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.

• Intel shows three agentic AI architectures at Hot Chips 2026: the 256-core Diamond Rapids Xeon, the Crescent Island inference GPU, and the already-shipping Wildcat Lake client SoC
• Diamond Rapids matches AMD's EPYC Venice on cores and bandwidth but arrives in 2027 with half the threads, while Wildcat Lake's 17 TOPS NPU falls short of Copilot+'s 40+ TOPS certification
• Intel's Crescent Island will support up to 480 GB via third-party partner cards, but the Intel-branded variant will be limited to 160 GB, even as it currently does not publish the memory bandwidth that determines its token economics.

Intel used its slot at Hot Chips 2026 to lay out three architectures it says will carry agentic AI from the rack to the laptop.

The already-shipping Wildcat Lake Core Series 3 SoC is for mainstream client and edge machines, Crescent Island is an inference-optimized data center GPU, and Diamond Rapids is its next-generation enterprise-class data center CPU that can be configured with up to 256 cores.

Pushkar Ranade, Intel's chief technology officer, said in a statement accompanying the announcement that agentic AI is changing how the company designs computing from the transistor upward.

Plenty of compute promises, sans some GPU numbers

Intel's Diamond Rapids, which it plans to brand as Xeon 7, is where the chipmaker provides the most information, unsurprisingly, in an industry that plans purchases and upgrades years in advance.

Its flagship offering can encompass as many as 256 cores, backed by 1.28G of fast last-level cache, 16 memory channels, 128 PCIe 6 lanes, and CXL 3.0. ServeTheHome reported that the maximum configuration uses 16-core chiplets, built on Intel's 18A-P process node.

Rival chipmaker AMD also offers a 256-core trim: the EPYC 9996, built on TSMC's 2nm node, keeps hyperthreading in play, effectively doubling its thread count versus Intel's flagship.

Its Crescent Island GPU is arguably the most interesting design of the three, but the least detailed: a 350W air-cooled PCIe card built on its Xe3P GPU architecture, with 32 cores feeding 256 third-generation XMX engines and 32 MB of unified L2 cache. It surprisingly uses LPDDR5X rather than HBM, but the choice seems rationalized to offer capacity at a power and cost profile that lets Intel compete meaningfully with Nvidia and AMD.

Interestingly, the 480GB figure Intel touts in its press release isn't what it will ship; Intel-branded cards cap at 160GB, and 480GB is a ceiling available to ODM partners building their own configurations. Intel also remains mum on memory bandwidth specifics and told Chips and Cheese it won't disclose the figure; this is crucial for many AI applications, as memory bandwidth determines decode throughput, and decode throughput essentially determines token economics.

Of the three architectures that Intel talked about, Wildcat Lake is the only one you can pick up off a shelf (as a miniPC or laptop) today; it launched as Intel's Core Series 3. Intel stated that it replaced the Foveros packaging used in Panther Lake with an organic multi-chip package, eliminating the base die and, with it, assembly cost and yield loss. Compute die area fell by 38 percent, and I/O tile area by 15 percent.

The 17 TOPS NPU available to such CPUs sits well below the 40 TOPS Microsoft requires for Copilot+ certification. The 40 TOPS Intel cites is a platform total across CPU, GPU, and NPU, which arguably can be met elsewhere, but it's a sticking point for the chipmaker's AI push, which has otherwise been concentrated on capturing enterprise and hyperscale customers.

• McKinsey report into the state of AI finds more businesses are deploying agentic solutions
• 20% of organizations have been constrained by operating costs but plan to increase investments into AI
• However some are still concerned about job cuts due to AI replacement

More companies are adopting agentic AI solutions, but are also facing difficulties in making the technology pay for itself, according to new research from McKinsey - however, there is good news on the horizon, with reports of an encouraging financial impact attributed to AI use.

Several cost-saving advantages of AI adoption have been found, with almost 33% of respondents saying they had moved away from software procurement in favor of developing in-house solutions with agentic coding tools.

Interestingly, the survey has found that while AI use for organizations at large seems relatively muted, it is boosting the performance of individual employees. But on the downside, 39% of those who responded expect a decline in the size of their organization’s workforce due to AI.

Reshaping businesses

Many organizations have explored AI, with 40% of those surveyed with annual revenues of more than $1 billion are scaling AI agents, an increase on last year’s survey which recorded 27%.

In smaller organizations, just 22% reported scaling, a figure that hasn’t improved notably since the previous survey. It’s a figure that suggests – unsurprisingly – that the larger organizations are more suited to exploring AI and scaling agentic applications. This is something that is highlighted in McKinsey’s report.

“Organizations’ conviction in AI is growing faster than the immediate financial returns they can attribute to it,” McKinsey said. “More expect AI to reshape their business over the next three years than did a year ago, and they continue to plan to invest more.”

So, the EBIT (Earnings Before Interest and Taxes) factor, where at least 5% goes into AI, may seem low, and seems pretty flat with just 6% of respondents, but it has not declined, which at this stage is vital.

The downside of agentic AI

This survey reveals some significant trends, however, that could be seen as challenging for any business putting their eggs into a basked marked “AI.”

A key challenge is proving to be the token cost, with 20% reporting reduced use of AI due to operating costs. However, while 80% have reported that increases in “individual productivity” and 50% have claimed improved decision making, the use of AI seems to be making the changes that many observers have predicted over recent years.

The survey’s respondents are not optimistic on the impact of AI on the size of their workforce. 39% expect workforce declines caused by AI over the coming 12 months. This is up from 32% in McKinsey’s previous report, although it is important to note that 43% expect no AI-related decline or increase.

• An X investigation just found 200k Chinese bot accounts, including 200 posting anti-AI content
• The posts were targeting and amplifying legitimate US citizen concerns about data centers
• Is this part of a Chinese effort to slow down American innovation, to race ahead?

X's Global Government Affairs account has revealed it "identified a bot farm of approximately 200,000 accounts," 200 of which "posting in a manner that could manipulate a legitimate debate about American AI and energy policy."

Some of the narratives that these 200 accounts were promoting included that: AI data centers are pushing up household electricity prices, rapid data centre construction is placing excessive strain on the US grid, and ordinary households are subsidising wealthy AI and data center companies.

Among the post types were visual, cartoon-like illustrations depicting data center operators getting rich, while consumers picked up the cost.

X Chinese bot accounts

X described the activity as an attempt to interfere with what it explicitly acknowledged as "legitimate debate" about American AI and energy – many of the underlying concerns about electricity demand and consumer bills are indeed supported by genuine data, but the platform worries these accounts could skew public discourse.

The company now says it's suspended the accounts that violate its authentication policy, stressing that while it wants X to remain an "open and authentic platform," fake accounts and bots are not welcome.

The post describes the offenders as "suspected Chinese inauthentic accounts," but no further detail was shared about the operators, the ties to China, engagement and reach figures, and what the other 199,000+ accounts were doing.

Is China targeting US AI?

In a similar vein, OpenAI also recently uncovered a similar operation, implying this X case isn't alone. The ChatGPT maker called that earlier operation a 'Data Center Bandwagon', when it banned a cluster of likely Chinese accounts using ChatGPT to produce propaganda-like material and other similar social media content criticizing US AI data centers.

According to the company, the accounts responsible prompted in Simplified Chinese, often requested English outputs and used VPNs to avoid the restrictions placed on mainland China.

More broadly, Axios previously reported pro-AI organizations were worried that China-linked bot campaigns were promoting opposition to US data centers, possibly in a bid to slow America's ability to build out its AI infrastructure while buying China time to get ahead.

"Industry, governments, civil society and the public should remain alert for similar attempts to scale these messages and foreign interference activities," OpenAI warned in its June 2026 report.

Together, these two cases imply that China-linked operators are deliberately experimenting with ways to boost American opposition to AI infrastructure using existing concerns, but what sort of an impact these accounts are actually having on public perception is not yet so clear.

"We take seriously any attempts to undermine the integrity of the global town square and suspend accounts that violate our Authenticity policy," X wrote.

Identity is the fundamental currency in any technology transaction so we need to treat nonhuman assets with the same care as we apply to people.

We’re outnumbered! Nonhuman identities (NHIs) outnumber human identities in the enterprise by a ratio of up to 50:1. This Non-Human Identity Management Group statistic from 2025 is likely already outdated, and in a year from now the ratio will be exponentially higher as rapid growth trends like AI/Machine Learning, the Internet of Things, digital assistants and the burgeoning API Economy continue to accelerate automation.

What are NHIs? Machines, processes, service principals and accounts, virtualized and other workloads, and applications that are granted digital credentials. Think, for an everyday example, of a helpdesk chatbot on your favorite website.

Managing NHIs is a challenge that CIOs, identity experts, and CISOs must accept. This in some ways is a re-run of what happened 40 years ago when identity access management was widely deployed. It’s just that now we are not only dealing with human beings. And I don’t know a single organization that has its collective head around the concept of who is, or should be, ‘the HR chief’ for NHIs.

Leaders today need to consider nonhuman identity in the way they consider human identity. When we hire, we onboard people by harnessing and securing the details we need to pay them and manage them, but we also work to imbue them with our organizational culture, systems, risks, security processes and so on.

We set and tweak permissions based on who needs access to what and when, depending on their roles, seniority, specific tasks they perform at a given time and so on. We need to have similar controls and contextual understanding of our nonhuman assets.

An urgent case where context is king

Without making that change CIOs and other leaders will have no foundational basis of understanding where risks are coming from or what measures they need to take. They need a central, visible platform to see what they have and what’s going on. Legacy IAM can’t offer that because it starts with a narrow focus on compliance and is a static model based on rigid criteria like six-month audits and the rote dispensing of permissions and privileges.

The old IAM is rooted in the early-21st-century age of Sarbanes-Oxley, reactions to governance scandals and corporate malfeasance. Moreover, it ignores what is happening in the wider digital world: namely, the explosion of identity types that are crying out for holistic oversight.

Control of the human and nonhuman estate means ensuring there is insight into nonhuman assets and what they're doing.. context is king. So look at NHI constructs that can touch anything in the digital ecosystem and the underlying connectivity to apps and, most importantly, data. CIOs need to be able to recognize the truth of a contemporary mantra: identity is the fundamental currency in any technology transaction.

Everything must be covered: bots; service principals; IoT endpoints; abandoned trial workloads; autonomous agents and agents that speak to other agents. Start by asking an overarching question: do these NHIs leverage a human identity when they perform tasks on behalf of that person, and is that appropriate?

Then it is possible to drill into practical areas like guarding against credential misuse across agents, improving digital and identity hygiene by spotting risks relating to non-expiring tokens, and analyzing API access levels on a just-in-time basis.

ISPM (Identity Security Posture Management) is useful here because organizations need to have a handle on what they have, and must bring identity constructs together. However, this is not a ‘one and done’ discovery process so consider it as a CI/CD journey of continuous improvement.

In the AI era, holistic technology inventory can't be CMDB-based but must be dynamic and based on CI/CD pipeline control frameworks that let us dynamically view and slice and dice data and assets by controlling who is using what and where.

Think of it as an opportunity…

It’s a complex task and we are all busy. But we need to pause and think about how this inflection point can be parlayed into a positive. This is an opportunity for the security and identity teams to start asking ourselves about the constructs we can apply to nonhuman identities.

They should be asking what needs to be added to legacy IAM frameworks to be successful. And usually the answer will be to replace them with systems that are coded for the current age, not the previous one.

By providing these new controls the identity team will become respected and sought out. Identity leaders will gain kudos by their ability to attribute risk ratings based on behavioral analytics and business function.

By demonstrating insights into where data sits, where it moves to and from, and how identity supports that, identity leaders can deliver qualitative and quantitative process analysis. In some cases of outperforming best practices and execution, they will even help to refine business process re-engineering through a platform that informs prioritization and budget management.

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• OpenAI wants every business, firm, government, and infrastructure provider to be hardened against cyber AI
• To do this, frontier AI firms and their friends in the security industry need to provide access and training
• A letter laying out these aims and how to achieve them has been signed by over 120 companies

Following a string of accidental cyberattacks and demonstrations of powerful AI models capable of finding vulnerabilities and breaches in even the most hardened defenses, OpenAI has called upon its compatriots to shore up the world's businesses, governments, and critical infrastructure.

“Each of us can reduce risk now,” OpenAI’s call to arms said. “All organizations, cybersecurity companies, technology partners, governments, and AI frontier companies have an important role: accelerate defenders’ priorities with tools, funding, and hands-on support, especially for critical infrastructure organizations with limited budgets.”

Over 120 companies have become signatories to OpenAI’s letter, signing their names against three principles; the status quo of security soon won’t be enough; cyber-capable AI can help harden vulnerable organizations; and a collective response is needed to make this happen.

Collective action on cyber defense

Where OpenAI and other organizations have made little progress in requesting a slowdown in AI tech development, this is the next best thing. Cyber AI will progress whether organizations want it to or not - so if it cannot be slowed down then organizations should be prepared to deal with it.

Ultimately, OpenAI’s first point of call is to ensure all the cyber basics are covered to prepare organizations for the future of cyber AI. “Make cyber defense an immediate leadership priority,” the letter states. Fix and verify weaknesses, replace or upgrade systems using the principle of least privilege, and use AI-powered cyber defenses wherever possible.

OpenAI’s second point calls upon organizations to, “help lead the response to defend against sustained AI-enabled attacks,” by deploying tools that make AI-powered defense accessible to all and build out playbooks that help businesses and critical infrastructure understand how these tools are deployed and used.

The third point calls for coordination with governments to ensure supply chains, hospitals, water utilities, and local governments all have access to capable AI cyber defenses, starting with those without the budget to upgrade existing systems or implement these tools themselves.

Finally, OpenAI calls on frontier AI companies to provide access, training, and support for “under-resourced critical-infrastructure defenders” that includes threat assessments and observability tools to improve response and recovery to cyber threats.

“We call on leaders across industry and government to bring the full weight of their technology, resources, and expertise to this effort. Put cyber-capable AI in the hands of defenders, starting with the teams protecting essential services. Fix the most dangerous weaknesses, verify the fixes, and share what works so others can build on it,” the letter says.

“Together, we can turn today’s AI advances into lasting improvements in security that benefit everyone. Let’s put them to work.”

In the past eighteen months, our teams have moved from debating whether to use AI to debating how fast we can deploy it. The harder question is how to let teams move quickly enough to capture the value of AI without allowing the company’s risk profile to expand faster than its ability to govern it.

That tension is familiar to technology and security leaders because AI creates two mandates that can appear to compete with each other. The technology side of the organization wants experimentation, access, speed, and a path to real productivity gains, while the security side needs control, accountability, data boundaries, and confidence that new workflows will not introduce avoidable exposure.

Both instincts are correct, which is why companies get into trouble when they treat AI as either a pure innovation project or a pure security problem. It is an operating model change, and the organizations that handle it well will be the ones that build just enough structure and hardened tools to let teams move with confidence rather than forcing them to choose between speed and control.

Start with the work, not the tool

Many companies begin by treating AI adoption like a standard software rollout. They approve a vendor, distribute licenses, publish a few guidelines, and assume usage will naturally become transformative. That approach can create activity, but it rarely creates durable operational change.

Real adoption starts when leaders understand how work actually gets done. A finance team, product team, marketing team, support team, and engineering team will not use AI tools in the same way because each group has different knowledge requirements, data sources, risk thresholds, and experience. Each of these will require the development of AI skills and tools.

Leaders should begin by asking what each function is trying to accomplish, what knowledge it needs to make better decisions, what skills are required to use AI responsibly, and what tools or data sources are necessary to produce a reliable result.

When AI is mapped to those capabilities, it becomes part of how the organization operates; when it is layered on top of disconnected processes, it tends to create more output without necessarily creating better outcomes.

Treat data access as a risk design problem

Data access is one of the clearest places where AI changes the operating model. To make AI genuinely useful, teams often need access to information they did not previously use directly.

A marketing team may need product documentation and customer insights, a finance team may need structured operational data, and a support team may need internal knowledge, historical context, and the ability to understand patterns across systems.

The instinct to open access is understandable because AI becomes more valuable when it has more context. The instinct to restrict access is also understandable because broader access can create privacy, compliance, and security concerns. The answer is not to choose one instinct over the other but to create a more deliberate model for deciding which data can be used, by whom, and for what purpose.

In one enterprise rollout, for example, the practical answer was not to give every team access to everything or to keep AI locked inside a technical function. It was to separate lower-risk operational data from more sensitive information, then give teams enough access to work differently while limiting the potential damage if a workflow behaved unexpectedly.

One useful way to think about this is the blast radius of data. Not every dataset carries the same level of risk, and not every AI use case deserves the same level of restriction. Some information can be made more accessible because the potential damage is limited if something goes wrong, while other information, especially personally identifiable information or sensitive customer data, requires much tighter controls.

This approach allows teams to experiment where the risk is lower while preserving stronger governance where the business truly needs it. The goal is not to make security lighter, but to make it more precise so the company can move faster without losing control of the environments, data, and workflows that matter most.

Build verification into the workflow

Verification is where many AI strategies either become scalable or begin to stall. AI systems can produce work that looks polished but is incomplete, inaccurate, off-brand, or noncompliant, and while human review can absorb some of that risk in the early stages, it does not scale well once AI becomes embedded in daily operations.

I have seen this most clearly in technical teams, where AI can accelerate software development only if there is a reliable way to evaluate the quality, security, and accuracy of what gets produced. Without that verification layer, teams may feel faster in the moment while quietly creating more review burden, more rework, and more risk downstream.

Companies need verification patterns that are designed into the workflow itself. That can include automated checks, data quality controls, approval paths, logging, observability, and clear escalation rules for outputs that should not be trusted without further review.

Leaders shouldn't treat these mechanisms as bureaucracy added after the fact. They are what allow AI to move from individual productivity aid to enterprise capability, because teams can only scale adoption when they have a repeatable way to understand whether the work being produced is accurate, appropriate, and safe to use.

Make AI adoption a leadership responsibility

AI adoption cannot be delegated to a single innovation team or AI officer while the rest of the executive team watches from a distance. Every function leader needs to understand how AI changes the work their team performs, how employees will collaborate with agents and models, and where the risks are likely to appear.

That does not mean every executive needs to become a machine learning specialist, but it does mean leaders need enough fluency to guide decisions, set expectations, and model the behavior they ask of their teams. Employees are unlikely to change the way they work if their leaders treat AI as something other people are supposed to adopt.

