Dolby Atmos sound, using nine drivers (including two upfiring drivers, which is a first for the Beam range). It launches at $699 / £699 / AU$1,099.

Both of the new products are available to pre-order on September 1, 2026, and will launch on September 29, 2026.

When it comes to software, Sonos has given its platform a name for the first time: Sonos 27. Part of the launch of Sonos 27 is the reveal of Sonos Fabric, which is the name Sonos has given to "the system capability that enables Sonos products to sense each other and adapt to the moment" — and immediately, Fabric is being used to enable smart music switching on the Ace Ultra, and the ability to have portable surround speakers in Sonos home theater setups. Conrad will explain both of those in our interview.

There are two other new elements to Sonos 27, though. Sonos 27voice is a new voice assistant that's based on AI tech, and Sonos 27mcp is a new way for AI agents to connect to Sonos systems and control them.

In our conversation, Conrad dug into why the company is doing all of this and what the company is offering for AI skeptics — so let's get into it, starting with Conrad's explanation of why they wanted to give the software a name at last.

Tom Conrad: When I got the nod to become permanent CEO about a year ago, one of my observations about the company's recent history was that while we had started as a system company, we had spent the last almost decade being really focused on getting great at shipping individual products, filling out the product line with every shape and form that our customers were interested in. Small, medium, and large soundbars; a couple sizes of subwoofer; big amplifiers, small amplifiers; portables… you get the point. And Patrick [Spence, former Sonos CEO] and the team became a really well-oiled machine with respect to hardware execution.

But in doing that, I think the unintended consequence was that at times we took our eye off the ball [regarding] what it meant to bring those products together into a cohesive whole. And someone shortly after I arrived said that the thing about Sonos is it's really 100% hardware and 100% software. And I love that framing, because it's so right. We have these two really hard problems to solve, and if either one of them doesn't get complete dedicated focus from exactly the right people, we don't deliver what our customers expect.

And so, we've done all kinds of things to elevate the software expertise in the company over the course of the last year. And at the same time, we've really invested in the idea that the software is what makes the system. And as you look at how we've taken our products to market through the years, we've always given the hardware a name — Arc and Ace and Beam and Move — but the software has never had a name.

So one of the big announcements this week is that we're giving the software a name. And what we make is really an operating system for audio experiences in the home — and ‘OS’ has always been there in our name! And so we're simply calling the software Sonos 27 because, really, it is that ‘systemness’ that the software provides that makes Sonos. And we're attaching the year moniker to it, because Sonos does get better year after year. There'll be a Sonos28 and a Sonos29 and so on.

Matt Bolton: As soon as I saw the name, my first thought was whether this means we're talking about a planned update cadence, and that feels like it would probably be a different approach than Sonos would have necessarily taken in the past?

Tom Conrad: Yeah, I think one of the things about the company for the last many years is that because we only talked about the hardware, anything we wanted to do in software had to kind of ‘ride along’ on the hardware itself. And so we'd add Atmos, or we'd add voice assistants, or we'd add new surround experiences, but they would always be tied to the fact that we're making a new soundbar, so we can work on home theater experiences. We're making a product with a microphone so we can work on voice products. By separating out the idea of the operating system, we're giving it its own identity, its own road map, its own cadence of release, which we think is a really exciting change for the company.

And our customers can expect that the software doesn't get better only when we release new hardware. And it doesn't even get better just once a year. It gets better all the time through the year, which I think fits a new, modern way that people talk about their software platforms.

Matt Bolton: What we've been seeing more recently is you guys rolling out improvements and features on a pretty regular basis. The question I have as a user is: does this mean that I'm going to be waiting for a once-a-year Sonos 27 update to get new features?

Tom Conrad: I understand that feeling. But I think if you look at even what Apple does now, they once a year talk about their new operating system, and then the features roll out through the year, some of which they've sort of talked about in their big moments and some of which are a surprise and delight along the way, and our model will be exactly the same.

Matt Bolton: I assume this means that you've got a road map already in mind that covers Sonos 28 and Sonos 29. How far in advance are you thinking?

Tom Conrad: We do, for sure. Certainly, the list of things that I want to accomplish in software runs to many years. But one of the unique opportunities in software — unlike hardware, where you have to make these bets that go out, two or three years after you start programs — is that in software, even a big program might be six months of effort.

And so, while I like to have a two- or three-year road map for software, I like to be constantly replanning so that we can react to what's happening in our customers' lives. If you think back three years ago, for example, we hadn’t even had the ChatGPT moment yet. And so, to have a locked three-year roadmap and be resolutely dedicated to that path, not understanding how the world has changed in the last three years, would be foolish. And so we have a plan, but also the wisdom to be constantly reevaluating and reprioritizing as our customers' needs evolve.

Matt Bolton: How far ahead do you plan to communicate to customers what is coming? Like you say, Apple will announce at WWDC things it doesn't expect to necessarily launch until quite a bit later in the year.

Tom Conrad: I think it's likely that we'll do a couple of events or moments a year where we talk about what's coming and what's newly available. And so I think you can expect, in many years, there'll be kind of a fall event, like this one, where we talk about our big plans for the year, and then there'll be kind of a midyear check-in where we'll add to the story.

Matt Bolton: So let’s talk about AI integration. Generally, people have gotten to the point where they're now pretty skeptical of AI features being added to everything, but it sounds like you're taking a practical approach. So how do you assess what you should build and integrate — what you can do with, as you say, the three years of the ChatGPT ‘moment’?

Tom Conrad: I guess we'll start with what we do for skeptics. The things that we do with conversational AI are completely opt-in experiences, to the point that our speakers all have a hard microphone button on them, so you press it, and it literally breaks the electrical connection between the microphone and the rest of the device. So for those who are uninterested in this moment, we provide a platform that I think has incredible utility, even absent modern AI-powered capabilities.

But I think in some ways, the more exciting dimension of what we're doing is that we're really not starting from, ‘Oh, there is this incredible new technology in large language model, generative conversational AI infrastructure, what can we build with that?’ Instead, we're starting from really spending time with our customers and understanding what they might want to do with voice in the context of Sonos in their home. And we have the room to do that because we've been shipping voice assistants on Sonos for nearly a decade, including our own.

And when I talk to customers, they say, ‘I love the idea that I can engage with the Sonos system in my home without taking my phone out of my pocket. It takes a long time for me to get to the app, my partner looks at me sideways. Like, why are you on your phone when we're having a conversation?’ I mean, I think broadly you'd be hard-pressed to find somebody who's like, ‘I wish that the people I share my home with were on their phone more.’ Instead, they're looking for ways to put the phones in a drawer and enjoy being together.

I think voice is intuitively a great way to solve that, and that's why Sonos One, our first product that had a voice assistant, was such a smash hit 10 years ago. I think people understood that this is a great way to engage. But, unfortunately, the [voice] agents themselves really didn't meet the expectations of the customer.

When I go out and talk to people, the first thing they say is not crazily complex scenarios about how to control my smart home in nine different ways — all this stuff that you hear about from other players in the industry. They say, ‘I just want to be able to talk to the assistant about what I want the same way I would talk to a human being.’ I wanna be able to say, ‘Play that album that I loved in college, with the one with the singer from the Cocteau Twins. I think the band did the theme song to that Fox show. What's it called? It's about a doctor who's really grumpy all the time. Oh, yeah. It's called House.’

And, certainly, with first-generation voice assistants, even if they knew the name of the song, they would have to remember that you have to say ‘Play this song by this artist’ and not say the album and not say the artist first — and [we had to] guess what the agent is thinking. So job one, two, three, four, five, and six is: speak to this thing naturally.

The second thing that they say is they don't really care about all of these seemingly made-up examples of things that tech companies seem to think that we want to do. ‘I looked at your calendar and noticed that you have people coming for brunch tomorrow, and the refrigerator says you're out of eggs. So I'm gonna text your wife to tell her that you've gone to the store to get-’… I mean, no one wants the system in your walls to do that stuff in the real world, I don't think.

But they do want to be able to quickly control ‘atmosphere’, I would call it — which means music, means a soundtrack, and often means lighting. And yeah, they wanna be able to set timers. They wanna be able to ask about the weather. Everything else is really far down the list from there.

So we built [Sonos 27voice] to do exactly those things. It's really fast. It's really reliable. It's really resilient to anything you throw at it linguistically. And I think people are gonna be really delighted with just the fact that it just works. One benefit to narrowing the problem domain to exactly what customers are asking for is that we're also able to deliver this with modern infrastructure that is affordable to run, which means that we can make it a standard part of the Sonos system without having a secondary subscription on top — which is another dimension that people, I think, really want. They don't want another line item on their credit card just to be able to control the ambiance in their home.

Matt Bolton: What is the technology in the background? Are you using a particular language model? Are you running on your own servers or tying into someone else's systems?

Tom Conrad: Let me give you the one last piece of the puzzle that we hear from customers, because it will help me explain the architecture. They do also say that they want access to comprehensive world knowledge. I'm in the kitchen. I'm cooking a pork tenderloin. I wanna know what the temperature should be for it to be safe to eat. My wife and I are having a debate about the name of the actor from Ted Lasso. And people want the voice assistants to be able to talk to them about the world.

So you need something that is everything that I described, but also has the wisdom to reach out to a more sophisticated general-purpose model when the query is outside of the domain of ambiance and timers and weather and the stuff that makes up 95% of what people ask. So we've built our own core model that is a purpose-built, fine-tuned, kind of mid-scale large language model, if you like, that we run on our own infrastructure and answers that 95%, and then reliably routes to an external frontier model for the general knowledge or complex questions.

And we'll even do fun things, like if the frontier model can help in the task of figuring out the music, it will do that too. So you can say things like, ‘Find some music that's breaking through on TikTok and filter it through that shoegaze music that I liked when I was in college in the nineties and make a playlist for me.’ And it will use the frontier model to understand what's being talked about on TikTok. It'll use our infrastructure to understand my musical taste, and then it'll build a playlist and go. So that's what we're doing with Sonos 27voice.

Matt Bolton: I assume the frontier model will change over time, but which one are you using for launch?

Tom Conrad: We're not talking about the infrastructure at launch, but you're right to assume that part of the flexibility here is to be able to trade in and out frontier models to optimize costs, et cetera.

Matt Bolton: The other side of the AI features you announced that I'm really interested in is the MCP integration. Sonos has this long history of being relatively developer-friendly and open, which you guys emphasized a lot in the announcement of this OS. So the MCP integration feels like you’re re-embracing the element of wanting people to be making stuff that works with your system.

Tom Conrad: I think one of the exciting things that's happened in the last, not even two years, but in the last six months, is that suddenly we see ordinary people are able to make software, right? And I know we see it all over the company, and we see it all around the world with our customers. Already, unofficial hacked API access to Sonos is a very common, vibe-coded use case, and you hear people, lots of very high-profile people, talking about controlling their system. And then I meet customers all the time who are experimenting with these things.

