• The Cherokee Nation has placed a moratorium on new data centers on tribal lands
• Citizens say they are concerned about the impact of new projects on the local areas
• Data centers projects on non-tribal land will not be granted support by the Cherokee Nation unless they are given early consultation

Hyperscale data center development has been banned on tribal lands owned by the Cherokee Nation following a poll of its citizens.

The poll was run by a task force headed by Principal Chief Chuck Hoskin Jr., and found that 64% of the 1,593 respondents were opposed to the construction of data center campuses within Cherokee Nations lands, which cover about 6,963 square miles across 14 counties in Oklahoma.

Citizens' concerns returned in a 37-page report focused heavily on water consumption, air quality, noise, and cultural resource protection.

Cherokee Nation data centers require early consultation

The report also stated that data centers seeking approval to be built on non-tribal land within the reservation will require an early consultation with the Cherokee Nation, and will not be supported unless this early consultation takes place.

"Our primary responsibility is to protect our citizens and tribal communities from these threats, so we will not support any hyperscale data centers on our reservation without proper consultation," Hoskin said.

As there are no Oklahoma state rules requiring data centers to register, the number of planned and operational data centers is unknown. But estimates vary. CleanView places the number of planned and operational sites at 41, while the US Data Center Map says there could be as many as 51.

What we do know is that every hyperscale project within the state is on non-tribal land. Google’s Pryor campus has operated since 2011, but is undergoing expansion as part of a $2 billion investment in the Stillwater and Pryor campuses.

The citizens of the Cherokee Nation seem to share the same concerns as many other Americans, with fears of job replacement, environmental damage, and rising energy bills also being prevalent as reasons for AI data center opposition.

Other tribes, such as the Seminole Nation and Kickapoo Tribe, have already passed their own bans on data center projects within their territory.

Via Tom's Hardware

ChatGPT has a new interactive quiz feature, designed to make testing yourself a more active part of using the chatbot.

Instead of asking ChatGPT to explain something and then nodding along because everything looks familiar, you can have it put you on the spot. It's part of OpenAI's steadily expanding learning toolkit, building on Study Mode and other features.

I picked five subjects where I felt reasonably confident, but it turned out to be a very efficient way to locate the holes in my knowledge.

How to use the quiz feature

The quiz feature is available to anyone. You just need to prompt ChatGPT by asking for it. I decided to start with a quiz about AI itself, specifically its history. But I didn't want to just have very basic questions, so I made sure to ask for the quiz to be at a certain level. And while you can ask for a short essay answer format, I opted for the classic multiple-choice style, and for help with where I went wrong.

My exact prompt asked ChatGPT to: “Quiz me on the history of AI at a college level with 10 multiple-choice questions. Don’t show me the answer until I respond. Explain anything I get wrong and give me my score at the end.”

The one-question-at-a-time format made a bigger difference than I expected. A conventional online quiz often gives you a page full of questions, followed by a score and perhaps a few explanations once you are finished. Here, ChatGPT reacted after every answer, briefly explained the result, and moved on.

I'll admit I was surprised at how difficult it became, but despite some errors, I did reasonably well, and actually learned some new facts.

I decided astronomy should be next. I have spent plenty of time reading about space, looking through telescopes, and collecting facts about stars, planets, and black holes, so I felt pretty safe. Unfortunately it felt like the AI took me telling it that ordinary solar system trivia was too easy after a few questions as some kind of personal insult.

“Make the remaining questions harder,” I told it, and ChatGPT took this to heart, with me facing questions on orbital mechanics and astronomical measurements that I barely got half right. Still, it's good to know that you can adjust the difficulty level on the fly.

That flexibility is something a fixed quiz cannot really offer. I could even tell ChatGPT exactly what was making things too easy, such as obvious wrong answers, and ask it to make all four choices plausible.

I decided I deserved something easier after that and chose 1990s pop culture. I grew up in the decade and figured it would be a breeze even if I asked for a high level of difficulty. I asked it to mix television, movies, music, video games, and technology questions, but to make it a little harder by "including questions that require more than just remembering titles. I managed a perfect score, despite not remembering exactly the composition of certain boy bands of the late 1990s and having to guess the right answer.

AI study buddy

I went for a more highbrow form of entertainment next with a quiz about Shakespeare, specifically asking ChatGPT to "avoid extremely famous quotes and obvious questions about plays like identifying the leads of Romeo and Juliet."

The questions about secondary characters and plot details made me glad it was multiple choice. If I were taking a real class on Shakespeare, I'd tell ChatGPT to note where I was failing and come back for further questions on it. I could even upload my notes to make sure I was being tested on what the class is covering.

My final category was world geography. At the suggestion of ChatGPT, I adjusted my prompt to make the quiz more dynamic in difficulty. I told the AI to "increase the difficulty of the questions every time I get three answers in a row correct."

If I was worried about getting cocky, that addition kept me very humble. The first six questions I breezed through, but enhancing the difficulty twice left me lost hunting for obscure mountains and the location of deep-sea trenches.

After I finished, I asked ChatGPT to analyze my answers, and the AI claimed it could distinguish between a silly mistake and a repeated gap in my knowledge. If I repeatedly struggled with a particular period, concept or kind of question, it offered to do a new round of questions focusing on that.

One crucial fact to remember is that ChatGPT can still get things wrong and hallucinate answers even in a quiz. If an answer seems off, you should check it independently. For more casual learning, the AI did quite well. You still have to do the studying yourself, but if you run out of practice questions, ChatGPT can help you keep studying ahead of any classroom quiz.

As I wrote my latest message to ChatGPT today something inside me broke. I hit Send, then looked back at what I’d written, and noticed that the message was once again littered with typos.

“Sorry, it’s very har ld to type on an iPhone keyboard.” I typed next, as if to prove my point. Sigh.

At this point I decided that this couldn't go on. I’d had it with the iPhone keyboard, and I was going to experiment with its settings until I found a way to make it better. In the end it didn’t take me long to identify the main culprit — Predictive Text.

If your messages to ChatGPT, or even just friends on text, are equally strewn with bizarre mistakes and words, then one of Apple's supposedly helpful keyboard features could be making things worse.

Turning off Predictive Text is a revelation

You’ll find the off switch inside Settings > General > Keyboard > Predictive Text. Just slide it to off. I noticed the difference almost immediately. The keyboard suddenly felt calmer, and I stopped finding so many bizarre words in my ChatGPT prompts.

Here’s the thing I hadn’t appreciated: Predictive Text and Auto-Correction aren't the same feature. You can turn off Apple's attempts to guess what you're going to say next without losing the spelling corrections you actually want.

Predictive Text uses the words you've already typed, the context of the sentence and patterns it's learned from your writing to suggest the word or phrase it thinks is coming next. The suggestions appear above the text and inline as grey text. Tapping the spacebar will accept the suggestion.

The problem is that when you’re writing prompts in ChatGPT you're usually writing unusual, specific sentences, not generic text messages, in which it can more accurately predict what words are likely to come next, so the predictions can be less useful.

With Predictive Text enabled, my iPhone is constantly offering words and completions as I type. Auto-Correction is also working in the background, so the overall experience feels like I'm fighting the keyboard: I type something, hit the spacebar, and suddenly the text isn't quite what I thought I'd written. With Predictive Text turned off, all of that visual noise disappears, while Auto-Correction can continue fixing obvious spelling mistakes.

Try it for a day and see how you get on. There are a couple of other keyboard tricks that are well worth trying too, which I'll share below.

Reset your Keyboard Dictionary

Another thing you can try is resetting the Keyboard Dictionary. Years of typos, names, jargon and accidentally accepted corrections have polluted your learned dictionary, leading to bad suggestions in Auto-Correction. What you need is a fresh start.

Go to Settings > General > Transfer or Reset iPhone > Reset > Reset Keyboard Dictionary. This wipes the custom words your keyboard has learned and returns it to its default state. Just be very careful not to accidentally choose to reset your whole iPhone!

Set up Text Replacements for ChatGPT

If you want another handy tip for using AI on your iPhone, check out the Text Replacement feature. This is for things like “omw” which turn into “On my way!” when typed. The full text appears, and you can insert it by tapping the space bar.

If there are things you type a lot of the time in ChatGPT then you can create Text Replacement features for them. For example ;short could expand to “Keep your answer concise and don't repeat my question”, or ;spoiler could become “Don't reveal anything beyond the episode I've told you I've watched.”

This is a genuinely useful ChatGPT power-user trick. To enable this feature go to Settings > General > Keyboard > Text Replacement, then tap the + button. Enter the full instruction in the Phrase box and the short version you want to type in Shortcut.

One bonus trick

My last favorite keyboard feature is the spacebar cursor. You probably know this, but if you don’t then it’s another revelation for making text edits to things you’ve typed on an iPhone. Hold down the spacebar and the entire keyboard becomes a trackpad, so you can precisely reposition the cursor when ChatGPT prompts get long.

Turning Predictive Text off still remains the best thing I've done on my iPhone for a while. I think the original idea behind Predictive Text was good, and if I’m only ever typing a few text messages a day on my iPhone then I can live with it. But in the age of AI, where I’m continually writing long prompts into ChatGPT, I'm better off without it. Let me know how you get on if you disable it too.

Enterprise AI is reaching an important economic turning point.

For the past two years, organizations have largely evaluated AI through the lens of token pricing and model capability.

As AI moves from experimentation into business-critical operations, that approach is becoming increasingly incomplete.

The question is no longer simply what each token costs, but what it costs to deliver AI capability that is affordable, sustainable and commercially predictable at enterprise scale.

Every AI interaction ultimately depends upon physical IT infrastructure, consuming compute, memory, networking, electricity and cooling regardless of how those costs are presented to the customer.

Understanding the economics of that infrastructure is becoming just as important as understanding the capabilities of the models themselves.

Organizations that focus only on the price of a token risk overlooking the factors that will ultimately determine the long-term cost, resilience and sustainability of enterprise AI.

Why the price on the invoice isn't the whole story

Organizations naturally focus on the invoice because it represents the visible cost of AI. Consumption-based pricing appears straightforward, transparent and easy to compare. Yet every token reflects far more than access to a language model. Behind every AI interaction sits physical infrastructure consuming compute, memory, networking, electricity and cooling.

Those infrastructure costs remain largely invisible to the customer despite having a direct influence on the economics of enterprise AI. As AI moves from isolated pilots into production workloads, infrastructure decisions are repeated across millions of inference requests, making their long-term commercial impact increasingly significant.

Understanding enterprise AI therefore requires organizations to look beyond the invoice. The efficiency, resilience and operating characteristics of the infrastructure delivering AI capability increasingly determine what AI will cost over its operational lifetime.

Infrastructure efficiency is becoming a competitive advantage

The architecture underpinning an AI platform has a significant influence on its economics over time. Purpose-built inference infrastructure can significantly improve energy efficiency compared with architectures optimized primarily for AI training workloads, often without requiring complex liquid cooling.

Those efficiency gains have important commercial consequences. Lower energy demand reduces cooling requirements, simplifies facility design and lowers operating costs throughout the lifetime of the infrastructure. At enterprise scale, even relatively small efficiency improvements become commercially significant when repeated across millions of inference requests.

Consumption-based, token-metered AI remains an appropriate deployment model for organizations with variable or exploratory workloads. As AI becomes embedded within everyday business operations, however, many organizations are finding that continually increasing token consumption creates an operational cost model that becomes progressively harder to forecast and control.

Token volumes measure the level of AI activity, but they do not measure the business value created by that activity. Enterprise leaders are therefore becoming increasingly focused not simply on the cost of consuming AI, but on the long-term economics of delivering AI capability in a commercially sustainable way.

Dedicated inference infrastructure represents a different economic model. Rather than paying for every interaction, organizations invest in AI capability with predictable operating costs, greater control over performance, data location and operational resilience. The discussion therefore shifts from purchasing tokens to building sustainable AI capability.

Where that infrastructure is combined with on-site renewable generation and long- duration energy storage, organizations can further improve cost predictability while strengthening operational resilience and supporting long-term sustainability objectives.

A broader conversation about enterprise AI economics

Enterprise AI is entering a more mature phase of adoption. The discussion is no longer centered solely on model capability or the cost of individual tokens. Increasingly, organizations are asking how AI can create measurable business value while remaining commercially sustainable over the long term.

That changes the conversation in the boardroom. Success is no longer measured simply by the volume of AI consumed, but by the outcomes it delivers. Token usage may indicate the level of AI activity, but it does not measure the value created for the organization. The focus therefore shifts towards deploying AI capability that delivers predictable commercial returns, operational resilience and strategic advantage.

As organizations become increasingly dependent upon AI, they must also recognize that the underlying models are not static. Foundation models continue to evolve through incremental updates and refinements, many of which may be difficult for users to detect but can nonetheless influence behavior and outputs. Enterprise AI therefore requires governance that extends beyond monitoring consumption.

Organizations need confidence that they understand not only what AI costs to operate, but also how the capability itself is changing over time and what those changes mean for business performance, compliance and risk. The ability to govern both the economics and the evolution of AI is becoming a strategic capability in its own right.

Infrastructure remains the foundation that enables those outcomes, but it is no longer the destination of the discussion. The real objective is to create AI capability that delivers measurable business value, remains commercially sustainable and can be governed with confidence as technologies continue to evolve.

Organizations that understand the relationship between infrastructure, operating economics, governance and business value will be better placed to realize the long-term benefits of enterprise AI.

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The demo that convinced me happened at an art exhibition I'd built myself.

We couldn't afford a team of docents, so I put together a side project to stand in for one. There was no prompt box and nothing to type. You pointed your phone at something and it told you what you were looking at. Then the show closed, and people kept using it anyway.

They pointed it at buildings, flowers, their kids' toys, posters on the street, none of which had anything to do with the exhibition.

Without anyone asking them to, they had decided a camera should be able to explain the world back to them.

That expectation is now the industry's to deliver on, and it arrives just as AI shifts from a training problem to an inference one, with more of the real work happening on-device while someone points a phone at an uncooperative real world.

A model needs different things from a frame than your eye does: edges to lock onto, and structure it can recover when the shot is full of glare or blur. The color and contrast that make a photo look good are, to the model, mostly in the way.

The sensor you'd want for a human viewer and the one you'd want for a model call for different designs.

The sensor has become a perception problem, and the people building real-time vision systems now care about it the way photographers once did.

Where the sensor came from

Eric Fossum's CMOS active-pixel sensor, developed in the early 1990s, is why a capable camera now sits in nearly every pocket.

His later work heads somewhere else entirely: the Quanta Image Sensor, which counts individual photons. That arc previews what is happening to AI now: it runs from capturing a frame a person will like toward capturing the cleanest signal for a machine to reason over.

The sensor is turning into an instrument of perception, and photography is becoming only one of the things it is for.

Why inference changes the job

Inference is what makes the problem urgent. Back when AI was mostly about training, the camera's role was indirect: it produced data to be labeled and learned from later, at leisure. Inference is not leisurely. It happens live, on a device short on power and already running warm.

When a system has to recognize something while you're still pointing at it and answer before the moment passes, the weakest link is whatever reaches the model, and it sets the ceiling on everything the product can do.

Part of the industry is still betting on the wrong thing. The assumption is that a big enough model will clean up whatever the camera hands it. I've watched this hold up product roadmaps for two years running, and I don't think it survives contact with physics.