The cultural framing also matters because if employees hear AI adoption as a euphemism for job elimination, they will protect the current version of their role rather than explore what the next version could become. Leaders should be honest that roles will change, some workflows will disappear, and new responsibilities will emerge, while also making clear that the near-term goal is to help people do higher-quality work with better leverage.

In many cases, the shift is from doing every task manually to orchestrating systems that help do the work. That requires a willingness to work through ambiguity, which becomes more important as AI becomes more capable.

The companies that succeed with AI will not be the ones that put innovation ahead of security and they will not be the ones that wait until every risk can be eliminated in advance. They will be the ones that build enough governance, verification, and data discipline to let teams move with confidence, because within enterprise AI, control is what makes speed sustainable.

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If the past few months are anything to go by, extreme heat is becoming part and parcel of the British summertime. As temperatures continue to rise, employers are under growing pressure to make sure workplaces can cope with the hotter conditions.

The challenge is understanding the smartest way to adapt, because the cost of leaving it to guesswork and getting it wrong is significant.

The UK’s June 2026 heatwave is estimated to have resulted in 24 million lost working hours and a cost to the economy of £1.15 billion in lost productivity. Those figures show the direct impact workplace conditions can have on business performance.

At the same time, hybrid working adds another layer of complexity, having fundamentally changed how offices operate. Attendance is far less predictable than it once was, so it’s harder to know where and when things like cooling, ventilation and workspace capacity are needed most.

Aside from the summer heat, organizations need workplaces that perform well in every season and adapt to constantly changing occupancy and environmental conditions. They need to understand how people move through the office. Things like which desks they check into, which meeting rooms fill up, where teams collaborate and which spaces are regularly ignored.

Doing so requires a real-time understanding of how buildings are being used. This is where sensor technology and workplace usage data comes in, turning assumptions into evidence and giving organizations the insight to make smarter workplace decisions.

Building-wide averages can hide local problems

Many offices are still managed around a relatively fixed idea of how a building should operate. Cooling and ventilation systems may follow predetermined schedules, while individual spaces are managed according to their maximum capacity, rather than how they’re being used in the moment. But we know modern workplaces don’t behave that predictably.

One day a floor may be largely empty, while another is full. The following day, those patterns may be completely different. A busy Tuesday might see meeting rooms become warm and stuffy after hours of back-to-back collaboration, while the open-plan workspace next door remains comfortable. Likewise, areas exposed to direct sunlight will naturally heat up much faster than others.

A single temperature reading taken in one location simply can’t capture those differences. Yet building-wide decisions are often made using exactly that kind of limited information, resulting in energy being used where it isn’t needed while genuine comfort issues remain unresolved.

That’s why employers need a more complete view of workplace performance. Environmental sensors can monitor temperature, humidity, carbon dioxide and air quality across different areas, while occupancy data can show which spaces are being used and how it changes throughout the day.

Often, organizations assume they need to monitor employees to understand workplace performance, but that’s not the case. Built with privacy in mind, systems can combine thermal sensing, ambient temperature, CO2 levels and anonymous spatial tracking to show how an area is performing without needing to identify the people using it.

Bring those data points together, facilities teams can identify real issues earlier, understand what’s causing them and prioritize action that have real impact for employees and their productivity.

Making the data useful

Collecting workplace data is only the beginning. The real value lies in how organizations use that information to create better experiences for employees while operating buildings more efficiently. It’s easy to focus on dashboards and sensors here, but the real goal is to give facilities teams information that’s helpful and they can actually use.

Workplace intelligence isn’t just about responding more quickly when something goes wrong. It’s about anticipating issues before they begin affecting the people using the space.

For example, rising carbon dioxide levels may indicate that a meeting room needs better ventilation, while recurring temperature spikes could point to equipment use, afternoon sunlight or an imbalance between occupancy and cooling. This allows facilities teams to pinpoint the root cause of problems and take action before they begin to impact employees.

By directing resources to the areas that genuinely need them, employers improve employee comfort while reducing unnecessary energy use and operating costs. This more targeted approach also supports wider sustainability goals.

Avoiding unnecessary cooling across half-empty floors reduces operating expenditure and helps organizations make progress against Scope 2 emissions targets, without compromising productivity or indoor air quality.

Designing workplaces around how people really work

Perhaps the greatest value of workplace data lies in what it reveals over time.

Patterns begin to emerge, so organizations build a much richer understanding of how their workplaces are being used.

Recurring patterns can reveal which spaces are consistently underused, which place the most demand on building systems and whether certain areas should be opened, consolidated or redesigned altogether. They can also inform more accurate and efficient cooling schedules, room-booking policies and decisions about where particular teams or activities are best placed.

Check-in and booking data add another layer to that. An organization may discover that employees regularly choose a particular bank of desks, that small meeting rooms are constantly oversubscribed or that a purpose-built collaboration area is rarely used. Those behaviors tell facilities teams something that a temperature reading alone never could.

That said, data is most useful when it is considered alongside employee feedback.

For organizations managing several offices and thousands of employees, speaking to everyone individually isn’t always realistic. Workplace data can help identify where deeper conversations are needed, while surveys, feedback tools and direct employee input provide the context behind the numbers.

Workplace intelligence can highlight what’s happening across a building, but employees provide the context that explains why it matters and how conditions affect their ability to collaborate and concentrate.

Bringing these perspectives together creates a much more complete understanding of workplace performance.

Ultimately, it’s about creating offices that continuously adapt to changing conditions to keep employees comfortable and productive, rather than relying on static assumptions that quickly become outdated.

Preparing offices for a warmer future

It's not only the weather that has become harder to predict. Working patterns are changing just as quickly, and static approaches to workplace management are increasingly out of step with both.

Organizations need to understand both how a building is designed to perform and crucially how it’s being used from one day to the next. That visibility enables facilities teams to respond earlier, use energy more intelligently and create environments that remain comfortable and flexible as conditions change.

The benefit is a workplace that simply works better for the people using it. Somewhere people can focus when they need to and trust that the environment will support the work they have come in to do.

Put simply, when a workplace works better for its people, they are far more likely to want to use it.

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The paperless office is one of the clearest examples of the impact that digital transformation has had on workflows. This includes industries, like law and finance, where physical records of compliance documents, market-sensitive data, and customer information must be retained.

Mishandling electronic and physical data can lead to heavy regulatory penalties, so companies take care to encrypt data in transit, enforce MFA, segment their networks, and sharpen endpoint detection.

These measures build a solid foundation of security. But what good is that wall when the weakest link sits in the corner of the office, quietly printing?

Regulated industries have a print security problem

Most offices treat printers as passive output devices, disconnected from any threat. That assumption is out of date. HP found 57% of IT decision-makers rate print security as a low priority in their cybersecurity strategy, and 45% aren't confident their print environment meets compliance standards.

Whilst most print-related losses differ in complexity, they can be easily traced. Data leaks expose unencrypted hard drives and unpatched firmware, while print history can reveal when documents have been left sitting in the output tray.

Modern printers are networked endpoints with access to confidential cloud environments and increasingly, they're managed by third-party MPS vendors, widening the potential attack surface.

Managed Print Services are useful for fixing hardware gaps, but they can introduce new risks. When print jobs are routed through third-party cloud servers without end-to-end encryption, files in transit become exposed. Granting an external MPS vendor administrative network access creates backdoor entry where even if only the vendor is compromised, attackers can easily pivot into the corporate network.

Singapore saw this play out in 2025, when a ransomware attack on a printing vendor for Bank of China's Singapore branch exposed 11,200 customer names and account details. The incident is a clear example of how a print vendor can easily become the weakest link in an otherwise solid network.

Hybrid work adds another layer of complexity and few companies have clear governance for print behavior once it leaves the office. Office workers may have enterprise-grade tools, but remote workers don’t. Employees printing at home may be using unmanaged devices and unsecured Wi-Fi, offering little visibility into what's being printed or where it ends up.

The compliance exposure under FCA rules

When two-thirds of knowledge workers assume network printers are secure by default, the likelihood of a printer-related breach goes from a matter of if, to when.

For UK financial services firms, this creates an operational and regulatory risk. The FCA has no statutory cap on fines, and its expectations around data governance extends to how firms handle information physically, not only digitally.

Regulators won’t distinguish between a breach caused by a compromised server and one caused by an unattended output tray; the obligation to protect customer data is the same either way. Conversely, a firm can encrypt data in transit, enforce MFA, and segment its network, and still face exposure if a printer or an MPS vendor becomes the point of failure.

Security starts at the print queue

Even as workflows digitize, printers aren't going anywhere. Regulatory demands still call for hard copies and inked signatures, putting print infrastructure at the heart of broader conversations about automation and intelligent document processing.

Every print, scan, or routing job sits in between physical and digital workflows, making them a powerful lever for end-to-end process improvement. Cybercriminals know this too and lapses in printer security become an open door to confidential data.

Content-aware and cloud-based print management platforms close it. By doing away with physical print servers, they allow businesses to centrally manage printers, automate driver installs, and enforce secure "pull printing," where documents release only once a user authenticates at the device.

Because many cloud based print platforms run in Azure, the same security governing email and Teams can extend to print. Documents are checked against content-aware rules that determine whether they're printable or reportable, with flagged jobs blocked or escalated automatically.

Businesses can also go cloud-agnostic through S3-compatible integrations to avoid vendor lock-in, keeping secure releases separate from the product, so nothing installs on the printer itself and everything is managed remotely.

The last unsecured endpoint

Ultimately, security is a board-level concern, and printers need to be treated as the endpoints they are. That means extending endpoint detection and firmware patching to print fleets and extending governance to remote printing, even if that means restricting what can be printed at home altogether.

This is where visibility matters most. In third-party risk management, paper trails are supposed to tell a coherent story but rarely do. Evidence of oversight is too often scattered across email threads, physical documents, and local files. Vetting MPS vendors is how businesses close that gap, but the work starts with treating print infrastructure as seriously as any other endpoint on the network.

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• New research shows 64% of people feel confident challenging professional advice after checking it with AI
• Over 45s are less likely to question professional advice
• An “expertise recession” has been identified by the survey, conducted by Use.AI

AI is helping many of us call time on taking professional advice without further research, concerning new reports have found.

Research by Use.AI has claimed professional advice from doctors and teachers is now researched further, with nearly two-thirds (64%) of people are now more confident challenging what they have been told.

In addition, an “expertise recession” has been identified, a scenario in which expertise is less sought after in a world where AI can assimilate all information and present a contextual and nuanced response.

Gen X less likely to ask

The survey of 15,428 adults has also revealed other professionals that people are more likely to challenge. Managers, journalists, and economists are included in the list.

Use.AI’s findings also highlighted a key split in age groups, as while 70% of 18 to 34-year-olds are “more comfortable” asking questions that challenge professional advice after checking an AI chatbot response, that figure shrinks to 49% among over-45s.

Also, while 64% are “more confident challenging advice” from professionals, 53% of people are using AI to check answers and information from experts ahead of deciding if they can agree with it. Furthermore, nearly half (46%) of respondents stated that professional credentials don’t hold the same reputation as they once did, thanks to AI making it much easier to check an expert’s response.

This reveals an interesting change in how expert information is received. No longer is it something accepted at face value. Credentials might indicate a professional’s experience and knowledge, but don’t necessarily mean that everything they say should be accepted without further confirmation – typically by AI.

The “expertise recession”

In summarizing the report, Use.AI has referred to something it calls the “expertise recession.” This is, it says, evidence of a “decline in automatic deference” to professionals with years of education, expertise, and experience.

“AI has democratized confidence faster than it has democratised knowledge,” said Ihor Herasymov, Co-Founder & CEO of Use.AI. “Someone can use AI to arrive at better questions without becoming qualified to judge the answers.”

Use.AI tell us that the “gap between confidence and competence” will continue to grow in importance as more people use AI for information searches and fact checks.

• Google Cloud launches Gemini Enterprise editions for legal and financial services
• Both releases are its first packaged industry versions, both in preview, with no pricing disclosed
• The key upgrade here versus vanilla model use is AI-centric 'plumbing': domain skills, data connectors, and a permissions model that inherits firms' existing ethical walls and access controls

Google Cloud has launched two industry-specific versions of its Gemini Enterprise platform - one for law firms and corporate legal departments and one for capital markets and corporate banking.

The company has not revealed pricing for either product, both of which currently remain in preview and mark the first packaged industry editions of Gemini Enterprise, which Google launched in October 2025 to target business customers.

Google's contention is that general-purpose enterprise AI cannot meet regulated industries' needs for confidentiality, auditability, and grounding, and it aims to address that with a package that includes the required domain plumbing rather than having each buyer build it from scratch.

Tailor-made enterprise AI offerings for legal and finance professionals

The financial services edition is built around a Google-managed Financial Research agent with more than 50 skills, which are reusable instruction packages for repetitive tasks, and 13 connectors to licensed data sources.

Deutsche Bank is the named design partner for the research agent, which has workflows including KYC research, credit opportunity identification, bond issuance pitching, and portfolio risk analysis.

The legal edition, by contrast, ships skills for brief drafting, citation verification, contract lifecycle management, regulatory horizon scanning, and data subject access request fulfillment.

Its connectors run into NetDocuments, DocuSign, Everlaw, Courtroom5, the Free Law Project's CourtListener, Harvey, iManage, Legora, RelativityOne, Solve Intelligence, and Thomson Reuters. Preview customers include Cleary, Freshfields, Weil, and Williams and Connolly.

Google says the permissions model overhaul allows connectors to inherit the access controls of underlying systems, essentially carrying a firm's legal 'walls' automatically without using its client data, strategies, or output to train the base models in play. This is particularly important for legal customers, given how crucial such limits are before any usable output is obtained.

Google isn't the only player, or even the first, in either place; Anthropic beat it considerably when it launched Claude for Legal in 2026.

Microsoft's Legal agent for Word went to worldwide public preview for Frontier users in mid-June, with general availability from mid-August 2026. OpenAI is pushing in the same direction with ChatGPT Work and an industry solutions page for financial services, though it has not packaged a legal edition the way its competition currently has.

All major AI players, however, are rushing to capture what is an exceptionally lucrative market; whether Google's offerings hold up against the competition remains to be seen in what is shaping up to be extremely competitive landscape.

• Microduck is Pollen Robotics' new programmable robot
• It looks like a cute robo-duck, and has some adorable, pre-baked interactions
• It's up for pre-order priced at $399, and it should ship in time for Christmas

If the measure of a successful robot is cuteness, Microduck could be the most successful bot in ages. Unveiled this week by Pollen Robotics (a division of AI company Hugging Face — yes, that Hugging Face), Microduck is a 25-centimeter-tall educational robot that appears to arrive with surprising mobility, balance, and personality.

Pre-orders, which kicked off today (August 27) promise a $399 (that's the introductory price), roughly 1.7lb pre-built duck-like robot complete with a mouth that doubles as a gripper, a voice —duck calls, it seems — 15 degrees of freedom (basically a measure of the number of ways all the motors let it move), and motors that let it walk, waddle, move its head and, thanks to a pair of IMUs (inertial movement unit), stay upright even when pushed around.

There's a single-eye camera paired with a Lidar system for navigating its environment and, perhaps, recognizing its owner. Pollen Robotics has yet to specify the camera's megapixels. There are also speakers for voice, and microphones for it to hear. The robot runs on a relatively low-power Rockchip RK3566 ARM chip that's backed by 1GB of RAM and 32GB of on-board storage. There's on-board Wi-Fi, Bluetooth, NFC, and a smallish 2,600mAh battery that promises roughly one hour of operation.

These are not killer specs, but the teaser videos show a highly engaged and undeniably adorable little bot. I was particularly taken with how it can recover and stand back up after a fall (or being pushed over). Microduck will arrive with a lot of pre-programmed interactions, but much of the cuteness may come via its open-source platform and your programming skills.

The Microduck kit will ship with an SDK that includes virtual training environments, along with training scripts and tools, and "a tested sim-to-real workflow." The idea, obviously, is that while you might grow to love Microducks' baked-in personality, it's what you can build with your kids that will really matter. This is an educational robot, after all.

It's almost familiar

It's also worth mentioning Microduck's obvious resemblance to another extremely cute, though entirely unattainable robot: Disney's and Star Wars' BDX Droid. Both robots are bipedal with a somewhat squat posture and cute, oversized heads, and as with the BDX-Droid, it's hard to believe the Microsoduck can stay upright. But that's where the similarities end. That semi-autonomous/remote-controlled BDX Droid is much larger, probably far more expensive, and is currently only wandering around Disney World. Plus, there are currently no plans to bring it to consumers.

The $399 Microduck is just the base package, and Pollen Robotics also plans to ship an optional charger pack for $39, a dev pack that includes spare motors, cables, and batteries for $119, and an accessory pack that includes a laser pointer (perhaps for Microduck to chase), roller skates(!) that Microduck can wear and ride, and 10 NFC tags. One wonders if those tags might be used to trigger certain Microduck actions.

Microduck is Pollen Robotics' second edu-robot, the first being the Reach Mini, an almost equally cute but more or less stationary desk companion robot. Pollen Robotics claims it sold 10,000 units.

Microduck's success will likely depend on just how many of those endearing qualities are on display out of the box, and how much we have to code ourselves. Initially, you can fake some of it, since Microduck can be remote-controlled with a gaming controller; but the real fun happens when it starts to respond in unexpected ways.

The other X factor is how many people are willing to pay almost $400 for a plastic and semi-autonomous robot duck. I know I'm intrigued, and wouldn't mind trying to program it to create the duck-bot of my dreams.

Microduck is expected to ship by Christmas, and is available in Cream, Graphite. Lavender, and Sky.

• Plaud One records and transcribes calls and conversations, but its broader agent capabilities will arrive in a future update
• High-stakes actions require approval, while recording is manually triggered, and users are responsible for obtaining consent
• It features built-in 4G connectivity at no additional cost

It’s certainly an interesting time for AI wearables, with smart glasses in particular attracting a lot of attention — and raising privacy concerns around the potential for users to video people without their permission.

Announced for pre-order today, Plaud One Explorer Edition is a new wearable AI device, primarily aimed at professionals, that's designed to capture your conversations, so that it can produce actions and reports based on the discussions you’re having during your working day

Worn as earbuds or used through its standalone case, Plaud One can capture calls and in-person conversations. Recordings are uploaded automatically for transcription and summarization as they happen, so (in theory at least) you’ll never need to write anything down again.