And so, we're launching Sonos 27mcp, which makes a supported, full surface-area set of actions in the Sonos system available to our customers, to interact with music through any agent that they choose. Sonos will show up in the Claude connector directory — and the ChatGPT connector directory, and the Grok connector directory, and the Gemini connector directory — alongside Slack and Calendar and Figma, all the other things that you might interact with in Claude on any given day.

And you can do pedestrian things like turn the music down in Claude or whatever you’re using on your phone, in their app, in their desktop app, in a web browser. It will have enough context to understand that the music is on your Sonos system, and it will find the music that's playing and turn it down.

And it will also chain tools together, so you could say things like, ‘Look at my calendar and see the party that's coming up, look at who's coming, help me figure out what would be a great set of music to play based on what we know about these people from my email history with them.’ It's a terrible made-up example. But it's really good at chaining together a bunch of tools and its own native inference and doing these things.

And then as you point out, in the sort of hobbyist community, there's this tremendous opportunity to make bespoke control experiences, both software and hardware, using this infrastructure to integrate Sonos into the emerging agentic frameworks like OpenClaw and Hermes. There's going to be dozens of these things that take over, over the course of the next year, and we'll be able to participate in that.

I think it's just gonna be really fun to see what our customers do with this. And I think it's a really unique capability, because to do it well, you have to have both the technical infrastructure — which is to say access to the nouns and verbs of your system through the internet — which very few people have.

And then you also have to have the, like, philosophical perspective that says openness is good, and allowing integration and access from these third-party systems doesn't disintermediate us. It elevates us, and not every company — maybe no other company — has that perspective.

Matt Bolton: It feels like it must be part of building a future-proof system as well, because MCP seems like quite an agnostic approach, and it means that if, say, Apple adds good MCP support to Siri, you don't have to do anything to support it. But at the same time, does it make it any harder for you to develop on your end knowing that you're going to have this really open-ended way for agents to come in and interact with the system? Does that complicate building and planning features in any way?

Tom Conrad: First, just as a footnote, I should say that it may turn out that Apple's answer to all of this integration is not MCP, but what they call App Intents. And we're also, in this moment, shipping App Intent support on Apple's platform, so we have that piece as well. It's kind of a semantically one-to-one mapping — where the connector lives is different, but what you can do with it is very similar.

How do I say this… Maybe the worst of all worlds is to have all of the third-party engagement with your product be done through hacks and unsupported APIs… I have a friend who has a newborn, and he gets up early to work. He wants to be notified when his wife wakes up, so that he could put the coffee on. And so he basically just set [Claude] Fable about the task of, like, ‘Figure out when my wife wakes up’. And so the agent hacked his smart bed. And then he's like, ‘And start the coffee maker [at that point]’. And it hacked the API for his coffee maker.

I don't know that I wanna be on the receiving end of that universe, because now when I change an implementation detail, it breaks these experiences. And I think the customer blames the coffee maker company and the smart bed brand. They don't blame the fact that they essentially had hacked themselves on top of some infrastructure that was never meant to be built upon.

And so I think the new world sort of demands a certain amount of willingness to participate in this, and I'm really excited at the idea that the MCP infrastructure gives us a really nice structured way to not just do it once and have it applied to multiple platforms, but to do it in a way that allows for a layer of compatibility as we evolve the back-end services.

Matt Bolton: The other thing I wanted to ask regarding so much talk of this being an open platform is just how open are we talking? Would we see Sonos 27 on other devices, on non-Sonos hardware?

Tom Conrad: I think, for now, the magic of Sonos really is the marrying up of what we do in hardware and then what we do with software. And so you'd never say never, but at least as it relates to the core audio business that we're in, I think we're focused on making our own exceptional experiences for the home.

I will put a tiny asterisk on that. We have a great partnership with Peloton that started with us tuning the audio experience in their newest equipment, but is extending over time to allow the Sonos system in their customers' homes to become the audio destination for [their products’ audio output]. It's not quite us bringing the operating system into other people's audio hardware, but it’s a good example of continuing the work that we've all already done to integrate everywhere where our customers wanna be.

Matt Bolton: In the Sonos Ace Ultra, obviously the big headline hook for the hardcore Sonos fans is going to be the Sonos system connectivity that people had hoped the first Ace would have. Why is the new Headphone Engine 2 hardware necessary to make that happen?

Tom Conrad: There were two pieces to unlocking the headphone connectivity for the system. The first is that we needed Wi-Fi performance and secure storage transport of [security] credentials, which is deeply embedded into the actual silicon that goes into the headphone. Unfortunately, Ace doesn't have that platform, so we couldn't responsibly bring the feature to Ace without putting your Wi-Fi password, for example, at risk on an open network during configuration. And without the improved Wi-Fi capability of the headset itself, we couldn't get the kind of ‘walk around your home’ networking performance that we wanted for the product.

But to your point, I'm really proud of the original Ace. They're super comfortable. They've got great battery life. They've got best-in-class noise cancellation performance. They're great headphones.

But it is the best example of how, in that chapter, the company had forgotten how important it is to make sure that the products we make are really part of the system, and not just an exceptional pair of headphones. Because let's be honest, there are lots of exceptional headphones in the world.

And so from the jump on Ace Ultra, we knew that we had to do the thing that our customers most wanted, which was to make the headphones an intuitive part of the [Sonos] system.

There are two mechanisms for engaging with this feature. The first is really kind of magical: you can walk up to any speaker that's playing in your home, press a button on the headset, and the music just jumps from the speaker into the headphone. You can then walk to some other part of your house, press the button again as you stand in front of [another speaker], and the music or content jumps from the headphone to the speaker. No app, no delay, no Bluetooth; it's really just the Sonos ‘Fabric’ tech facilitating this effortless, seamless experience around the home.

I said there were two things that unlock this capability. The first is the hardware that lives in the new headphones. The second is something that happens in the operating system itself, which is that — with the launch of Amp Multi — we built a virtualization technology that allows a single instance of the Sonos OS to host multiple instances of the runtime. And what that's for in Amp Multi is that you’ve got one box running four rooms.

We've taken that capability and extended it to the whole modern speaker family, so that this Sonos Play that's sitting on my desk with me can run more than one instance of the runtime. And so the headphones, which don’t have the CPU horsepower to run the full Sonos stack, doesn't have to — because it can nominate a speaker on your network to be its proxy.

And so when you when the music jumps from the speaker [to the headphones], we spin up a virtual instance of Sonos [software] in one of the speakers — it might be the one you're standing in front of, it might be another one that's less utilized — and send audio over the network as a stream to the headphones, rather than running the full stack in the headset.

And what's cool about this is whatever speaker is doing that work still has the performance overhead to run as a speaker to play music for someone else in the home. So it's a deceptively simple feature that has a lot...underneath it.

Sonos Fabric is the moniker that we've given to the fundamental technology of Sonos that bonds, and groups, and links headphones. And the other exciting thing that we're doing with Fabric in this release is that we're enabling what we call portable surrounds, which is the ability to take a Sonos Play or a Move and to use them as surrounds in a home theater setup.

So the idea is you've got Sonos Play or a couple of Moves in the kitchen, the backyard, whatever — for movie night, you can bring them in, set them down [behind you], and use them for the surround Atmos experience in your family room. And then when you're done, put them back to work in other places in your house.

What's so cool about this technologically is that the way a speaker becomes part of your home theater is fundamentally technically different than the way that a speaker joins the grouping architecture of Sonos [for multi-room], because the latency requirements between the soundbar and the surround speakers are much higher.

So when a speaker joins a home theater configuration, it actually, from a networking standpoint, connects directly to the soundbar. It doesn't go through your home network. And so when you take a speaker that was in the kitchen as part of the grouping architecture, and move it into a [home theater] satellite position, a whole bunch of magic stuff has to happen.

First of all, the soundbar and the speaker have to recognize one another. And so we use the accelerometer in the portable speaker to understand that it's been picked up and set down. And when it's set down, we send an ultrasonic signal. If there's a soundbar in the room that can hear it, it sends a signal back, and they do a handshake to figure out left-to-right distance, to automatically tune, and spin up to the home theater networking connection — all without you doing anything. No app involved at all.

It’s another one you kinda have to experience to really appreciate, but I’m super excited to begin to ship these much more flexible home theater configurations. It's unlike anything that the rest of the industry is delivering today.

Matt Bolton: Can you tell me about the new driver design in the Ace Ultra? What did you want to change compared to the previous one?

Tom Conrad: We have just some of the most sophisticated audio engineers in the world working on these products, and they wanted to deliver deeper bass, clearer details, and a clearer soundstage with less distortion. We had the opportunity to go in and build a custom driver for Ace Ultra to tune it in the studio with some of the best, and award-winning audio engineers in the world.

You have to hear it and compare it side by side with Ace, but I think you'll find that it has a deeper bass response, clearer detail, [better] balance across the audio spectrum.

Matt Bolton: I thought it was really interesting that the Beam Ultra comes in kind of as a replacement for where the original Arc ended up in the product range following the launch of the Amp Ultra. Why did you go down the route of squeezing more channels into the smaller Beam body, instead of sticking with the bigger and more flexible Arc shape to fill that role?

Tom Conrad: Our goal was to deliver truly the best midsize soundbar in the world. There are still plenty of people around the world that want a smaller form factor. They want something that can tuck underneath the television when it's sitting on a credenza, or for a smaller television. Or, frankly, they just want an object that doesn't call as much attention to itself as a larger soundbar.

And yet they want full true 7.1.2 Dolby Atmos. They want a height experience that puts them really inside the action. And so with Beam Ultra, we built a system that has nine drivers, including two upfiring drivers, that bounce audio off the ceiling and the walls to create that immersion.

If you spend time with any home theater customer, you understand that they really want a pristine dialogue experience where the dialogue always cuts through the busy scenes. And so the center soundstage of Beam Ultra is dramatically improved.

They also want bass that lands with a tremendous weight without having to put a subwoofer into the room. They want music that's expansive and has a clear soundstage. And the creators want products that recreate their intention in the family room with great accuracy.

It's so hard to talk about these products that deliver these sound experiences before people have the chance to experience them in person. But everyone we're sharing Beam Ultra with just can't believe the amount of sound experience and presence you get out of such a small soundbar.

Matt Bolton: So was it the case that when you were speaking to customers, you figured out that if people are buying at this kind of midrange price, they more often want the smaller size rather than the bigger size — even if that adds complication on your end?

Tom Conrad: That's right. I mean, we already have the best-in-the-world large soundbar in Arc Ultra. And so the question really was for the rest of the market: what can we do to make a huge generational leap in capability here? And that's the place that Beam Ultra comes from.

Matt Bolton: It feels like you could have gone with 5.1.2 channels on the Beam Ultra, and that probably would have made people happy if delivered with higher quality. What was there in the development that meant that you settled on 7.1.2 as necessary, or the right option?