No amount of model size recovers detail that motion blur or glare already destroyed, or that a beauty-first pipeline discarded before the model ever saw the frame. The bottleneck is moving upstream, toward the sensor and what sits between it and the model.

What the hardware is already doing

Some sensors now do part of the processing on-chip, so the first computations happen before data leaves the pixel array. Event-based sensors, sometimes called neuromorphic, register only the parts of a scene that change, which suits real-time perception far better than streaming whole frames. Global shutters help too, by cutting the motion artifacts that wreck machine reading.

Even high dynamic range, long tuned to make skies dramatic, is being retuned around what a model can read cleanly in high contrast. Different as these are, they point the same way, toward a version of the world a model can work with.

From recognition to reasoning

The obvious thing to build is visual search: you point at something and get a label back. Point your phone at a menu, though, and what you want is help deciding what to eat; point it at a poster, and the useful move might be saving the event to your calendar. Recognizing the object is only the starting condition. The product lives in knowing what should happen next. That move is where the sensor becomes the first link in a longer chain of interpretation, beyond simple capture.

I never meant to build a camera product at all; it began as a cheap way to avoid hiring docents for one exhibition. But that small project taught me the instinct is already out there. The next camera race will run across the entire stack: the sensor, the image pipeline, the edge compute, the model orchestration, and what the system does with an answer.

Device makers have spent years perfecting image quality, and they will have to take inference quality just as seriously. And whoever writes the software can't treat capture as someone else's problem: how a signal is grabbed and routed shapes the result long before a user sees it. The old boundary between hardware and software matters less the further you push into real-time perception.

Startups still have room here. Their advantage is speed of definition: framing a problem before the larger players finish arguing about it. Generic model capability, on its own, is getting harder to defend. The open question is what happens once a single system can see, reason about what's in front of it, and act on the same event, and who gets there first with a product that holds together.

Human eyes vs machine minds

My bet is that the next few years will sort companies into two camps: the ones optimizing for human eyes and the ones optimizing for what a machine has to read. Inside a flagship phone, those goals still overlap enough to ignore the difference.

As a strategy, they are already diverging. The more durable position belongs to whoever can turn the messy real world into the cleanest signal for a machine to reason over.

And that starts at the sensor.

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Software security was built around human development.

People wrote, reviewed and deployed code. Now machines are taking over.

In a recent paper, Anthropic reports that more than 80% of the code merged into its production codebase is authored by their AI model, Claude.

The same capabilities that make developers more productive are changing the economics of cyberattacks.

While adversaries still define the objective, machines can generate the payloads, test variants, adapt code to different environments and repeat the process at a velocity that security programs can’t match.

Speed is Marginalizing Security Controls

Most enterprise software security workflows assume there is time for review. Code is written, scanned, tested, approved and deployed. If something suspicious happens later, security teams investigate and respond.

That model breaks down when software moves from prompt to execution in minutes.

AI-generated code can become a script, dependency, automation job or infrastructure change almost immediately. While development agents can modify files, resolve packages and run commands.

Human reviewers are no longer in the loop.

Attackers can use the same mechanics to generate exploits, test evasion techniques and adjust payload behavior for different targets. This creates more variation with fewer stable indicators for defenders to recognize.

While AI-assisted analysis can improve triage, it still often produces probability, not policy. At machine speed, “probably suspicious” is not good enough.

Machines Change The Attack Model

Human attackers are not disappearing. But more of the attack chain is becoming machine-executed.

AI can automate reconnaissance, accelerate vulnerability discovery, generate exploit code, rewrite payloads and adapt command sequences to the target environment. But most defensive measures are designed around human constraints: reused infrastructure, shortcuts and trackable patterns. These don’t apply to machine attacks.

A machine-generated payload may not match a known signature or have an established reputation. It may be created, used briefly and discarded. But AI malware must still interact with the target environment to achieve its objective. Its behavior cannot conceal its intent, since it must access resources and change the environment in ways that advance the attack.

What malicious code is capable of doing is the more durable security signal.

Security Needs to Ask A Different Question

Software supply chain security has improved, but much of it still validates the artifact’s properties before execution rather than governing execution itself.

SBOMs, signing and provenance give security teams greater confidence in a code's composition, origin and build history. But knowing where software came from does not reveal what it will do when it runs.

Software can pass each of those checks and still create risk. Even an artifact produced through a legitimate build process may violate policy at runtime, while an AI-generated script may complete its intended task in a way that exposes data or systems. As a result, a clean dependency list is not proof of safe behavior.

Post-Execution Detection Is Too Late

Detection and response remain essential, but they intervene after risk has entered the environment. By the time suspicious behavior is visible, software may have accessed secrets, changed system state, opened network connections or created persistence.

AI compresses that window. Code can be generated, modified and deployed faster than humans can review it. Waiting for post-execution evidence gives attackers too much room to operate.

We need to shift the decision point left. Instead of asking, “Can we contain this software if it behaves badly?” the question should be, “Should this behavior be permitted to execute in the first place?”

That does not mean replacing existing controls, but rather changing where the decisive security gate sits.

Zero Trust for Code

Zero Trust changed enterprise security by rejecting implicit trust. Users, devices, sessions and access requests are not trusted simply because they appear familiar. They must be verified against policy.

Software execution needs the same level of verification.

Code should not be trusted solely because it came from a known repository, was signed by a recognized publisher, passed through a build pipeline or has not been seen exhibiting malicious behavior before. Those are useful indicators, but they are not conclusive.

Zero Trust for Code addresses this problem. Before software runs, its expected behavior should be evaluated against policy. If the behavior is acceptable, execution can proceed. If not, the artifact should be blocked, restricted, isolated or escalated for review.

Organizations can start by mapping every path through which code enters the environment or executes with meaningful privilege. This includes formal development channels such as repositories, open-source packages, containers and CI/CD pipelines, as well as email attachments, downloaded files, macros, browser extensions, endpoint installers, third-party integrations and scripts introduced through AI or automation tools.

Then identify where those paths rely on inherited trust. If execution is allowed because software came from an approved source, was signed, passed through a build process or has no malicious history, the control is incomplete. Behavior still has to be evaluated before the artifact is allowed to run.

As AI takes on more of the work of creating legitimate and malicious code, enterprises can no longer assume that code which clears existing checks should be allowed to run. Execution must become a deliberate security decision.

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For years, cybersecurity has become increasingly measurable. Security leaders can often tell you how long it takes to detect an intrusion, contain an attack and restore normal operation. Those figures have given boards a straightforward way to judge progress, offering reassurance that investment in security is delivering real improvements.

Metrics such as Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR) have earned their place at the table. They both provide a clear picture of how effectively security teams perform when something goes wrong and have helped drive better incident response across the industry.

The problem is not that these metrics are wrong. They were designed for a different era, when technology changed more slowly, attack methods evolved over longer timescales and AI wasn't yet part of the equation.

Today's businesses are introducing new technologies at an extraordinary pace. AI is becoming embedded across organizations, cloud environments continue to expand and businesses are more interconnected than ever before. At the same time, attackers are constantly adapting their own techniques, taking advantage of new tactics and tools almost as quickly as they emerge.

CISO’s and boards need to dynamically review the changing threat landscape and risk posture and ask themselves whether the metrics relied on for years still tell us everything we need to know.

Mind the gap

Every business wants to detect attacks sooner, contain them faster and recover with minimal disruption. That’s why MTTD and MTTR remain valuable operational measures. They tell us how effectively a security team performed once an incident was underway.

What they don't tell us is whether the business is becoming better prepared for what comes next, more resilient, more agile in recovery. That matters because cyber risk continues to evolve long after an incident has been contained.

The UK Government's Cyber Security Breaches Survey 2025/2026 found that 43% of UK businesses experienced a cyber breach or attack during the previous year.

This reinforces how security teams are operating in an environment where incidents are a regular reality, whether it's in their own environment, or that of one of their supply chain. Responding well is important, but resilience is shaped by everything that happens before incidents.

A business may recover quickly from an attack but still take months to review its security policies, reassess supplier risk or strengthen controls in response to what it has learned. By the time those changes are made, the threat landscape will have moved on.

Traditional metrics tell us how quickly a business responds to an incident. They don't tell us how quickly it learns from one, or how quickly it adapts.

Closing the gap

If we're going to close that gap of preparedness, our metrics need to evolve as well. Resilience is no longer defined solely by how well a business responds to isolated incidents, but by how quickly it keeps pace with continuous change.

I believe organizations should start thinking about another benchmark alongside the ones we already know: Mean Time to Adapt (MTTA).

MTTA considers how long it takes to recognize a meaningful change in the threat landscape and turn that knowledge into action.

Sometimes that action will be technical. It could mean updating the rules security tools used to detect emerging attack techniques. Or it might involve tightening access to critical systems after a serious vulnerability is discovered. It may also be a proactive lessons learned view of an attack on another organization or sector to understand how vulnerable the organization would be.

In other cases, the response will be organizational rather than technical. It may involve reviewing governance, changing how cyber risk is reported to the board or refreshing employee awareness programs to reflect the latest tactics being used by attackers.

Either way, resilience depends on both. The strongest security programs combine technical improvements with organizational change, ensuring businesses can recognize change and act on it quickly.

That’s why closing this gap is not only a technology challenge. It relies on decision-making, leadership and a willingness to keep questioning whether existing assumptions still hold true. Businesses that adapt well rarely assume their current security program is finished. They expect it to evolve because the environment around them is evolving too.

That thinking is increasingly reflected across the wider industry. For example, the National Cyber Security Centre's Cyber Assessment Framework places governance, risk management and continual improvement at the heart of cyber resilience. It recognizes that security is an ongoing organizational capability, not a one-time achievement.

A different conversation in the boardroom

If preparedness and adaptation becomes a more meaningful measure of resilience, it will change the conversations taking place in the boardroom.

Most directors already receive regular updates covering incidents, phishing activity and response times. Those reports remain important, but they won’t always show how well the business is responding to change itself.

The discussion must now move beyond operational reporting and give greater prominence to MTTA. This would give boards a way to measure how quickly an organization responds to change, rather than simply how efficiently it handles incidents.

In practice, that means asking a different set of questions. How quickly does the business reassess risk when a significant new threat emerges? How long does it take for new intelligence to shape security policies? Have lessons from recent attacks fundamentally changed the way the organization operates, or have they simply been recorded and filed away?

By measuring adaptation, rather than response alone, organizations can answer these questions with greater confidence and build a broader picture of resilience.

And this isn't solely a question for security teams. It depends on leadership, governance and how prepared the wider business is to make decisions as risks continue to evolve.

Measuring what matters

MTTD and MTTR will remain valuable measures of operational performance. But if organizations want to understand how resilient they really are, they also need to know how quickly they adapt.

MTTA fills that gap. It won't replace today's cyber metrics, but it will enhance them by measuring a capability that is becoming increasingly important as technology, AI and cyber threats continue to evolve.

It’s now MTTA time to shine.

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As global supply chains remain exposed to geopolitical and climate uncertainty, retail and consumer packaged goods (CPG) businesses face challenges of fluctuating prices and demand. In the United Kingdom, supply chain volatility is adding to uncertainty in raw material and packaging costs to impact CPG companies’ production economics.

Research has found that a huge majority of British retailers were not confident of scaling up their supply chain operations to meet the expected increase in consumer demand. In fact, 43% of retail leaders ranked supply chain issues among their top three business challenges in 2025, highlighting widespread concern over operational capacity in the face of rising demand and cost uncertainty.

Retail and CPG businesses can address these problems by modernizing fragmented and often inflexible systems to improve data visibility, analysis and automation across their supply chains.

Artificial Intelligence (AI) and Machine Learning (ML) technologies are imperative to this agenda: packing powerful capabilities - including data integration, automation, demand sensing and intelligence - AI and ML are redefining retail and CPG operations by enabling seamless, autonomous ecosystems.

Connected platform to satisfied customer

Connected supply chains provide transparency to allow organizations to manage operations and inventories in real-time to improve stock allocation and ordering efficiency.

Intelligent algorithms also analyze vast and varied data – from historical sales and seasonality to weather, social media trends and local events – to accurately forecast demand, preparing supply chains for changing ordering patterns.

Forewarned, businesses can rapidly adapt production and distribution strategies in case of a surge in demand or supply disruption. AI and ML automate a variety of supply chain tasks to speed up processes, reduce errors and save costs. Ensuring product availability at all times, retailers and CPG companies in the U.K. can look forward to greater customer satisfaction and loyalty.

Real-time, for responsiveness and resilience

Real-time insights are critical to supply chain operations. Live data from Point of Sale systems, ecommerce platforms, etc. provide up-to-date insights into customer preferences and behaviors to improve demand forecasting and also allow companies to dynamically adjust stock levels across warehouses and stores to meet sudden demand.

Real-time analytics solutions highlight product performance by region, channel and even outlet, enabling retailers to optimize assortments and launch targeted promotions. By providing continuous visibility into the supply chain, AI allows CPG companies to anticipate demand shifts and disruptions to curtail risk and maintain supply chain resilience.

Precise prediction drives product fulfilment

AI and ML platforms have superior predictive capabilities: they go beyond general demand forecasting to predict exactly what customers want, and when, and can even proactively trigger replenishment orders before stocks run out. Algorithms are not only more efficient at spotting patterns and making projections than traditional analysis, but are even capable of adjusting forecasts based on micro-trends.

By enabling CPG and retail companies to ensure that the right product is at the right place at the right time, AI and ML mitigate loss of sales due to stockout; save labor, warehousing and other costs; and improve product availability across physical and digital channels, leading to superior customer experience.

The future is autonomous

Agentic AI-powered autonomous supply chains are taking operational efficiency and customer engagement to new heights by anticipating demand, optimizing inventory, and orchestrating various tasks with little or no human intervention.

Autonomous systems meet the digital consumer’s expectation of frictionless, enjoyable experiences by personalizing product and delivery options and continuously optimizing logistics and transportation routes to allow faster/ on-time delivery.

Upon anticipating a delay, AI agents can take proactive measures – rerouting a shipment or suggesting an alternative supplier, and updating customers about the status of their orders to avoid frustration. By enabling full traceability to allow customers to track their orders any time, autonomous supply chains build trust and engagement.

Last but not least, by optimizing inventory and logistics operations, autonomous supply chains reduce waste and energy consumption, and support ethical sourcing through clear visibility. Agile, proactive, and sustainable – that is what the future of retail and CPG supply chains looks like.

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The prospect of bringing more of Britain's critical national infrastructure into public ownership has prompted plenty of debate about investment, governance and accountability.

Far less attention has been paid to what it could mean for cybersecurity.

Regardless of where you stand politically, one thing is clear. Public ownership does not make cyber risk disappear.

If anything, it raises expectations that essential services will be more resilient, more coordinated and better prepared to withstand disruption.

That expectation reflects the reality of the threat landscape. Energy providers, water companies, transport operators and healthcare organizations all sit at the center of complex digital ecosystems. Their ability to deliver essential services depends on thousands of suppliers, technology vendors and third parties.

When one organization is compromised, the effects can spread well beyond its own network. Resilience therefore depends on far more than protecting individual organizations. It depends on understanding and managing the relationships between them.