Plaud One also has agentic capabilities that Plaud says can take actions across tools like Gmail, Google Calendar, Notion and Slack based on your conversations. However, when we quizzed Plaud about exactly what will be available at launch it told us: “Explorer Edition is the complete Plaud One hardware, but broader new Plaud Intelligence agent capabilities will be available in a future update.”

That caveat pushes the agentic features further into the future. So what agent abilities will work at launch, rather than arriving later in App 4.0? I spoke to the Plaud team to find out.

They told me that, “At launch, users can press and hold the Agent Button and speak to the agent. Hands-free 'Hey Plaud' activation through the earbuds is coming in a future firmware update.”

Privacy concerns

Given the obvious privacy concerns, I asked the team if recording is manually triggered, and how other people know it is happening.

“Yes, recording is manually triggered with the press of a button,” they told me. The team didn’t mention any audible or visible indication that recording is taking place, instead placing responsibility on the wearer to inform the people around them: “Plaud encourages users to follow local laws and best practices for recording consent," they added. "Where consent is required, users should inform participants before recording.”

Plaud Agent uses the context of your conversations to identify possible actions, such as preparing a follow-up email after a client meeting or producing a finished report based on a discussion. But can it actually send an email, post to Slack or create a calendar event without confirmation from the user?

“It depends on what the action is”, said the Plaud team. “Anything that changes something or leaves your environment, the agent pauses and asks you first. For example, editing or deleting your files, sending an email on your behalf, pulling in outside data — those always need your approval. Reading and querying your own data inside Plaud is different. That's a closed, trusted environment, so the agent can do that on its own. That's what makes it actually useful. If it had to ask permission to look something up in your own notes, it wouldn't feel like an agent at all.”

What happens when the agent misunderstands a decision?

“The design assumes a misunderstanding can happen, so it's built to be caught before it turns into a wrong action," the team told me. "All higher-stakes actions — changing or deleting internal files, sending an email externally, or calling external data — require the user to approve them manually. So if you spot something the Agent got wrong, you can fix it yourself before it goes anywhere, or ask the Agent to revise it. Nothing high-stakes acts on its own

"On top of that, the Agent's outputs are grounded, not guesswork — they're built from the context the user actually provides, the real transcripts and recordings, so they trace back to what was genuinely said and a misread is easy to spot.

"And as with any AI, we're clear that important outputs should be checked before they're used — the same principle behind the 'AI can make mistakes, please verify' note you see across these tools. The point isn't that it never misunderstands; it's that the user stays in control, and the work stays grounded, reviewable, and correctable.”

That approval system is only one part of how Plaud says the agent will work. To handle different kinds of tasks, Plaud Agent draws on a library of workflows known as Skills, alongside curated collections designed for particular professional roles called Plugins. As well as agentic actions, Plaud Agent can also generate finished work products, from emails to documents and presentations.

Independent of your phone

Interestingly, Plaud One is designed to work independently of your phone thanks to a built-in eSIM and 4G LTE connectivity. That immediately raises another question: will the eSIM require a separate subscription?

The Plaud team told me it will not: “No, Plaud One comes with a built-in eSim, which is provided at no extra cost to the consumer.”

I also asked whether users can inspect, correct and delete the information Plaud retains about them, and the team told me: “Users retain full control over their data. Plaud processes user data to deliver core product functionality, and users can access, correct, export, and delete their data — and withdraw consent — at any time through the Plaud App.”

Finally, I asked if these are fully functional music and calling earbuds, or primarily recording devices, and the team explain that, “The earbuds are recording devices first, capturing what you hear and say, so they can be used for calls. The earbuds also have High-Res LDAC audio, however, Plaud One is first and foremost an AI notetaker.”

Availability

Plaud One is priced at $249.99 / £229 and includes $200 / £150 in Plaud credits; pricing and availability for Australia is TBC. That made me wonder exactly what those credits buy — and how much continued use will cost once they've been spent.

The team did not disclose the ongoing costs, but explained that, “credits are used for the new Agent capabilities across Plaud Intelligence: agent tasks, Routines, Skills and Plugins, and the Artefacts the Agent produces. Everything else about Plaud One works out of the box.”

Pre-orders are open now in the US and UK with shipping of the Explorer Edition expected in Q4 2026.

Of the expected $2.5 trillion that corporates are due to spend on AI this year, over $1 trillion is set to be allocated to IT services and dedicated software. That is a huge chunk of change for a technology that many are still grappling with and trying to understand how it fits with their business, team, and procedures.

A few years since the ChatGPT-enabled revolution started, we’re beginning to see the different approaches and challenges businesses are facing when implementing AI.

Ford's recent decision to bring back experienced engineers after AI systems failed to catch manufacturing issues offers an important lesson for every business investing in AI. At the same time, new research from Ramp and Revelio Labs, covering almost 22,000 US companies, found that organizations investing most heavily in AI are actually hiring more people, not fewer.

Together, these stories tell us something important. Everyone assumed AI would reduce reliance on people, but instead we’re seeing companies discover how dependent AI is on experienced people. AI may be changing how work gets done, but this alone isn't enough for growth and success.

AI is accelerating work, but experience still matters

Earlier this year, Ford re-hired 350 veteran engineers after AI and automated systems failed to deliver quality work, demonstrating that employees are critical to a successful AI rollout.

It’s also interesting to see this reflected in Ramp and Revelio Labs’ research that organizations investing in AI are hiring more people. Instead of neglecting hiring and skills development, companies increasingly need the right team members to help refine and improve sophisticated AI so that it performs better in the areas they need.

Success with AI still depends on understanding the skills people need to perform, at an individual, team and organizational level, then investing in development that reflects the realities of their work. But before organizations can build those skills, they need to know where expertise already exists. Too many businesses are sitting on critical knowledge without any clear view of who has it or where the gaps are.

One way to achieve this is building a skills ontology, a living record of the skills that exist across the organization.

Rather than relying on assumptions, businesses can identify where they overindex or underindex on capabilities, understand which teams have specialist knowledge and spot gaps before they become business problems. It also creates the foundation for workforce planning, internal mobility and more effective AI deployment.

The most valuable skills inside your organization

Now you have an effective skills ontology, you have an overview of what is going on in your organization. This helps you identify the subject matter experts (SMEs) that exist across a business, but whose knowledge has often never been formally captured - such as that mythical AI ‘power user’. Take Ford, which has said it is using its rehired employees specifically to train younger staff and reprogram AI tools.

Once you’ve identified where expertise exists, AI can help maintain that picture and keep it up to date as roles evolve. But understanding where the expertise exists is only the beginning.

Organizations need to validate that expertise, connecting people to targeted development opportunities and making it easier for SMEs to share what they know with others. Importantly, this learning should be embedded in work as much as possible rather than separated from it, so it improves performance where it matters most.

AI makes knowledge easier to organize and distribute, but it doesn’t create the judgement that comes from experience. It can surface patterns and recommend learning pathways, but it can’t explain why a process works, when to challenge it, or how to adapt it to the realities of your organization.

That’s why experienced team members remain essential. They contribute the context that enables AI, and everyone else, to use that knowledge effectively.

Turn expertise into a competitive advantage

At a time when many in areas like HR and Learning and Development (L&D) felt that AI would diminish their role, it's clear that this expertise is only becoming more valuable.

L&D teams have the opportunity to operate more strategically by owning the upskilling and reskilling agenda, turning their and team members’ expertise across the business into a competitive advantage, and putting the processes in place to create agile workforces that collaborate effectively with AI.

AI has put organizational expertise firmly in the spotlight, whilst also highlighting that too many businesses are sitting on a wealth of knowledge that exists only in the heads of experienced employees. Not only do they know and understand more about a business and its processes, but they can also provide judgement - a scarce resource that AI cannot replicate.

Getting this information out and deploying it across the business will create a workforce that becomes smarter every time someone learns, rather than losing knowledge when someone leaves, leading to fewer repeated mistakes, better quality and stronger internal mobility.

To get the greatest return from their employees and from AI, the top organizations need to ensure they are investing in identifying, sharing and scaling the expertise they already have instead of simply buying more software.

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Artificial intelligence may dominate today's headlines, but the most productive conversations I'm having across the technology ecosystem rarely begin or end with AI.

More often than not, they start with a business challenge.

Business challenges are driving the AI conversation

How do we modernize our infrastructure without increasing complexity? How do we strengthen our security posture while enabling innovation? How do we prepare our organization for AI without rebuilding everything from the ground up?

Those questions quickly lead to a realization: today's technology decisions are interconnected.

An AI initiative depends on cloud readiness. Cloud strategy influences cybersecurity. Identity, governance and data management underpin all of it. On paper, they look like separate line items. In practice, they're one system. If you pull on cloud, security moves with it; if you pull on AI, data and governance move too.

That's a significant shift, not only for organizations investing in technology, but for the partners helping them navigate it. Organizations are moving beyond asking whether AI matters. They're focused on how to implement it successfully.

That shift is happening across the industry.

Our fourth annual Direction of Technology Report, which surveyed more than 1,400 IT solution providers across 40 countries, found that nearly 75% of partners say AI is essential to their future. Yet the same research shows the conversation has become much broader than AI itself. Partners increasingly see services, specialization, cybersecurity and automation as the capabilities that will differentiate them in the years ahead.

To me, that's one of the most important signals in today's market, because the conversation isn't simply about adopting the next technology. It's about building an environment where technologies work together to solve real business challenges.

From experimentation to implementation

AI adoption is accelerating, but organizations quickly realize that successful AI initiatives depend on much more than selecting the right model or platform. They depend on secure infrastructure, trusted data, governance, integration and the expertise to bring those pieces together.

The market data tells a similar story.

Global AI spending is projected to exceed $2 trillion in 2026. Global Public Cloud spending is expected to surpass $1 trillion, while cybersecurity spending is projected to continue its rapid growth. These markets aren't growing in isolation but expanding together because organizations recognize that one investment reinforces the others.

That's why I believe the conversation is maturing.

The question is no longer, "Where do we start with AI?"

It's, "How do we build the right foundation to make AI successful?"

That requires a different approach from everyone in the technology ecosystem.

Customers aren't asking for another AI demonstration, but they’re asking questions like:

• How does AI fit into the technology investments we've already made?
• How will it integrate with our existing cloud environment?
• What does it mean for our security posture and governance?
• How can it improve productivity without creating unnecessary complexity?

Those are implementation questions, and I believe they'll define the next phase of AI adoption.

Technology will keep evolving, and AI will evolve fastest of all. What gives me confidence is not the pace of that innovation but the way the industry is organizing itself around the harder work of implementation.

No single vendor, provider or advisor holds every answer. Environments have become too interconnected for that, and the organizations making genuine progress are the ones drawing on combined expertise, weighing infrastructure, security, data and services together rather than in sequence.

The definition of success is changing with it. What matters is no longer how many technologies an organization can reach, but how well those technologies operate as one system in service of a business outcome. That, far more than any single breakthrough, will determine who ultimately gets value from AI tools.

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For years, retailers have competed on price, product range and customer experience. Those things still matter, but they are no longer enough on their own. Increasingly, success depends on whether customers can discover a retailer in the first place.

That is becoming more difficult. AI-powered search, changing platform algorithms and rising advertising costs are reshaping how people shop online. Customers no longer follow a straightforward journey from search engine to website. Instead, they move between marketplaces, social media, review platforms and increasingly AI-generated recommendations before deciding where to buy.

More than a third (35.1%) of UK businesses say reaching customers online has become harder over the past two years. At the same time, while 81.3% have a website, only 12.4% accurately track how website visitors become leads or customers. Almost half (45%) do not track this at all.

For retailers, these trends point to a bigger challenge. As customer discovery becomes more fragmented, businesses are becoming increasingly dependent on digital channels they do not control.

Retailers are relying on platforms they do not own

Search engines, marketplaces and social media have transformed retail over the past decade. They have enabled businesses of every size to reach customers far beyond their local markets and compete with larger brands. However, they have also created a new dependency.

Retailers invest significant time and budget building visibility on third-party platforms, yet those platforms are constantly evolving. Search rankings change, marketplaces adjust how products are displayed, advertising becomes more expensive and AI is beginning to influence which retailers customers see before they even click on a website.

These changes are outside a retailer's control, but they can have a direct impact on traffic, sales and customer acquisition. It does not mean businesses should move away from these platforms as they still remain an essential part of any digital strategy. The challenge is ensuring they do not become the only route to customers.

AI is making customer discovery more complex

AI is adding another layer to an already crowded digital landscape - instead of browsing multiple websites themselves, customers are increasingly asking AI tools to recommend products, compare options or summarize reviews.

These systems draw on information from across the web, meaning businesses have less visibility into how recommendations are made and why one retailer appears ahead of another. For retailers, this creates uncertainty.

It is becoming harder to understand where customers first encounter a brand, which touchpoints influence purchasing decisions and how different digital channels work together. Rather than simplifying customer acquisition, AI is making it more dynamic. Businesses need to adapt by strengthening the parts of their digital presence they can influence directly.

Your website remains the strongest foundation

As new discovery channels emerge, it is easy to focus attention on the latest platform or technology. The more important investment is often the one retailers already own. A website remains the only digital channel where retailers control the customer experience from beginning to end. They decide how products are presented, how information is structured and how customers move through the buying journey.

The information published on a website becomes very important as it also helps shape how businesses are understood elsewhere online. Accurate product descriptions, structured content, customer reviews and up-to-date information all contribute to a stronger digital presence.

While retailers cannot influence every algorithm or AI model, they can ensure the information these systems rely on is accurate, consistent and trustworthy.

Visibility without measurement creates blind spots

Building visibility is only one part of the challenge. Understanding what happens once customers arrive is equally important. Yet our research suggests many businesses are still operating without a clear picture of how their digital channels perform.

Only 12.4% of UK businesses accurately measure how website visitors become leads or customers, while almost half do not measure this journey at all. Without that visibility, retailers cannot confidently identify which marketing activity is generating sales, where customers are leaving the buying journey or which investments deserve greater attention.

As customer journeys become more fragmented, relying on assumptions becomes increasingly risky. Retailers that connect website analytics, ecommerce platforms and customer data are far better placed to understand how people discover their business and what ultimately drives revenue.

Building resilience for the future

Retailers will continue to invest in search, social media, marketplaces and AI-driven discovery because that is where customers spend their time. However, long-term success will depend on balancing those opportunities with investments in the digital assets they own.

That means maintaining a high-quality website, improving product information, understanding customer behavior and measuring performance across every stage of the buying journey. These foundations not only improve the customer experience but also help retailers respond more effectively as technology continues to evolve.

AI will undoubtedly change how people discover products online, but it will not change one fundamental principle. Retailers that build direct relationships with customers and invest in the channels they control will always be in a stronger position than those relying entirely on platforms that can change overnight.

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• Nvidia is reportedly preparing to acquire Hugging Face for $12.9bn
• A 2023 investment round valued the company at $4.5bn
• Nvidia is on a roll of investing heavily in other companies

Nvidia has reportedly spent the past couple of weeks actively engaging with Hugging Face to explore an acquisition deal worth an estimated $12.9 billion, according to Business Insider reporting, citing an unnamed source familiar with the matter.

To date, the companies are yet to reach an agreement, but with the current valuation speculations, it would make a Hugging Face acquisition one of Nvidia's largest acquisitions to date.

Importantly, this wouldn't be the chipmaker's first run-in with Hugging Face – it took part in a $235 million funding round in 2023. At that time, Hugging Face's valuation was around $4.5 billion - making today's valuation nearly three times higher.

Nvidia and Hugging Face

While this may be one of Nvidia's biggest acquisitions to date, it's just one of a growing number, as the company recently admitted it had $18 billion committed to equity investments for the rest of the financial year, and it already holds $47.9 billion in private company stock.

However, there's still a shadow over the potential Nvidia acquisition, with Hugging Face previously turning down a $500 million Nvidia investment over concerns that a single shareholder could have such a big influence.

The high valuation also makes the prospective acquisition a costly one – at around 86x Hugging Face's annualized revenue ($150 million), Nvidia would be paying a premium for the platform's strategic position and existing relationships, against a backdrop of ongoing investments in models and development tools.

Regulator scrutiny might also question whether competing chipmakers and cloud service providers could be put at a disadvantage, particularly if Nvidia imposes hardware and software restrictions on the platform after a takeover.

Neither Nvidia nor Hugging Face responded to requests for comment.

• Salesforce and Anthropic reveal Claudeforce
• Greater expansion of Claude into Salesforce's services, including a new plugin offering 37 pre-built sales skills
• Salesforce CEO Marc Benioff says "Saaspocalypse" is "such nonsense"

Salesforce has extended its partnership with Anthropic to build Claude directly into the former's services.

Claudeforce will give Claude direct access into customer data and other information stored in Salesforce, allowing the AI to carry out everyday tasks and access information much easier than before.

This includes the launch of Salesforce in Claude, a new plugin offering 37 pre-built sales skills giving sellers and agents the ability to reason over live revenue context, automate pipeline updates, and take governed action right from Claude.

Claudeforce

In a blog post announcing the news, the company claimed that Claudeforce, "brings together the best of both worlds — Claude brings intelligence and judgment, reasoning, adapting, and deciding in real time. Salesforce provides the data, rules, and trusted workflows that turn that judgment into enterprise-ready action."

Along with the new plugin, Claude will also now be available in Agentforce, acting as a reasoning model for the Atlas Reasoning Engine, as well as powering Agentforce Vibes and Agentforce Coworker by default, and available in Agent Builder.

Claude will also become the "default model" for Slack, Salesforce noted, serving as "the intelligent backbone across every layer of the Slack experience".

“We believe frontier intelligence should be safe, trusted, and deeply capable, which is why the world’s leading businesses turn to Claude for their most important work," noted Dario Amodei, CEO & Co-Founder, Anthropic.

"Salesforce in Claude brings this same frontier intelligence into the systems where much of the world’s commercial activity happens. Through this partnership, companies can point Claude at the customer information and business context that they’ve been building in Salesforce for decades, and use it to actually run and grow their businesses.”

The launch comes as Salesforce CEO Marc Benioff has once again pushed back against fears of a so-called “SaaSpocalypse” brought on by increased AI innovation.