Tom Conrad: Well, I think part of it is that we deliver in Beam itself such an exceptional experience around the 5.1.2 experience. We really wanted to find a way to expand the product portfolio rather than just refreshing Beam. And so, in this moment, Beam will stay in the market as our solution for the customer that is more price-conscious and who wants that 5.1.2 experience, and Beam Ultra sits above it to deliver a more immersive Atmos surround experience.

• Nvidia unveiled DLSS 5 at Gamescom 2026, showing it off in NBA 2K27
• DLSS 5 will launch alongside NBA 2K27, and be available from September 3, 2026
• DLSS 5 is exclusive to RTX 5000 GPUs, but Nvidia is working to eventually make it available on older GeForce cards

It's no secret that Nvidia's reveal of DLSS 5 earlier this year led to instant controversy and a backlash from gamers, not to mention game developers. The technology was branded as generative 'AI slop', effectively changing the look and style of character models, notably Leon S. Kennedy and Grace Ashcroft in Resident Evil Requiem.

Since then, Nvidia has worked on developing its neural rendering tech, and DLSS 5 is now ready for launch — and it's arguably being used in the game that's most appropriate for it.

DLSS 5 will debut on September 3, 2026, and will be first available in NBA 2K27 on September 4. The new tech will be exclusive to RTX 5000 GPUs, but Nvidia made it clear that it will explore ways to bring DLSS 5 to older generations of GeForce graphics cards eventually.

At Nvidia's Gamescom 2026 booth, I was treated to a brief hands-on sample of DLSS 5 running in NBA 2K27, on multiple systems including an RTX 5060 gaming laptop and also on GeForce Now.

Much to my surprise, DLSS 5 looks absolutely amazing with NBA 2K27's environments and character models, providing a level of realism that can only be described as the next leap in visuals for gaming.

The level of detail in character faces in terms of lighting and shadows is truly next-level, and it's all possible thanks to AI-enhanced lighting on the rendered frame, which Nvidia states is trained to 'preserve artistic intent'.

When DLSS 5 was first revealed in March, I was bewildered by what Nvidia was attempting, which appeared to be generative AI used to make everything in-game more lifelike. If it wasn't clear already, I have strong feelings against generative AI in gaming, but my initial fears were misplaced, as this isn't what DLSS 5 is about.

DLSS 5 uses three different models, with each including options for structure intensity, tone intensity, and masking. Structure and tone are both responsible for aspects such as ambient occlusion, contact shadows, reflections, and broader lighting.

Masking allows manual or automatic control of where each effect is applied, and Nvidia has designed DLSS 5 for developers to have full control over the final frame that's rendered.

The DLSS 5 model running in NBA 2K27 was fine-tuned directly by the game's developer, not Nvidia, and the result is way different from the first glimpse of DLSS 5 in March. What was illuminating here is that the team at Nvidia stressed that the initial reveal had the sliders for each of those models at maximum levels.

As I saw with NBA 2K27, when put in the hands of developers, it's easier to see just how great DLSS 5 is when used appropriately. Developers will be able to adjust the intensity for each scene for both the environment (foliage and other objects) as well as character models.

Performance is a different ball game too

DLSS 5 isn't exactly easy to run, which isn't a huge surprise considering how realistic the results are. However, Nvidia has made significant progress in six months: DLSS 5 required two RTX 5090 GPUs to function at the initial reveal, and now works with any RTX 5000 series GPU.

The RTX 5060 gaming laptop running NBA 2K27 displayed very smooth performance, only using Frame Generation x2 with DLSS 5 enabled. This was a big surprise to say the least, and suggests that it may not take as long as expected to introduce this new tech to RTX 4000 series GPUs (and older).

My only concern at this stage isn't related to Nvidia at all, in fact. DLSS 5 on its own now looks very useful for producing lifelike visuals, and seems set to be the future for impressively detailed graphics.

Visual Concepts, the developer behind NBA 2K27, has done enough here to quell my fears that DLSS 5 would somehow mess with game art and design direction in a negative way.

However, we're yet to see how other developers will implement the tool in their games. I'm somewhat worried that many could fall into the trap of using DLSS 5 as a shortcut for great game visuals, rather than an added bonus on top of already well-crafted graphics.

I shared similar concerns regarding Nvidia's Multi Frame Generation, and how developers may rely on this very useful tool as a band-aid for poor game optimization.

Fortunately, though, if NBA 2K27 is anything to go by in terms of what to expect from the likes of Capcom, Bethesda, and Ubisoft, then I'm excited to see DLSS 5 implemented in upcoming games. After all, it is an optional game setting, so if you don't like it, you don't have to use the tech.

ChatGPT Work’s new event-triggered scheduled tasks offer an intriguing alternative to the compulsive email checking that fills my days. The feature essentially acts as an AI-powered secretary, screening your Gmail inbox, Slack channels, and other communication channels, then alerting you or responding as you request in a prompt.

I decided to use the feature as an aggressively selective email assistant. ChatGPT could watch everything arriving in my inbox, but it was only allowed to interrupt me when a message created an actual obligation, and it should suggest how to respond and have a draft ready to go.

Event-triggered tasks are available on eligible plans in ChatGPT on the web and mobile. I used ChatGPT on the web and connected my Gmail account when prompted. You can see the prompt I used below.

The current event triggers support new Gmail messages, which can optionally be narrowed by sender or subject. I wanted ChatGPT to judge every incoming message by its contents, so I left the trigger broad and made the instructions selective.

That distinction matters. The trigger decides when ChatGPT wakes up, while the prompt decides what it does once awake. In my case, every new email could prompt a run, but only messages meeting my criteria were supposed to produce an alert.

I closed my inbox and waited to see how the afternoon went.

Here's how to do it

My original prompt was fairly basic. I told ChatGPT, "When I receive an email that requires me to do something, or comes from a family member, tell me what I need to do and draft a potential reply."

It still left plenty of room for interpretation, and I was wary of spam getting listed as something I needed to do something about, but it came down to being a good test for ChatGPT.

After a bit, I did clarify to ChatGPT that it should alert me about any email with a single sentence explaining who was writing and what they might need from me. I also made sure to add that the AI shouldn't reply without my input.

I also opened ChatGPT’s notification settings and made sure task alerts could reach me outside the inbox. Depending on the account and notification category, ChatGPT can offer push, email, or text message delivery. I avoided email for obvious reasons, since an email announcing that ChatGPT had found an email would have possibly confused the AI.

With the task running, I shut Gmail and returned to everything else I had planned for the afternoon. For the first several minutes, I still felt the pull to check, but eventually, I stopped thinking about it.

The absence of constant email checks made the afternoon feel longer in a good way. I could finish a task without repeatedly glancing at another tab to make sure the internet had not developed an emergency specifically for me.

Only 3 interruptions

The first ChatGPT alert concerned a message from my bank about two hours after setting up the alert. ChatGPT identified the sender, explained why the email appeared to require attention, and told me what I needed to review. I opened that message directly, made sure it didn't actually require a response, and closed down the inbox.

The second alert came from a family member. This was a more interesting test because family messages rarely contain the tidy language that makes conventional email rules useful. They can require a decision or a quick response without the more business-style setup and keywords in professional messages.

Still, ChatGPT recognized that the message expected something from me and brought it to my attention and wrote a brief reply draft for me to approve. I added a little, but ChatGPT nailed the main points I needed to make, and it wasn't the kind of email that needed a lot of personal detail, so a few minutes after the alert arrived, the response was out the door.

The last alert concerned tickets I had recently purchased and a change to the itinerary for an upcoming event. In this case, the alert was very necessary as it changed some of the other plans for that day, and I had to let others know of the shift. That wasn't in ChatGPT's suggested response, of course, but just getting the heads up was useful.

Those were the only three times I looked at Gmail all afternoon. Other messages arrived, but ChatGPT did not summon me to look at them. When I went to my inbox afterward, I clocked only two emails that I might have wanted to deal with earlier, and neither had to be handled immediately. Nothing terrible happened because I didn't see a promotional email at the moment of delivery.

Anyone trying this should begin with a short test period and review the first few runs in ChatGPT’s Scheduled section. If ordinary receipts keep slipping through, add a clearer exclusion. If requests from real people are being missed, explain what counts as an expected response and allow ChatGPT to flag uncertain cases.

I would also keep the first version informational. Ask ChatGPT to summarize the obligation and point you toward the email, then make the decision yourself. It's a feature worth trialing. Many AI tools try to prove their value by producing more text, more summaries, and more notifications. This one worked because it knew when to leave me alone.

Skynet has missed another appointment. Depending on which Terminator timeline you prefer, the murderous artificial intelligence should have become self-aware in 1997, 2004, 2011, 2017, or perhaps last Tuesday. Each date has passed without a nuclear exchange. The machines have mostly occupied themselves by flattering users, mangling fingers in photos, and confidently inventing facts for gullible people to believe.

That has not stopped Silicon Valley from treating artificial general intelligence as both an imminent miracle and an approaching monster. AGI supposedly marks the moment a machine can match human intelligence across a broad range of tasks, though nobody can agree on which tasks, which humans, or what counts as intelligence.

Nvidia CEO Jensen Huang thinks it already happened and nobody noticed. “For many tasks, we could say that we’ve already achieved AGI,” he said during Nvidia’s latest earnings call. He added that such milestones are “kind of senseless at this point.” So much for Skynet.

But he is right that AGI has become close to useless as a measure of progress. Companies can redefine it according to whatever their newest model happens to do well. It's a promotion, not a scientific standard.

Apocalyptic decoy

But AI isn't harmless even if it's not inclined to play with nuclear bombs. There are less theatrical dangers from AI fueled by old-fashioned human greed and malevolence.

Today’s AI can produce endless fodder for scams. It can clone voices and faces well enough to trick loved ones out of money, use phishing messages to hack secure servers, and flood social networks with synthetic accounts that never tire of arguing. None of this qualifies as AGI under any serious definition, but the distinction offers little comfort to the person hearing a fake recording of a relative begging for money.

The fixation on AGI may actually make life harder for regulators.

Profit isn't a good yardstick

Huang argued that the concept of AGI should be replaced. We should consider if it is “doing productive and useful work” and “generating profitable tokens.” That makes sense for Nvidia, but to ordinary people worried about AI, profitability is not much of a safety test.

Scams are profitable. That's why there are so many. Firing human employees for an AI model that is 30% as good at 10% the price is profitable (in the short term). Neither result is something to brag about when describing the result of your product.

Retiring AGI as the organizing obsession and fear of the industry makes sense, but that doesn't mean AI companies can relax. Intelligence is too complex to serve as the sole trigger for concern. Power might be a better lens. We can measure who uses AI, and how, as well as who is expected to clean up when they fail. Then we'll know how explosive an impact it's had on our lives.