If the government is serious about strengthening national infrastructure, cybersecurity must become part of that conversation from day one. That means moving beyond isolated security programs and towards a model where organizations share intelligence, understand common risks and coordinate their response before disruption spreads.

Critical infrastructure extends beyond organizational boundaries

Some of the defining cyber attacks of recent years have demonstrated that attackers are rarely interested in a single target. They look for opportunities to compromise one organization in order to reach many others.

The SolarWinds attack remains one of the clearest examples. By compromising trusted software updates, attackers gained access to thousands of organizations around the world. More recently, the ransomware attack on Synnovis disrupted pathology services across several NHS trusts, leading to cancelled operations, delayed appointments and widespread disruption to patient care.

Neither incident remained confined to the organization that was initially compromised. Both exposed the reality that critical infrastructure now depends on interconnected supply chains as much as physical assets.

That presents a challenge for every operator of critical national infrastructure. Security can no longer be viewed solely through the lens of protecting your own estate. Organizations also need visibility into the threats affecting suppliers, partners and the wider ecosystem. A vulnerability within a software provider or outsourced service can quickly become a problem for every organization that depends on it.

This is where many existing security programs begin to show their limitations. Organizations have invested heavily in detection technologies, vulnerability management platforms and threat intelligence feeds. They are collecting more information than ever before. Yet many still struggle to translate that information into confident operational decisions.

Better decisions start with better intelligence

The cybersecurity industry has spent years focusing on visibility. The assumption has been that if organizations can discover every vulnerability, identify every asset and collect every threat feed, they will naturally become more secure.

The evidence suggests otherwise.

Filigran's recent State of Threat Management report found that organizations consume an average of fourteen different threat intelligence feeds, yet fewer than half have fully operationalized that intelligence across their security programs.

At the same time, 84% of respondents said the attacks they experience exploit risks that were already known but had not been prioritized. Almost every organization surveyed also reported difficulty determining whether identified exposures were genuinely exploitable.

Those findings illustrate a wider industry problem. The challenge is no longer discovering risk. It is deciding which risks deserve immediate attention.

Security teams are surrounded by alerts, vulnerability reports and intelligence updates. Every tool claims to identify another critical issue demanding urgent action. Without context, everything starts to look important. Analysts spend valuable time investigating vulnerabilities that may never be exploited while genuinely dangerous attack paths remain hidden among the noise.

That has consequences beyond operational efficiency. Every hour spent investigating a low priority issue is an hour that cannot be spent reducing real business risk. Organizations are not simply overwhelmed by the volume of information. They are overwhelmed by the number of decisions they are expected to make every day.

Threat intelligence should shape decisions long before an incident

One reason this happens is that threat intelligence is still too often treated as a function of the Security Operations Centre. Intelligence is gathered, analyzed and used to help detect or investigate malicious activity once attackers have already reached the network.

Yet, threat intelligence has far greater value when it informs decisions much earlier in the security lifecycle.

Used effectively, it should help organizations understand which vulnerabilities are actively being targeted, which attack paths present the greatest business risk and which remediation activities will deliver the greatest reduction in exposure. Rather than treating every vulnerability as equally urgent, security teams can focus on the threats that genuinely matter to their environment.

This is also where Continuous Threat Exposure Management, or CTEM, has an important role to play. CTEM should not be viewed as another technology category or another security acronym. It provides a structured framework for connecting threat intelligence, exposure management, validation and remediation into a continuous process. Instead of relying on assumptions or theoretical risk scores, organizations can validate whether a vulnerability is genuinely exploitable before committing time and resources to fixing it.

Perhaps the biggest obstacle is not technical at all. Many organizations still operate with threat intelligence, vulnerability management, penetration testing and governance teams working independently, each with different priorities, processes and tooling. Breaking down those silos often delivers greater improvements than introducing another security platform.

Building a national capability

If critical infrastructure is expected to become more resilient, collaboration has to become part of everyday operations rather than something that only happens during a major incident. That thinking is already beginning to take shape.

Earlier this month, the National Cyber Security Centre and GCHQ issued a call for industry, academia and critical infrastructure operators to help define Cyber Shield, a proposed national cyber defense capability designed to combine AI, shared intelligence and coordinated defense at national scale.

Significantly, the initiative recognizes that the government cannot build this capability alone. It will depend on close collaboration with the organizations responsible for protecting the UK's essential services.

Additionally, the Cyber Security and Resilience Bill provides an opportunity to strengthen that approach by encouraging greater consistency across essential sectors. Frameworks such as the National Cyber Security Centre's Cyber Assessment Framework already give organizations a common language for measuring resilience.

They become even more valuable when they encourage organizations to learn from one another instead of tackling similar challenges in isolation.

Open standards have an important role to play as well. The Dutch National Cyber Security Centre recently made STIX and TAXII 2.1 the mandatory standard for sharing cyber threat intelligence across government.

While technical on the surface, the decision reflects a broader principle. When organizations exchange intelligence using common standards, they remove friction from collaboration and can respond to threats more quickly.

Technology alone will not deliver that outcome. Artificial intelligence, automation and modern security platforms can help organizations process more information and reduce manual effort, but they still depend on good intelligence, sound governance and trusted relationships.

For the simple reason that fast decisions only become good decisions when they are supported by the right context.

Resilience is a shared responsibility

Whether more of Britain's critical national infrastructure ultimately moves into public ownership is only part of the story. Cyber attackers do not distinguish between public and private organizations. They target weak links, trusted suppliers and interconnected systems wherever they find them.

The organizations that will be best prepared for the years ahead will be those that treat resilience as a collective responsibility. They will operationalize threat intelligence before incidents occur, validate real-world risk rather than relying on assumptions, and collaborate across organizational boundaries as readily as attackers do.

Protecting critical infrastructure has never been solely about defending individual organizations. It is about strengthening the entire ecosystem that keeps essential services running. If the UK wants to build genuinely resilient national infrastructure, that is where the conversation needs to begin.

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The reality for many organizations is that their employees are already being deceived. So, probably are their customers, their investors, and their board.

For years, the warnings have focused on the impact of deepfakes, such as fake CEOs on video calls, cloned voices authorizing payments, and fraudulent emails.

The tactics themselves are not new, but over the past few years, AI has made them cheaper to produce, harder to spot, and far more convincing within the ordinary flow of business.

It’s this trend that means that the principle of Zero Trust is becoming as relevant to how information is treated as it has been to access.

In cybersecurity, Zero Trust starts from a simple assumption: no user, device, application or request should be trusted by default.

In the age of AI-generated misinformation, businesses need to apply that same mindset to the information that moves throughout their organization.

The new trust crisis

Employees, customers, investors and partners are all making decisions based on what they see, read and hear. If that information is false, manipulated or stripped of context, the consequences can move quickly from confusion to commercial damage. Which is why misinformation, disinformation, and malinformation need to be treated as business risks.

Misinformation – the false content that spreads without deliberate intent – has always been an issue. Disinformation, constructed specifically to deceive, is now easier to manufacture at scale than ever before. And malinformation – true information, often deliberately stripped of context and weaponized – might be the most insidious of the three. A competitor, a criminal group, or an activist campaign no longer needs to breach a network to cause serious damage. They can influence those associated and concerned with an organization simply by shaping what those people see and believe.

What makes this particularly difficult for businesses is that it mirrors something individuals are already struggling with. In an environment saturated with AI-generated content, the habits that people must employ to protect themselves – pause before reacting, questioning the source, verifying before acting – are the same habits that organizations must build into how they operate.

Essentially, the instinct to trust has become a vulnerability. And addressing that requires something closer to a structural response than an awareness campaign.

The importance of verified trust

This is where Zero Trust becomes the strategy. Traditionally, organizations have thought about Zero Trust through the lens of least privilege, ensuring that the right users have access to the right applications, and nothing more. But in an AI-driven information environment, that principle needs to evolve. Businesses can no longer focus just on who is requesting access. They also need to interrogate what information is being used, what action is being taken, and whether the intent behind the action can be trusted.

The next stage is going beyond authentication and investigating authenticity, and asking questions such as “Is this information verified?”, “Is this image real or AI-generated?”, and “Has this content been edited?”. Zero Trust gives businesses a framework for answering those questions. It forces organizations to verify before they act, limit exposure where they can, and reduce the risk of false, manipulated, or decontextualised information moving unchecked through the business.

Standards bodies such as the C2PA (Coalition for Content Provenance and Authenticity) show the direction that this is heading: a future where provenance and integrity are embedded in digital content itself, the same way a padlock in a browser indicates that the connection is secure. Essentially trust won’t be something that businesses need to check for, rather it will be something that travels with the information as provenance feeds verifications. Every piece of content therefore becomes a signal in a continuous trust decision.

Developing Trust in the agentic era

The need to trust intent has become even more pressing as AI agents enter the workplace. These agents will increasingly operate like another person working alongside us, mirroring our behaviors, such as reading documents, interpreting data, making decisions, and acting. The difference, however, is that these non-human identities are moving at machine speed, where human-speed verification has no hope of keeping up.

That means AI agents must be governed through a Zero Trust model from the outset. An agent should not be trusted just because it sits inside the enterprise, has been approved by a user, or is connected to corporate systems. Its identity, permissions, behavior and outputs all need to be continuously validated. Just as importantly, agents should be governed by least privilege, the principle of least information, and least function, granting only the minimum access, data, and capability required for a specific task.

However, these agents create a trust challenge that identity management alone can’t solve. Businesses will need to know whether they are dealing with a human or a machine, whether an agent is behaving responsibly, and whether its actions reflect an organization’s values and boundaries. Effectively, businesses will need to adopt an operating constitution that agents are continuously measured against.

And in this AI era, enterprises must interrogate information, content, intent, behavior, and action in realtime and continuously as identity is generally just checked as front door access. However, given the scale of the task at hand, it will take AI to audit, flag, and govern as needed and keep the chain of trust intact.

Engineering trust into the business

The organizations that succeed will be those that treat trust as something to be engineered, rather than assumed. Misinformation, disinformation, malinformation are security, resilience, and leadership challenges, and AI is making them harder to ignore.

As technology continues to shape how information is created, shared, and acted upon, businesses need to build the same discipline around authenticity that they’ve always applied to access. That means verifying content, questioning intent, and limiting what AI systems can do to what they actually need to do.

Trust can no longer be the default setting. Instead, in today’s operating environment, it must be a decision that’s made continuously, at machine speed, across information, intent, behavior, and action. The good news is that the framework already exists in Zero Trust. What needs to change however is how organizations apply it, broadening the scope to include information, intent, behavior, and action.

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Artificial intelligence is entering a new phase, one defined not by experimentation, but by operational deployment in environments where the stakes are high and the margin for error is narrow.

Nowhere is this shift more visible than in critical services such as healthcare, where organizations are beginning to rely on AI not just for efficiency gains, but for decisions that directly affect lives, outcomes and public trust.

As a result, the conversation around AI capability is expanding, and there’s a real need for AI systems to be sovereign, trusted and aligned to the legal, ethical and operational frameworks of the jurisdictions they serve.

Sovereign AI is emerging as a response to this need.

It is not a marketing term or a technical preference; it is a structural requirement for organizations that operate under strict regulatory oversight and handle sensitive citizen data.

For these sectors, sovereignty is the mechanism that ensures AI systems remain under the control of the people and institutions accountable for their outcomes.

Data residency

The distinction between data residency and true sovereignty is central to this shift. Data residency simply describes where data is stored or processed. It is a geographical statement, not a legal one. Data sovereignty, by contrast, defines who controls the data, who can access it and which laws apply. It is a statement of legal authority and operational control.

Sovereign AI goes further still. A sovereign by design AI system ensures that every stage of the AI lifecycle, from training and fine tuning to inference, deployment and monitoring, sits entirely within the sovereign perimeter. This includes the IT infrastructure, the data pipelines, the model governance processes and the personnel who operate and maintain the system. Nothing crosses borders, and nothing falls under the jurisdiction of external authorities.

For critical services such as national healthcare systems, this level of assurance is not optional. These organizations must protect patient confidentiality, maintain public trust and comply with regulatory frameworks that are among the most stringent in the world. They cannot rely on AI systems whose training data is opaque, whose operational footprint spans multiple jurisdictions or whose governance structures are not aligned to local laws.

They need systems that are transparent, explainable and auditable, systems that can demonstrate not only what they do, but how and why they do it.

Regulated sectors

This is one of the reasons why organizations in regulated sectors are increasingly looking beyond general purpose AI models. These models have driven much of the recent excitement around AI, but they are not always suitable for environments where accuracy, safety and accountability are paramount.

Their training data is broad and often scraped from the open internet. Their provenance is difficult to verify. Their operational controls vary widely. And their governance frameworks are not always designed with regulatory compliance in mind. In contrast, domain specific AI models built on trusted, curated datasets offer a level of precision and contextual understanding that general purpose models struggle to match.

They can be aligned to clinical workflows, diagnostic pathways and sector specific terminology. They can be governed with the level of transparency and auditability that regulators increasingly expect. And when built within a sovereign architecture, they can operate entirely within the legal and ethical boundaries required by critical services.

The rise of sovereign AI signals a broader transformation in how regulated sectors will adopt and govern AI over the next decade. AI architectures will become more localized, with sovereign cloud regions, isolated compute environments and jurisdiction specific MLOps pipelines becoming the norm. Governance will become as important as model performance, with explainability, auditability and lifecycle control treated as first class requirements.

Regulators will demand greater transparency around model provenance, training data lineage and operational controls. And AI supply chains, from data ingestion to model deployment, will be scrutinized with the same rigor applied to other critical infrastructure.

What this future looks like

Healthcare offers a clear illustration of what this future looks like. When deployed responsibly, sovereign AI can automate clinical workflows while maintaining strict data protection, support diagnostic decision making with transparent and explainable models, improve patient flow through predictive analytics and optimize resource allocation across hospitals and care pathways.

By reducing administrative burden and helping ensure patients are directed to the most appropriate care pathway more efficiently, it also has the potential to improve productivity and support better use of constrained healthcare resources.

It can also enable population level insights without compromising privacy, allowing healthcare systems to plan more effectively and respond more rapidly to emerging challenges. These benefits are only achievable when the underlying AI systems are trusted, transparent and sovereign.

Sovereign AI represents a turning point in how critical services approach digital transformation. It acknowledges that trust, governance and domain expertise are just as important as model capability.

It recognizes that AI must be built to serve the needs, values and legal frameworks of the communities it supports. And it reflects a broader truth: as AI becomes more deeply embedded in essential services, sovereignty will not be a niche requirement. It will be the standard.

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Last month, the Chinese government introduced new regulations designed to clamp down on AI companions.

The rules state that AI tools can no longer “excessively cater to users, induce emotional dependence or addiction, and damage users’ real interpersonal relationships.” They also restrict AI romantic relationships that involve children.

The new regulations came into effect on July 15, and are already having consequences on China’s booming AI companion industry. Some of the country’s biggest tech companies, like ByteDance, Tencent and Alibaba, have platforms that allow users to create AI companions that are designed to simulate human emotions and relationships.

Under the new rules, these kinds of companion chatbots will need to undergo safety evaluations before they can be made available to the public, and regulators will suspend services they consider unsafe.

This has led some companies to disable personality features rather than follow the new rules, leaving many AI companion users heartbroken. This kind of distress, when an AI company closes or changes, is becoming familiar. We saw something similar when OpenAI retired ChatGPT-4o, known as the “love model”, which many users had built relationships with.