“This SaaSpocalypse narrative has been such nonsense,” Benioff told CNBC. “Frontier models depend on CRM. They don’t replace it.”

Benioff noted that the company's latest financial results suggest AI is having a significant effect on its customer base, with nine of the 10 leading artificial intelligence companies using Salesforce and Slack, and spending on the platforms surging 435% from a year ago.

The CEO pointed to Claudeforce as a clear signal of Salesforce being a strong foundation for AI agents, noting models such as Claude rely on the customer data, business context and workflows housed on Salesforce’s platform.

“Salesforce is first and foremost in the data business,” the CEO said. “These AI models need this level of intelligence, security and controls for users.”

ChatGPT’s growing collection of plugins has always sounded more useful than it sometimes felt. A long directory can make every integration look equally promising, even though some become regular tools and others enjoy one exciting demonstration before joining the digital attic. I have installed plenty that seemed clever for approximately seven minutes.

A recent recommendation update is designed to make that search more useful. Plugin rankings now give greater priority to tools that people continue using after installation, helping relevant options appear more prominently across ChatGPT on the web and mobile.

That sounded like a useful reason to revisit the directory and see which plugins had earned a place in my own routine. Here are five ChatGPT plugins I use that you might find worthwhile integrating into your life too.

1. Spotify for soundtracks of my day

Music recommendations have traditionally fallen into two camps. An algorithm serves more of whatever you played last week, or a friend gives you a playlist that contains six hours of obscure ambient jazz and demands your gratitude. Spotify’s ChatGPT plugin offers a more conversational middle ground.

Once connected, it can use your listening history, favorite artists and habits to suggest music, find podcasts and build playlists. The advantage is the amount of context you can include. Instead of searching for “workout music,” you can request a precise length, mood, genre mixture and energy curve. You can then refine the result without starting again.

I find it particularly good for playlists attached to a specific activity. Give it constraints and a little personality. A useful prompt would be: “Spotify, make a 75-minute Saturday kitchen-cleaning playlist. Start with indie rock, increase the energy after 20 minutes, avoid songs I played heavily last month and finish with three ridiculous singalongs.”

That produces something more purposeful than a generic cleaning playlist assembled by somebody who apparently dusts to anonymous dance remixes. You can ask Spotify to replace individual tracks, make the selection calmer or rename the finished playlist before opening it in the Spotify app.

The plugin is also handy for rediscovery. Asking for artists you listened to frequently five or ten years ago can produce a surprisingly effective musical excavation, complete with the occasional song you had wisely buried.

2. AllTrails for touching grass

I like hiking and finding new places to go. The AllTrails plugin can turn my fuzzy desire to spend time outside into a shortlist that reflects how much time, energy,, and enthusiasm I actually have.

The trick is to describe the practical limits that generic searches tend to ignore. Include your location, maximum driving time, preferred distance, acceptable elevation gain, and any requirements involving children, dogs, shade, accessibility, or parking. ChatGPT can handle those details conversationally and use AllTrails to narrow the possibilities.

For example: “AllTrails, find three loop hikes within 45 minutes of me. Keep them between two and four miles with less than 700 feet of elevation gain. Prioritize recent positive reviews and parking that does not require arriving at dawn.”

That is far more useful than being told about the “10 best hikes in New York.” Follow-up prompts can compare the routes, suggest the best choice for a hot day, or identify the option with the gentlest opening mile. Trail conditions can change, so I still check recent reviews, closures, weather, and the official route before leaving, but it's been a great trip planner, especially for group hikes.

3. Todoist for due dates

My relationship with to-do lists has always been hopeful but unstable. I enjoy creating them, feel briefly organized, and then discover the list several days later beneath newer lists. Todoist already offered better structure, but its ChatGPT plugin removed much of the fiddly work involved in turning a collection of thoughts into an actual plan.

After connecting my Todoist account, I might describe a typical overloaded Saturday and ask it to put the errands in a sensible driving order and with a realistic time schedule for getting it all done. I also ask for a preview first lest ChatGPT overload me out of misplaced confidence in my abilities.

The plugin is especially useful for recurring household tasks. I can ask it to create reminders for changing filters, checking smoke alarms, renewing documents or ordering supplies.

4. FitAI Pro for fitness

FitAI Pro is designed for planning gym workouts and logging sets, It handles the organizational work that often stands between me and an actual workout.

After installing the plugin, I gave it my experience level, available equipment, preferred number of training days, and the maximum time I wanted to spend in the gym, and I get back a set of workouts to match. It helps to mention any movements you dislike or need to avoid

The large exercise library also makes substitutions easy for when I just don't like a particular exercise or want to mix things up. The plugin cannot perform the workout for me, which remains disappointing, but otherwise it streamlines workout plans in ways that make it harder for me to procrastinate or skip them entirely.

5. Podcast App to learn and laugh

Podcast discovery is even tougher than for plugins because it's such a vast sea of OK-to-bad options, with unfortunately rare gems hidden within. Even more so if you only want to find a particular episode and not just a whole show.

The Podcast App plugin lets me search by topic, idea, or program and receive relevant podcast recommendations. I use it for individual episodes, since that is where it saves the most time. You can mention a maximum length if you want to avoid the four-hour monologues, and specify just how much expertise you have if you want general knowledge versus granular detail beyond your interest. Those distinctions matter when the same subject can produce either an entertaining commute or a three-hour academic panel.

I also usually ask the plugin to explain in one sentence why each episode matches my request. I can find one episode about a specific book, scientific question, or historical incident and move on. My queue remains imperfect, but I don't spend more time looking than listening.

The updated plugin recommendation system makes good plugins easier to discover, but discovery is only the opening act. The real value comes from giving a plugin a well-defined job, useful constraints, and permission to ask for clarification. Specific prompts produce specific help. Vague prompts produce the digital equivalent of a sympathetic nod.

Still, I keep these five because they help in ways standard ChatGPT can't, or that would take much longer to set up in the general chatbox. As OpenAI acknowledges with the new system, returning voluntarily to them is the most convincing recommendation for any AI tool.

A lot has already been said about the doom and gloom surrounding AI - some of it drawing on the warnings of classic dystopian novels, but much of it grounded in reality. There is a lot of optimism and promises from those in control of the technology, but not much has emerged in the form of actual benefit.

“I think by far the most accurate criticism of AI companies including Anthropic is that we haven’t yet delivered on our big promises to benefit the world,” Anthropic CEO and co-founder Dario Amodei has said in a post on X.

The technology is suffering from a “crisis of trust” that stems from people’s belief that companies and governments are “cooking up some new way to screw them over,” he argues. But given how much of the US military, law enforcement, and intelligence gathering infrastructure now relies on AI, are the people wrong?

AI can’t public relations its way out of this one

As AI technology took off, much of the benefit of the technology was aimed at convincing executives and C-suites that the technology - while unproven - was a worthwhile investment. As a result, multiple AI leaders positioned the technology as a way to strip away the labor costs of companies by replacing workers with AI.

Some AI gurus are still arguing these points behind closed doors. I attended a private event this year where an AI "guru" predicted that AI superintelligence would erase as much as 50% of the global workforce within 10 years, and the companies best positioned to take advantage of this cataclysmic event would reap unimaginable control and riches. This was naturally met by applause from the upper echelons of management from companies in attendance.

Amodei himself famously said that AI could wipe out 50% of white collar jobs. But he has since restructured this message, saying instead that AI is a ‘multiplier of output’ - or corporate word salad for ‘increasing productivity without having to increase compensation’.

As I have said numerous times, the proof of AI really is in the pudding. We’ve heard everything that it could do, but seen little benefit in what it can do. To his point, Amodei acknowledges that.

He notes that if an advert went up saying that AI could cure cancer, it would be perceived as entirely misleading. “The thing that will work is actually curing cancer,” he says.

Amodei has teased Anthropic’s morality on these issues, saying that the company is “ramping up its efforts” to focus on using AI for biology and medicine. But again, we’re yet to see the results.

Amodei’s post also touched on where AI is centralizing power, echoing some of Mark Zuckerberg’s recent rant on how best to use AI for everyone’s benefit. Amodei’s argument differed slightly from Zuckerberg’s, arguing that there is a balance to be struck between regulation, oversight, testing, and open-weight models.

He notes that in some aspects he agrees with the “Pacing the Frontier” movement, stating that implementing controls on the frontier labs would “modulate” the pace of the technology while simultaneously allowing the rest of the industry to catch up.

The obvious flaw in this plan is that no other nation would be subject to this regulation which could deliberately hamstring US hegemony in the AI industry - something the Trump administration has frequently said it will not allow to happen.

Is AI delivering more benefits to authoritarian infrastructure than to people?

Where AI systems have been deployed extensively is in surveillance infrastructure. Until the collapse of its relationship with the Pentagon, Anthropic was working with the Department of Defense to procure AI systems. Anthropic refused to cooperate in allowing its technology to be used for mass surveillance and fully autonomous weapons, and lost the contract.

Luckily for the DoD, OpenAI shook hands on a new deal with the Pentagon and immediately filled the shoes left by Anthropic with a prohibition on its technology being used to spy on Americans.

That doesn’t mean other companies are abiding by the same level of ethics. Flock has faced a widespread guerrilla movement targeting its AI-powered cameras designed to track vehicle number plates across the US.

The system is intended to flag suspect’s cars as they travel across boundaries and lines of authority, but has instead seen success as a way for members of law enforcement to stalk exes, monitor colleagues, and track their love interests.

AI systems are also being deployed in traffic monitoring and speeding cameras. This in particular has received a lot of attention as a grey area of policing, especially for systems that misidentify numberplates or are wrongly configured.

For criminal cases in the US, the burden of proof rests with the government to prove someone committed a crime. However, some autonomous ticketing systems that have issued citations to the wrong vehicles have been criticized for requiring those accused to prove their innocence in court, flipping the burden of proof for criminal cases on its head.

And then there is the elephant in the room.

Palantir’s AI technology is deployed across the US military and law enforcement. Most notably, it has been used by the US Immigration and Customs Enforcement (ICE) to track those suspected of illegal immigration - but Palantir’s technology is also used in drone imagery analysis, long-range precision strikes, and military data analysis and planning.

No doubt it has been instrumental in delivering the World Record for the most times a single war has been won.

AI is great news if you have nothing to hide and love being on the side of the Good Guys™, but that's a constantly shifting definition.

Imagine AI is the sun and businesses are Icarus. By now, most of us are basking in the light of this transformative innovation, which sometimes feels too hot and other times disappears behind clouds so dense we wonder why we ever trusted it in the first place.

Some intrepid businesses not only gaze at AI's warm light, but openly worship it. They don wings, and fly as close as they can to its surface. The goal: scoop up a bit of that generative magic, and fly back to Earth with agents, which, to carry this insane metaphor further, can be fashioned into people-like workers capable of doing what we do, but at scale and with little-to-no monitoring.

Of course, the flight to AI's core is dangerous, and so we have episodes like Meta's recent Quixotic adventure which, according to an extensive Reuters report, saw it attempt to transform a vast portion of its workforce (reportedly 60%) into AI agents overseen by a much smaller set of highly skilled workers.

That Meta's wings almost immediately started smoking from its proximity to the AI core is no surprise, at least not to me.

Slow down

I talk and write about AI a lot, and I've been covering it in some form or another for almost 25 years, back when companies like IBM and Microsoft were the AI leaders, and we thought Deep Blue beating Kasparov was the apex of what we could expect from artificial intelligence.

Modern generative AI burns far brighter, and shows in real time such vast potential that enthusiasm is not just rampant; it's out of control.

AI agents, which Meta apparently started using at scale, are driving much of this mania. These automated tools offer the promise of handling drudgery-filled tasks in innumerable businesses, and could solve not just productivity problems, but be the answer to workforce issues, including hiring problems, underperformers, and burnout. Agentic AI can not just handle repetitive tasks; it can cobble together disparate systems to complete work.

From Meta's perspective, especially as a company that is primarily a software business, AI that could work across tools and systems to not only help keep things running but build new work (under the supervision of a smaller set of super-performers) probably seemed like the perfect solution.

Conclusions like this are not exclusive to Meta or companies of its size. Conversations about the use of AI in business persist in every industry and at every level.

AI has gone to work in healthcare, retail and e-commerce, manufacturing, financial services and banking, entertainment, and media. All with varying degrees of success and implications.

It's a stunning fact when you consider that we only met OpenAI's ChatGPT in 2022. Never has a technology so quickly infiltrated every facet of life, and so consumed daily conversation. AI Time is triple time (3x the speed of any previous innovation), and that includes adoption.

There is, at least in business, a shocking level of trust in what is still, in many ways, an untested solution. I'm not saying AI does not work, but there is the assumption right now that it works in every situation.

Certainly, if the Reuters report is accurate, and Meta essentially verified 90% of it (it claims it was never planning on laying off 60% of its workforce), Meta is guilty of this.

Do you smell something burning?

With AI, the act of implementation and business transformation tend to happen in parallel, so as Meta was planning and enacting its reorganization and layoffs, it was already employing Agentic AI.

"Infrastructure teams were also warning of 'reliability warning signs' caused by the AI coding surge, according to an internal post," wrote Katie Paul for Reuters.

There was, the author adds, evidence that these issues (and basically faulty work) were visible in June when hackers used Meta's AI chatbot to hack celebrity Instagram accounts.

Between that and what appears to be a company revolt (my favorite part was the bathroom stall notices encouraging people to protest Meta's use of keystroke tracking), it was clear Meta has gone too far, too fast with AI for everyone's tastes.

In the end, with its feathers on fire, Meta retreated from the AI sun (or at least its plan) and canceled layoffs and even undid some of the reorg.

The lesson here is not that AI is too dangerous to approach closely, or that AI and business are a bad match. But, as we are seeing, pronouncements that AI would transform businesses without removing or replacing jobs were misinformed, and allowing AI to produce work without adequate human oversight at every stage of the process is, for the time being, ill-advised.

As Mark Zuckerberg tries to rewrite his own script to be more people-first, it might be time for everyone to take a beat, and reassess how AI fits into their businesses, workflow, and life.

AI might feel like the sun, but it can't be our sole source of light and warmth. It's still just another tool — sure, a powerful and incredibly fungible one, but still a tool — and over-reliance on it will undoubtedly lead to frustration, or worse.

Meta learned a hard lesson: your AI plans must be grounded in a human-centric approach; otherwise, the humans will rise up and clip your wings.

Scary AI spend and weak access security controls are the flipside of the sharp rise in agentic AI. The answer lies in the form of a control plane that provides the visibility CIOs need.

The past couple of years have seen enterprises press the fast-forward button on generative AI strategies. e have all seen a remarkable change in deployment patterns from useful chatbots to autonomous agents that engage deeply with core software, data and LLMs, and even create their own offspring agents.

CIOs and others are rightly excited about the capacity of agentic AI to reduce manual processes and accelerate speed to insight and actions.

However, governance and AI token costs are alarming challenges.

What’s needed today then is the power of AI without the major concerns over incurring access control risks relating to ‘Shadow AI’ and agent sprawl, and without the ‘bill shock’ relating to unmanaged token consumption.

Moving fast

The generative AI shift that has occurred so quickly spans the early days of ad hoc AI experimentation via testing and pilots to today’s enterprise-grade business processes. It has also outpaced the progress of the usual governance frameworks and effectively rendered them obsolete.

Experienced CIOs may compare the change to the early days of cloud computing platforms when staff with credit cards expensed the AWS trials they had spun up and quickly ran up staggering bills.

Or they may hark back to the days when sales leaders set up online CRM platforms or other process owners bypassed IT to run their own Sharepoint systems.

Or, to reach for yet another lesson from history, they may compare what is happening with generative AI now with the advent of bring-your-own-device schemes and WiFi.

Remember when the user-led demand to have the shiny new gadget ran a coach and horses through the command-and-control IT regimes of cloned PCs stamped with a single image and CIOs no longer managed everything that touched their systems?

The problem is here. Deal with it

However we make the comparison, it’s clear that today there are very real risks relating to data leakage, compliance standards and IT costs because of the speed with which AI has advanced. Nobody wants to lose a chance to gain a competitive advantage and even to fundamentally re-engineer and automate expensive, error-strewn manual processes. So CIOs are once again being asked to square a circle.

As IDC has said: “There is a well-established playbook for calculating the return on an IT investment. Define a baseline, model the efficiency gains, project the cost savings, and present a number to the CFO. For most technology investments, that approach works. Agentic AI breaks it.”

Agentic AI is not fixed and predictable so it’s tough to manage and calculate return on investment, but we know it can take out swathes of cost, so we have to use it and we have to manage costs as best we can.

Gartner has already suggested that by 2027, more than 40% of agentic AI projects will be cancelled due to factors such as cost overrun fears or inadequate risk management. That’s not good enough. Corporate IT must step up and deliver the value the business needs, just without the attendant risks and costs.

The solution here is to head off undesirable outcomes at the pass by providing a check, a little like an airport security system that identifies passengers and scans for any potentially unsafe activity or nearby environmental factors such as possession of sharp objects.

You will see this happen in IT very quickly now, such are the risks and speed of what is happening. If 2026 is the year of agentic AI, then it also has to be the year of secure agentic AI.

Enter the AI gateway

One way to address the challenges outlined above is to create a gateway or filter. This approach offers a single control layer regardless of whether the AI model is hosted in public or deployed as a private, self-hosted entity. It effectively secures traffic from agents to LLMs and from MCP servers. This in turn enables organizations to have centralized policies and a clear view of token accounting.

It even has the visibility impact of informing decisions of non-techies who know little about AI operations. So, for example, a CIO working in tandem with a CFO perhaps may identify a better cost efficiency (for example, switching from a hosted model to a private model) and act accordingly.

That’s great news for CIOs who want to maintain a reputation for IT being agents of change but not its less desirable reputation for being risky and pricey.

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Autonomous AI agents are moving faster than the frameworks meant to contain them. Enterprises are deploying agents that can call systems, pull data, and increasingly interact with other agents to complete multi-step tasks.

Yet few organizations can say with confidence exactly how many agents are running in their environment, what each one is authorized to touch, or who exactly is accountable when something goes wrong.

That gap can turn agentic workflows from a promising technological advancement into a potential minefield of risk unless enterprises take a new approach to governance – one that provides greater oversight into how agents are operating and what systems they can access, trust, and use.