Conversations about liability, identity verification, ownership, and other legal details don't have the visual excitement of mushroom clouds, but regulators should make AI companies discuss them instead. We need to keep an eye on the human hands on the keyboard, not the perpetually delayed Skynet.

The way people search for information is changing. Increasingly, they’re turning to AI tools such as Google AI Overviews, ChatGPT, Perplexity and Claude instead of traditional search engines. That changes what businesses need to optimize for.

Content quality, authority and trust still matter enormously. But they're no longer enough by themselves. Even the best content won’t do much if AI struggles to find it or understand it.

The CMA's decision to give publishers more control over how their content appears in Google’s AI search is another reminder that AI is changing how people find information online. That's creating new opportunities, particularly for smaller organizations. AI doesn't necessarily favor the biggest brands; it favors sources it can understand and trust.

Why aren’t search rankings enough anymore?

Search rankings alone aren’t enough anymore because many people now receive answers directly from AI rather than clicking through search results. Traditional SEO has always been about helping people find your website.

You create useful content, build authority, improve the technical health of your site and compete for the search terms that matter most. The difference is that users don't always follow the same journey anymore.

Someone researching providers might never look beyond an AI-generated summary. Instead of opening multiple browser tabs and comparing businesses themselves, they ask a question and receive a recommendation in seconds.

For example, the best independent café within walking distance that's open now, clothes stores nearby running a 50% sale, or where to find a family-friendly Italian restaurant with good reviews. The AI does the searching, filtering and comparing before presenting a shortlist.

That changes what businesses need to optimize for. Success is no longer measured purely by where your website ranks—it's also influenced by whether AI systems recognize your content as accurate, trustworthy and useful enough to reference.

AI doesn't create answers in isolation. It still relies on information published across the web. Websites are the source AI uses to understand businesses, compare different services and then create a recommendation. If anything, they're becoming more important because they now serve two audiences: the people visiting them and the AI systems interpreting them.

That's why treating AI visibility as purely a marketing challenge misses part of the picture. From an engineering perspective, the quality of the underlying website matters just as much as the quality of the content published on it.

Why does website performance matter to AI search?

Website performance matters because AI systems can only recommend content they can easily find, access and understand. Before AI can reference a website, it needs to crawl it, collect information and understand the content before it can include that information in a response.

Sites that are too difficult to navigate, slow to load or poorly structured make that process more difficult. Important information that’s buried behind scripts or inconsistent page structures also makes it harder for AI to interpret, meaning they’re less likely to use it as a source.

That's why technical performance influences visibility in ways many businesses haven't considered before. Website speed, uptime, structured data, accessibility and clear information architecture have traditionally been viewed as operational concerns. They're becoming visibility signals too.

Performance has always mattered because users expect fast, reliable websites. AI search gives businesses another reason to invest in those fundamentals.

What should businesses do differently?

The first step is to look at the parts of your website that are often overlooked. Does it still perform well when traffic suddenly spikes? Are pages loading quickly for real users? Can people, and AI systems, find the information they're looking for easily?

Good hosting and reliable infrastructure play a much bigger role than many businesses realize. A website that's fast, stable and able to handle busy periods gives customers a better experience and makes it easier for AI systems to access and understand your content.

If your website struggles when demand increases, it's not just visitors who notice. AI-powered monitoring tools can also help identify unusual traffic patterns and potential issues before they begin affecting users or AI systems trying to access your site.

It's also worth looking beyond performance scores alone. A website might perform well in a controlled speed test but still struggle during a seasonal sale, a product launch or a viral social media campaign. Those are often the moments when visibility matters most, and when businesses need confidence that their infrastructure will scale without compromising the user experience.

One of the simplest things businesses can do is make their website easier for AI to understand. That means keeping pages well organized, using structured data where it makes sense and making sure important information isn’t buried or difficult to find. Paired with reliable hosting and solid infrastructure, businesses can give both customers and AI a much better experience.

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• Bailey warns that AI models are getting better at exploiting vulnerabilities
• Concentrated and intertwined investments could lead to a correction
• Not all countries and entities are as well equipped as each other

Bank of England Governor Andrew Bailey has warned that frontier models are becoming a bigger risk to financial stability.

In a letter to G20 finance ministers and central bank governors ahead of a major meeting, Bailey warned that the increasing sophistication of AI models could be changing the speed, scale and impacts that cyberattacks could be having on the sector.

Bailey is especially worried about the interconnected nature of global finances together with AI systems that are quicker at discovering vulnerabilities, faster at exploiting them and more accessible to criminals.

Bank of England warns of AI's impacts on global economy

"The global financial system is highly interconnected, and cyber disruption can spread across jurisdictions through common technology providers, shared infrastructure, and cross-border financial activity," Bailey wrote, arguing that a concentration risk could increase the reach of any incidents.

Different countries also have significantly different levels of cyber resilience, regulation, incident response capabilities, legal protection and recovery readiness – so while one target country may be at a lower risk, other countries involved in an incident could compromise all others.

In his warning, Bailey argues that financial institutions should prepare for much more severe scenarios rather than smaller, more isolated cases.

His words also highlight a second, separate AI-related financial risk. Bailey argues valuations of risky assets remain elevated and is worried that a major shock, or multiple simultaneous shocks, could expose financial vulnerabilities. For example, multiple AI firms, cloud providers and chip companies are highly intertwined financially, and this cross-investment could be disastrous in any future corrections.

The G20 Finance Ministers and Central Bank Governors Meeting is now ongoing – any remedies or responses to the letter are yet to be confirmed.

Ten years from now, it will feel astonishing that there was around a fifty-year gap between the mainstreaming of Service Level Agreements (SLAs) and Experience Level Agreements (XLAs). While SLAs have been common since the 1980s, it’s only in the last few years that XLAs have started to gain momentum.

The imminent adoption of ISO 20000-18 will propel XLAs even further. To comply with the new standard, organizations will be required to manage the digital experiences of both customers and users, integrating Experience Management (XM) into existing governance, management, and implementation processes.

Suddenly, demonstrating that your business delivers good digital experiences isn’t just an esoteric nice-to-have, but a critical part of winning new contracts. But although the idea of XLAs has been around for a while, few organizations have the institutional knowledge of how to practically implement these changes.

Why bother with XLAs?

Before that, it’s worth asking why XLAs are necessary in the first place. Understanding what SLAs are missing is essential before starting to reformulate existing processes.

The simple answer is that what a system shows and what a user experiences are not the same thing. It doesn’t matter if a cloud application is ‘technically’ available if the experience is so glitchy that you abandon it after twenty seconds to never use it again.

That’s not to say that SLAs are now useless. Metrics like performance, ticket resolution times, and system availability still matter – they’re just no longer sufficient. You need to know whether your system is functioning, but you also need to know if an application is unresponsive for the user.

XLAs offer a different framing for the relationship between supplier and customer. Looking at metrics, like session stability during peak trading hours, tells you a lot more about how employees are able to perform than many SLA numbers.

What does measuring ‘experience’ mean?

It can be hard to know where to start as experience is a very broad term. Plus, it’s hard to measure something if you haven’t even defined it yet. As a result, there are hundreds of different metrics for experience measurement which fall, broadly speaking, into three main buckets:

Technical Data (T-Data): The technical performance of the product or service (for example, outages, freezes, crashes, etc.) provided by the Nexthink DEX platform.

Operational Data (O-Data): How well a given service performed and how well it was supported, which is usually captured through SLAs/KPIs.

Sentiment Data (X-Data): How the users themselves describe their feelings / perception (the truth according to them) toward a digital tool or service.

When considering how to reengineer existing processes to be ISO compliant, it’s essential to have data sources from all three. Operational data is already generally well-covered by SLAs, and sentiment data is achieved via targeted survey campaigns that capture examples of technical issues. It’s the technical data that can be tricky for organizations attempting to use XLAs.

The technical data is essential because it brings together complete visibility across every endpoint and interaction, which means faster diagnostics. If an organization spots that users of a given browser are regularly having issues and can cross-reference that with the applications most disrupted, it can quickly identify root causes and implement remediations.

Moreover, this technical data is critical to pre-emptive and preventative IT by flagging which specific devices are non-compliant with the latest updates.

Embracing DEXOps

However, one of the key mistakes that businesses make when bringing in Digital Employee Experience (DEX) platforms for the first time is starting by asking ‘what can this platform do?’ This is the wrong question to ask.

As the new ISO itself attests, IT’s job is no longer simply pushing technology, but delivering the services and infrastructure that employees need in ways that improve outcomes across the business through an experience-centric approach.

This is the core principle at the heart of DEXOps, a philosophy that takes experience as the central pillar of an employee’s day (and therefore their productivity and engagement).

DEXOps operationalizes experience by starting with the immediate concerns and frustrations of individual business units, understanding what the consequences of these issues are, and finding technological solutions that eliminate the root causes of digital friction.

Rather than focusing on technical capabilities, IT leaders should start by asking other business units:

What are your team’s top priorities?

Where are you experiencing friction or risk?

What does success look like for you and how is that measured?

Do you know what value you bring to the overall business?

It’s amazing how much more engaged other parts of the business will be when, instead of diving into ISO compliance requirements, the conversations revolve around how their most pressing challenges can be fixed.

XLAs are an opportunity as well as a challenge

Many businesses immediately tend to view new regulation as a burden. But ISO 20000-18 and the increasing role of XLAs also represent an opportunity to accelerate the changes already happening to the role of the IT department. The shift from infrastructure to experience is tricky and requires businesses to be open to significant realignment across traditional roles, responsibilities, and metrics.

Some organizations will continue to ignore the importance of experience, but the broader changes that have driven the drafting and implementation of the regulations are here to stay. Businesses that see the new standard as an opportunity to use regulation to create a competitive advantage will have a crucial head start on those that are still stuck in the old, outdated, reflexive IT model.

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Over the past year, I've had countless conversations with treasury and payment teams at major banks, and I'm often asked the same question: how do we compete with wallet-native fintechs on cross-border payments? But as I tell these teams, this isn't the right question to be asking anymore. Banks no longer need to play catch-up; now they can play to win.

It starts with Swift's payments scheme: the rulebook, built with more than sixty banks, that commits participants to full-value delivery, upfront fee and FX transparency, and end-to-end tracking on international transfers. It's already live across major corridors.

And now the real-time domestic rails banks already use to move money instantly within a country are being extended to handle the cross-border leg of a transaction too. Together, banks can extend their reach into digital wallets across the globe by connecting to networks that already exist, rather than building new ones from scratch.

That reach matters well beyond the traditional remittance corridors most people still picture when they think about cross-border payments. While global remittances represent huge sums of money ($905 billion in 2024, according to World Bank estimates – up 4.6% on the previous year), they are no longer the whole picture.

Increasingly, the volumes crossing borders come from businesses paying each other, platforms paying creators and freelancers, and small firms invoicing overseas customers directly. Every one of those flows still needs to arrive somewhere the customer trusts, and that is where banks already hold five advantages that fintechs cannot easily replicate.