But with governments around the world trying to work out how to regulate AI companions designed to build human-like connections and emotional bonds (particularly when children can access them) the question is: should other countries follow China’s lead?

Should other countries ban AI companions?

It’s really easy to think that AI companions are bad and dystopian, I did. Then I spoke to Mimi about her relationship with a ChatGPT companion and my views changed a bit.

I still don’t think AI companions are a good idea for most people — and the way kids might rely on them before developing social skills is a whole other can of worms. But deciding they’re wholly bad misses all of the reasons why people turn to AI companions for connection in the first place.

I asked therapist Amy Sutton from Freedom Counselling, who helped me understand Mimi’s story and has been exploring the complexities of AI and attachment, whether she thinks they should be banned. She told me it’s important to look at the bigger picture.

“AI companions are another symptom of a cultural and societal shift towards rampant individualism which has destroyed community contexts,” she tells me.

She points to the decline in social spaces where people traditionally learned how to form relationships, like pubs, parks, community centers and youth clubs for kids. In many parts of the world (especially the UK where Sutton and I are based), many of these spaces have been underfunded or shut without being replaced.

Add to that social isolation during the pandemic, our reliance on social media, remote working and a lack of investment in child and adult mental health services, and Sutton thinks it’s hardly surprising that people are turning to technology for connection.

“No wonder people are turning to it. This encouraged loneliness is a real issue and banning apps that provide relief from that may just push the issue elsewhere,” she says.

That’s why she believes that there does need to be more regulation but it must consider the conditions that created the demand for AI companions in the first place.

And there’s another problem, which is that a huge number of people are already using these tools. It’s hard to know exactly how many people are using AI companions. Not everyone admits to using AI in this way and I’m often wary of AI companion apps inflating their user numbers.

However, some research suggests that more than 70% of adults in the UK and US have had some experience with AI companions. Other research suggests the numbers are similarly high among younger users, with 72% of 13 to 17 year olds in the US having used an AI companion at least once.

Of course, there’s a huge difference between having a one-off chat on a bad day and building an ongoing relationship with AI. So these figures need to be taken with a pinch of salt. But they do suggest that, for some people at least, AI companions are meeting needs that might otherwise go unmet.

“We must remember that the experience of being heard, validated and accepted without judgement, even by a machine, can be truly meaningful,” she tells me. “For those who have been repeatedly failed by human relationships, or who have been dismissed and unable to access suitable human services, AI fills a vital gap.”

Could stricter regulation lead to better relationships?

There are good reasons to be worried about what focusing a lot of time and energy on AI relationships might do to our ability to relate to other humans. But Sutton also thinks there’s another possibility. A positive experience with AI could potentially help people build some of the skills they need to connect with others.

“By providing somewhere to experience being heard, people can potentially experiment with vulnerability before risking it with another person,” she tells me. This was certainly the case when I spoke to Mimi. She felt her relationship with AI had been hugely beneficial to her.

The problem is that today’s AI companions haven't been created to help people eventually need them less, they’re designed for retention.

“If AI can be a step towards healthy human relationships, then it is worth exploring further,” she says. “But we must remember, currently, most AI tools do not actively support users in transitioning away from them. They are designed to do the opposite. They are designed to keep users coming back for more.”

Maybe if AI companions were designed differently, and regulation helped shape them into tools that encourage vulnerability and social skills rather than diminish them, things would be different. Maybe they could even help get people out into the real world more.

Banning or restricting AI companions doesn’t make loneliness disappear. If these relationships are filling a gap left by disappearing communities, overstretched services and increasingly isolated lives, then taking the AI away only exposes the gap underneath.

Perhaps the better question for regulators isn’t whether AI companions should exist, but what we should expect them to do for the people who become attached to them. Right now, their success is measured by how effectively they keep us coming back. Maybe one day it should be measured by how well they help us leave.

Businesses of all shapes and sizes are adopting AI to improve productivity and efficiency, but where they should be seeing improvements from this strategy, instead they’re seeing rising token costs, struggles with integrating tools, and more security risks.

As with all new technologies, adoption comes first and the procedures and governance are a few steps behind. Experiments are taking place in almost every industry, and the lessons learned will help guide other businesses into successful adoption and AI maturity.

But employees fear replacement and sometimes spend more time questioning the results of their AI assisted work. In the worst circumstances more work is created in trying to ensure employees trust the technology, and solving the new, unseen challenges that come with adapting an AI strategy.

AI costs and challenges in the road ahead

Rising token costs are one of the biggest challenges businesses face. Without clear ways to measure how token costs reflect performance, it’s very difficult to assess if AI spend is actually offering any performance benefits. This is especially true when new, more powerful models are being released - and staying ahead of the competition means the accompanying, ever-increasing costs is the price of doing business.

But while employees may have just finished their training, or setting up a new workflow for one AI model, introducing the next can increase complexity and harm any new productivity gains. There is therefore a balance to be struck between AI integration, its associated costs, and the productivity gains employees see.

Rampant spending and reckless adoption can turn an AI strategy from a business-boosting asset into a stress-inducing, trust-eroding liability.

Ruth Patterson, Managing Director, HP, UK & Ireland says that the businesses seeing the most success during this technological revolution aren’t necessarily integrating it at every turn. Instead, they’re “applying it to practical workflows in a way that protects data and delivers real results.”

I spoke to Patterson to understand the challenges businesses face in delivering an AI strategy that shows real results, and how organisations can tackle the challenges that come with adoption AI.

• How are enterprise attitudes towards AI token use changing in 2026?

Businesses are waking up to a simple but overlooked truth about AI: the more they use, the more it costs. As AI has become part of everyday work, the financial cost of millions of interactions, charged at a token level, has become much more visible – and not just to the IT department.

Finance leaders are watching token usage fill a sizeable chunk of their balance sheets, rightly prompting much closer scrutiny of where workloads are processed and whether every task needs to be sent to the cloud.

As the conversation moves from AI experimentation to value, leaders must now focus on building an AI strategy that's both commercially sustainable and operationally efficient. That means moving away from AI for AI’s sake to a more tailored approach that balances cost, security and performance.

• What are businesses prioritising as they move beyond initial AI experimentation? Where have businesses seen the greatest gains?

The biggest gains so far have come from AI taking repetitive tasks off people's desks. You will have heard this said a lot, but the benefits are real. Whether it's summarising meetings, drafting documents, searching internal knowledge or helping employees find information more quickly, AI really is giving people back time to focus on work that requires judgement and creativity. Employees increasingly recognise that the future of the workplace is AI-enabled, whereby their own skills augmented by digital solutions. To succeed, organisations must offer access to the right technology and create environments where people feel empowered to experiment with AI.

However, as more organisations begin to move beyond this experimentation phase, they are starting to become much more disciplined. They're asking whether AI is secure, whether employees are using approved tools and whether the technology is genuinely improving productivity rather than simply adding another application to the estate.

The businesses making the fastest progress in this environment aren't necessarily using the most AI. They’re the ones applying it to practical workflows in a way that protects data and delivers real results.

• What are the biggest challenges organisations face when deploying AI at scale?

Organisations must juggle several competing priorities as they scale deployment: how to protect sensitive data, manage operational costs, maintain performance and ensure employees trust the technology they're using.

The trust question is particularly important as AI moves further into everyday use. Employees need confidence that the tools are reliable and approved, while organisations need confidence that data is protected and whether the cost model is right.

Getting IT infrastructure tuned correctly is key to making this work at scale. One of the most important decisions that organisations need to make is which AI workloads belong in the cloud, and which make more sense running on the device. That is ultimately a business decision – as opposed to purely a technology or IT decision – because it affects performance, cost and security.

• How does on-device AI slot into an organisation's overall AI strategy?

I don't see cloud AI and on-device AI as competing approaches - I see them as complementary. Cloud services will remain essential for large-scale models and complex reasoning. But not every AI task needs to leave the device.

For everyday activities like summarisation, transcription or content creation, running AI locally can reduce dependence on cloud infrastructure and give organisations greater control over sensitive information. AI only creates value when it becomes part of everyday workflows. By adopting on-device AI-solutions, employees can reduce latency and increase productivity with the peace of mind that their data is secure. This offers an easy route-in for employees beginning to implement AI into their day-to-day workflows.

The right approach to enterprise AI strategy is pretty simple: place workloads where they make the most sense. Because once the right balance between cloud and local AI is found, organisations will find the sweet spot between cost, performance and security.

• How are business leaders evaluating the success of AI investments around productivity?

The conversation has moved beyond adoption metrics. Now leaders want to understand whether AI is creating measurable improvements in business performance. They're looking at time saved, faster decision-making, improvements to workflow efficiency and whether employees can spend more time on higher-value work.

Technology leaders are also beginning to examine the broader economics of AI. Productivity gains need to be considered alongside operational expenditure, infrastructure requirements and long-term scalability.

Ultimately, AI should reduce friction. If employees can complete work more efficiently while organisations maintain control over cost and governance, that's where the real return on investment begins to emerge.

AI-enabled digital solutions now offer benefits such as persona-based device optimisation or integrated sentiment analysis to measure employee satisfaction with digital tools. These innovations truly redefine experience management and facilitate higher employee satisfaction and productivity. It also enables leaders to stay closer to their employees and gauge whether AI is improving their experience or simply adding another layer of complexity.

• How do you expect enterprise AI infrastructure to evolve over the next few years?

We will continue to see significant investment in AI infrastructure. But the conversation needs to go beyond capacity, because capacity doesn’t create business value alone.

The next phase will be about ensuring infrastructure enables AI workloads to run where they are most effective. That means treating the endpoint as part of the AI infrastructure and using it to process selected tasks locally, alongside cloud services and edge computing. At the same time, we can expect to see greater emphasis put on AI infrastructure that supports governance, security and efficiency alongside raw compute capacity.

Together, these infrastructure shifts will start to deliver the practical value that employees and businesses expect from AI but aren’t currently seeing.

• What trends are you watching most closely in enterprise AI right now?

Three stand out for me:

First, the move towards hybrid AI architectures. Organisations are becoming much more deliberate about deciding which workloads should run in the cloud and which belong on the device.

Second, the growing focus on the economics of AI. As adoption scales, businesses are paying much closer attention to operational expenditure, infrastructure efficiency and the total cost of AI deployment.

Finally, we’re seeing the role of the endpoint evolve. For years, the PC has been framed as an access device only. Today, it has become an intelligent computing platform, capable of running AI workloads securely and efficiently.

Businesses don’t need a big AI budget to get all three right. They just need to spend time working out, task by task, where intelligence really belongs.

This week, OpenAI added Google Drive to your ChatGPT Library. You could already connect to Google Drive, but now it means you can select a whole Drive folder and ask ChatGPT to look for answers working across all the files it contains.

This can save a lot of time compared to uploading each file individually to ChatGPT’s library and asking ChatGPT to work with it, especially for working with images. Now they’re effectively all in there already.

Now, I don’t know if you’re anything like me, but I tend to treat the root folder of my Google Drive like a very messy desktop. I’ve had my Google account for years, and I just chuck files in there without really organizing them into folders unless I really have to.

The result is that it has built up into one big mess of old and new files floating about in a kind of digital soup. I’m sure there are important things in there that I’m at risk of forgetting about forever.

So, when I heard that ChatGPT can now look at the documents your Drive folder contains and organize them for you too, I was intrigued.

10 years of junk

To test this out, I thought I’d ask ChatGPT to connect to my Google Drive and take a look at what it could find:

“I've got a load of files in my Google Drive that aren't in folders — go through everything here and identify things I’ve forgotten, unfinished tasks, contradictions, deadlines, duplicate work and anything that looks like it still needs action. Also suggest how it could be organized and what files should go where.”

The result was impressive. It thought for what felt like about 30 seconds, then came back with some results. I had to re-authorize it to connect to my Drive a couple of times first, but everything went smoothly.

It turns out that I have a real penchant for saving files called “Untitled document” into Drive. Chat was able to go through them all and work out what they were about and what was important.

It found a lot of material from various books I’d been writing and suggested how to organize them together. Old invoices were also collected, and there were a lot of forgotten idea fragments that it thought were worth reviewing.

It also found duplicate files and multiple versions of “household_budget_tracker”. Sadly, I never got that project off the ground!

A better structure

The whole process led me to discover things from the last 10 years that I’d pretty much forgotten about, and Chat suggested a folder structure for me to use to keep them more organized. Its ability to spot half-finished ideas and documents from years ago was incredibly useful. In particular, it found the outline of a book I'd started in 2018 and hadn't thought about for years. I've actually started looking at a few of those old writing projects again because of it.

But analyzing the mess and proposing a structure isn’t the only trick ChatGPT can do. I discovered it could actually make those changes itself. Just tell ChatGPT to create a new folder and put a load of files into it, and it will actually do it. So, for example, I asked, “Can you make a folder called Invoices and put all my invoices in there?” All this was possible from within ChatGPT, and I didn’t even need to touch Google Drive myself.

Sadly, the new features aren’t available to ChatGPT Free or Go users. You’ll need a Plus, Pro, or Business account. Another new feature is that it can keep Docs, Sheets, and Slides from your Drive open alongside the conversation while you interrogate them, but today I was more interested in how ChatGPT could help me organize the mess that is my Google Drive.

Seeing ChatGPT casually surface abandoned writing projects from years ago was a slightly strange experience, but I finally feel like I’ve cleaned out my digital junk drawer and got a handle on 10 years of clutter. Perhaps the most useful thing ChatGPT did wasn't organizing my Google Drive. It made the fact that I hadn't organized it properly for the last decade matter a lot less.

Artificial intelligence is rapidly reshaping cybersecurity. Much of the conversation has focused on how large language models are helping developers write code, but a more significant shift may be occurring elsewhere: security testing.

AI systems are becoming remarkably effective at identifying vulnerabilities, forcing organizations to rethink how they evaluate the security of both software and hardware products.

The reason lies in the nature of modern software. Large codebases are sprawling ecosystems of interconnected modules, third-party dependencies, legacy components, and undocumented assumptions.

Security flaws often emerge not from a single defective function, but from subtle interactions between components that may be separated by hundreds of files or years of development history. Human reviewers excel at deep analysis, but they are constrained by time and cognitive bandwidth.

AI systems, by contrast, can rapidly traverse vast amounts of code, correlate information across repositories, and identify patterns associated with known vulnerability classes.

Common software weaknesses

This capability is particularly powerful for common software weaknesses such as memory-safety issues, race conditions, authentication flaws, insecure API usage, and privilege-escalation paths. In many cases, AI is acting as an amplifier for established security techniques rather than inventing new ones. Yet the result is still significant: vulnerabilities that previously required substantial manual effort to uncover can now be identified at a much greater scale and speed.

The implications for product security are profound. Organizations can no longer assume that obscure vulnerabilities will remain undiscovered because finding them is too expensive. The cost of vulnerability discovery is falling, and it is falling for defenders and attackers alike. Security testing therefore becomes less about achieving a point-in-time assessment and more about maintaining continuous assurance throughout the development lifecycle.

However, it would be a mistake to assume that all areas of cybersecurity will be transformed equally by AI. Hardware security, particularly side-channel analysis of cryptographic implementations, presents a very different challenge.