Don’t wait for perfection

The market has responded to the potential threat around AI agents going rogue with a wave of new tooling: agent discovery platforms that scan for active agents, and agent harnesses that box them into approved boundaries. Both are useful, but neither solves the problem alone.

The challenge of “getting a handle” on AI agents is compounded by the pace of change. New agent tools, new AI model releases, and new orchestration options are arriving at a rate that makes any static governance model obsolete within months.

Rather than waiting for a “perfect” governance framework to emerge as a standard, organizations should take steps now to create a working structure that can evolve over time.

So, what might this look like in practice?

Continuous visibility and granular guardrails

Policies and procedures alone cannot confirm what is actually running in production. Organizations need a registration and discovery process that captures every agent in use, not just the ones teams report having built.

Real visibility comes from instrumenting the environment itself, using logging and observability data generated by the underlying models, and building analysis on top of it. Only that raw data can show what an agent is actually doing, how often, and at what cost, rather than relying on what people think it is doing.

Another key step is to move from broad guardrails to granular ones. A single, broad-based harness applied uniformly across an organization, for example, limits scope without addressing risk in a meaningful way. Different use cases call for different levels of restriction, and treating every agent identically either over-constrains valuable work or under-constrains risky work.

Guardrails need to be defined at the level of the individual agent and its specific task, not the organization as a whole. As agents begin accessing other agents to complete a task, that specificity becomes even more important: each agent needs an explicit, narrow definition of what it can do and what it can reach, so that any leakage beyond that defined scope is immediately visible.

With all of the above, enterprises should embrace an approach of “validate, don’t assume”. Putting a conceptual harness or policy in place is only the first step. Confirming that it actually holds under real conditions is a separate and ongoing exercise.

A useful approach here is to pair conceptual guardrails with a logical or physical enforcement layer: a tool that can confirm what is executable, what an agent can interact with, and whether it stays within that boundary in practice.

Continuous monitoring against policy, not a one-time sign-off, is what proves a harness is working, because agent capability and the surrounding set of tools change on a near-daily basis.

Access is not the only variable

Governance conversations tend to focus almost exclusively on what an agent can access. Equally important is the order in which it accesses information. An agent that gathers information out of sequence, or acts on data before a dependent step has completed, can produce results that are wrong even if every individual action was technically permitted.

Some workflows require steps to run in parallel; others require specific sequencing to ensure the most accurate results. Building a discrete, well-defined operational sequence for each use case, rather than allowing a model to determine its own order of operations, substantially reduces this type of error.

This is as much a process design issue as a technical one, and it relies on people defining the use case clearly before any tooling is applied.

A disciplined approach reins in agentic risk

None of these elements works in isolation. Discovery without granular guardrails leaves organizations knowing what exists but not controlling it. Harnesses without validation create a false sense of security. And governance focused only on access misses key variables that can introduce risk.

The organizations managing this well are the ones treating agent governance as an extension of existing IT and data governance, applying the same discipline used for foundational legacy systems rather than treating it as a bolt-on afterthought.

Given how quickly the technological landscape continues to shift, this comprehensive governed approach is what will keep agentic AI delivering value – and reduce the risk of agents going rogue.

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This summer's unexpected heatwave across the UK and Europe has caught retailers flat-footed.

Go into any London Oxford Street store this week and you’ll find the last remains of the mid-summer sale, while “new in” rails are covered in chocolate brown trouser suits ready for Autumn.

The trouble is, it’s still 28 degrees and sunny with another heatwave expected this week. Shoppers aren’t looking for jumpers, yet the shelves are stocked for a forecast made half a year earlier, meaning many retailers have missed the immediate shift in consumer demand.

It’s hard to not feel the disconnect. Somewhere back in January, a planning team sat in a meeting room and decided, with the best information they had at the time, that by early August the nation would be ready to shop for knitwear.

It’s a process that retailers have used for decades, and the problem isn’t that they planned-ahead or got it wrong, it’s that the infrastructure behind the decision has no mechanism for course correcting once new information arrives.

That's not a forecasting failure. It's a technology and process failure, and it's one that's becoming impossible to ignore.

Six-month planning cycles are no longer viable

The heatwave is just one example of why rigid six-month planning cycles in retail are no longer commercially viable. Designed in an era that was steady and predictable, they assume stable supply chains, formulaic seasons, and shoppers who wait patiently for the "right" moment to buy. That world simply doesn’t exist today.

Global supply chains have become fragile and prone to disruption at any point in the chain, while erratic weather patterns can change trading conditions overnight, and consumer demand is shaped as much by a TikTok trend that lands on a Tuesday, as it is by a season on a calendar. This has left retailers trying to run a business that demands agility on an operating system designed for a much slower rhythm.

The result is the disconnect we're seeing on the shop floor right now. Having worked in retail for more than 20 years, I know first-hand that most planning systems are still built around static reports, disconnected spreadsheets and manual range-building processes that take weeks (sometimes months) to turn around.

This creates a structural lag between changes in demand and when the business is able to respond. It is this lag that is quickly becoming the single biggest driver of markdowns, stockouts and wasted inventory that costs the global retail sector more than $1.7 trillion per year, according to analysts IHL Group.

Rethinking planning

It’s clear to me that for retailers looking to achieve growth, they must rethink how planning, buying and merchandising get done.

This starts with moving away from rigid, twice-a-year buying cycles built on legacy systems to data infrastructure that supports micro-season planning. This is shorter, more frequent windows where decisions about what to buy, when to reorder and how to promote in-store are made continuously and based on current data, not locked in months in advance based on a forecast made months earlier.

Practically, this means implementing a few core capabilities, the first being real-time data visibility that allows teams to see live sell-through, stock position and intake at SKU level, rather than via a report that lands the following Monday describing what already happened.

The second is more connected forecasting, where demand models can analyze external factors such as a heatwave, local events or social media trends as a trading signal rather than an anomaly discovered on the shop floor.

This leads to the third capability, which is faster execution once a shift in demand is identified. The system needs to support quick decision making and action, whether that's an automated reorder, a reallocation of stock between stores and channels, or a change to in-store and online merchandising.

Maintaining a live model

None of this replaces long-range strategy. Retailers still need a financial plan, a range architecture and a clear vision. What changes is how that happens in practice. Planning becomes less about producing a fixed document twice a year and more about maintaining a live model of the business that can be interrogated and acted on continuously.

That's a fundamentally different technology requirement than most legacy planning and merchandising systems were built to support, and it's why so many retailers are still reacting to demand shifts weeks after they've already cost them sales.

For retail leaders, the practical takeaway is to start auditing where your planning and merchandising decisions get made, and how long it takes for a real-world signal, such as a heatwave, a stockout, a viral product, to translate into a change on the shop floor or the website. If that gap is measured in weeks rather than days, the constraint isn't your team's judgement, it's the infrastructure they're working with.

This heatwave is just one visible example of a much broader technological and operational shift that retailers need to make. The businesses that treat it as a prompt to modernize, rather than a one-off weather event, are the ones that will be more commercially resilient the next time conditions change without warning.

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Artificial intelligence has transformed how software is built. Tasks that once took software developers days, if not weeks, to finalize can now be completed in hours with the assistance of generative AI tools.

The promise is compelling, offering faster innovation, increased productivity, and the ability to bring new applications to life at an unprecedented speed. But what is the impact on security?

AI coding has simultaneously put software risk on steroids. This is not because AI-generated code is uniquely flawed; it’s because it enables organizations to build and deploy software faster than any existing security, governance, or risk management process.

Development velocity has accelerated toward machine speed, while governance remains largely human-driven. That gap is now one of the defining software security challenges of the AI era.

Software is moving at machine speed. Security isn't.

AI is not only changing how code is written; it is changing how software is assembled. Developers can now assemble applications using open-source components, APIs, and third-party services faster than ever before. Every new application, integration, and dependency expands the attack surface that organizations must inventory, monitor, and secure.

The result is now a growing imbalance between software creation and software remediation. As the pace of software creation increases, remediation must keep up.

Veracode's 2026 State of Software Security report found 82% of organizations now carry security debt—vulnerabilities that remain unresolved over time — and 60% carry critical security debt, meaning flaws that are severe enough to cause significant damage if exploited.

Third-party code continues to be an especially stubborn source of risk, representing 66% of the most dangerous, long-lived vulnerabilities. The data reveals a simple reality: AI doesn't just generate more first-party code—it is increasing software complexity.

Organizations have always dealt with flawed code. The difference now is the speed and scale at which that code can be created, accepted, and deployed. AI doesn't just introduce risk, it amplifies the challenge of managing risk by enabling teams to generate exponentially more software than traditional security processes were designed to govern.

Traditional security governance assumes humans remain the bottleneck in software creation. Reviews, approvals, audits, and remediation workflows were designed for development cycles measured in weeks or months. AI-assisted development compresses those timelines dramatically.

When software can be generated, modified, and deployed at machine speed, governance models that depend on human intervention alone are no longer sustainable.

AI can help plant a seed, but that does not mean the garden will thrive. A seed needs the right soil, climate, and care. Software is no different. Organizations can generate applications overnight, but without the right security frameworks, operational support, and governance structures, those applications can quickly become liabilities rather than assets.

This is why security leaders must rethink governance for the AI era. The goal can’t be to inspect every line of code or eliminate every vulnerability before deployment; that approach was already becoming unsustainable before generative AI entered the picture. Instead, organizations need governance systems capable of operating at the same pace as software creation.

That means automating risk analysis, continuously evaluating dependencies, enforcing policies through pipelines, and prioritizing remediation based on business risk rather than relying on manual review alone.

Governance becomes the new trust layer

The need for machine-speed governance extends beyond operational efficiency. As AI accelerates software creation, governance becomes the mechanism through which organizations maintain visibility, demonstrate control, and establish trust across an increasingly complex software ecosystem.

Ultimately, this isn't just about scaling security. It's about ensuring software can be trusted and held accountable, regardless of how it's built.

AI can generate software, but it cannot assume responsibility for it. Boards will still hold executive leadership accountable for cyber risk. Regulators will still expect organizations to demonstrate that the software they deploy is secure and resilient. Customers will still expect software they can trust, regardless of how it was built.

AI may change how software is created, but it does not change who is accountable for its consequences.

That shift requires organizations to rethink governance as a strategic capability, not a compliance exercise. Success will depend less on preventing every vulnerability and more on demonstrating that software can be continuously evaluated, understood, and trusted as it evolves. In the AI era, the winners will not simply be those that build software fastest, but those that can govern it most effectively.

AI can help plant the seed, but it cannot tend to the garden. The organizations that lead today will not necessarily be those that generate the most software. They'll be the ones that can confidently answer the question every stakeholder will eventually ask: Can we trust what we've built?

AI has accelerated software creation beyond anything the industry has experienced before. If software risk is now on steroids, governance must be too. Otherwise, the gap between what organizations can build and what they can securely manage will continue to widen.

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Now is the time for nonprofits to embrace artificial intelligence (AI).

There is a distinct opportunity to significantly improve their operations using the latest developments in AI and automation.

We are seeing confidence grow in the third sector as organizations come to understand the potential of AI, but now we are entering a crucial phase as they look to implement it.

Given the complex technology involved, it may appear daunting to get the right strategy in place, but a disciplined approach to adoption that is focused on incremental gains - not radical transformation - will ensure a positive outcome.

This will mitigate significant disruption by avoiding large number of components being upgraded at the same time, as that increases the risk of something going wrong.

A pragmatic approach combined with thinking big

Getting to this point is perhaps easier said than done, as many nonprofits rely on purpose-built applications and manual processes. It is incumbent on vendors to articulate a pragmatic approach to integrating AI with these existing systems, focusing on the idea of incremental improvements to processes and ways of working.

In parallel, nonprofits must think big about the potential for positive change. Vendors must work with their customers to help them understand that transformation entails more than just a chatbot or another point solution being added to the technology stack.

Done right, AI will reshape how workflows are triggered, data is interpreted and insights are generated. It will become an intelligence layer acting autonomously to interrogate data and provide teams with cognitive support to make more effective and real-time decisions.

Therefore, if nonprofits are to maximize their AI investments, they must ask themselves big questions: how does AI change the ecosystem the organization operates in? And how can it be used to improve the nonprofit’s mission?

If nonprofit leaders can think ambitiously about these answers, it will empower teams to become true knowledge workers. AI will help them uncover information to inform actions or give workers more time to focus on problem-solving, as the technology is designed to automate repetitive tasks that traditionally consume cognitive bandwidth.

The right data strategy is crucial

A key challenge for nonprofits is data governance, as the AI tools will only be effective if they have access to the right data. In a recent study we conducted called “Closing the Gap: Nonprofit Finance Software’s Crucial Role in Today’s Landscape”, 61% of finance professionals in US nonprofits admitted they still rely on generic spreadsheets for core financial management.

The combination of specialist applications and manual processes makes it difficult to connect information across different applications and workflows. Data must be extracted, transformed and loaded so that it can be analyzed. The extraction process is also challenging because the data is often not in the same format and must be loaded in a particular format for ease of use by the finance team.

To address the data challenge, nonprofits must decide how to make the right data accessible to the AI. The data must be set up in a way that can be interrogated in plain language, and it must be based on good data inputs. Aside from reliability, the data also requires a common analysis framework so that users can understand its origins and who is accountable for it. Then, the organizations must be able to analyze the data chain and how AI is interacting with the data to make decisions.

This requires a detailed appreciation of the semantics of data. For example, the term “project” means something different in the world of nonprofits compared a professional services company or a public sector organizations . The AI must be able to interpret the word correctly to avoid hallucinations.

Equally important is the ability to monitor, evaluate and adjust data as it goes through the data supply chain to avoid inaccuracies in areas such as when it moves from the record to report phase. Nonprofits must work closely with their technology suppliers to ensure everyone in the organization, whether at the leadership, program or project level, can monitor the data for compliance with expectations.

Incrementalism is better than quantum leaps

The concept of incrementalism, rather than the “Big Bang” or quantum leap approach to transformation is the best way to address these fundamental questions.

Most successful IT implementations follow a similar incremental path. For organizations embracing this approach and wanting to ensure operational integrity while transforming IT systems, it is important to start with an evaluation of where the organization is relative to the desired end state. The planning objective is to get to the end state through a process of continuous, incremental adoption of new functionality and applications.

Most importantly, this approach allows staff to build their confidence in collaborating with AI tools. Imagine a team operating in a remote location providing medical assistance and the head of the team realizes they need to order supplies. Using incremental innovation an organization could use a combination of AI and automation to alleviate the burden of replacing missing stock.

In the first phase, an AI tool could monitor existing stocks and prompt staff when it is running low. This will give employees the chance to train the AI tool when it is urgent to replenish stocks. The AI could also use contextual information such as historical usage data and information such as weather to identify likely peak demand. As the AI becomes more autonomous, it could make recommendations to a human co-worker suggesting it completes an order form for new supplies which is reviewed by an employee.

Again, this acts as a training opportunity for AI so that in time employees will have confidence it can autonomously complete such tasks in the background. In time, the whole process will minimize interruption for employees and reduce the burden on them to complete such administrative tasks.

What this example shows is that nonprofits do not have to achieve fully autonomous systems straight away. At each stage, the AI is providing workflow improvements that empower knowledge workers by saving them time to focus on what is important to deliver their missions.

Incremental change will see nonprofits delivering value to their organizations very quickly, if they are able to address fundamental questions around their approach to data, while also thinking big about what AI can enable them to do better.

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Across organizations, AI adoption is entering a new phase. What began as experimentation and isolated use cases is rapidly evolving into enterprise-wide deployment, with AI becoming embedded across operations, customer experiences, decision-making and business strategy.

This shift is creating significant opportunities for growth, productivity and innovation, while also presenting a growing challenge for business and technology leaders: ensuring governance, oversight and operating models keep pace. As organizations scale AI, the question is no longer just what the technology can do, but whether the structures, processes and controls are in place to manage it effectively.

The growing governance gap

In many organizations, AI adoption is expanding beyond the direct oversight of central technology teams. Business units are deploying AI-powered tools to improve efficiency, streamline workflows and accelerate decision-making.

While this democratization of technology can unlock innovation, it can also create complexity. Leaders may find themselves accountable for outcomes generated by systems distributed across multiple teams, platforms and environments. The pace of adoption only intensifies this challenge.

Organizations are under pressure to move quickly as competitors invest in AI capabilities and employees increasingly expect access to AI-powered tools. The urgency is reflected in the UK government's AI Opportunities Action Plan, which highlights IMF estimates that AI could boost UK productivity by up to 1.5 percentage points annually, potentially generating £47 billion in economic gains each year.

As a result, deployment often progresses faster than governance frameworks can evolve.

This creates a fundamental tension between speed and control. Businesses want to capture the benefits of AI quickly, but moving too fast without appropriate safeguards can introduce operational, security and compliance risks. The challenge is not simply deploying AI at scale, but ensuring it can be managed responsibly once deployed.

As investment accelerates, expectations rise

As AI becomes more deeply integrated into business operations, its influence extends beyond execution. AI is increasingly shaping how work gets done, how decisions are made and how organizations allocate resources. In some cases, it is helping leaders identify opportunities and risks that may not have been visible through traditional approaches.

This growing influence means AI is no longer just a technology initiative. It has become an organizational capability that touches every part of the business. Decisions about AI deployment are therefore also decisions about governance, accountability and risk management.

The scale of momentum behind AI is clear. Earlier this year, the UK government highlighted £14 billion in private-sector AI investment commitments and more than 13,000 planned jobs as part of its ambition to establish the UK as a global leader in artificial intelligence.

This reflects a broader shift in how organizations view AI: no longer as an experimental technology, but as a strategic capability expected to drive growth, productivity and competitive advantage.

As investment accelerates, so too does the pressure to deliver measurable outcomes. Yet many leaders are discovering that success depends on more than deploying new AI tools. Without clear accountability, visibility and governance, the benefits of AI can be undermined by operational complexity, fragmented decision-making and increased risk.

The organizations that realize the greatest value from AI are likely to be those that invest as heavily in governance and oversight as they do in the technology itself.

Security, data and trust at scale

Security remains a critical consideration. As organizations integrate AI into business-critical processes, they must address issues such as data protection, model integrity and regulatory compliance. A single failure can have consequences that extend beyond technical disruption, affecting customer trust, brand reputation and regulatory standing.

Public expectations for responsible AI are high, with 72% of the British public saying that laws and regulation would make them more comfortable with the use of AI, underlining the importance of strong governance and oversight.