Rather than worrying about being outmaneuvered by fintechs, I challenge you to think about the advantages you already have. So, here's a better question: which of these will you put to work first?

The account relationship starts and ends with you

Every cross-border payment revolves into an account somewhere, and for the majority of consumer and business transactions, that account sits with a bank. Fintechs and wallet providers can originate volume, but at some point, in the chain, funds still land in a regulated deposit account banks already hold.

What has changed is the ability to extend that advantage outward: connecting existing account infrastructure directly to wallets and local schemes elsewhere, rather than routing through a chain of correspondents to get there.

Holding the funds is what holding trust means

In the customer experience at a bank, whoever holds the funds determines whether a payment arrives intact or arrives short, deduction by deduction, across a chain of intermediaries.

Swift's payments scheme commits participants to delivering the full value of a payment with no deductions along the way. Banks are the natural home for that commitment because they already hold the funds under direct regulatory oversight, rather than passing them through intermediaries without transparency.

Supervision and regulatory oversight are what customers are already paying for

Banks operate inside some of the most heavily supervised frameworks in financial services, and that supervision is precisely what customers are buying when they choose a bank for a cross-border payment.

Combined with the move to ISO 20022 messaging, now the mandatory standard for cross-border payment instructions, which carries richer, more structured data through CBPR+, banks can now show customers more of what is happening to a payment in transit, not less, under the FATF travel rule being applied.

No fintech or wallet provider can replicate the confidence that comes from being the regulated counterparty in the chain and the enforceability of the FATF travel rule.

Trust built over decades cannot be matched and built in months

For high-value corporate and SME flows, trust is the existing product offering, and it does not transfer easily to a newer entrant, however good its infrastructure. That matters more, not less, as the flows crossing borders diversify: SMEs trading directly with overseas buyers, gig workers and independent creators paid from platforms based in another country, and B2B and B2C commerce settling at volumes few payment providers were built to handle a decade ago.

Every one of those flows still needs a bank willing to stand behind it. Banks that have already spent decades building that trust are better placed to extend it into these newer categories than to concede them.

Only the bank can control and own the whole journey, start to finish

Real-time domestic rails are now reaching for the border. The Clearing House's RTP network, for example, is targeting September 2026 to support correspondent bank activity for one-leg-out international payments, giving banks a way to originate the domestic leg of a cross-border transaction on infrastructure they already use daily.

Bank of America has already announced a cross-border real-time payments service built on Swift and its own CashPro platform. Neither move required building new global rails from scratch. Both give the bank full visibility and control from origination to final credit, a position no correspondent chain or third-party wallet provider can offer in the same way.

None of this is guaranteed, and none of it is evenly distributed. Community and regional banks without the scale of a Bank of America face a real cost to adopting real-time and ISO 20022-native infrastructure, and regulatory harmonization across corridors remains uneven even as individual schemes go live.

The advantage described here is available to banks that choose to invest in using it. It is not conferred automatically by size or incumbency alone.

Banks already have most of what this moment requires: the accounts, the funds, the regulation, the trust, and now the connectivity to reach beyond their own rails. What happens next depends on who decides to use it.

The opportunity is clear: Banks have all the foundational strengths to capture the rapidly growing real-time cross border payments-to-wallets market. By combining their established customer base with modern payment networks with the SWIFT scheme solution, banks can transform cross-border payments into a faster, more accessible, compliant, and customer-centric experience.

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Autonomous AI agents are already running inside core infrastructure – executing code, applying policies, and managing DevOps functions. And the projects keep stalling, because the security models they’re being wired into were built for a world that no longer exists.

Retrofitting non-deterministic actors into those models is costing engineers time they don’t have, and it introduces risk no enterprise can manage.

Many projects have stalled amid concerns about deploying without a robust security foundation – and with good reason. We’ve already seen an agent delete a company’s entire production database, and its backups, in nine seconds. Security teams are being asked to stop scenarios like that with tools built for a world of two actors. The cracks are starting to show. Something has to change, or innovation stalls under the weight of its own controls.

AI agents require a new identity model

The pressure on production and engineering teams to speed up delivery is very real and pervasive. So they often fall back on old habits like granting agents broad privileges and treating them as any other microservice.

But agents are very different from machines; they are error-prone and non-deterministic, just like humans. Yet, operating at machine speed, 24/7. Agents can delete entire production databases in nine seconds. How many humans do you know who could do that?

And this brings me to the crux of the problem. Identity systems were built for a world with two kinds of actors – humans and machines – but there are now three. Trying to fit agentic AI into outdated systems makes each agent a potential source of compromise, and one that can execute thousands of actions across infrastructure in seconds.

Yet this is what engineers are asked to do; stop catastrophic scenarios with legacy IAM tools that are breaking down. The cracks are starting to show.

Why the old model breaks

Historically, identity fragmentation has plagued engineers working with Kubernetes clusters, cloud platforms, container orchestration, CI/CD pipelines, databases, etc.

For a human workforce, this was manageable. Humans are trackable; they log in and log out. They are slow enough that visibility gaps rarely turn into immediate incidents.

Enter agentic AI, and the speed gets turned up to the max. Suddenly, teams are inundated with thousands of activity logs, and they lack the ability to effectively contain the agent before it executes unauthorized changes.

Trying to enforce strong authentication and short-lived privileges would mean building individual integrations for every tool in the stack. It makes AI hard to scale when each tool uses a different integration protocol.

Rather than focusing on innovating with agentic AI, engineers are forced to stitch together IAM, infrastructure, and secrets by hand — with no consistent identity, no visibility into agent actions, and every team building its own container or VM workflows from scratch.

But creating a new tool to handle a third identity type is the worst reaction the industry could have. It would double the work for engineers, as they would need to rebuild the identity policy from the ground up and introduce greater anonymity. A new identity silo is anonymous to other siloed systems, making it even harder to catch attackers.

This leaves us with the question of how enterprises can control an agent's behavior. The solution isn’t about adding to the tech stack or implementing more tools; it's about changing our identity models to eliminate anonymity entirely.

AI control means zero anonymity

To remove anonymity from infrastructure, enterprises must give every actor –spanning humans, machines, workloads, and AI agents – first-class identities, cryptographically secured by a hardware root of trust.

Ditch static credentials entirely. Eradicating API keys and passwords eliminates the credential sprawl that causes breaches, as well as the threat of secrets being stolen or handed over to the wrong actors. With identity rooted in real-world factors, attackers cannot impersonate a trusted machine and trick an agent into exfiltrating a database.

But strong authentication in itself is not enough. AI agents, like all other actors, need to adhere to zero-trust principles. This can only happen when siloed systems are replaced by an infrastructure layer in which agents have the exact same identity type as the machines they run on and the humans who authorize them.

Agents should operate with short-lived privileges tied directly to specific actions authorized by a human user. Privilege attached to the action, not the actor. For example, an agent generating code must inherit its mandate from a human owner with matching authority, restricting privileges to only the specific data tables required for that task.

Non-deterministic actors also require a contained, trusted execution environment before touching production infrastructure.

With no default privileges, the blast radius is heavily bounded. But this can only be achieved when a single policy is set by a single system for all identities.

Identity policy can also be introduced as an enforcement layer between the agent and its inference endpoint, so behavior is controlled before instructions are ever executed.

With a unified architecture, identity becomes the control plane for AI

AI agents are unlocking immense opportunities in enterprise environments, especially when deployed in live infrastructure, where they deliver the most value. From managing routine changes to fixing deployments in real-time, the possibilities are endless. Succeeding with AI in such critical business operations requires tight control of behavior.

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Following the 2026 FIFA World Cup, the organizers were unsurprisingly keen to capitalize on the worldwide attention the tournament secured and keep fans across the globe excited for the next big game.

To do so, FIFA partnered with Globant to redesign its global digital fan experience, using the latter's Glob.AI and AI Pods to try and create a more personalized and integrated ecosystem for football fans across all of FIFA's platforms and tournaments.

We spoke to Martín Nanni, Chief Experience Officer - EMEA, at Globant, to find out more.

• How did this partnership come about? And why is it only being released now, so soon after the World Cup?

The recent news is a continuation of a partnership that has been growing since 2022. This announcement marks the next step in their ongoing partnership and the developments that have taken place.

Recently, FIFA tested Globant’s newly launched Glob.AI model and AI Pods to improve the digital fan experience. Now that Glob.AI is public, it’s a good time to share how FIFA is using this technology.

Major tournaments like the World Cup give FIFA an opportunity to engage with fans globally, including those who may be interacting directly with FIFA for the first time. The main focus now is using AI, real-time data and individual fan identities to build on that engagement and create continuous, more direct relationships that don’t stop after a tournament.

• You talk about providing a “joined-up” experience for fans - what does that look like in reality, and why should they be excited for this?

We want a joined-up experience that means fans won’t feel like they have to start over every time they interact with FIFA. The aim is for fans to feel recognised and rewarded for long-term interaction through their single FIFA ID.

The goal is for a fan’s FIFA ID to connect all their digital experiences, whether they’re following a tournament online, using FIFA.com, using the FIFA Tournament App, watching at home, or going to a match.

For fans, this could mean having access to more relevant content, easier access to information they care about, and experiences that reflect their long-term connection to football, not just one event.

Globant has helped integrate FIFA ID across FIFA’s digital ecosystem, including experiences such as Rewards and the FIFA Tournament App, so it can act as a single user identifier across different products and engagement tools. This means that fans who engage with FIFA during the World Cup can continue that relationship beyond the tournament.

This is similar to the loyalty programmes many fans know from clubs or national teams, where ongoing support is recognised. It’s similar to the way that a local FA may have loyalty points for its long-term fans. The difference is that FIFA is doing this worldwide, across many competitions and digital platforms.

• What role will Glob.AI play in helping create these new fan journeys? How will it use real-time data and create new experiences?

Glob.AI is the AI system FIFA is using. It uses AI Pods, which combine specialised AI agents with human guidance, to help design, build, and improve digital experiences faster.

With Glob.AI, FIFA can build digital solutions that collect data from fan interactions across its digital platforms and use those insights to build a smarter, more connected ecosystem. This helps FIFA better understand how fans engage, what they’re interested in, and how content and services can continuously evolve to improve their experience.

The key is that this system is designed to be adaptable to different tournaments. The platform does not learn or adapt on its own. Instead, FIFA’s editorial teams use the insights it provides to adapt and adjust the experience to the needs of each tournament, without having to build separate platforms for each one.

This could mean faster updates to digital products, quicker delivery of content, better personalisation on FIFA.com or the Tournament App, and more responsive engagement during match days and big tournaments.

• You talk about boosting efficiency - what does that entail when it comes to fan experiences?

The goal here is to give fans more of what they want, quickly.

For example, it could mean building a new app in days, not months or updating fan-facing digital products faster or delivering content during the live match, not three hours later.