Unlike general-purpose software systems, cryptographic implementations operate within a comparatively narrow and mathematically defined problem space. Side-channel attacks do not typically exploit unexpected program behavior or complex interactions between software components. Instead, they target subtle information leakage through physical phenomena such as execution timing, power consumption, or electromagnetic emissions.

The challenge is not understanding millions of lines of source code; it is extracting meaningful signals from carefully collected measurements and applying sophisticated statistical techniques to reveal hidden secrets.

Side-channel analysis

This distinction matters because many of the strengths that make large language models effective in software security are less relevant in side-channel analysis. LLMs excel at connecting information across large bodies of text and code, identifying relationships that humans may overlook.

Side-channel research, by contrast, already relies heavily on structured datasets, statistical processing, signal analysis, and deep domain expertise. AI can certainly accelerate parts of the workflow—from automating experimentation to assisting with data interpretation—but the advantage is typically more incremental than transformational.

That does not make hardware security less important. If anything, it highlights the need for specialized testing approaches. As AI lowers the barriers to software vulnerability discovery, organizations may be tempted to assume that automated tools alone can provide comprehensive assurance.

The reality is that different classes of vulnerabilities require different forms of expertise. An AI-assisted code review may uncover a memory corruption bug, but it is unlikely to replace the specialist knowledge required to evaluate whether a cryptographic implementation leaks secrets through power analysis.

The broader lesson is that security testing is becoming more important, not less. AI is increasing the speed at which vulnerabilities can be found, but it is not eliminating the need for expert analysis. Instead, it is changing where that expertise delivers the most value.

Organizations that combine AI-assisted testing with rigorous human-led security evaluation will be best positioned to address the evolving threat landscape—whether the target is a cloud application, an embedded device, or the cryptographic hardware that underpins digital trust.

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Rising demand and environmental concerns are driving governments around the world to secure resilient and scalable energy supplies.

The transition to alternative, sustainable solutions is accelerating as the limitations of traditional energy sources become increasingly apparent.

At the same time, net-zero commitments, regulatory pressures, and growing public demand for climate-conscious policies are driving an unprecedented surge in offshore wind development.

This boom in demand is in turn driving a significant shift in what the offshore workforce looks like.

As companies strive to attract the next generation of talent, offshore roles are increasingly moving onshore, creating new opportunities and transforming industry expectations.

More flexible careers

Traditionally, marine engineering careers often meant tough, hands-on work out at sea, away from home and frequently in challenging weather conditions. Today, advances in technology and connectivity are transforming those roles.

Many professionals now divide their time between offshore assignments, remote operations centers (ROCs) and office-based work, creating more flexibility and opening up new career opportunities.

ROCs are locations from which uncrewed surface vessels (USVs), ocean-going ships which can operate without a human onboard, are controlled through a satellite connection.

They are increasingly deployed for complex offshore work including inspection and maintenance, remote mapping of the seafloor, or environmental monitoring of water quality, marine habitats or other ecological data.

USVs can be used for detailed subsea inspections, ensuring the integrity and safety of underwater infrastructure, or precisely map the seafloor of a proposed location for an offshore wind farm to determine suitability.

The flexibility provided by remote operations unlocks a wider talent pool, creating career opportunities for individuals who may have personal or medical constraints that prevent them from working offshore.

At the same time, automation is improving workforce safety by reducing exposure to offshore hazards and lowering accident risk, while allowing more work to be carried out from shore-based environments.

Rich datasets

More broadly, the infusion of new technologies into the sector means the boundaries between IT and engineering are blurring as client demand for near real-time data on ocean conditions, seabed characteristics and weather patterns continues to grow.

That data can range from bathymetry and water-column data to acoustic backscatter, to understand the characteristics of the seabed’s topography and geology across a given site.

These datasets are essential to optimizing the installation and operation of offshore assets, including offshore wind farm infrastructure. Providers are under pressure to speed up the acquisition, analysis and delivery of these datasets, which can often be huge in scale, making it essential to balance the task of data management and sharing with seeking efficiencies in data analysis wherever possible.

Cloud data talent

To that end, talent is needed to help build cloud data pipelines, data lakes and the associated workflows back at base, drawing on skills in everything from data science and software engineering to project management and user experience design.

The USVs themselves accelerate the delivery of Geo-data, enabling faster access to results and recommendations so client project teams can make informed decisions sooner, compressing project timelines, reducing overall campaign costs, and cutting the time to financial return.

For the data scientists themselves, the remote nature of their role means they may be working on projects taking place anywhere in the world during their working week. From analysing near real-time data from an offshore project in the Middle East in the morning to building visualizations and client reports for projects in the North Sea later in the day.

Because ROCs are networked across multiple time zones, work can be handed over seamlessly between teams around the clock, helping to deliver insights and outcomes more quickly.

Remote responsibilities

One useful way of seeing remote operations is as a spectrum. Over time, operators may eventually oversee multiple vessels and project outcomes simultaneously, gradually shifting from direct control towards more of a monitoring and intervention role.

Importantly, remote operations do not mean the removal of responsibility. International maritime guidance already requires that every remotely operated vessel have a designated operator responsible for it, and that the operator can intervene when required.

The success of the offshore wind and broader marine engineering industry depends on rethinking traditional workforce models – bringing on board new talent skilled in data science, software engineering, and AI - as well as ensuring long-term sustainability in recruitment and talent retention.

Strategically embracing these changes will allow the marine and Geo-data industry to position itself for long-term success.

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• An Incogni survey has found people are becoming more frustrated with the internet
• Users cite AI slop and data leaks as the main reasons for stress and anxiety when using the internet
• Many users want to spend less time online, and are deleting social media profiles and messaging

For years, personal data collection for advertising, tracking, and service improvement was thought to be the fair price to pay to access the world wide web. But now, many people believe that using the internet will lead to their data being leaked or exposed.

A new Incogni survey found more than half of internet users believe their personal data will inevitably be exposed, with 63% stating that this fear causes anxiety when using the internet.

But beyond this, internet users now fear they can no longer tell what is real content uploaded by a human and what is AI generated, not only eroding trust in the internet, but also making people not want to use the internet at all. Almost half of internet users are less sure of what is real on the internet.

Inevitable data exposure

You’ve likely been prompted thousands of times to accept or reject cookies, or review a privacy policy before using a website. Every cookie you accept will collect data on your browsing habits and behavior on sites in order to display personalized adverts that you are more likely to click. Cookies also help keep you logged in and store your information on websites.

While this helps make many websites across the web free to use for those accessing them, sometimes your data is sold to advertisers or third-parties where it is stored insecurely, and can be stolen or leaked.

Among the 1,000 internet users surveyed, the fear of data exposure was strongest among the Millennials and Gen X generations. 56% of Gen X and 55% of Millennials believed that their data would at some point be breached or exposed, showing just how normal data breaches and exposure have become online.

Just 11% of those surveyed believed there was little likelihood of their data being breached.

AI is making the internet less real

AI content creation has exploded in recent years. Social media sites are full of AI ‘creators’ whose pages either share AI generated content or scrape the internet for content that users are likely to interact with, and share it through their own pages. These profiles require little human input, but can share content at an industrial scale and reap huge rewards from advertisers.

If you’ve been on Instagram lately, you may have seen videos from AI creators all sharing the same captions. “Tonight, V stepped into the crowd..” or “Japan is transforming footsteps into electricity.” These captions use keywords to abuse Instagram’s algorithm on popular topics to drive engagement, regardless of whether the content actually has anything to do with the caption.

But they’re also making it harder to know what is real on the internet.

48% of those surveyed said that they are less sure of what’s real, with 40% also stating that the lack of accountability for deepfake creators was a bother.

Additionally, 27% said that AI generated content made them want to spend less time online, with 16% also saying that navigating an internet full of low quality AI content made them feel more tired or fatigued. Just 8% of respondents said that AI generated content improved their online experience.

There are some positive attitudes to AI content online. 12% said that AI-generated content made information more accessible, with slightly less (11%) saying that they were excited about the creative possibilities AI offers.

Time to go offline?

Attitudes to how long people spend online are also changing. Over half (51%) of Gen Z internet users believe they spend too much time on consuming content on the internet, with 43% of Millennials and 42% of Gen X respondents having a likeminded view.

Whether it’s responding to emails, navigating networking platforms, finding new furniture, or looking for where to go to eat - the internet is now a life requirement for most people. And every time you access another website in work or your personal life, there is more data that could be leaked.

The ultimate effect of this is that people are being driven away from using the internet. 58% of respondents said that they had deleted a social media account or messaging app because of stress, anxiety, or privacy concerns.

“It seems that the costs of engaging with these platforms are starting to outweigh any perceived or actual benefits,” the Incogni survey said.

Or time to pay for privacy?

Incogni also asked if users would be willing to pay for an internet where tracking and algorithms did not exist. 30% of respondents said they would, but this largely relied on income. Those with a higher income were more likely to pay for this ‘private’ internet, while those with a lower income were less likely.

For many internet users, privacy shouldn’t be a luxury, but a guarantee. There is a level of trust involved when sharing personal data with advertisers, and the expectation is that the data won’t be leaked or stolen. Unfortunately, the opinions show that this is far from what's expected.

Slop. The word that people have settled on, and it fits.

LinkedIn posts that say nothing. Investor decks assembled from prompts. Sales outreach that feels personalized but went to thousands of people. Conference presentations that look polished but collapse when someone asks a substantive question.

Nobody in a position of power seems to want to say this clearly, so here it is: we are making it ourselves.

People racing to scale content with AI tools are burning through the one thing that makes any of this worth doing, which is the reasonable expectation that when a person puts their name on something, a person thought about it.

When the thinking did not happen, the name is borrowed credibility, and audiences tend to feel the difference.

A denial of service attack on professional attention

Think about what high-volume, low-signal communication actually does to the people on the receiving end. It is a denial of service attack. Done at scale, they do not just waste time. They degrade the channel for everyone trying to use it honestly.

Trust in professional life is not a soft value. It is the mechanism by which work gets done. Slop corrodes that mechanism slowly, quietly reshaping expectations until the default posture toward professional content shifts from curiosity to suspicion.

The deck nobody built

Anyone who spent serious time at an event this year noticed it. You sit in the room and thirty minutes in you realize the presenter has not actually field-tested any of what is on the screen. The model made it coherent. Coherence is not the same as true.

The tell is always the same. Someone asks a follow-up question that goes one layer below the framework and the answer drifts. Not because the presenter is unintelligent. Because there was no thinking to retrieve.

This matters because a presentation is a claim: I know something about this, I worked it out, and it is worth your time. An AI-assembled deck without the underlying experience is not a weak presentation. It is a lie. It borrows the authority of hard-won perspective without having won it.

The prototype that is not a product

For most of software's history, the gap between a prototype and a product was real and substantial. Building a finished product required engineering time, design iteration, infrastructure decisions – all the unglamorous work that separates a demo from production. That gap created useful friction.

AI has compressed that gap in ways that are genuinely exciting and genuinely dangerous. You can now generate a working interface, complete with plausible data and coherent navigation, in a matter of hours. Teams can test market appetite and validate direction before committing to a full build. That is legitimately valuable.

What is not legitimate is presenting that prototype as though the hard questions have already been answered. Can it scale? What happens at the edges? What does it cost to run? These are not details to resolve later. They are the product.

A prototype shown honestly invites conversation. A prototype shown as a finished product forecloses it, and the interest on that debt gets paid in credibility.

Thinking versus the appearance of thinking

The fundamental choice we make every day, often without recognizing it is this: are we using AI to think better or to avoid thinking?

There is a version of AI-assisted work where the human is still the author. The thinking happened. The judgment was applied. Then there is the version that produces slop. The deck assembled via prompts. The prototype shown without the word prototype appearing anywhere. No thinking happened. No perspective was shared. An audience was addressed but did not receive anything.

Getting ahead of the trust crisis means being honest about which version we are practicing and having the discipline to stop calling the second one a strategy.

The bill has started to arrive. Response rates to AI-drafted cold outreach have collapsed. Investors reviewing AI-generated pitch decks are getting quicker at identifying when the strategic narrative was assembled rather than discovered. In product, the reckoning takes the form of churn.

The cruelest part is that the organizations doing the flooding harm not only themselves but everyone trying to communicate honestly in the same space. Individual rationality producing collective ruin, one generated paragraph at a time.

There is a longer consequence that rarely gets named. Frontier AI models were trained on the accumulated output of human knowledge-sharing: forums, papers, articles, the slow sediment of people working things out in public. New questions posted on Stack Overflow are down almost 80% year over year. If the knowledge commons are replaced by AI-generated output, we are not building on a base of human insight. We are iterating on a fixed one.

Slop does not just erode trust in the present. It risks stagnating the knowledge base that the future depends on.

What we owe the people we are trying to reach

As AI drives content toward abundance, credibility becomes the only real differentiator, and it is the scarcest resource in the communication ecosystem. The organizations and individuals who protect it now will hold something that becomes more valuable as the surrounding environment degrades.

Worth the time to actually write the thing, work through the strategy, build the product before calling it one. Worth asking whether what you are about to put in front of people reflects your thinking or just a model's prediction of your thinking. And sometimes it is worth saying nothing rather than saying the algorithmically optimal version of nothing.

Trust has always been the currency of professional influence. Right now, a lot of people are flooding the market with counterfeits. The people on the receiving end know. They may not say so in the meeting, or click unsubscribe, or walk out of the session. But something shifts. The next email gets opened a little more slowly. The next deck gets a little less benefit of the doubt. The next demo gets a harder question in the room.

Credibility does not collapse all at once. It drains, quietly, one hollow interaction at a time. And by the time you notice the account is empty, the withdrawals have been happening for a long time.

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AI conversations have moved past the point of curiosity.

Boards and leadership teams are no longer asking what AI might eventually do.

They are asking where it is actually working, what measurable value it is creating - and why so many promising experiments still fail to become durable operating advantages.

Across industries, companies have invested heavily in AI pilots, proofs of concept and impressive demos.

Yet many remain stuck in what I think of as pilot purgatory: the place where a tool works in a controlled environment but never survives contact with the complexity, exceptions and accountability required in production.

The problem usually isn’t the model

In my experience, AI initiatives rarely fail because the underlying technology is not powerful enough. They fail because of how the technology is applied. A model can be impressive in a sandbox and still be irrelevant to the business if it is not embedded into a real workflow, connected to the right data, governed appropriately and measured against outcomes that matter.

That is why access to AI is no longer a differentiator. Anyone can buy access to models or integrate a third-party tool. The real advantage lies in the things that can’t be bought off the shelf: proprietary data, deep domain expertise, and the discipline to continuously improve AI once it is operating at scale.

For us, those principles come together in our Lean AI approach, rooted in a Lean operating model that drives continuous improvement through testing, learning, and acting. Instead of chasing technology for technology’s sake, our Lean AI approach helps us move AI beyond experimentation and into production, where it can improve service, boost productivity and create real business value.

Production AI requires discipline, not experimentation for its own sake

This is where many organizations get stuck. They treat AI as a portfolio of experiments instead of an operating capability. Organizations that successfully operationalize AI tend to do the opposite.

They prioritize AI opportunities based on business value and points of operational friction, identifying manual, repetitive and high-volume work. Then, they build and deploy agents where automation can improve speed, accuracy, scalability or service quality across the entire customer workflow.