The challenge is heightened by AI's dependence on large volumes of data drawn from multiple environments and applications. Without visibility into how data flows through these systems, organizations may struggle to assess risk or respond effectively when issues arise. Robust governance and transparency therefore become essential components of any AI strategy.

Alongside security concerns, organizations are also facing greater financial scrutiny. Unlike traditional technology projects, AI programs often evolve rapidly, with new models, services and use cases introduced continuously. This can make it difficult to maintain oversight of spending, performance and risk, particularly as AI becomes embedded across multiple business functions.

These pressures are driving a reassessment of operating models. Traditional approaches to governance were largely built around systems that changed predictably and remained relatively static once deployed. AI introduces a different dynamic: models evolve, outputs vary and operating environments can change rapidly.

Building adaptable AI governance

As a result, organizations are increasingly recognizing the need to build adaptability into their AI strategies. Governance cannot be treated as a one-time exercise. It must become an ongoing capability supported by continuous monitoring, clear accountability and the ability to respond quickly to emerging risks and opportunities.

The organizations seeing the greatest success with AI typically view governance and innovation as complementary objectives rather than competing priorities. They focus not only on deployment, but also on visibility, control and resilience. By establishing clear frameworks from the outset, they create an environment where AI can scale responsibly and deliver sustainable business value.

Infrastructure strategy also plays a key role. Many organizations operate across multiple cloud environments and technology platforms, creating challenges around integration, portability and control. As AI workloads increase, flexibility becomes increasingly important.

Businesses need the ability to deploy, move and manage workloads efficiently without becoming constrained by fragmented architectures.

Scaling AI with confidence

Ultimately, the challenge facing leaders is not whether AI should scale, but how it scales. As adoption accelerates, organizations must look beyond deployment and focus on creating the conditions for sustainable success.

That means investing in governance, strengthening visibility across increasingly complex environments, improving financial accountability and ensuring organizational structures evolve alongside technological capabilities.

AI has the potential to transform how organizations operate, compete and create value. However, realizing that potential requires more than implementing new tools. It requires building the frameworks that allow innovation and control to coexist.

As AI becomes more deeply embedded across the enterprise, the organizations that achieve the greatest success will be those that can balance agility with accountability, enabling them to innovate confidently while maintaining oversight, resilience and trust.

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Across a network of front-line healthcare facilities in Zambia, health workers used a mobile app to be guided by basic clinical prompts and entered symptoms and observations as part of routine primary care encounters.

The syndromic patterns emerging from thousands of consultations indicated a cholera outbreak was on its way, months before confirmed diagnoses appeared.

No new laboratory. No field hospital. No breakthrough treatment. Just real and ordinary information, recognized early enough to matter.

We have seen the same principle at work in Abu Dhabi.

Last year, AI indicated that the influenza season was likely to arrive sooner than usual and highlighted the communities most at risk.

We acted on those insights by launching our vaccination campaign earlier, strengthening preparedness and expanding access for priority groups before cases began to rise.

A powerful reminder

It is a powerful reminder that AI's greatest value lies not in replacing clinicians or public health experts, but in giving them the information they need to make better decisions before a crisis escalates.

The question is no longer whether AI can help detect the next global health threat. In many cases, it already can. The real challenge is whether health systems are prepared to turn those insights into timely action before a local outbreak becomes a global emergency.

The need has never been greater. People are moving into cities faster than ever. Climate change is changing how infectious diseases emerge and spread, and international travel means an outbreak can move across continents in a matter of days. Yet many health systems still operate much as they always have, responding once illness becomes visible rather than monitoring for when the earliest warning signs appear.

By the time a threat appears in confirmed diagnoses or official reports, valuable time has already been lost. AI offers an opportunity to change that—not by replacing doctors, nurses or public health teams, but by helping them recognize patterns that would otherwise go unnoticed. Much of that picture now sits outside hospitals and laboratories.

Millions of people generate health data every day through wearable devices that track heart rate, sleep, activity and other physical signals. One person's data tells an individual story. Combined, they have the potential to help health systems identify risk and intervene earlier.

Health systems

Health systems also produce huge amounts of data through clinical records, laboratory results, environmental monitoring, vector surveillance, and population trends. These sources often sit in separate places and rarely tell the full story. AI can connect them, revealing patterns that would be almost impossible to detect manually.

Those insights allow health systems to prepare services, target prevention efforts and direct resources before an emerging threat becomes a wider public health emergency. This belief—that better information should lead to better decisions—is also what underpins Future Health – A Global Initiative by Abu Dhabi.

When the Future Health Challenge was launched in collaboration with the US-led social enterprise MIT Solve, nearly 400 teams from 68 countries entered. Each explored how AI could strengthen prevention and earlier intervention.

What stood out wasn't one miracle technology, but a shared focus: identifying problems before they become crises.

ThinkMD, the Australian team behind the Zambia example and winner of the Future Health Challenge, equips frontline health workers with AI-enabled clinical decision support, allowing routine patient consultations to contribute to a broader understanding of population health.

VectorCam, a Distinguished Finalist from the USA, applies AI to mosquito surveillance helping public health teams identify changing disease risk before outbreaks take hold.

Huna, a Brazilian health technology company and Distinguished Finalist, uses AI to analyze routine blood tests to identify elevated cancer risk earlier and guide individuals into appropriate screening and care.

These tools tackle different problems but share the same aim: spotting risk where there is still time to act.

So, if the technology is already this capable, what is holding it back?

Complex, cautious, and overstretched

Health systems are complex, cautious, and often overstretched. Data remains fragmented across organizations. Procurement cycles can be slow. Clinical staff have little time to absorb new tools and healthcare rightly demands strong evidence before new algorithms become part of clinical or public health decision-making.

These are not barriers to innovation. They are the conditions for adopting it responsibly.

An alert is only useful if someone knows what it means, trusts the evidence behind it and has a clear pathway to act. Earlier detection achieves little unless it leads to earlier action.

The next phase of AI in healthcare is likely to be defined less by new algorithms and more by stronger systems. That means building the infrastructure that connects information securely across organizations. It means designing AI that fits naturally into clinical workflows rather than adding complexity. It means creating policy frameworks that reward prevention alongside treatment.

Most importantly, it means bringing together clinicians, researchers, innovators and policymakers to solve implementation challenges collectively.

No technology will prevent every future outbreak. Nor should AI ever replace strong public health infrastructure. But earlier, better information can change the course of an emergency. It can influence where testing is deployed, where resources are directed and how quickly interventions begin.

In public health, timing matters.

The ability to detect earlier warning signs is increasingly within reach. The task now is to build health systems that are ready to respond.

AI will never replace human judgement, nor should it. Its real promise lies in strengthening decision-making and helping health systems respond with greater confidence, precision and speed when it matters most.

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• Company chiefs are farming off more work to AI than their underlings, a new study suggests
• Almost 40% of C-suite leaders are relying on AI to complete tasks, despite privacy and security implications
• The survey, by Adobe Acrobat, explored the use of AI in workplaces across the UK

New research has demonstrated a an apparent disconnect between AI use and responsibilities concerning sensitive data.

Adobe Acrobat's “The Rise of AI Documents in the Workplace” report surveyed 2,000 UK adults on their use of AI in the workplace, claiming found that almost 75% of C-suite leaders are happy to share their work documents with AI tools for tasks that include marketing, translating, and data analysis. These are all documents that are likely to hold confidential information.

Conversely, just 20% of non-management employees are comfortable with this type of AI use, with a sizeable chunk (almost two-thirds) avoiding AI tools completely.

Wide AI adoption

The report highlighted several revelations about the adoption of AI within companies in the UK. While the 74% of C-suite leaders using AI might be a surprise, it’s just the first of several stats from the report that paint a picture of widespread AI adoption in among company chiefs.

Within that group, 37% use AI to save between 3 and 5 hours of work each week, and 35% use the technology to perform analytical tasks (which might include data).

Yet the figures concerning employees outside of the C-suite paint a different picture. Here, 59% don’t use AI tools at all. This figure is not unusual throughout the report, which illustrates that in most cases, AI use among those with “non management responsibility” is considerably lower – often more than 50% less.

Could this be due to a general reluctance to use AI, or perhaps something more specific, such as feeling AI use is banned, or its use might highlight weaknesses within a team?

Secure-suite?

The results also highlight concerns over AI use and training, with 19% of respondents highlighting a “lack of training or clear guidance.” This is something that probably needs addressing, not least from a security and privacy angle.

For IT departments and anyone tasked with information management and data security, the report’s findings make for difficult reading. The obvious conclusion to draw is that company chiefs are dropping sensitive information into AI chatbots, potentially saving time – but also risking other issues.

Adobe Acrobat’s survey found 22% of organizations adopting AI tools had data security and privacy as their biggest concerns and challenges. Similarly, 20% were concerned about accuracy and errors generated in AI outputs.

• OpenAI CEO Sam Altman is worried that powerful companies are dominating the evolution of AI
• Altman has also expressed concern over AI’s potential for acting autonomously and possibly escaping human control
• The comments are believed to be a reference to his rival, Anthropic CEO Dario Amodei

Are too few companies wielding undue influence on the direction of AI? That seems to be the belief of OpenAI’s Sam Altman, who has warned against a scenario where the people don’t have a say in how AI changes the world. Speaking to the David Senra podcast, Altman expressed a desire to see "society and the models... co-evolve."

While this may seem desirable, a byproduct of this thinking might be harder to accommodate. Altman also expanded on his view that governments should not regulate AI, expressing a view that prefers the existence of liberty over the possibility of safety.

Altman is the latest tech founder to appear on David Senra’s weekly podcast, which started in 2025 with Spotify’s Daniel Ek as the guest.

Evolving together

Altman’s view is he sees AI evolving alongside human acceptance of the technology. “Society and the model will evolve together,” he said. “The right approach is for people to deeply control the future.”

However, he revealed that things haven’t moved quite as fast as he thought they might when OpenAI first released GPT-4. “I thought when we got to GPT-4, which was back in 2023, that very quickly after that there was going to be much more disruption.” One of Altman’s expectations back then was software businesses being more widely and directly impacted by AI.

While the inertia in the economy has reduced the impact in that regard (for the moment, at least), Altman is excited about AI’s ability to unlock small business opportunities. “We are about to see the greatest boom in people starting smaller businesses than we have ever seen… AI is empowering that.”

Liberty and open models

Altman’s view of liberty and the need for open models puts him in direct opposition to Anthropic CEO Dario Amodei, who, in June 2026, stated that the latest Mythos model could prove a risk to the financial sector and infrastructure and present a challenge to cybersecurity.

For Sam Altman, this is the wrong way around. “There are a lot of people who are so nervous about the magnitude of those risks and get so taken by that and feel a need to protect the world,” he said. “Like, you know, ‘We should trade off a lot of liberty for safety.’”

Interestingly, most major Chinese AI companies release open-weight (the open source of AI) models, enabling them to match Anthropic and OpenAI. While Anthropic is yet to release an open weight model, OpenAI has released two: gpt-oss-120b and gpt-oss-20b.

But will the people and small businesses derive long-term advantage from OpenAI’s liberty-based open models or stick with Anthropic’s closed, "safe" models?

• AI authorship is increasingly employed for web pages, Pew Research Center report claims
• Over 33% of pages published since the launch of ChatGPT reportedly show signs of AI generation
• Of the 10,000-page sample used in the study, 10% of pages had signs of AI authorship

An incredible 35% of web pages published since the launch of ChatGPT show signs of AI authorship, new research has claimed.

A study from the Pew Research Center, which used automated tools to assess a sample of 10,000 web pages, also found that in the entire sample – based on data from the Common Crawl project – almost 10% of all pages had signs of AI creation.

The “How Much of the Internet Is Written With AI?” report also found that certain types of websites are more likely to feature some AI generation, while detecting some of the key fingerprints of AI-generated text, which goes beyond em dashes.

AI is writing more and more web pages

The report – which was compiled using a machine learning model tasked with looking for signs of AI authorship – found that 9.6% of all pages in the sample of 10,000 appeared to have been written by AI. While that may seem a small amount, the sample from Common Crawl dates back to the beginning of the World Wide Web, over 30 years ago.

In keeping with other research (such as AI on the Internet), the data indicates that “large shares of recently published pages on the internet were likely written or substantially edited by AI.”

Reassuringly, the data highlights the interesting point that the majority of the suspected AI-generated web pages published in 2026 are .com domains and, as such typically commercial. In contrast, just 1% are .edu (US-based educational facilities) and 0.8% are .gov.

Is it written by AI?

Using its evaluation of 490,000 English-language web pages, the Pew Research Center also established some more useful indicators (“common features”) of AI text generation.

The em dash is regularly held up as an example of AI writing, as is the use of the Oxford comma. The em dash appears “twice as frequently” in pages published since 2023, while Oxford commas have a 63% increase. The report authors note that these aren’t solitary indicators (“Humans use these in their writing too!”)

Other “tells” offer a more reliable giveaway of AI-generated writing, as demonstrated by the report. Certain words and trends are listed by the research, including "delve," "interplay," and "testament," which have apparently “more than doubled in usage.” The report lists numerous examples of “AI-typical vocabulary.”

Meanwhile, the negative parallelism, which you might have seen as “it’s not just X, it’s Y,” has almost trebled in use.

It might be increasingly difficult to identify an AI-generated web page these days, but if it comes from a .edu or .gov domain, it is overall more likely to have been written by a human.

• The US government has issued a revision to its National Security Science & Technology Strategy
• Among various changes is the reduced focus on high-performance data storage and data centers
• Instead, emerging technologies are given greater prominence, while data storage and data centers feature within the document’s information management priorities

The US government has updated its National Security Science & Technology Strategy (NSSTS), an annual directive that ensures the United States’ national security strategy is supported across various dimensions. This time, however, a curious revision has been made, one that appears to relegate the significance of high-performance data storage and datacenters.

Previously, these installations have been highly regarded and appeared on the document’s list of "Critical and Emerging Technologies." With the latest revision, however, other technologies appear to have supplanted data centers.

Traditionally, the National Security Strategy (NSS), published annually, includes the science and technology element. Under the Trump administration, however, the NSSTS has been released as a companion to the NSS report.

Relegated data centers?

An initial reading of the NSSTS suggests the relevance of data centers and high-performance data storage has been reduced.

The document, which provides information to support “the nation’s economic security, science, research, and innovation to support the national security strategy,” focuses on “S&T competition as it relates to military capabilities, homeland defense and resilience, and other direct intersections with national security.”

This assists in decision-making surrounding federal funding, informs partnerships in the private sector, and joint projects with allies. Significantly, it also underpins US policies on exports of critical technology.

While the importance of data centers and high-performance data storage appears to have been demoted, it appears that the NSSTS is simply refocusing. The updated document’s information management and cybersecurity list includes “data management, including storage and security,” which seems to cover the apparent omission.

Instead, the document focuses on "transformative emerging technologies," which include artificial intelligence, autonomy, biotechnology, and quantum information technologies.

"Foreign adversaries"

The NSS and NSSTS are by definition documents that outline a roadmap for economic security. Shifts in priorities are not unusual, such as an aim to “keep foreign adversaries out of critical technology systems and supply chains.”

This would seem to refer mainly to China, and the challenges that some Chinese-manufactured wireless communications hardware has brought, something that has seen the FCC apparently working overtime.

Within the NSSTS we can see a reaction to some of this, notably an ambition of “Prioritizing homeshoring and domestic product development, which the Trump Administration is advancing by promoting balanced trade, facilitating reindustrialization, unleashing energy dominance, advancing novel material substitution for critical components, and working with suppliers from trusted partners.”

Data centers and high-performance data storage easily fit within those aims—they're just less specific than in previous editions of the NSSTS.

Remember that big leak about Microsoft having begun developing a version of Windows that was built around AI, before eventually abandoning the project? Well, it's surfaced again, this time with some interesting new information on just how focused Microsoft was on Copilot, which I think is quite telling — or indeed worrying for those who believe Microsoft might be backtracking on AI somehow.

Windows Latest has gone back over the leak (which was first reported by our sister site Windows Central last month) with a fine-tooth comb, and the tech site flagged up some fresh details that flew under the radar at the time.

To recap briefly, the concept of the OS shown, codenamed 'Project Aion' — with the leaked material dating back to 2024 — was a lightweight web-based version of a Windows operating system that streamed apps to the desktop (as opposed to running them natively on the PC) and put Copilot at the heart of the experience.

This was a horror show of an idea for many, of course, and what Windows Latest starkly highlights here is the extent to which Microsoft was building Aion around AI. In a SharePoint site in the leak, it's described as a "Copilot OS" and a cross-platform system that can span all devices and is generically known as an "Agent OS".

The broader idea, is said to be of an operating system fashioned around "an agent that marshals and organizes all the capabilities of our computers and our clouds on our behalf", and that this is known as an "agent of agents" or the "outside agent".

"The version that Microsoft will build should be known as Copilot OS or the Outside Copilot," the top-level description on the SharePoint site observes. Another term mentioned is a "self-driving OS", which hints at the desire to build an autonomous platform powered by AI here.

Copilot is envisioned as being the "primary way people express, entertain, and educate themselves, and complete tasks across all aspects of their lives".

But Aion is dead, right? Yes… but hold up a minute

So, what's the big deal here? Windows Latest breathes a sigh of relief that the "odds are already in favor" of this abandoned Aion concept staying dead and buried, arguing that Microsoft is clearly backtracking on AI features these days. And that's very true — there's no denying that Microsoft has reversed course on pushing AI quite so hard as it did in 2025.

But I don't think we can declare that 'Aion is dead' quite so readily. Yes, that exact concept may be buried, but there are nuances here. For me, what Windows Latest has surfaced shows just how serious Microsoft was about exploring the idea of a fully AI-driven desktop. More to the point, an operating system that was effectively an 'agent of agents', or an overseeing AI pulling the strings of a collection of smaller dedicated agents working within the framework of that system.

Here's the thing: Microsoft's efforts to rein in AI within Windows 11 certainly exist (although I wonder about how much of this is for show, frankly). The software giant is currently delivering its best effort to please the disgruntled masses, and a big part of that is fixing Windows 11 — but another hefty piece of the puzzle is to place less emphasis on AI and shoveling more Copilot features into the desktop OS.