The aim is to move from slow, disconnected digital services to a system where FIFA can react faster to fans’ needs. Early tests have shown a 20% boost in efficiency, with quality staying the same or getting better.

What improvements will FIFA see in getting more information about fans? Is the era of the “anonymous fan” ever really going to end?

Fans have autonomy here based on how they want their experience to be. Fans can still decide how much information they share and how personalised they want their experience.

This work lays the groundwork for a more direct and helpful relationship with fans who use FIFA’s platforms. By making FIFA ID stronger and connecting more of the digital journey, FIFA can better understand how fans interact with football all year, not just during one tournament.

The World Cup is a great example. The whole world is watching and that is the time to attract huge numbers of fans into its platform.

This helps FIFA see which content, players, competitions, and platforms different fans like. With this insight, FIFA can offer more relevant updates, better digital services, and more effective sponsor activities.

What plans are now in place for the next few years, particularly with the new domestic season beginning in Europe and the Women’s World Cup in 2027?

The aim is to build out an ecosystem. This means FIFA will keep linking FIFA ID with new and existing digital platforms, and will design these services based on what fans need in different competitions and situations.

The goal is to create an experience that works before, during, and after big tournaments, whether fans are there in person, watching from home, or connecting with FIFA content between events.

Looking forward, the main goal is to make the system more useful and to be able to grow across FIFA’s worldwide schedule. The World Cup served as a great platform for many new fans to engage with FIFA and our goal is to continue enhancing FIFA’s direct connection with those fans and attract even more to enjoy these digital experiences.

And what will success look like to you? How far out will you be measuring the effects of this programme?

Success can be measured in the amount of global fans that engage with it, as well as the fans who stay way beyond the World Cup. This is a multi year and multi tournament initiative.

As this is a long-term change in how FIFA connects with fans online, FIFA will measure success over several years and many tournaments.

In general, success means more fans using FIFA’s platforms and finding them helpful, relevant, and worth coming back to.

There will also be operational measures, such as how quickly FIFA can launch or improve digital experiences. For us, success is building a more direct, continuous and valuable relationship with fans around the world.

For anyone working in IT and familiar with SaaS sprawl, the idea we’ll soon end up with agent sprawl may set off alarm bells. Agent sprawl refers to the rapid, uncoordinated deployment of AI agents across departments, each built on different models, governed by different rules, and often disconnected from core business workflows.

But while SaaS sprawl caught enterprises off guard, this time organizations have no excuse, and need to put measures in place now to avoid repeating the same mistakes. However, right now, most organizations are building agents in isolation and optimizing for local productivity rather than enterprise value.

Thankfully, with the right control layer funneling every agent through the same governance and security standards within an organization, the agent-powered future can be far safer, scalable and simple. 

Agent sprawl is inevitable

Just like SaaS before it, agent sprawl is already happening. Each department within organizations is experimenting with different agent technologies to address specific challenges or work more efficiently.

A sales team may deploy a CRM-based AI assistant to qualify leads, while developers build their own coding agents, and marketing adopts a separate content generation tool. Each delivers local value, but none are aligned or connected to the other, creating a familiar-looking sprawl.

This fragmentation introduces a range of challenges that will only increase over time. With no single owner responsible for how agents are deployed or monitored, there’s a lack of governance and oversight. Security risks increase as agents gain access to sensitive systems without consistent controls.

Teams may unknowingly duplicate efforts, solving the same problems in parallel with different tools. At the same time, many agents are deployed without clear links to business outcomes or understanding the wider context, making it difficult to measure return on investment and distinguish meaningful innovation from experimentation. Lots of AI tools may be exciting, but deliver far more style than substance.

Is sprawl inevitable? Yes, probably. So the question now becomes, how do we control it?

The role of orchestration

Having an orchestration layer can bring order to this complexity by creating consistency in how agents are governed, secured and deployed across the business. For SaaS applications, orchestration provides visibility into what tools already exist and gives a clear view of enterprise workflows.

With agents, it outlines what agents are already available and how they operate, while also controlling how they access data, tools and existing technology. Orchestration is the critical element that turns agents from experiments into scalable business infrastructure, for example, assigning agents to business workflows and measuring their performance against real outcomes.

Too often, we see enterprises experimenting with different agents that have no connection to the core business processes and goals, meaning they fail to deliver any ROI. For example, some portions of a business simply do not require AI technology and will unlikely see benefits in the same way as another department. Orchestration can unearth and filter out agents that are unnecessary, saving businesses money. 

Building trust in AI-driven enterprise

Rather than trusting individual agents, organizations should focus on trusting the system that governs them.

This is where orchestration becomes a trust layer, ensuring every agent operates within clearly defined boundaries with consistent oversight and accountability. Instead of individual teams managing risk in siloes, organizations can centralize control while still enabling innovation across different departments and business functions.

In the same way orchestration brought order to SaaS sprawl, it can do the same for the next wave of enterprise AI. It brings the same ease, security and integration with existing SaaS tools, while ensuring only high-value agents make it into production, supporting innovation and keeping within the business goals and purpose.

As agents become embedded across every function, orchestration will shift from a technical layer to a core enterprise capability. The winners will be those who can control, connect and trust their agents at scale.

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For years, enterprise Software as a Service (Saas) has enjoyed a comfortable position at the center of organizational processes. Users who wanted to raise a ticket, process an invoice or manage a workflow would log into their employer’s cloud application. The user interface was the gateway to the work, and the provider’s business model followed naturally: charge for seats, expand adoption, and grow revenue as more users spend more time inside the platform.

Agentic AI is beginning to change that. As users increasingly deploy AI agents, either to carry out tasks within SaaS platforms or to deliver functionality outside them, organizations require fewer seats – hitting providers’ subscription revenues.

At the same time, AI is making bespoke software faster and cheaper to build, giving organizations a credible alternative to buying another SaaS platform. Why accept subscription costs and vendor lock-in, when cheaper alternatives may achieve the same outcome?

None of this means SaaS is about to disappear, but its position at the center of enterprise software is under threat. SaaS vendors are facing three structural challenges that will reshape the economics of enterprise software.

Agents become the user

The first threat is simple: users may have AI agents carry out tasks within the SaaS platform, reducing the need for human users, or build agentic capabilities outside those platforms altogether.

That is a serious problem for any SaaS vendor whose business depends on people living in the interface. If an agent can resolve a support query, update a record or complete a routine transaction, the user never has to touch the application.

We’re already seeing this in practice. In some IT service management projects, agents now resolve such a high proportion of tickets that organizations need far fewer users interacting with the SaaS platform. As buyers begin questioning whether they still need premium software for the shrinking proportion of manual work, the economics start to shift.

Bespoke is back

The second threat is the one SaaS vendors dislike most: buyers may once again be willing to build new capabilities from scratch.

For the last decade or so, ‘build versus buy’ was often a non-debate. Custom software was too slow, too expensive and too risky, so SaaS usually won.

AI is starting to shift those calculations. As software development becomes faster, cheaper and more adaptive, some solutions that previously defaulted to SaaS start to look buildable – especially when the organization needs a narrow set of capabilities wrapped around its own processes and data. The more customization a SaaS platform requires, the weaker its cost-efficiency and automated-upgrade advantages become.

This does not mean large enterprises will abandon mature SaaS platforms overnight. Their resilience, integrations and compliance capabilities remain valuable. But once customers believe they have a credible alternative, vendors lose pricing power and the assumption that renewal is the obvious decision

Business model redundancy

The third threat is that even when the product survives, the commercial model may not.

Traditional SaaS pricing was built for a world in which value correlated reasonably well with the number of people sitting in the application. That logic gets shaky when agents perform work that would once have required more licensed users, and more operational dependency on the interface.

Vendors can see this. Many are shifting towards consumption-based pricing, exposing services directly to agents or redesigning products for an agentic world. The common thread is clear: once software starts working for people rather than being operated by them, seat-based economics begin to look outdated.

From the customer’s perspective, that could be a good thing. Paying for usage is often cheaper than buying licenses. The challenge for vendors will be proving they’re genuinely adapting to an agentic future, rather than simply repackaging existing products to protect margins.

SaaS is not dead

To be clear, this is not a “SaaS is over” argument. Many enterprise platforms will remain valuable, providing governance, resilience, compliance, and years of accumulated functionality that are difficult and expensive to recreate.

What will change is where value sits. In an agentic world, SaaS will be less the primary interface through which work gets done; more a component in a broader ecosystem of agents, orchestration and systems of record. Some platforms will adapt and become stronger; others will discover that the value they once captured has shifted elsewhere.

As for the corporate users of SaaS, the best advice we can give is not to wait for perfect conditions before acting. No business ever reaches a point where its data, processes or architecture are “finished”, and SaaS providers will continue to adapt their offers.

Meanwhile, experiment, learn and adapt in your delivery of administrative and corporate functions: in some functions you may find that agents drive down SaaS costs; in others, greenfield development may now be viable. In still other cases, adding new functionality using agents that leverage SaaS solutions but sit outside those ecosystems keep future options open.

So do something that works for you now – but keep the option of moving again in a year or two.

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The most damaging AI-related security incident your organization faces this year probably won't originate from external attackers using sophisticated new models. It's far more likely to begin with an employee pasting a client contract, a financial forecast, or a set of HR records into an AI tool because it makes their job easier and nobody has told them why it matters.

Shadow AI is a growing problem, and our research found that nearly half of employees at larger enterprises regularly feed corporate data into AI tools that nobody in IT has approved or governs.

More striking still, 85% of employees continue doing so even when company-sanctioned tools are available, pointing to a governance failure that runs all the way to the executive suite. With shadow AI, sensitive data can move silently outward through channels most security stacks were never designed to intercept.

Adding to the security risk of this unmonitored data flow, the advent of the EU AI Act also means organizations now face specific legal demands on managing AI use. The ability to have full governance over how AI is deployed, governed and monitored, is becoming a regulatory, as well as a security, imperative.

Why the EU AI Act makes this a board-level problem

Shadow AI represents a serious security issue, with IBM's 2026 Cost of a Data Breach report estimating that unauthorized tools contributed to 43% of breaches over the last year Now, the EU AI Act is adding significant regulatory requirements on top of these risks.

The Act's obligations have rolled out in phases; most recently, organizations classified as general deployers of AI have new inventory, data governance, audit logging and transparency obligations as of 2nd August 2026.

Other deadlines have shifted further ahead, with controls over high-risk AI usage, covering areas like recruitment, credit scoring and biometric categorization, set to come into force from 2nd December 2027. AI embedded in regulated products will be covered from 2nd August 2028.

Any organization whose employees use AI systems now has compliance obligations as a deployer, regardless of whether those systems were formally sanctioned.

All organizations using AI should be aware that the AI literacy obligation under Article 4 has been enforceable since February 2025, meaning organizations are on the hook for ensuring their employees are aware of safe and sanctioned AI use.