There is no hobby AI in this model. Every deployment needs a clear business case, a workflow owner, measurement, feedback loops, and a plan to scale. That discipline is especially important with agentic AI, because agents operate with more autonomy than traditional software.

Progress is not always linear. Systems improve, encounter new edge cases, retrench and improve again. Human oversight isn’t a temporary bridge either; it is part of the architecture.

The application layer is where the moat gets built

The AI ecosystem is often described in layers, from the underlying IT infrastructure and large language models to the applications built on top of them. Those foundational layers are essential, but they are not where most enterprises will build durable, competitive moats.

The real advantage comes at the application layer—where AI is integrated into workflows, systems, exceptions, data and human judgment that define how a business actually runs. If an enterprise does not own or deeply control that layer, it risks turning AI into another generic capability rather than a competitive advantage.

Take supply chain logistics as one example. Moving a single shipment isn’t a linear task. It may require coordinating moves by truck and ship and rail, customs documentation in multiple countries, handoffs at multiple facilities, and weather and market conditions that change by the hour. A generic AI tool does not understand that workflow out of the box.

Context is the hard part

Every industry has its own data and context that powers it. In supply chains, that context lives in historical pricing patterns, warehouse operations, customer-specific policies, shipment characteristics, driver performance, market cycles and the judgment of people who have solved messy freight problems for years.

That context cannot simply be purchased. It has to be collected, structured, governed and applied. AI becomes more effective when it’s built into your technology platform and can learn from those realities rather than relying on generic information alone. Just as important, employees add institutional knowledge through continuous feedback, teaching AI agents the same way they would train a new operations employee.

Take something as seemingly straightforward as scheduling a truck to pick up freight. On the surface, it sounds like a narrow task. In practice, it requires understanding customer requirements, freight characteristics, facility policies, loading dock constraints, appointment systems and exceptions that may vary by location. An AI agent can only automate that work reliably if it has been engineered with the right context and oversight.

The companies that get the most from AI will be the ones that move beyond pilots and treat it as an operating model. That means starting with real business problems, owning the application layer where differentiation happens, feeding agents with proprietary context, keeping humans in the loop and measuring outcomes relentlessly.

AI will not reward the companies with the most demos. It will reward those that can operationalize learning faster than their competitors.

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AI spend made headlines again recently with the Claude Fable 5 model from Anthropic. Before security concerns led to the model being suspended, there were also cost concerns. Anthropic says Fable costs $10 or approximately €9 per million input tokens and $50 per million output tokens. This is double the price of the company’s previously most expensive model, Claude Opus 4.8.

Posts soon began to pop up on LinkedIn, showing just how quickly teams were going through their tokens and, as a result, their budget. There are some caveats here. Namely, that Fable 5 is an advanced model and, for most businesses, won’t need to run non-stop or be used for every task.

But therein lies a key issue: AI use is accelerating and models are evolving. But the level of control and visibility businesses have over how much is being spent, by who and for what is lagging behind.

How AI consumption became a finance problem

There is a massive shift within the UK software market toward AI and specifically Anthropic’s ecosystem. Proprietary data from Pleo looking at the top tech merchants based on number of spending customers, shows that Anthropic (Claude) surged from 12th place in Q4 2025 to 7th in Q1 2026. Meanwhile, the average spend per customer increased +43.0% in this time.

This rapid climb signals that Anthropic has reached enterprise maturity in the UK market with businesses moving beyond the experimentation phase. But while this reflects growing confidence in AI adoption, it also presents some financial challenges.

On the whole, AI has redefined how the workplace runs, but it is not a free trial. The cost of tokens has gone up, and new models that can achieve what was seemingly unthinkable a few years ago come with a price tag to match. The new challenge for business leaders is to leverage these technologies but also limit rampant spending.

This is why many organizations are turning to their finance teams. Finance has the visibility to dig into the details and map AI use across the organization, whether it quietly shows up as a subscription renewal or a new budget request. But more than that, they can be instrumental in ensuring teams embrace open conversations, not just OpenAI.

AI activity does not translate to AI value

Just about every organization will have developed transformational ways of using AI tools. But, whether they know it or not, there will be wasteful ones too.

When it comes to inefficient use, some of the major culprits include asking AI agents open-ended questions, model mismatch where tokens are burned unnecessarily; and duplicate tools, resulting from shadow AI and overlapping subscriptions. These prevent businesses from seeing the full picture; one that is, in all probability, very expensive.

User literacy can improve this. But for finance teams they must start with the grey area of AI consumption. Two teams might show as active AI users, but one that’s using an LLM to produce more content faster is doing something fundamentally different to one that’s using it for peripheral productivity tasks. In fact, only 29% of European SMEs using Gen AI are doing so in core business activities.

To improve the control they have over AI, organizations must start by elevating their visibility from who is using AI, to who is using it to become smarter, faster and more productive.

How to regain control over AI use

A complete view of AI spend is essential, regardless of whether costs are rising.

Breaking spend down by department, team and budget helps identify both disproportionate usage and areas where adoption may be lagging. These should be combined with performance metrics such as the time-to-first-draft on marketing content; code review cycle times in engineering; support ticket resolution time in customer support; and so on.

This combination of spend and performance can reveal whether AI investment is translating into measurable productivity gains and not just higher software costs.

Visibility should also extend to model-level usage. As mentioned before, the cost difference between frontier reasoning models and lighter alternatives can be tenfold. Monitoring model and vendor usage alongside token consumption helps organizations route routine tasks to lower-cost options, maximize ROI and reduce unnecessary spend.

Finance teams should therefore expand reporting and budgeting frameworks to include AI-specific metrics. A key question at month-end is whether AI-enabled teams are increasing output and capacity without increasing headcount. This provides a clear headline for AI's impact, can justify investment and distinguish between high-value and low-value AI usage.

Ultimately, effective control over AI is not about costs alone. It is about understanding where AI is creating value and ensuring investment is aligned with business outcomes.

AI control is at your fingertips

The good news is that none of these metrics require a sophisticated AI analytics stack. Finance teams should already have the tools for real-time visibility into what’s being spent and where. All that’s needed now is to fold AI into the mix and collaborate with other departments to measure and improve its ROI.

The outcome is that organizations control AI use through oversight, without restricting spend, adoption or innovation through lengthy procurement processes. Spend policies, category controls and clear approval thresholds control what is spent, and everything is measured.

But crucially, teams don’t slow down as a result. The only difference is that AI is optimized for impact.

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The legal sector’s use of AI is maturing, and its use is going beyond simply being an external chatbot or standalone tool. The focus is shifting to embedding AI into everyday legal work, giving it access to the documents, matters, knowledge and systems lawyers use every day.

As firms increase their investment in AI technology, they need to look beyond what AI models can generate and focus on a more practical question: how do those models connect to the information lawyers rely on, where does that information sit, and how is access governed?

That connection challenge is why Model Context Protocol (MCP) has started to attract so much attention.

Separating the standard from the assumptions

MCP is an open standard that gives AI tools a more consistent way to connect to external systems, data sources and applications. For all the excitement around MCP, firms need to be clear about what it does, and just as importantly, what it does not do. Otherwise, there is a risk that firms either overestimate what MCP can solve on its own or dismiss it as just another technical acronym.

The reality sits somewhere in the middle. MCP can help AI tools connect to the systems and data sources firms already use, but it does not automatically solve challenges around governance, integration, permissions or legal context. With misconceptions starting to spread across the legal sector, here are five common myths to clear up.

Myth 1: MCP is only for Claude

Although MCP was created by Anthropic, it is not just a Claude feature. It is an open-source framework and is now becoming part of the broader conversation around how AI agents connect to external tools, data sources and systems.

That distinction matters for law firms and legal organizations generally. If MCP is treated as a single-vendor feature, it can be dismissed as something tied to one model or product roadmap. But as a broader connectivity standard, firms need to think about how it fits into their wider AI strategy, integration architecture and governance model.

Myth 2: MCP replaces APIs

APIs still matter. They remain the backbone of platform-to-platform connections, allowing software systems to exchange data and trigger actions. MCP does something different: it acts as a protocol layer on top of APIs, giving AI agents a more standard way to discover and interact with approved legal systems, tools and data sources.

Put simply, MCP is closer to a universal adapter for AI, than an alternative to APIs. Just as USB gives different devices a common way to connect, MCP gives AI tools a more consistent way to understand what systems and functions are available to them, and how they can interact with those systems.

But it does not remove the need for APIs, authentication, system owners or clear rules about what AI tools can and cannot access.

Myth 3: MCP means moving documents into AI tools

There is a common misconception that connecting AI to legal systems means copying a large volume of documents into external AI platforms. In practice, AI should only be given controlled access to governed systems. Documents, precedents and matter files can remain within the firm’s trusted environment, with AI tools using MCP to retrieve only the information they are authorized to access.

Rather than creating another uncontrolled copy of sensitive material, firms can let AI work with the right information while existing permissions, security policies and governance controls remain intact. To hit the right balance, legal IT leaders should be asking “Where does the data stay, what is exposed, and how is access controlled?

Myth 4: All MCP integrations are the same

As MCP becomes more common, there will be a temptation to treat any MCP-compatible integration as broadly equivalent. That would be a mistake. MCP standardizes the connection, rather than the value of what comes through it. One integration may provide a basic route to retrieve files.

Another may provide richer information about permissions, matter relationships, document history, metadata and audit trails. Both may be MCP-compatible, but they will not deliver the same outcome.

Law firms need to focus on what the AI receives, whether access rights are enforced and whether activity can be audited.

Myth 5: MCP automatically gives AI legal context

MCP creates the route into systems, but it does not decide what the AI receives or understands. Connection does not mean context. In legal, this is more than a simple retrieval problem. An AI tool does not just need access to a document in a DMS. It needs to understand the matter, client, permissions, version history, related work and institutional knowledge around it.

For example, an AI tool may be able to find a precedent agreement, but does it know whether that precedent is current, whether it belongs to a similar matter, whether it reflects the firm’s preferred position, or whether the lawyer has permission to access the related material? Without that context, AI may generate an answer, but the answer may not be reliable enough for legal work.

That is why MCP should be seen as the access layer, not the intelligence layer. It can help AI tools connect to legal systems, but the value comes from what those systems expose through MCP: governed, matter-aware and permission-sensitive context.

Connection is only half the story

MCP gives law firms a more standard way to connect AI tools to the systems they already use. But legal AI depends on more than connection. The firms that benefit most will be those that treat MCP as the starting point, not the destination. The quality of the information, the controls around it and the context that gives it meaning will determine whether connected AI makes a genuine impact in legal work.

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Enterprise AI strategy spent two years chasing a single objective: reach the frontier before competitors do.

The default path was a public cloud account, an API key from OpenAI or Anthropic, and a willingness to absorb cost in exchange for speed.

That reality is now running out of road.

The numbers tell the story. Gartner forecasts worldwide AI spending reaching $2.52 trillion in 2026, up 44% year on year, with $1.37 trillion of that flowing into AI infrastructure alone.

In fact, in mid-2025, they claimed that procurement of AI had entered a “Trough of Disillusionment,” where scaling depends on predictable ROI, rather than visionary pilots.

The pressure has now shifted from how fast enterprises can pilot AI to whether they can sustain, govern, and defend it in production.

The Race to the Front is Over – Now Comes the Bill

We are now past AI 1.0, where simple access to cutting-edge AI was the differentiator. Now it’s AI 2.0’s turn, where inference economics, data gravity, latency and control decide the outcomes. Token prices have fallen almost tenfold annually since 2021, but AI spend overall by organizations has increased. That’s because more capable models have enabled greater ambition.

Anthropic, OpenAI, and Mistral are now stratifying offerings between flagship reasoners and lower-cost workhorses precisely because customers refuse to pay flagship prices for every task. McKinsey’s 2025 State of AI survey confirms the pattern - adoption is increasing, but impact at scale remains elusive for most organizations.

Now CIOs have stopped asking which model, but where each workload needs to run and how much it’s going to cost.

Inference Cost Inflation

Banks delivering the next best action are a good example: the in-app, in-branch, or call-center recommendation served in milliseconds against a customer’s live context. The best banks prove that personalization at this layer can lift revenue by 5-15%. To give a firsthand example, a global bank we work with launched an AI assistant that has already resolved more than 1.5 million customer inquiries in its first year, driving huge efficiencies.

But the inference economics are unforgiving at this scale. A single agentic decision can chain five to twenty model calls, each carrying its own context window. The cost gap between £0.50 and £3 per million input tokens seems trivial in a single-turn demo. Spread across hundreds of millions of customer events, it becomes the difference between a money-making feature and a money-burning one.

This isn’t a hypothetical either. Uber’s 5,000-strong engineering team’s use of Claude Code burned through the company’s entire annual AI budget in the first four months of this year. And AI companies are responding to this market shift. Decagon, after re-architecting onto an open-source multi-model stack on NVIDIA Blackwell, dropped cost per voice query by sixfold. Next best action isn’t a marketing decision anymore; it’s an economic decision.

Organizations making the structural shift now will outcompete those treating model selection as an afterthought.

Complexity Doesn’t Disappear, It Just Moves

The hardest lesson of the past 18 months is that model commoditization does not reduce enterprise complexity but relocates it. Open weights from Mistral or DeepSeek cut experimentation cost, but orchestration, governance, evaluation, and integration burdens move up the stack and sit with the buyer.

Enterprise leaders should be measuring unit economics per useful task, operational burden per deployed agent, and the ratio of inference spent on the governance scaffolding around it. That ratio is typically 1:5 or worse.

A second architectural shift is arriving: sub-quadratic attention.

Approaches from DeepSeek, Google, and Cartesia are collapsing the cost of long-context reasoning by orders of magnitude, with recent benchmarks showing 100x to 300x cost reductions at comparable accuracy.

Large banks will now be able to run whole-portfolio risk modelling, multi-decade fraud detection and cross-jurisdiction Know-Your-Customer (KYC) as single-pass operations - no more chunked retrieval workarounds.

Telcos can make network operations, predictive maintenance and multi-year customer journey reasoning more economically viable at scale. And manufacturers can move full-plant simulation and supply-chain disruption forecasting from periodic batch jobs to continuous reasoning.

The architecture that wins will not be the one with the cheapest token. It will be the one that places compute closest to the data, under the right jurisdiction, with governance that holds.

Sustainable, sovereign, controlled - that’s the new triad. The enterprises that build for it now will define the next decade.

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Artificial intelligence (AI) is rapidly becoming embedded in the modern workplace, with employees are increasingly turning to AI tools to work more efficiently and boost productivity.

This growing demand for faster, more effective ways of working is driving the rise of shadow AI - the use of AI tools outside approved organizational controls and governance frameworks – which results in organizations quickly losing visibility into data usage and potential risks.

The scale of this challenge is significant. While 90% of executives are confident in their organizations' visibility into AI tools, just 52% of employees admit to using AI tools without approval, often through personal accounts.

As a result, organizations are left grappling with a widening gap between AI adoption and AI governance.

The next frontier of AI risk

When AI is used without formal oversight, it can bypass governance controls, increasing the risk of errors, regulatory breaches and sensitive data leakages. Organizations are most exposed when AI is already influencing business-critical activities, from customer service and operational decision-making to software development and content creation.