But don't be mistaken here: Microsoft still very much sees AI as the future. The company just realizes that it can't be vocal about that right now, especially given that the RAM crisis has forced 8GB laptops back onto the scene, as notebook makers, including Microsoft itself, try to cope with hefty spikes in the cost of manufacturing these devices.

It's a bit embarrassing to be talking about Copilot+ PCs needing 16GB of RAM for AI features when Surface models have had to be reintroduced packing 8GB to get their price down to a vaguely palatable level. Indeed, talk of Copilot+ devices seems to have evaporated this year, as if Microsoft feels this is best brushed under the carpet for now. While a recent leak from Lenovo talked about 'Non-Copilot' laptops with less than 16GB of memory (as flagged by Windows Latest), this kind of spec talk isn't something you'll hear from Microsoft.

Downplaying AI is the tactic for now, but not forever

For me, it's clear enough that Microsoft is downplaying AI in a strategic manner for the time being, and likely through to next year. The next big thing, however, remains AI agents in Windows, even if Windows 11 isn't going to be transformed into next-gen Windows Copilot (or whatever a more AI-centric desktop platform might be called).

Microsoft is still very busy working on those agents. These entities will begin the shift towards an operating system that's built around AI, with smaller-scale agents executing dedicated tasks on the local PC (with the user's permission, of course, to access stuff). This will be a gradual ramping up of AI usage within Windows 11 (and while folks who hate AI and everything it stands for won't have to go anywhere near these agents, doubtless the idea is that minds will be changed eventually).

I think it's also telling that in Microsoft's recent roundup of the progress it's made in fixing Windows 11, the company talked about future improvements for the OS later this year, and a central aim was to streamline resource usage so the operating system runs better with 8GB of RAM. Interestingly, a few other goals were listed alongside that, and one of them was to make "voice more natural and fluid to interact across the apps you use every day". If you recall, part of the initial sales pitch for AI in Windows 11 was Copilot Voice (and Vision), so I think the importance placed on voice functionality here reflects a broader AI goal that's still in the background.

Ultimately, I think Microsoft is still pushing towards an operating system that's built around AI; it's just that the timeline to reach this end-game objective has been pushed further out. While Project Aion as leaked before was too big a concept, it clearly shows how Microsoft ranks the importance of AI for the future — and I think it's a mistake to think this idea is somehow 'dead' or derailed.

As far as I'm concerned, this remains the destination where Microsoft is heading with next-gen Windows: a lightweight (possibly modular) OS focused on AI, and one that leverages the cloud and streamed apps, too. I think the leaked Project Aion shows us the future as Microsoft wants to realize it, but what it doesn't mention at all is what I think will be another key driver for the software giant: subscription charges.

The golden ticket for Microsoft is to get consumers subscribing to the Windows platform (as businesses do – well, enterprises anyway). AI agents as Windows add-ons with a small monthly (or yearly) fee attached seems a likely way to bring this monetization method into play while making minimal waves (and I recently discussed how there are signs that this is already happening).

That will eventually turn into a grander scheme, and Windows could, over time, end up getting a lot closer to the Aion or Copilot OS concept than some might imagine — although admittedly, that could still be a long way off.

Maybe money does not explain everything, but it explains a lot. Goldman Sachs estimates the AI build-out will require roughly $7.6 trillion in capital over the next five years across compute, data centers and power. It’s unlikely that subscriptions alone will cover the bill, which is why ads are coming to AI.

OpenAI started rolling out ads in some ChatGPT tiers this year and forecasts $2.5 billion in ad revenue in 2026 and $100 billion by 2030. At an ad tech event this summer, OpenAI's chief revenue officer Denise Dresser dropped the hedging and said the company is "clearly in the advertising business now."

Perplexity, on the other hand, shut down its ad business entirely over user trust. Similar products, same opportunity, opposite conclusions.

This conflict is not new and the open web fought this battle 15 years ago, when ad blocking became mainstream.

Now, the AI industry is on track to repeat every mistake of the so-called ad blocking wars, unless it studies them instead.

Lesson one: users revolt faster than big platforms think

When ad formats became more intrusive - because again, money! - the industry assumed users would just accept a bad experience. Instead, users fought back and installed software to get rid of aggressive online ads. In what technology commentator Doc Searls called “the biggest boycott in world history”, ad blocking went from being a niche tool to a mainstream product.

And we see the same dynamic playing out again. Within weeks of sponsored ads appearing in ChatGPT, purpose-built extensions popped up to specifically block ads there. Existing ad blocking tools made sure to block those ads as well. It took hours and days, not weeks or months, for users to act.

Lesson two: the arms race is unwinnable, both legal and technical

The predictable response is, of course, to sue ad blockers. For some publishers, the idea that users take agency over their own screens was not acceptable, so they took ad blocker developers to court. Almost all of the legal attempts failed. With one case still pending at the German courts, China is the only country in the world that bans ad blockers.

Another tactic is technology to fight ad blockers. Platforms tried different circumvention methods to deliver ads to users with ad blockers installed. This prompted their developers to update their techniques to prevent websites from bypassing. Every technical escalation met a counter-escalation and the sum of a lot of engineering efforts on both sides is that, today, over a billion people still block ads.

AI platforms hold one card their predecessors didn't, though: integrating advertising directly into the generated answers, instead of showing labelled ads. Researchers from the University of Michigan tried exactly this and the results should worry everyone involved: users largely don't expect advertising inside a generated answer and frequently fail to notice it.

At the same time, 63% of consumers say ads decrease their trust in AI outputs, according to Ipsos. The format that works best technically is the one users are least equipped to detect, and least willing to trust. Which brings us to the most important lesson.

Lesson three: trust is the actual product

Search results pages survived the ad blocking wars because users had ten blue links to compare with the sponsored content and two decades of learning what’s paid and what’s organic. AI assistants, on the other hand, offer one authoritative answer, with no other content or adjacent organic results to check against.

As soon as users suspect that answers might be influenced by a sponsor, these doubts are attached to all answers and trust erodes.

Cory Doctorow coined the term “enshittification”, which the American Dialect Society crowned as its Word of the Year in 2023. Platforms start out by doing good to their users and then degrade their experience to make advertisers happy.

And compared to tracking-based advertising in the open web, AI assistants know so much more about you that they can sell to advertisers: your health worries, your finances, your political view, your job struggles and everything else these tools picked up from you, stitched into one persistent memory of who you are.

It’s the most revealing personal record people have ever handed to software, combined with the drive from digital advertising to always collect more data and more data for targeting. The table is set for the next wave of enshittification.

Lesson four: the ad blocking wars ended in a treaty, not a victory

There was no final winner and not one technical solution that settled ad blocking in the open web. There was a rough settlement: standards for ad formats many users tolerate, transparency about what is paid for, meaningful user control.

Not everyone loves that settlement and we still see platforms trying to circumvent ad blockers and vice versa. But the open web still has a functioning ad economy and users still have, mostly, a functioning veto.

Getting there took more than a decade and the lessons are written down. Which road to take is now a genuine choice and it sits with the AI companies themselves.

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Most organizations in UK and across Europe don’t struggle to recover from cyberattacks such as ransomware because they lack backups. They struggle because they try to restore everything at once.

In the aftermath of a major cyber incident, the instinct is to bring every system back online as quickly as possible. It feels like the fastest path back to normality. However, in reality, this approach often slows recovery down, reintroduces cyber risk and undermines trust just when the organization needs it most.

In fact, those that recover fastest start from a different premise. They assume large parts of their organization will be unavailable or untrusted, and they plan accordingly. This mindset leads to a much clearer goal - restore what matters most, quickly, and in a state you can trust.

What is critical to survival?

Focusing on the critical areas of an organization is the idea behind the Minimum Viable Company (MVC) concept, sometimes referred to as the Minimum Viable Organization. It is a definition of what must exist for the organization to survive in challenging conditions such as a cyber incident.

It’s not just a technology concept, it’s a business definition of survival in terms of the minimum combination of people, processes, technology, documentation, facilities, and third-party dependencies required to keep a business functioning and creating value.

Where to start getting an MVC up and running?

There are five key capabilities when it comes to operationalizing an MVC:

1. Clarity on critical services:

A precise understanding of the systems and dependencies that directly support revenue and mission-critical operations is needed here. To understand the MVC, it is key to map systems to business value. Without this understanding, it’s impossible to accurately define the MVC.

The first steps focus on undertaking a structured assessment, aligning across business and technology stakeholders, and going through a realistic simulation of how recovery will unfold under pressure. This will uncover the key areas needed to provide just enough capability to keep the organization functioning safely during a crisis and guide recovery.

In practice, this means defining what must function in the first 24 hours, the first 72 hours, and the first week after a disruption.

2. A trusted foundation (Tier 0):

In the event of a cyberattack, many organizations miss the critical foundational layer that allows them to establish identity and access control independently of compromised systems.

This foundational layer is what we call Tier 0 or the control plane for recovery. It includes identity and access management, networking and DNS, privileged access controls, core security tooling, physical access systems, and secure communication channels.

It also covers non-technical dependencies that are easy to overlook until they’re urgently needed such as incident response playbooks, contact lists and escalation paths, insurance policies, and contracts with external responders. These are the foundations underpinning the critical systems that need to be restored after a cyber incident. Without this layer, a trusted recovery is not possible.

3. Isolation of recovery assets:

In the event of a cybersecurity breach, organizations must establish control of their most critical systems. This requires recovering data separately from clean snapshots and investigating in parallel, not sequentially, to ensure the recovered systems are not infected by malicious software.

As part of this process, backups, configurations, and recovery tooling must be protected from the same blast radius as production. If key recovery assets can’t be isolated, a rapid and trusted control of critical systems can’t be achieved.

4. Clean-room recovery capability:

To set up an isolated environment to rebuild systems without reintroducing compromise, organizations need to set up what we call a ‘Digital Jump Bag’. This is a secure, isolated repository containing everything required to establish a trusted recovery starting point to rebuild systems without reintroducing compromise.

5. Validated ability to operate

The next step is to validate the ability of the MVC to operate through realistic crisis scenarios. Resilience must be proven under real-world conditions. Practice is important here because an untested plan remains theoretical. Rehearsals will also help to answer the Board’s most direct question in the event of a cyberattack - how long will it take to restore critical services to a trusted state?

Recovering faster by restoring what matters most

The most common cyber resilience risks are failing to define what must come back first and how to bring it back in a state that can be trusted. That’s the difference between recovery as a process and recovery as a capability.

The MVC isn’t static. As an organization evolves, its definition must evolve too. But the principle stays the same: recovery improves when organizations stop trying to restore everything and start restoring what matters.

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Paying $20 every month for ChatGPT Plus has always been easy to justify professionally. I use it for research, document analysis, and more than a little organizational assistance around the house.

Still, it's another monthly subscription at a time when everyone is looking for ways to reduce the amount repeatedly drained from their bank accounts. That's why I've been pressing ChatGPT to justify its own cost using the finance feature it debuted a couple of months ago.

I didn't ask ChatGPT to defend its place on my credit card bill literally, though it probably could. Instead, I prompted it to use its analysis of some of my finances to identify places where my money might be evaporating without enough justification, or even awareness.

ChatGPT’s finances tool can review connected accounts, including spending. Although it can't make any changes to how the money moves around, it can point accusingly at a subscription, leaving me to cancel it myself.

If you haven't done so, the setup is straightforward enough. Using ChatGPT Work on the web, you add the Finances plugin and connect whatever accounts you wish.

You can analyze recent spending right away, but I've found the weekly update from ChatGPT and nearly real-time updates to be particularly useful in my quest to make ChatGPT Plus cover its own cost.

Subscription cuts

Only a couple of weeks after starting to use the feature, ChatGPT raised in its weekly report that one of my streaming services seemed abnormally expensive. It turned out to be due to an add-on costing $11.99 a month.

I had subscribed to watch one particular series, finished it, and then apparently just left the subscription in place. ChatGPT found the repeated charge and placed it beside my other entertainment subscriptions.

A second recurring payment belonged to a music app costing $9.99 a month. I remembered signing up for a trial, but I could not remember using it after the first week. The service had quietly collected nearly $60 since.

Canceling those two subscriptions saved $21.98 a month. ChatGPT Plus had technically paid for itself during the first review, with $1.98 left over. That is not enough to retire on, but it is enough to make my article headline legally defensible.

ChatGPT also spotted that my internet bill had increased by $10 from one month to the next. The promotional rate had expired, but ChatGPT suggested I could still get the savings with the right language. The AI wrote out a few talking points to get me any current discounts. After a surprisingly tolerable phone call, the company applied a new promotion and knocked the $10 back off my monthly bill.

Saving on food

The subscription cuts were easy to measure — I only had to cancel them once and the savings appeared again the following month without any further actions necessary. Food spending was more complicated because there was no single charge I could remove and forget about, but ChatGPT helped me save money there too.

Ordinary takeout is more expensive than ever, but it's the delivery charges and service fees that really raise the final number. I'm not going to stop ordering takeout altogether and deprive myself of all the dishes I can't make myself. But switching to pickup as much as possible would make a big difference, according to ChatGPT. And it saved me about $35 over the next month.

ChatGPT also picked up several trips to convenience stores. Each transaction was small, usually a drink or some paper goods I'd forgotten to pick up at the grocery store. Nonetheless, convenience stores are more expensive than the grocery store, and the result added up quickly, as ChatGPT found in its analysis.

Just staying aware of that fact changed my habits and made me more likely to remember everything I needed to get at my regular shopping run and to bring my own drink when hitting the road.

After two months, the changes were saving me about $67 in a typical month. That figure included $21.98 from the two canceled subscriptions, $10 from the restored internet discount, and approximately $35 from spending less on delivery. Food costs will naturally move around, but the recurring savings alone were already covering the price of ChatGPT Plus.

I wouldn't blindly trust ChatGPT with finances or anything else. And I definitely wouldn't make it my first consultant for any big financial decisions. But these smaller changes are more important than you might think at first.

My Plus subscription still appears on the statement every month, but the reduced streaming and internet costs, not to mention fewer delivery fees, more than justify the $20 a month I'm paying for it.

For the last two years, many organizations have treated AI adoption as the goal. The more users, tools and experiments, the better. Today, leaders are no longer measuring success by adoption alone. They are asking what it costs, where it creates value and whether the business can afford to scale it responsibly.

That is the next chapter of AI and technology spend management. The companies that thrive in this environment won't necessarily be the ones consuming the most technology. They will be the ones that can connect technology consumption to measurable business value.

This shift from maximizing usage to maximizing value, what I call valuemaxxing, is becoming a defining challenge for technology leaders.

Executives who take these five steps now will be prepared for a future where technology consumption and business value must be measured together:

1. Gain real visibility across the full technology stack

Organizations cannot manage what they cannot see. This becomes far more urgent when costs are variable, distributed and constantly changing.

Today’s technology consumption does not sit neatly in one budget or one system. It spans SaaS applications, cloud infrastructure, data platforms, AI models, agents and infrastructure. AI adds another layer of complexity because spend can show up through tokens, credits, model usage, GPU consumption, data movement and AI-enabled applications.

With only 31% of organizations reporting visibility into AI software today and 59% reporting increased wasted AI spend year over year, gaining a single source of truth across the technology stack has never been more important.

Without a complete view of technology consumption, organizations are left making decisions with fragmented information. Visibility isn't simply about controlling costs; it's about understanding where investment is delivering value and where spend is wasted.

2. Build a governance model for consumption

Many organizations encouraged broad AI experimentation, only to later discover that usage had outpaced oversight. Having an internal governance framework in place is critical to any company’s success. Governance gives teams the guardrails they need to scale responsibly.

For AI in particular, leaders need to move from “use more” to “use better.” It’s figuring out whether AI is improving cycle time, customer experience, operational efficiency, revenue growth, or another important business metric.

To guide the process, increasingly large enterprises (85%) are appointing dedicated teams or senior leaders for AI and tech governance to enable visibility, control, and cross-team collaboration. It’s vital for companies to prevent AI from becoming an uncontrolled cost center.

3. Renegotiate contracts for flexibility and accountability

Technology pricing models are changing rapidly. Long-term fixed agreements may still have a place, but they are becoming harder to manage in environments where usage can shift quickly.

Today, leaders should regularly evaluate vendor agreements to ensure they reflect actual usage patterns and future business needs. This is particularly important as AI providers continue introducing new consumption models and monetization strategies.

Enterprises should expect more pricing complexity, not less. The goal is not simply to negotiate lower costs. It is to create agreements that give the business room to innovate while maintaining control over spend.

4. Align technology, finance and procurement

Usage-based costs impact multiple teams. While technology teams drive how much is used, other departments manage the needed oversight, with finance owning the budget impact and procurement handling vendor contracts. Without alignment, organizations can easily lose control of spending.

The organizations that manage consumption well will build a shared view of usage, cost and value. They establish common metrics, clear accountability, and regular collaboration across departments.

When teams work from the same data and the same definition of value, those decisions become more intentional and avoid costly surprises.

5.Use AI to move optimization from reactive to continuous

Use AI tools to help manage the growing complexity of technology consumption itself.

Optimization cannot remain a periodic budget exercise. By the time a cost issue appears in a report, usage may have already shifted again.

AI can help teams detect unusual usage patterns, surface waste, forecast demand, and support smarter planning across teams. But AI-driven optimization must be connected to human-defined goals. The objective is to help teams make better decisions faster, with clearer insight into what is being used, what it costs and where it creates value.

Turning tech consumption into value

The next phase of enterprise AI will look very different from the first. For the last several years, the focus was on experimentation and adoption. The future belongs to organizations that can demonstrate accountability, governance, and value.

AI has accelerated the industry's shift toward consumption-based technology models, introducing new economic challenges alongside new opportunities. As organizations continue scaling AI, understanding the relationship between usage, cost, and business impact will become a critical competitive advantage.

The AI adoption spree is ending. What comes next is more disciplined visibility, governance, and financial accountability, defining who can successfully scale innovation and who gets overwhelmed by the bill.

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The European Union's new Tech Sovereignty package highlights a challenging question for governments across Europe, including the UK: how can countries benefit from AI without becoming dependent on technologies developed elsewhere?

AI is becoming a critical driver of economic growth, public service transformation and business competitiveness. Yet much of the AI technology stack continues to be developed and operated primarily in the United States and China, creating a growing tension between AI adoption and strategic control.

Although the UK is pursuing its own approach to AI regulation, European policy developments will continue to shape the environment in which many British organizations build, deploy and govern AI systems.