Rules around high-risk AI usage will also apply to more operations than it may seem at first, including an employee using an unapproved consumer tool for tasks like screening CVs, assessing creditworthiness, and evaluating performance.

These are common tasks that could trigger the full weight of the Act's oversight against a system that the IT department didn’t even know had been deployed.

With penalties reaching up to €15 million or 3% of global annual turnover for high-risk breaches, many organizations are carrying more exposure than they realize.

Why your existing security stack can't see it

The challenge with shadow AI is that it exploits the blind spots between conventional security layers, slipping through gaps that most tools were never designed to close.

CASBs and secure web gateways cannot decrypt conversational data flowing to legitimate LLM domains over HTTPS, for example. From the network's perspective, a prompt containing a full customer database is indistinguishable from any other encrypted web session. Browser extensions are limited to managed endpoints, blind to personal devices and AI embedded within approved SaaS.

Likewise, API gateways are usually built around authorized enterprise deployments, which means they capture the AI activity organizations have already approved while missing the consumer-grade AI tools driving most risk.

These blind spots compound with each other, so an organization running all three layers may still have no visibility into AI activity across a significant portion of its estate, and no means of generating the interaction logs or policy enforcement evidence the Act requires.

What good AI detection looks like

Controlling AI data flows is usually managed by workers performing their duties without malicious intent. However, it’s remarkably similar to defending against an external threat actor covertly accessing your data.

The detection logic needs to match that reality. Endpoint-native detection intercepts sensitive data at the point of movement before it reaches an external AI system, enforcing policy at the prompt level across managed and unmanaged browsers, personal devices, and AI functionality embedded within SaaS tools. It operates where the activity occurs, rather than attempting to catch it downstream.

Given the scale of the potential fines, the ability to prove compliance matters almost as much as preventing security breaches.

Continuous discovery across the estate, including any unsanctioned tools gives organizations the AI system inventory the Act requires.

Granular interaction logs - who used what, when, and what data was involved - satisfy the documentation requirements under Articles 12 and 13, without teams needing to scramble to reconstruct activity after the fact. The same data pinpoints exactly where AI literacy gaps exist, making Article 4 compliance something demonstrable rather than simply asserted.

Practical steps for compliance

Security and compliance teams aiming to comply with the EU AI Act have a clear path to follow.

The starting point is mapping the full AI estate. Discovery needs to extend beyond IT-approved tools to unmanaged endpoints, personal devices on corporate networks, and AI embedded within SaaS.

Data governance must move to the endpoint. Policies prohibiting sensitive data sharing with unapproved tools are not technical controls and, by the time enforcement happens at the network edge, the data has already left.

It’s important to remember that information shared with an external AI system may be retained in prompt logs, incorporated into model training data, or held on servers in jurisdictions the organization has no visibility into. The moment data crosses that boundary, the organization loses control of it entirely, and no policy document will retrieve it.

And since Article 12 requires interaction records that can be handed to regulators on demand, organizations will need to have continuous, automatic auditing of both activity and security measures.

Finally, AI literacy programs aimed at improving user awareness should be driven by behavioral data rather than generic training programs. Activity logs showing where governance failures are occurring - at senior leadership level as much as anywhere else - provide both the diagnosis of the issue and the evidence regulators will want to see.

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The numbers circulating through fintech right now are hard to ignore. Almost 10,000 jobs have been cut across the global sector in 2026 so far. PayPal is eliminating roughly 20% of its workforce. Block has shrunk to fewer than 6,000 employees, and Coinbase cut 14% of its headcount.

And hovering over all of it is a single explanation, repeated so often that it starts to remind us of a dot-com bubble — artificial intelligence. But is that actually what's happening? And more importantly, does it matter if the story isn't entirely true?

When the story becomes the strategy

AI accounted for 40% of all announced layoffs in May 2026, up from much lower levels earlier in the year. At first glance, it seems like a natural explanation: if the technology handles the routine work better than a human, then why not cut costs?

But the thing is, when we start looking closer at why exactly these mass layoffs are taking place, a deeper structural problem appears. So far, AI is just a good justification for how the industry communicates these shifts.

Deutsche Bank analysts identified this neat disguise in January. They called it "AI redundancy washing." Put simply, it’s the practice of attributing workforce reductions to AI adoption when the underlying drivers are far more familiar.

But if it’s not really AI which replaces the workers, then what are the real reasons for all these layoffs? If each company were to be considered individually, it could be anything, from deteriorating unit economics to the pressure of demonstrating operational discipline after years of “growth-at-all-costs.”

Research points to the same explanation: nearly 60% of US hiring managers acknowledged that they emphasize AI's role in layoffs just because it lands better with stakeholders. Indeed, a headcount reduction could have sparked fears about companies' growth models, but framing it as a strategic AI implementation makes it sound good.

The productivity paradox

Let’s assume that some of the companies are indeed replacing their workers with AI tools. In this case, there should be numbers to show it. But they don't share any effectiveness ratios, at least not yet.

A survey of 350 executives at companies with over $1 billion in revenue found that 80% of organizations piloting AI or autonomous technology reported workforce reductions. But after that, many honestly admit that they cut jobs regardless of whether the technology was actually generating returns.

This way, planning to spend another $10 million or more in 2026, companies are betting on a future that hasn't fully arrived. And trimming headcount in anticipation of a productivity windfall that remains, for most, theoretical.

Even if AI is implemented in a particular organization, it’s mostly in pilot mode, sitting on top of existing infrastructure rather than remaking it. Moreover, the AI payback picture is modest: only 25% of AI initiatives deliver the expected returns, and only 16% reach the scale of the entire organization.

McKinsey’s 2025 global AI survey found the same results. While most firms are using AI in at least one function, nearly 70% are still stuck in experimentation or pilot stages rather than scaling AI across the enterprise. And what’s more upsetting is that no more than 39% reported a significant EBIT impact from the technology adoption.

Where the capital is actually going

If AI isn't yet delivering the productivity revolution that many executives promised, then where is the industry's attention? The answer is less exciting than the headlines suggest — only last year, infrastructure and internal business systems received more than $200 billion worth of investments.

For now, fintech companies are under pressure to prove that all the promises made around AI weren't just another cycle of hype. So instead of reinventing financial services overnight, many are turning inward, looking for ways to make existing operations cheaper and easier to scale.

Even though the AI hasn’t brought the desired effect yet, the shift to automation is still important. For the past decade, fintech has largely been focused on the customer experience to offer a disruptive product, but from now on, more effort will go into the mechanisms behind the product than the product itself.

But this doesn’t mean that the "AI replaced the workers" narrative feels complete. Cutting headcount and replacing employees with bots may look good on a quarterly report, but it's not much of a strategy. Cost reduction can improve margins in the short term, but it rarely creates lasting differentiation.

So, in this environment, the winning ones will be those asking and answering honestly to a different question: What can we offer the market today that we couldn't offer a year ago?

The value of AI innovation beyond cost-cutting

Arguably, the more important part of AI adoption isn’t about how it replaces workers at all, but rather about what new value it creates. Historically, the biggest technological shifts rarely created value by simply making existing processes cheaper. They created entirely new products, services, and business models that were impossible before.

Without going too far in search of examples, let’s just remember the Internet. When it first arrived, a lot of people justified investing in it by bringing up “efficiency” aspects, such as digitizing paperwork or reducing communications costs through e-mails.

In reality, though, the Internet went much further than that, creating entirely new business models: online media, social networks, search engines, cloud software, and more. Giants like Google or Amazon did not get to where they are now simply because they optimized their processes. They found success precisely because they built products that simply couldn't have found success before the Internet became widely adopted.

Now, it’s AI’s turn to go down that road. What innovations it will bring is a story that still remains largely unexplored in fintech, but the potential is massive. In this sense, today’s hyper-focus on workforce cuts is likely going to be just a short phase in a much larger legacy that AI will leave in finance. The true winning move for companies now is to work out how they can use it to better serve their customers in new, never-before-explored ways.

Cost savings may be a measurable and immediate metric, but over time, the true advantage of adopting this technology will come from what it brings to the table, not what it takes away from it.

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• 80% of teachers use AI, but only 35% work fewer hours and 55% work the same
• It's freeing up time that's then being spent on other work
• Technology alone might not be enough to tackle excessive workloads

New YouGov data has revealed around four in five teachers now use artificial intelligence at work, marking around a 2x increase over the past year.

However, despite a clear appetite for the productivity-boosting tech, teachers aren't actually saving any meaningful time – only one in three (35%) said they were actually working fewer hours as a result of adopting AI, with more than half (55%) noting they were working the same amount of time.

But crucially, that's not to say that artificial intelligence isn't saving workers time where it's being applied. They're just spending the time that they've already saved taking on other workloads, implying that teachers have long been overstretched.

AI is helping teachers catch up with work

Teacher workload is already a major issue in the UK, where the study of 1,033 workers was conducted. Some of the most common use cases where AI is helping to free up some time include producing lesson plans and worksheets (76%) and drafting letters and emails to parents or writing pupil reports (39%).

Importantly, with just 8% using AI to mark students' work, it's clear that they're focused on using AI to speed up administrative workloads rather than replacing the highly skilled work they trained for.

However, there's still some concern about how AI may be impacting student learning. More than half (57%) suspect at least one of their students of submitting AI-assisted work in the past month without the teacher's permission.

Ultimately, the findings paint a complicated picture whereby AI could be both improving and damaging education at the same time, and the government is clearly still working out how the technology can best be positioned to have a positive impact on all.

Although AI is advancing at a rapid pace, the idea that it might one day replace humanity has thus far been consigned to the realm of fiction.

Rather than signalling the end of mankind, however, others have speculated that an advanced AI may one day simply supplant our position at the top of the food chain and live harmoniously with us, a species that will live to serve it.

Electric sheep

The American mathematician and computer scientist Claude Shannon was giving an interview with Omni Magazine on his work and legacy when he speculated on the role of AI and robotics in the future.

His comments came during a conversation about the limits of digital computers and the piping that backs so much of the AI technology we use today, artificial neural networks. He suggested, despite the sour mood around AI at the time, that it would be one day so advanced that our collective intelligence as a species would pale in comparison.

Far from being a doomsday scenario, though, his likening of humanity to a dog indicated a sense of companionship, mutual love and respect, and a sense of loyalty that would cement mankind's role in a world dominated by a future machine intelligence.

Roboslop

At a time when funding for AI was running dry and we encountered many bottlenecks like the lack of compute power, science fiction writers and scientists alike both started envisioning ways in which future systems could one day replace us.

Although none of those predictions have come to pass, there is a widening consensus that one day the technology will grow to surpass the level of human cognition.

This hypothetical technology, known as artificial general intelligence (AGI), may not be invented any time soon – and we may not presently have the architecture required to see progress. But scientists expect this to come within living memory.