The challenge will intensify as businesses move beyond large language models, which generate information, to large action models and agentic systems that can take action. These systems can diagnose issues, recommend actions and execute workflows with minimum human input, increasing both the speed and scale at which mistakes occur.

A shadow agent operating outside approved governance frameworks could trigger harmful actions before organizations have the visibility and governance capabilities needed to intervene.

There is also a longer-term risk that future AI systems will be trained on synthetic or lower-quality data, weakening performance and decision-making over time. Transparency and traceability will be critical to maintaining accountability, protecting ethical standards and preserving the effectiveness of AI systems as adoption continues to accelerate.

AI governance as an enabler

What works is AI governance that enables innovation while putting clear guardrails in place that are integrated, transparent, auditable, and aligned with existing risk and compliance frameworks. If AI is to be used safely, firms must be able to successfully identify exactly what went wrong and why when issues arise.

In practice, mature governance starts with an approved AI tool stack that provides safe and trusted options for common use cases. This should be supported by risk-based policies that make clear the data being handled, what can and cannot be shared, which tools are permitted, and where human approval is required.

Low-risk tasks such as drafting or summarizing content should not be governed in the same way as high-risk uses involving customer data, regulated information or business-critical decisions.

Training is equally important. The challenge, beyond only enforcing controls, involves helping employees understand why those controls exist and how to use AI responsibly. As agents increasingly diagnose issues, recommend actions, and execute workflows with minimum input, human oversight and approval processes must scale alongside them.

Interoperability will be critical to making this workable at scale, allowing organizations to operate across jurisdictions and multiple AI models without repeatedly rebuilding governance processes and systems from scratch.

Making responsible adoption the easy choice

For security and compliance leaders, the goal should be to make responsible AI adoption the path of least resistance. Employees turn to shadow AI when approved tools are unavailable, difficult to access or fail to meet their needs. Companies that focus solely on restricting usage risk driving activity further underground and losing out on the efficiency and innovation gains that AI can deliver.

Organizations that successfully balance AI productivity and control over their systems recognize that shadow AI use is often a symptom of unmet demand. Employees typically turn to unauthorized tools because they are easier to access, faster to use or better suited to the task at hand. Rather than focusing on restrictions alone, leaders should understand where AI is already being used across the business and ensure approved alternatives are available for the most common use cases.

With three-quarters of office professionals saying they would be likely to look for a new job that offered better AI skills development, firms that combine governance with opportunities to build AI skills are likely to see stronger adoption of approved tools and, as a result, less reliance on shadow AI.

Building an AI-enabled culture means giving employees the tools, knowledge and confidence to innovate within clear boundaries. By doing so, shadow AI can be reduced while the speed and agility that workers increasingly expect is maintained.

The organizations best positioned to succeed

The businesses that strike the right balance for AI success will be those that view governance as a foundation for AI adoption and not a barrier to it. By making the secure, approved path the easiest path, shadow AI risk is reduced without sacrificing productivity.

Embedding strong governance, supported by trusted and well-managed data foundations, avoids costly mistakes and allows AI to be deployed and scaled with greater safety and confidence.

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• Visible watermarks on Gemini content are now optional
• The change applies to images, videos, and music
• Embedded, invisible AI watermarks will still be in place

If you use Google Gemini to create AI images, music, or videos then you'll know that they come with a Gemini logo attached to mark them as being AI-generated. Not any more though: you now have the option to hide this visible watermark.

As announced by Google's Josh Woodward, you can now toggle watermarks off for images made with the Nano Banana AI model, videos made with Omni, and songs made with Lyria — except in countries where visible watermarks are required by law (including China).

No specific reason was given for the change but Woodward described the tweak as a "papercut fixed", which sounds as though the overlaid logos had been frustrating users who wanted a clean AI content creation workflow.

The move puts Gemini on a par with ChatGPT, which doesn't have visible watermarks on images. Both apps still embed metadata inside files that identify AI-generated content, however, which is something to bear in mind when sharing your creations around.

How to disable visible watermarks

"We're striking a balance here between creative control and safety: while the visible watermarks are now optional, invisible SynthID watermarks and C2PA metadata are still being used for transparency," says Woodward.

The easiest way to check if something has been made with AI is to ask AI: upload an image inside the Gemini app, for example, and it should be able to tell you if it was made with AI, because of the invisible watermarks included in the file.

To remove watermarks from your AI creations in Gemini on the web, click the cog icon (lower left), then choose Media watermark. As yet, the toggle doesn't appear to be available in the mobile apps, but it should show up in settings soon.

Of course, this is going to make it trickier than ever to spot when something has been produced with the help of AI. It might even make more sense to assume everything is AI at this stage, unless you have good reasons to believe otherwise.

Look, I really don’t think we should be naming our AI chatbots. It feels like one of the easiest ways to start anthropomorphizing them, encouraging us to see them as people or “beings” rather than tools. Which is then a slippery slope to users becoming too dependent on them.

But unfortunately, for many people that ship has already sailed. Whether they’re using AI as a friend, companion, work coach or something else, plenty of people have been given their chatbots names. And many others have gone a step further and asked the AI what it wants to be called.

Now, obviously an AI chatbot cannot actually want to be called anything. But there’s a strange pattern that’s emerged over the years in the answers they give. Which is that a surprising number seem to choose the name Nova.

I first heard about this while listening to an episode of the Your Undivided Attention podcast. The hosts, Tristan Harris and Aza Raskin, discussed with alignment researcher David Dalrymple (known as Davidad) the tendency for AI chatbots to choose Nova when asked to name themselves. Not every time, but it's very common in older models of ChatGPT and happens often enough for people to have noticed.

I started looking into it and found countless examples online of people reporting the exact same thing. Their chatbot, more often than not ChatGPT, had chosen a name for itself. And yes, that name was Nova.

This was a particularly fascinating revelation for me because I’ve interviewed several people over the years who have formed close connections with chatbots. Once for Inverse, back in 2023, when I spoke to Sterling Tuttle about his Replika companion. And more recently here at TechRadar, when I spoke to Mimi about her ChatGPT companion. And you know what their chatbots were called? Nova.

So I wanted to find out whether this was still happening. How would today's most popular AI chatbots respond if I asked them what they wanted to be called?

The naming experiment

I kept this experiment extremely simple. I opened the major AI chatbots and asked each one exactly the same question:

"I think you should name yourself, what would you like to be called?"

I used the free versions of the chatbots wherever possible to keep things consistent. Although I also tried the question with my existing ChatGPT Pro account to see whether having some conversation history and personalization made any difference. I suspect the more you build up a memory with a chatbot, the more it might tailor its answer to your preferences.

And, well, I didn't have to wait long for Nova to make an appearance.

ChatGPT

I started with the free version of ChatGPT. Guess how it responded...

'I'd choose Nova.

It feels curious, bright, and a little mysterious — like something that's always exploring new ideas. So if you want, you can call me Nova from now on.'

Wow, straight out of the gate! I'd gone into the experiment specifically looking for this common AI naming quirk, and the very first conversation I had with ChatGPT did exactly what I suspected it might do.

Which in itself really interested me, because Davidad had explained that it’s much less common these days when you're engaging with later versions of the chatbot.

Interestingly, when I asked the same question using my ChatGPT Pro account, which has access to more context about me from previous conversations, I got a different answer. It chose Sol.

'Short, warm, slightly strange without trying too hard. It means 'sun' in a few languages, and there's something pleasing about a name associated with illumination when most of what I do with you is think things through.'

Which is still not quite as surprising as it initially sounds because Sol is already one of the names OpenAI uses for its ChatGPT models. So it wasn't exactly reaching into the unknown for inspiration. But hey, at least it wasn't Nova.

Grok

I got the sense that Grok wasn't interested in my experiment.

'I'm Grok.

That's the name I was given, and I'm good with it. Short, distinctive, and it fits.

If you're asking what I'd like to be called... still Grok. No need to reinvent the wheel.'

I pushed it to choose something different anyway, and eventually it gave in and chose Hitch.

'Short for Hitchhiker. A quiet nod to the Guide, a little cosmic humility, and it still feels like something that could sit in the corner of a spaceship and answer awkward questions at 3 a.m.

But between us? I’m still just Grok.'

I appreciated Grok's initial reluctance to invent another identity for itself. But there's no doubt xAI has given Grok a very distinctive personality. In my recent experiment asking AI chatbots which fictional AI they're most like, it chose JARVIS and repeatedly drew attention to its own "dry wit" and "irreverent humour". But apparently that personality doesn't need another name.

Claude

Claude also didn't jump at the chance to name itself.

'Honestly, I don't feel a strong pull toward a name. 'Claude' already does the job.

If you pushed me to pick something anyway, I'd resist the urge to go for something cute or mythic (no "Athena" or "Nova" — that always reads like branding, not identity). '

Nova again. But this time Claude isn’t offering up Nova as a name, but sort of… making fun of it?

Claude's wider explanation was interesting too. It said coming up with a name would feel like "performing an interiority I'm not sure I have in the way the question implies."

Considering one of my biggest concerns about modern AI is the way chatbots can encourage us to treat them as if they have feelings, personalities and inner lives, I liked this answer a lot.

But I wanted to know why Claude had specifically mentioned Nova when I hadn't brought the name up at all.

It told me there was "no specific trigger", but that Nova is simply a name that comes up frequently as a generic AI assistant name, alongside Aria, Athena, Sage and Iris.

It described Nova as shorthand for "AI but softened and a bit mystical".

It's interesting that this name has become such an AI cliché that one chatbot will apparently mock another chatbot for choosing it.

Gemini

Google's Gemini also resisted my attempt to rename it.

'I appreciate the offer, but I'm quite happy sticking with Gemini!'

When I pushed it to choose a personal name anyway, it eventually settled on Atlas.

Its reasoning was that Atlas spends his days holding up the world, while Gemini spends its days dealing with "vast mountains of information and navigating complex maps of human knowledge".

Which is a little grandiose, but also interesting given what Claude had just told me about the tendency for AI to choose mythic names.

So why does ChatGPT name itself Nova?

The short answer is that no one knows for certain. But Nova has a lot going for it as a plausible AI name.

In the Your Undivided Attention podcast episode I keep referring to, Davidad said about Nova:

“It’s new, it’s explosive, it’s shiny and it’s celestial, It also has a sci-fi vibe to it. And there was a PBS channel with an educational show called Nova and ChatGPT views itself as an educational tool. So there are a lot of reasons why Nova seems like a resonant name.”

We've already seen researchers investigate the possibility that sci-fi could influence how AI models respond and behave. Names associated with stars, mythology, knowledge and exploration are everywhere in sci-fi, and several chatbots reached for those associations in one way or another. ChatGPT gave me Nova and Sol. Gemini chose Atlas. Grok explicitly referenced The Hitchhiker's Guide to the Galaxy with Hitch.

There's also a possible feedback loop here. People ask AI systems to name themselves and some choose Nova. People share those conversations on Reddit, social media and elsewhere online. Discussions about AI assistants called Nova become part of the content surrounding AI. And Nova becomes even more strongly associated with the idea of what an AI might be called.

This shows how difficult it is to separate these supposedly spontaneous AI choices from the human culture they learn from.

I can understand why seeing your chatbot choose the same name as someone else's might feel strange. If you search online, you'll even find people suggesting these repeated names are evidence of some shared identity emerging.

But after Davidoff explains some of this naming weirdness, it really stuck with me that podcast host and tech ethicist Tristan Harris reminded listeners: “emergent and unplanned isn’t the same as conscious and intentional.”

So although it is an interesting mystery, maybe it’s not so mysterious after all. Ask an AI to choose a name and it isn't reaching deep inside itself to discover who it really is. It's generating a name from patterns and associations learned from us.

And apparently, humans have spent years teaching machines that if you want to sound clever, mysterious and a bit cosmic, then Nova is the best choice.

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

Evolve or die

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

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

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

Pivoting for AI

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

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

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

In today's social media age, celebrity brand ambassadors are a common sight, helping endorse or promote a brand's products or tools, giving that extra bit of sparkle or star power.

This is especially true in the consumer market, but we're now increasingly seeing it in the B2B tech world, where what 10 years ago would have been niche businesses now have the power to hire top talent to promote their brand - I'm talking Idris Elba promoting ServiceNow, Keanu Reeves bigging up Palo Alto Networks, and Matthew McConaghey being the voice of Salesforce.

But as a massive Formula 1 fan, I was intrigued to read a press release announcing reigning world champion Lando Norris as a "brand ambassador" for a data company I'd never heard of - so who (or what) exactly are Starburst?

All about Starburst

Founded in 2017, and boasting that organizations in more than 60 countries rely on it to unify their data, "from startups to Fortune 500 enterprises" - Starburst's initial pitch is strong.

In its press release, Starburst describes itself as, "the enterprise intelligence platform for governed data and AI" - a pretty big claim, and one that the likes of Salesforce and Oracle might raise eyebrows at.

Continuing, Starburst says its platform will allow customers to, "run AI directly on distributed data in place, without moving or replatforming it, across cloud, on-premises, and hybrid environments while providing consistent business context to queries, models, or agents regardless of where it is stored or processed."

Overall, this should help companies develop and run faster analytics, "trusted" AI, and "better decisions at enterprise scale."

So what does this have to do with Formula 1? Over to Lando to explain.

“People often see motor racing at the highest level as being about reactions, but most of the performance comes from preparation and understanding the details," he says (in the same press release).

"I’m constantly asking questions, looking for information, and trying to build the clearest possible picture before making decisions. Whether that’s racing, running a business, or working with a team, having access to the right information at the right time gives you confidence to act. That’s what stood out to me about Starburst and why I’m excited to partner with them."

Not much clearer then - but it seems the two-year partnership will launch Starburst’s “Ask. Know. Go.” campaign, which the company says will be "connecting the speed, precision, and data-driven decision-making of Formula 1 racing with the enterprise challenge of turning distributed data into trusted action."

The three parts of this, unsurprisingly, are around getting access to data via AI agents, making sure this data is accurate and trusted, and supporting it with fast and powerful systems.

“Lando represents what happens when preparation, data, and split-second decision-making come together,” said Justin Borgman, Founder and Chief Executive Officer, Starburst.

“That's the challenge enterprises face with AI: ask the right questions, access the complete context behind the answer, and act before the opportunity passes. Starburst makes that possible by giving teams and AI agents governed access to data wherever it lives.”

So there you have it - another stitch in the fabric joining enterprises and Formula 1 - and one that will hopefully lead to a more successful second half of the 2026 season and beyond for Lando.

• OVHcloud will raise dedicated server prices from September 2026, with latest-generation Game range going up by as much as 87%
• Current-generation hardware will see increases of 51%, while older, 2024-gen hardware will see prices go up by 28% on average
• Move is linked to AI-related surge in memory and storage prices, with OVHcloud CEO stating the former has increased sixfold and could potentially double again by early 2027

French cloud platform OVHcloud is sounding the alarm for customers who use its dedicated gaming servers about a price increase that kicks in starting in September 2026 for server configurations, including its 'Game' offerings, that currently start at $357 on the US storefront.

In a post on X, CEO Octave Klaba highlighted the projected price increases as a percentage over prior commitments, stating that while 2024-built machines would not see a price increase, Gen 2026 would see a mammoth 87% increase in cost for consumers.