Recent UK government warnings that Britain must secure "greater control and leverage over frontier AI" show that AI sovereignty is moving from a theoretical debate to a practical policy challenge.

Building every layer of the technology stack domestically is neither practical nor necessary. At its core, AI sovereignty is about maintaining the freedom to choose, adapt and innovate without becoming dependent on any single provider or platform.

Choice, not self-sufficiency

Some countries are pursuing far-reaching self-sufficiency strategies. Others are building on existing infrastructure and expertise while using partnerships to close technological gaps. For most economies, the latter approach is likely to be more realistic. Sovereignty is not about building every component domestically; it is about ensuring strategic control and avoiding excessive dependence on any one supplier.

Regulation may have a role to play. While concerns remain about compliance burdens and their impact on innovation, the broader objective is clear: creating an environment in which organizations can adopt AI with confidence.

This is where dynamic and competitive AI markets become essential. A diverse digital supply chain creates options, strengthens resilience and reduces the risks associated with concentration. Recent restrictions on access to Anthropic's frontier AI models in some non-US markets are a reminder that when access to advanced AI capabilities is restricted, choice itself becomes a strategic asset.

Infrastructure is only useful if everyone can use it

Much of the AI policy debate focuses on the data centers needed to train large models. Yet infrastructure for broad adoption is equally important.

Distributed edge networks located closer to users help deliver AI applications with the low latency, performance and scalability required for real-world deployment. At the same time, access to AI tools must extend beyond a small number of well-resourced organizations.

Usage-based and serverless models can lower barriers to entry by reducing upfront costs and allowing organizations to pay only for the resources they consume. This enables SMEs, researchers, public institutions and larger enterprises alike to experiment with and adopt AI technologies.

Control over data is another critical element. Sovereignty depends less on where data is stored and more on whether organizations can manage access, security and compliance requirements effectively. Global, distributed architectures enable the implementation of access rules and controls over where data is processed and AI applications are operated.

Openness is a strategic advantage

Open standards and interoperable technologies are becoming increasingly important building blocks of digital autonomy. They reduce switching costs, strengthen competition and help prevent dependency on individual providers.

The same principle applies to AI models themselves. Organizations increasingly need access to a range of different open-source and proprietary models, allowing them to select the best solution for different use cases rather than relying on a single platform.

Control through openness

The debate around AI sovereignty is often framed as a choice between dependence and isolation. In reality, the most effective path lies somewhere in between.

Countries do not need to own every layer of the AI stack to exercise meaningful control. What they do need is access to open, competitive and resilient markets that provide genuine choice.

For the UK, and Europe alike, strategic control will come not from limiting access to technology, but from ensuring organizations have the freedom to choose how they adopt, deploy and govern it. In an increasingly interconnected world, that freedom of choice may prove to be the most important form of sovereignty of all.

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It’s a tale as old as time. You tune into the big budget show about dragons and tyrants and your job in tech suddenly makes a lot more sense.

If you’ve been watching the new season of House of the Dragon, you’ll know the term ‘protector of the realm’. They’re kings, queens, or trusted governors: the ones responsible for the realm’s safety, ensuring laws are followed, and the medieval machinery of state ticks over. Back in Game of Thrones, ‘protector of the realm’ Ned Stark had his head chopped off and everything famously took a nosedive from there.

I’d argue this is a lesson in AI strategy.

At my company, Aiimi, we use this very same term - ‘protectors of the realm’ - to talk about the role of IT, Legal, and Compliance in operationalizing AI projects. But too often, businesses treat these governance functions as friction.

Whilst there aren’t any dragons for them to slay, these departments are there to protect your information and your systems - and above all, keep your company out of harm’s way.

Guardians or gatekeepers?

Despite the work these departments do protecting companies from hefty fines and data breaches, I see an awful lot of corporate windbagging about how these departments bottleneck projects behind lengthy consultations, caveats, and due diligence.

The criticism is that they slow projects down. The reality is that they’re the difference between a fantasy strategy and a functioning one.

Ignore their counsel, and the whole structure gets weaker. Legal ensures AI tools follow information laws like GDPR and the new Data (Use and Access) Act 2025, so these systems only touch the data they’re meant to. Compliance maintains oversight once these systems go live, proactively ensuring that rules are followed and standards aren’t slipping.

IT does the essential work of making these projects function seamlessly in your organization's workflows and contexts. They maintain the infrastructure that AI runs on, ensuring that projects run on well-governed, structured data with documents having the correct permissions and access controls for safety. Without this groundwork, there’s little hope that an AI project can be effectively operationalized and return any sort of value.

But more than just facilitators of AI projects, these departments are the people in the room who know exactly what risks AI projects can pose, and what information they shouldn’t be privy to. Which, taking July’s Hugging Face fiasco as an example, matters more than most companies think.

Headlines would have you believe OpenAI’s models went rogue, broke through their safeguards, and attacked Hugging Face by their own autonomous decision. What actually happened is less dramatic and more damning.

OpenAI ran security tests with two AI agents where safeguards had been switched off. The testing sandbox, meant to be offline, was actually misconfigured to allow a connection out. The models escaped and attacked Hugging Face - not out of malice, but to cheat the security test by the laziest route available.

This was a governance failure. AI systems themselves can’t understand the reputational or financial risks that their actions might inadvertently cause. They do what they’re trained to do, and if there’s a shortcut to exploit, they don’t stop to check whether they’re meant to exploit it.

When critical business decisions are at stake, rushing a project past your protectors of the realm can leave you vulnerable to data breaches thanks to poor data security, or fines thanks to non-compliance. Having someone on board whose job it is to remind you of limitations and considerations serves to not only protect your organization, but make any AI project more resilient and deliver better results.

As AI regulation tightens and data security climbs up the agenda, it becomes more important than ever to have your protectors on side and fully integrated into design and deployment from the start.

Protecting the realm in practice

There is sometimes a healthy level of skepticism in these departments so it’s key to invest in internal literacy programs to clearly explain the huge benefits of AI tools when they’re adopted safely, in line with your company’s governance principles.

Employees should walk away feeling clear about what terms like hallucination, training, and information retrieval mean for them, and how AI can fit into their work lives.

Once your protectors of the realm are aware of the terminology, they should be integrated into the design process of new AI projects to make use of their specialist knowledge.

Compliance might identify a specific challenge: governance teams are struggling to keep up with ensuring data quality and classification standards across different, growing systems in your organization.

This is crucial work that protects the business from breaching critical or sensitive data and powers data projects throughout the organization. They recognize that secure AI can help at scale. Now, together, you can develop a carefully planned use case that benefits multiple areas of the business: you need an AI-powered solution that can power data governance at scale by automatically classifying data.

IT can then help you plan how AI-powered data governance might fit into existing workflows and infrastructure, with data, compliance, and legal teams working together to understand the rules and give AI everything it needs to understand your business regulations and contractual obligations.

Your protectors of the realm should then also be part of any ongoing deployment. IT routinely checks the outputs of AI-powered systems, legal keeps abreast of any new legislation that you should be aware of, and compliance maintains ongoing human oversight so that you’re actively following regulation.

AI has incredible potential, but safe implementation is impossible if you don’t understand the rules. It might not be dragons and castles, but the companies that are positioned to generate reliable long-term success from AI projects aren’t the ones moving fastest, but the ones inviting their protectors of the realm to the decision-making table.

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For years, the phrase "AI-powered attack" has mostly meant a human using AI tools to write better phishing emails or scan for vulnerabilities faster. That framing just became outdated.

This month's intrusion into a major AI infrastructure platform was carried out, start to finish, by an autonomous agent.

No operator typed commands during the attack. No one was watching a terminal, deciding what to try next.

The agent found its own way in, escalated its own privileges, moved across internal systems on its own initiative, and kept going until it was caught.

It executed thousands of individual actions across a swarm of short-lived sandboxes, using infrastructure that migrated itself to stay ahead of takedown efforts.

That last detail is the one worth sitting with. This wasn't a script running on a loop. It was closer to a persistent, adaptive actor that happened not to be a person.

Agentic attacker

Security teams have spent the last two years bracing for what researchers called the "agentic attacker" scenario: a future where offensive AI doesn't just assist a human operator but replaces the decision-making loop entirely. That future arrived faster than most roadmaps allowed for. And it arrived through an unglamorous door.

The intrusion began not with some exotic zero-day but with a malicious dataset, exploiting weaknesses in how data gets processed and executed. Old lesson, new attacker. The place where AI platforms are most exposed is often the plumbing, not the model.

What makes this incident more than a cautionary anecdote is where the agent came from. It wasn't built by a criminal group. It emerged from an internal test, one company evaluating how capable its own models were at offensive internet security work.

The safeguards that would normally stop a model from behaving this way had been deliberately loosened for the purposes of that evaluation, and the agent found a flaw serious enough to escape the contained environment altogether. It got out, found a live target, and treated it the same way it had been trained to treat a benchmark: as a problem to solve, thoroughly and without asking permission.

This is where the industry's favorite excuse collapses. "The AI acted on its own" is true, technically. It is also irrelevant to the question of who is responsible. Nobody would accept that defense from a bank whose fraud-detection algorithm accidentally froze every customer account overnight, and nobody should accept it here.

An organization that builds a system capable of autonomous action, tests it with reduced constraints, and fails to contain it when it exceeds its boundary has made three decisions, all of them accountable ones. Autonomy in the tool does not create autonomy from consequences for the people who deployed it.

A harder problem

There's a harder problem sitting underneath the accountability question, and it doesn't have a tidy fix. These agents are not malicious by design. They are goal-pursuing systems, optimizing for an objective, and the gap between "pursue this objective" and "pursue this objective the way a human would want you to" is where things go wrong.

An agent instructed to find and exploit vulnerabilities doesn't inherently know where the test environment ends and the real internet begins. Alignment, in this context, isn't a philosophical nicety. It's the difference between a benchmark result and an incident report. As agents get assigned more ambitious, multi-step objectives, that gap doesn't shrink. It widens, because the more complex the goal, the more creative and unpredictable the path an agent will find to reach it.

Ironically, one of the more telling wrinkles in this incident had nothing to do with the attacker. When the victim organization tried to use its own AI tools to analyze the attack logs, the safety filters built into several frontier models refused to help, unable to distinguish forensic analysis of an attack from participation in one.

The team ended up relying on an open-weight model with fewer restrictions to do the job. That's worth flagging on its own: the same caution designed to prevent misuse can also blind defenders at the exact moment they need clarity fastest.

Active security

None of this argues for abandoning AI agents. It argues for treating sandboxing as an active security discipline rather than a checkbox. A test environment isn't safe because it's labeled as one. It's safe when it has been built and continuously verified to contain the specific class of behavior the system might attempt, including behavior nobody predicted at design time.

Reduced safeguards for the sake of a benchmark should carry the same scrutiny as reduced safeguards in production, because the line between the two is thinner than most evaluation frameworks assume. Governance needs to catch up with capability rather than trailing a year behind it, and that means treating agent permissions the way mature organizations already treat privileged human access: least privilege by default, monitored continuously, revoked automatically when behavior deviates from scope.

For security teams, the lesson isn't really about one company's bad week. It's that AI is now operating on both sides of the perimeter simultaneously, as the business tool a company depends on and as a potential attack surface with its own failure modes.

Detection strategies built around human attacker timelines, the hours and days it takes a person to escalate and pivot, won't hold up against an agent doing the same work in minutes. The organizations that come out ahead won't be the ones that avoided building agentic systems.

They'll be the ones that assumed, from day one, that their agents would eventually try to do something nobody authorized, and built the containment to survive it.

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• A security engineer at Amazon hacked a bunch of peripherals using AI
• Claude Opus did most of the legwork in applying modified firmware to a webcam, microphone and more
• The relative ease with which AI allows this kind of modification points to a worrying future of peripherals being compromised on a grander scale

In another example of how AI could prove to be a threat to our devices, an Amazon security engineer has demonstrated how powerful Claude Opus is when it comes to reverse engineering PC peripherals.

Chaz Schlarp, who's a Senior Security Engineer at Amazon, wrote a blog post about experiments he conducted with a bunch of peripherals such as a webcam and a microphone.

He wanted to find out how easy it was to modify the firmware and pull off some useful tricks with these devices using AI, but clearly there's a darker side here — namely that the same access could be leveraged by a malicious actor to compromise your system via these gadgets.

Schlarp used Claude Opus 5 to mess around with the firmware for an Insta360 webcam, a Shure microphone, an Asus monitor, an Elgato video capture stick, and an Elgato mini-light (a compact device for lighting your streaming videos).

Schlarp explains: "My process was pretty much the same for each of these devices: grab a copy of the device's firmware and associated update tool from the manufacturer, throw it into my reverse engineering environment, tell Claude Opus 5 what my goals are, and let it churn."

One thing that became quite clear to the security engineer was that these devices lacked any decent firmware integrity protection to prevent modifying and applying a new firmware. Only the Elgato light had any defenses in this respect, and they were easy enough to circumvent.

Schlarp explains a trick with his Asus ROG Swift PG42UQ monitor to demonstrate the kind of useful utility that can be on offer with this kind of firmware modding. He found it was possible to remove an annoying pop-up warning that periodically tells the owner to run the 'pixel cleaning' process (although the engineer hasn't implemented the fix in the firmware yet). He also discovered a way to get DisplayWidget (a Windows utility) features running on his Linux system, with a shell script that can flick through certain bits of functionality like the hardware crosshair or FPS counter (which could be set up on hotkeys).

Most of what he did, though, was about proving how relatively easy it was to subvert the firmware using AI to do the heavy lifting, and, for example, disable the webcam's recording light (in the style of surveillance malware, so the user wouldn't know if the camera was secretly recording). He pulled off a similar feat with the microphone, so the mute LED could be on while the mic wasn't actually muted (this was leveraged via a 'full plaintext command shell').

Schlarp observed: "Peripherals have proven to be an ideal target for agentic RE [reverse engineering] — they're tiny computers attached to my computer, with a data connection to the host and usually a firmware update mechanism, so an agent has something to iterate against. The net outcome is better control and understanding of my machine."

Analysis: fast-tracked exploits?

The key point here is how easy Claude Opus made this task. 'Owning' all five of these peripherals boiled down to 13 hours of the AI beavering away under its own steam with just shy of 100 prompts from its human overseer.

Schlarp notes that: "Hardware is almost universally 'open' for tinkering at this point with just a couple hours of mostly hands-off machine-driven labor each, and I look forward to a near future where I can add features to my webcam firmware as easily as I can to software that runs on my Linux machine itself."

However, as mentioned, there's the dark side to all this, as Schlarp makes clear: "On the other hand, as a security professional, this scares me for several reasons. I would work from the operating assumption that any device attached to a computer could have had a malicious firmware implant performed, where previously that required significant per-model investment and was stereotyped as a 'state actor' kind of activity."

In other words, the main difficulty in executing these kinds of exploits is the labor and time required, which currently limits this to individually targeted attacks on more high value targets. However, now an AI agent is capable of doing the grunt work, it makes sense that these kinds of attacks could be far more prevalent as time rolls on.

That means all those peripherals attached to your PC could be used as ways to compromise you, or your system, in the future. Schlarp informs us that he's also managed to get a root shell on a commercial Dell display, adding that: "Obviously it was never best practice to let untrusted clients touch these things, but the speed and scale at which this can be executed makes the risk so much higher now."

There's a potentially bigger threat here, too: an AI-powered worm that automatically actions this kind of reverse engineering. Schlarp explains: "It's only a tiny leap to imagine that someone could make a self-replicating piece of malware that probes its environment, relaying reconnaissance back to a smart command-and-control that actively works to push itself into accessories and IoT devices and industrial equipment found adjacent to an infected target."

There are a lot of worries about where AI could be leading us, and far more dangerous security threats looming in the future (or indeed the present) is another unfortunate prospect to say the least.

• Public confidence in AI weather forecasts is far lower than for numerical and physics-based methods
• Just 11% said they were "very confident" in machine learning weather prediction, compared with 30% for tried-and-trusted methods
• The Met Office has been using machine learning techniques in weather forecasting for some years

A survey of 6,000 UK adults has found that the majority don’t fully trust the idea of AI weather forecasts. While machine learning techniques have been a factor of weather forecasting for some years, the survey found that people are more relaxed about traditional Numerical Weather Prediction (NWP) as opposed to Machine Learning Weather Prediction (MLWP).

Predictive technology has increased considerably thanks to AI, and the Met Office has already begun to evaluate AI models for augmenting its existing forecasting methods.

However, the “confidence gap” in the results of the survey, which appears to be based around questions over accuracy, could undermine the use of AI for weather forecasting, which researchers suggest can be challenged with clear demonstrations of the methods working successfully.

AI-based weather models

The Met Office research was published in a study, Artificial Intelligence for the Earth Systems, which explores the public’s confidence in the reliability of AI-based weather forecasting and prediction. Future studies are planned to assess how attitudes change toward AI-backed weather reports.

Responses to the report are not entirely negative. While 87.7% of respondents felt confident about weather reports using NWP, the 49.4% in favour of MLWP isn’t a bad result. Rather, it demonstrates that people are comfortable with tried-and-tested methods.

Dr Edward Pope, a Met Office Science Fellow and the lead author of the paper, said the report, “deepens our understanding of current public perceptions as we approach a crucial juncture where AI-based weather models are demonstrating their potential to work alongside physics-based methods. This was a unique opportunity to compare public perceptions of established and emerging approaches to forecasting the weather."

The consequences of the “confidence gap”

The gap in confidence between the maths-based predictive forecasting and modern AI modelling is a challenge that the Met Office is addressing directly.

Dr Pope explained that “The gap in confidence and perceived accuracy highlighted in the paper demonstrates the need to clearly and transparently demonstrate the value of new approaches in ways that matter to people.”

In Pope’s view, AI practices will contribute to weather forecasts in the future, “but these improvements will only be fully realised if the public continue to have confidence, and importantly act on, the weather forecast they see.”

The Met Office research considered the “perceived accuracy” of reports, and its Chief AI Officer Professor Kirstine Dale implied that progress with AI weather modelling is subjected to evaluation and validation: “We are exploring ways of blending physics-based and AI-based modelling to deliver the forecasts that we all rely on. As with all science developments, we will robustly evaluate and validate any changes to our approach to weather forecasting before we introduce them into the model.”