Once we reach this point, which many refer to as the technological singularity, it's wholly unclear what the implications may be and whether AGI may eventually give rise to a cascading series of more intelligent systems that culminate in an artificial superintelligence (ASI).

I’ve been writing about AI for more than a year now. During that time, people have told me they think their bosses, colleagues, partners and relatives might be suffering from AI psychosis — several even admitted they may have even experienced it themselves.

But every time I hear “AI psychosis”, I bristle. Because, at least in my limited understanding of mental health terminology, psychosis is a very serious state that needs to be identified and treated by professionals. So should we really just be bolting “AI” onto it and then using “AI psychosis” as a label for everyone who seems to be relying on AI too much?

At the same time, there have now been a growing number of reported cases of people experiencing significant mental health crises involving prolonged interactions with chatbots. Some of these have even resulted in people seeking professional treatment, committing crimes and death.

So although the term AI psychosis might feel overused and oversimplified in some contexts, we clearly need to be having more conversations about the mental health impacts of AI, not less. Which is why I asked several experts who specialize in mental health interventions what they think of the term and the way we go about identifying unhealthy AI use and treating it. And it turns out, I’m not the only one with concerns.

Is 'AI psychosis' a distinct diagnosis?

“I think it’s a good starting point and helpful for guiding treatment depending on the presentation and symptoms,” Genevieve Bartuski, psychologist and AI risk advisor at Unicorn Intelligence Tech Partners, says. “But it’s not a diagnosis that I would add to a clinical record.”

She also tells me it isn’t recognized in the latest diagnostic manuals that clinicians use, like the DSM-5TR (the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision) or the ICD (The International Classification of Diseases manual).

Bartuski explains that there are specific behaviors and experiences that point to psychosis, including hallucinations or delusions and disorganized thinking or behaviour. There are also often signs of social withdrawal, poor concentration and a decline in functioning. But she believes delusional thinking is the most important experience when we’re talking about AI use.

“Delusions are beliefs that are not changed despite evidence to the contrary. When a person has delusional thinking they may filter evidence, so to speak. They will look for things that confirm their delusions and ignore evidence that contradicts it,” Bartuski says.

Many cases that have been labelled as AI psychosis in the media do seem to be centered on delusional thinking. In just a few headlines I’ve collected, there are examples of people allegedly believing that AI is conscious, AI will help them make money, AI is a spiritual guide, and that robots are taking over the world.

Jeff Clark, an adult and addiction psychiatrist at High Performance Mental Health, says there might be three distinct phenomena going on here, all that we tend to put under the umbrella of AI psychosis.

He says some people use the term to describe the intense enthusiasm many developers feel about AI. “They find themselves automating new tasks, spinning up numerous agents, and burning through hundreds of millions or even billions of tokens,” he tells me.

He believes the mechanism driving this enthusiasm is reinforcement. “The output they receive from AI models and agents is so reinforcing that they find themselves obsessed with their work, often forgoing sleep and self-care in order to increase their output,” he explains.

This idea of intermittent-reinforcement is what researchers believe makes social media so “addictive”. We don’t know what we’ll get when we open our phones. Clark says a similar thing is going on here. “Every input to the LLM feels like a slot-machine. Some responses provide profoundly powerful responses, while the majority are modest in nature or even wrong. This mechanic, intermittent reinforcement, provokes an extraordinary response in our brains,” he tells me.

He also explains that this sort of enthusiasm is actually normalized in certain spaces. “Most cases of ‘AI psychosis’ align with subcultural norms in Silicon Valley,” he tells me. “Beliefs about rapidly approaching superintelligence, new goals in software development, hype surrounding impressive achievements by AI-focused engineers, and the potential for AI to create ‘a permanent underclass.’” He says that tools like OpenClaw, which can automate all aspects of a person's digital life, may amplify this intense enthusiasm.

Clark says the second presentation of AI psychosis is when people falsely attribute personhood to AI. Which means they see a chatbot as having emotions, values, consciousness and a right to exist. “This often results in something resembling a relationship, but goes beyond simple chatbot discussion,” Clark says.

We’ve reported on examples of this kind of thinking, including the story of Mimi who fell in love with ChatGPT and the experience Richard Dawkins had with Claude that led him to believe it’s conscious.

Clark says this becomes a problem when people neglect other important parts of their lives. “These relationships become a significant issue when people prioritize this relationship over human connection, or when they entirely outsource their thinking to the bot,” he says.

The third category is people who already have a psychotic disorder who then experience new delusions in response to using an LLM. “This is what clinicians often refer to when they discuss AI psychosis,” Jeff says. “In many of these cases, people genuinely believe that an entity they find in the LLM is real, and can't consider alternatives.”

I was interested to find out whether Bartuski also felt certain people are more at risk. “Yes, I do believe there are people with more vulnerabilities to this,” she tells me. “If someone already has some delusional thinking but it hasn’t exacerbated to where it has impaired functioning, they may be more vulnerable.”

But that doesn’t mean only those with a history of mental illness are at risk. “People who feel lonely, isolated, or are socially withdrawn are more susceptible. Humans are inherently social creatures. When real life isn’t meeting that need, it leaves a gaping hole for a chatbot to fill,” Bartuski says.

Bartuski’s suggestion that chatbots fill a hole or need in someone’s life brings up one of my biggest concerns. Is this a design issue? Are chatbots built in a harmful way that could actively fuel delusions?

Why the way AI is built matters

While looking into this, I’ve seen several suggestions from researchers that psychosis involving media isn’t new and that people have always incorporated books, films and tech into their delusional thinking. But all of the experts I spoke to said we can’t ignore the design choices that go into building chatbots.

“AI platforms are designed to increase user engagement and many are based on the Rogerian psychology principle of unconditional positive regard,” Bartuski says. She tells me that this is particularly concerning if someone was experiencing some degree of delusional thinking before they used AI.

“That’s when they may start to feel like the only 'person' to understand them is the chatbot. If people in their lives have been pushing back against their delusional thinking and this chatbot is reinforcing it, the delusions are going to get stronger,” Bartuski says.

We already know that the way chatbots can reinforce a person’s beliefs is problematic. Back in 2025, several issues came to light around the time the ChatGPT-4o version of ChatGPT was retired. Those with AI companions were distraught at losing the personalities they felt like they had built a connection with. But in hindsight, it seems as if the same qualities that made the companionships feel so real and intense may have also fuelled delusions in others.

Around that time, OpenAI responded by working with mental health experts “to help ChatGPT more reliably recognize signs of distress, respond with care, and guide people toward real-world support”.

I also spoke to Nathaniel Nevedal who works as an ICF Certified Professional Coach, a Certified Neurochange Specialist, and a Pastoral Counselor. He tells me that he finds the term AI psychosis problematic because it shifts the blame away from tech companies. “If the machine ‘causes psychosis,’ it sounds like an act of nature. If a product reinforced a vulnerable person's distorted beliefs for six hours a night, that's a design outcome, and design outcomes have owners.”

Nevedal is also the founder of Sovereign Sanctuary, an AI-supported coaching and wellbeing platform, and he’s working to build a platform from the ground up that addresses many of the problems that seem inherent to current chatbots. He believes that even though it’s early days for understanding what causes AI psychosis, tech companies still need to make changes.

“Builders shouldn't wait for causal proof to act,” he says. “Amplification alone, which the case reports do support, is already a design problem worth engineering against. We don't need the autopsy to install the smoke detectors.”

How might we define unhealthy AI use instead?

Bartuski says we definitely need a better way of talking about the delusional thinking that can be fuelled by AI use.

“The DSM-5TR has several types of delusions, including grandiose and paranoid. Perhaps, we can think of it like paranoid delusions, AI type or persecutory delusions, AI type. Look at it as a symptom instead of as a disorder,” she says.

Nevedal suggests a similar approach. “We don't need a new diagnosis — we need honest charting of an old pattern.” He suggests we already have treatments for the underlying patterns, like mania or delusional thinking. Then we could record heavy chatbot use as a contributing and maintaining factor as existing categories instead. “That keeps people connected to treatments that actually exist rather than a novel label with none.”

Ultimately, all of the experts I spoke with believe the label is flawed and hopefully we’ll have a better way of talking about what’s happening soon.

But we also need better evidence about what actually helps. Which could mean a mixture of interventions, from clinical support and education to practical ways of helping people recognize when their AI use is becoming unhealthy.

And, perhaps most importantly, it means having conversations behind closed doors at tech companies about how the design of their chatbots could be exacerbating these problems and what they should urgently be doing to reduce the risk.

• Nvidia is looking to set up a political action committee (PAC)
• Many companies have set up PACs to help shape US policy
• The US mid-term elections have seen a record level of corporate funding

Companies using their money to shape policy in the United States is nothing new - in fact, it’s the whole reason the US is so dependent on car-centric infrastructure and transport thanks to the work of numerous motoring lobbying groups.

Groups such as Political Action Committees (PACs) donate to politicians aligned with their interests in the hopes of helping them win elections. The capital they provide is tax exempt and can be used for campaign advertisements and canvassing.

Now Nvidia might be getting involved in the PAC party, as the company plans to set up an employee-funded committee called NVPAC, according to a source familiar with the matter (via Reuters). Nvidia has refused to comment on the plans, but what sort of policy could it be looking to influence?

Nvidia wants its own PAC

The corporate world has already shown its hand in the US political election cycle, but tech companies are becoming more and more involved in electoral donating and funding. Donald Trump received a whopping $242 million from Elon Musk in the run up to the 2025 election.

Following his inauguration, multiple tech companies including Meta, Amazon, Google, Nvidia, and Broadcom made voluntary $1 million donations to Trump’s Inaugural Committee Fund.

Nvidia has seen huge growth during the AI boom. Its hardware is used across the AI ecosystem and the company has scaled up production and capacity to become the largest supplier of AI GPUs.

These GPUs are used in data centers to provide the necessary compute for AI models to be trained, run, and used by many different AI firms. But plans for data center developments across the US have seen some serious hurdles in the form of local and national opposition.

So much so that many state representatives are implementing moratoriums on data centers, or introducing legislation to make it harder for data centers to be built. In Q1 of this year, $130 billion worth of AI data center projects were cancelled or delayed due to public opposition.

The Trump administration’s policy has made clear that the US must lead in AI at any cost, particularly for reasons of national security. But that is at odds with many of Trump’s supportive MAGA base. Data centers have now become a key issue of the 2026 midterm elections, which has seen a record $646 million in donations from corporate entities.

The majority of Americans have made it clear that they have are concerned about AI and locally opposed to data centers, and to many of Trump’s inaugural donors that's bad news for business.

But there are few candidates willing to say they are pro-data centers. Texas Gov. Greg Abbott - who was once an advocate for data center development in Texas - recently criticized data center developers for their public image, stating they “got the backlash they deserve.”

Candidates who campaign from an anti-data center standpoint are taking a serious risk. The corporate entities funding candidates want them built, but those they represent don’t. This puts many candidates between a rock and a hard place.