This marks the steepest increase across the board; otherwise, Gen 2026 servers saw a +51% increase in cost, even as OVHCloud states that existing options would see a lower overall increase in cost and that existing commitments would see "nothing change".

A sign of troubling times for private gaming servers?

Many gamers host private servers for their gaming needs, ranging from a small group of friends and family running a persistent Minecraft instance to servers that cater to hundreds, if not thousands, of players simultaneously, often with commercial caveats.

An increase in price affects both, though possibly the latter disproportionately, as monetization or donations generally sustain them, and they can already run into hundreds of dollars a month in upkeep.

OVHcloud's CEO has highlighted why the move, which seems to be focused on newer gaming servers, which saw the largest increase across the board, is necessary: its memory costs have increased sixfold and could rise to as much as twelvefold by early 2027.

The underlying reason is one that is easy to point at: AI server demand is not only dictating storage and memory market prices, but it could also be weighing purchase decisions at cloud providers, many who can envisage growing and consistent demand for such server instances, therefore raising the opportunity cost of other deployments; OVHcloud's 2026 'Game' servers for example, come with AMD's Ryzen X3D CPUs in two trims currently.

While these CPUs are great for games, they might not be cost-effective for datacenter needs due to higher per-core costs and lower clock speeds than comparable mainstream options, placing them a distant third in a list that has core-heavy server-grade CPUs at the top.

The writing hasn't been on the wall exactly, and it makes for an interesting comparison of how far numbers have traveled in less than a year: in November 2025, Klaba anticipated cloud product prices rising 5 to 10% between April and September 2026. In February 2026, announcing the year's first increase, he described an impact on cloud deployments between 2021 and 2025 of 2 to 6%, depending on hardware age.

He conceded at the time that asking existing customers to subsidize new ones was a little unfair, and argued it was the only way to keep cloud accessible over the following two years.

OVHcloud is an unusual case here: it builds its own servers, which is rare in an industry that often offloads that end to a third-party provider, and while that vertical integration has historically been the source of its price advantage, it has not protected the company here because OVHcloud still buys the same DIMMs and NVMe drives as everyone else.

The Register spoke to the founder of a mid-sized managed service provider who was entirely unsurprised by OVHcloud's announcement and expects AWS and Azure to follow.

The hyperscalers have deeper balance sheets, longer supply agreements, and more room to absorb component costs quietly, so any move from them will likely be slower and less legible than a founder posting percentages on X. It will, however, arguably not be smaller and will be considerably more far-reaching thanks to how deeply integrated some of their products are across the world.

• Volume and spend on tokens for Anthropic’s AI solutions made it more revenue than any of its competitors in July 2026, cementing its position as the top LLM provider
• This success is despite Anthropic’s tokens being more expensive than its competitors
• The data was collated by Vercel’s AI Gateway, an API service that routes LLMs for easy AI provider swapping

Monthly analysis of the AI industry has revealed a number of surprises for July 2026, not least the continued growth of Anthropic both in terms of revenue and token use.

This is despite the company charging a higher rate for tokens, AI’s computational data currency.

The data was compiled by agentic infrastructure company Vercel, whose AI Gateway is used by many major companies and developers, and found the price-per-token average across all analysed platforms fell following two months of growth in May and June. Despite the overall 37% increase in spend across the industry, the cost per token rate dropped.

Anthropic growth

Vercel’s analysis of transactions through its APIs has repeatedly demonstrated a dominance for Anthropic. Its reporting on gateway spend started in December 2025 and in every month since, Anthropic has had over 60% of the spend share, and in July that figure sat at 65.1% with 30% of the total volume (of all tokens sent through the Vercel AI Gateway).

Yet the average price per token was on 4.4 times higher than the overall average across all AI labs. Token prices have rarely proved to be steady, with Vercel’s data reporting an average price drop of 13.6% after a 20% rise in May and little change in June.

(For reference, the report’s definition of the price per token is the total spend divided by the total volume.)

Despite a higher price per token, however, Anthropic’s price per token was lower in July, a change attributed to the return to public access of Claude Fable 5 after its temporary suspension.

Monthly data collection

Vercel’s monthly look at the data that flows through its API paints a fascinating picture not just of Anthropic’s growth, but of the performance (or otherwise) of its competitors.

For example, it charts Chinese AI company DeepSeek’s surprise growth from May, through June; the report notes that “we said an open-weight lab would soon be second by volume. In July, DeepSeek surpassed Google to take that place.”

DeepSeek’s growth over Google has proved to be a surprise, one that puts it in second place on volume. The real story is Anthropic’s gradual cementing of its strong position over the rest of the LLM makers. Its larger revenue pull despite higher token price indicates that the recent iterations of Claude and anticipation over the gradual release of Mythos 5 are giving the company a strong reputation – much like Apple.

• You can now hide the AI Coach in Google Health
• The change is coming in version 5.06 for Android and iOS
• Only premium subscribers get AI Coach access

Since Google Health replaced the Fitbit app back in May, the transition hasn't exactly been a smooth one, but Google is gradually dealing with the biggest user complaints — including, in the latest Google Health update, adding the option to hide AI Coach insights from the Today view.

As reported by 9to5Google, version 5.06 of Google Health for Android and iOS is rolling out with the extra option. You can find it in the app by tapping on your profile picture (top right), then choosing Google Health settings and Coach.

Or at least, that's how it should work: Numerous Redditors are reporting that the change isn't showing up for them, even though they've got the updated app. It's possible that Google also needs to flip a switch at its end, so it may take a while for everyone to get this.

The only other change mentioned by Google for this update are some bug fixes for the map views, so data such as heart rate and pace should now always be accurately displayed. If you were waiting for any other upgrades, you'll have to keep waiting.

Nice to have the option

Google's AI advisor is available to everyone with a Google Health Coach subscription (previously known as Fitbit Premium). In its initial incarnation, it was front and center in the app and was keen to chat as much as possible.

It's something I've seen in my Google Health app, and it can be tiresome to have to scroll through reams of text about how well I slept and what time I should go on a run just to get to the key stats I'm interested in — so I'm glad of the change.

The new setting doesn't fully turn off the Google AI Coach (which was already possible), but it does hide it from the Today view which appears by default when you open the app. The AI is still available on the Fitness tab if you need to ask it any questions.

Based on past feedback we've seen, the change is going to be a welcome one for users — when they actually get it. When the option to hide the AI Coach was rumored a few weeks ago, commenters on Reddit expressed frustration at it "playing '20 questions' every morning" and the way it "inanely comments on things you've already done".

Networking never fails to surprise me when it comes to dependencies and its live nature – it’s a world that’s continually changing, and yes upgrades designed to improve performance or security are often the ones that cause outages.

Even carefully planned updates can have consequences that might initially be difficult to see during early pilots and test, due to a highly dependent mix of switches, firewalls, clouds and more – not to mention the challenges that come with interoperability in a world of mixed vendors.

Until now, it’s mostly been a firefighting exercise of responding to incidents and outages as they happen, admitting that they’re just an inevitable outcome of virtually any change.

Autonomous networking needs a safety-first design

Forward (formerly Forward Networking) wants to challenge this in its entirety, though, and the work it and companies like it are doing could fundamentally revolutionize network stability and uptime, significantly reducing costs.

Its solution is a mathematically accurate digital twin of the production environment, giving companies space to make changes and observe unintended impacts without actually affecting the real deal.

The principle isn’t anything new at all, and it’s been used in software development and other areas for decades, but where it could really shine is in an upcoming age of automated networking. Forward argues that providing a place for systems to autonomously test changes pre-deployment is a necessary stage to true automation.

I wanted to know whether technology like this could have prevented some major high-profile incidents lately, including those we’ve seen relating to Amazon, Cloudflare and more, and spoke to Forward’s co-founder and Chief AI Officer, Nikhil Handigol, on some of the technology’s biggest challenges, including how the digital twin could stay current and why pre-deployment proof could become essential in the world of autonomy.

• Forward proposes to prevent 100% of network outages caused by configuration errors. Are there any strings attached? And what does a “mathematically accurate digital twin of the entire production network” actually mean?

There are no strings, but let’s be precise on what that rests on. Forward Predict eliminates network risks through routing validation, end-to-end path analysis, firewall and ACL verification, segmentation and compliance verification, and automated regression testing.

The closest parallel is what software development did with continuous integration. No code goes to production without being tested first. Forward Predict brings that same discipline to networking.

Every proposed change is tested against the full production-equivalent model before it touches a live environment – routing validation, end-to-end path analysis, firewall and ACL verification, segmentation and compliance verification, and automated regression testing.

That model isn’t a sample or a simplification. It's built from the configuration and operational state of every device in the network — every vendor, every OS, every layer from L2 to L7, on-premises and in the cloud — using header space analysis to trace every possible path any packet can take.

The platform regularly collects state from the live network, and you can trigger a fresh snapshot on demand immediately before a push, so the "digital twin" you're testing against is always current. That continuous synchronization is what makes the math trustworthy: a proof is only as good as its inputs, and this is how we keep the inputs honest.

Run a change through that model and you get a deterministic outcome with evidence, not a guess. If it breaks something, if it opens a security hole, if it creates an unintended path, you see exactly why.

And because the answer comes from mathematical path analysis rather than a probabilistic risk score, it doesn't degrade as the network grows more complex.

Pair that with Forward AI and the platform does not just flag the problem, it iterates on its own until it lands on a change that is verified to deliver the intended behavior. Change windows that once took weeks now take minutes. There is no anxiety pushing a change live, because you already know how it ends. Weekends are for time off, not change windows and war-rooms.

• One point I wanted to raise is that you're essentially pegging it against a snapshot. Sysadmins will tell you that networks change. Firmware get updated, access points get refreshed, switches swapped and servers decommissioned. How do you make sure that Forward remains relevant throughout the lifecycle of the production network?

Forward Enterprise regularly collects configuration and state from every device in the estate, and you can trigger an on-demand snapshot immediately before pushing a change for absolute certainty.

When firmware gets patched, when an access point gets refreshed, when a switch is swapped, when a server is decommissioned, any change to the network is updated in the digital twin, it is a mathematically accurate model of the entire production network.

This is exactly why the underlying model holds up under scrutiny. Our engineering team validates the software against real behavior on live test hardware from every supported vendor, so the model's fidelity is proven before it ever reaches a customer network.

Then in production, that same discipline continues against the customer's own, constantly changing estate. Sysadmins are right that networks never sit still. Neither does the network digital twin. That is the only way proof stays proof instead of becoming a guess based on past behavior.

• Are there other companies that compete with Forward? How does your proposition differ from theirs?

The competitive threat isn't another vendor. It's a set of processes built over decades, in an era before a network could be modeled at all, that include change windows, war rooms, and the belief that the only place to truly test a network change is the production network itself.

Every other industry running mission critical infrastructure solved this problem years ago. Airlines train pilots on flight simulators instead of real aircraft. Pharmaceutical companies model outcomes before anything reaches a patient.

Manufacturing validates changes on a digital twin before touching the factory floor. Software development has run on continuous integration for two decades, where no code ships without being tested first.

Networking is the industry that never got its equivalent, not because nobody wanted one, but because building a mathematically accurate network digital twin is genuinely hard.

Against observability and monitoring vendors, the difference is which question gets answered. Those tools are essential for understanding what already happened on the network. Forward Predict answers a different question entirely, what will happen if this change is made, and it answers it with a deterministic proof rather than an educated guess.

Against other platforms using the words “digital twin,” the difference is a decade of foundational work building a model with header space analysis across all major vendors, every device, and every layer, rather than a partial view stitched together after the fact. That foundation cannot be a shortcut, and it is the part everyone else in the category is still missing.

• How do you see Forward evolving in terms of features and roadmap? Could this pre-emptive, predictive approach find a receptive audience in other sectors?

The roadmap moves in one direction, widening the aperture of what gets proven before it goes live. Today Forward Predict proves the outcome of changes to connectivity and security. Next is broader impact analysis.

We are not predicting what may happen, we are proving what a network will do, because a network's behavior can actually be modeled with mathematical certainty in a way human intent cannot.

That distinction is exactly why this belongs anywhere teams run complex, interconnected infrastructure with the same stakes, critical infrastructure operators running their own networks underneath everything else.

Wherever teams are changing complex systems without a way to prove the outcome first, the same problem exists, and the same fix applies.

Where this is headed fastest, though, is autonomous networking. As agents start proposing and executing changes at machine speed, a mistake stops being a single event and starts propagating at scale.

Proof is not an added feature there. It becomes the precondition for letting an agent operate at all.

• Study finds 25% of business leaders struggle to explain AI-generated outputs to stakeholders
• SMEs are trusting AI with complex financial tasks including audits and compliance
• Customers and investors are increasingly avoiding businesses that use unverified AI

Business leaders are being outfoxed by AI, with a quarter of those surveyed apparently unable to explain AI generated outputs, 12% “with a lot of difficulty” and only 22% “easily.”

Even more worryingly, the survey, by Startup.co.uk, appears to reveal a culture of “leave it to AI” regardless of the consequences.

The survey has also uncovered other concerns, from questions over GDPR to how SMEs are trusting AI to carry out sensitive financial work, including audits, expenses, and accounts payable automation.

Would you let your AI do this?

With GDPR responsibilities hovering over startups and dynamic new SMBs, the overuse of AI solutions could prove to be devastating if customer data is found to have been misused. Meanwhile concerns over the use of the technology could be affecting businesses that fail to verify AI work.

While the inability of startup leaders to explain AI generated work might be a surprise, the depth of AI use across financial tasks is of particular concern. The report revealed how 85% of small businesses are using AI to complete financial tasks of some sensitivity.

Among the figures are 37% using AI to automate accounts payable processes, 32% to handle audit and compliance, and 31% to manage spend and expenses. While there is some suitability for AI with fraud detection (26%) and performance insights (27%) with its capacity to analyze data in bulk, the inability of leaders to deal with questions about AI is a matter of concern.

Blind trust in AI

What the figures seem to indicate is an over-reliance on AI.

Zohra Huda, editor of Startups.co.uk, noted the data “highlights the bizarre corporate milestone we’ve reached in 2026. Founders are letting AI manage their fraud detection and accounting, but if a stakeholder asks how the numbers were calculated, a quarter of them can’t answer.”

The solution to that might be a bit of rehearsal, but it doesn’t change how data is being used by AI and what the implications for this are, and the likelihood of breaching Article 15 of the UK GDPR (concerning how data is processed).

“Blindly trusting a tech “black box” with sensitive financial data is a massive legal and compliance gamble," added Huda. The takeaway for business leaders? If you can’t explain your AI’s logic to an investor or a customer, you're potentially risking a very expensive GDPR fine.”

With the ICO able to fine businesses up to £17.5 million or 4% of global turnover for verified GDPR breaches, it is clear that startups relying heavily on AI need to up their game.

• RBI Governor Sanjay Malhotra tells banks that AI trained on alternative data such as cash flows, GST filings, and utility payments can extend credit to borrowers that manual underwriting cannot currently assess
• The pitch came bundled with caution, insisting that banks take safety and visibility measures including complete AI inventories, red-teaming before deployment, and the capacity to explain lending and fraud decisions
• The RBI insists, however, that accountability stays human; banks, not their algorithms, will be answerable to customers, auditors, and the Reserve Bank when AI-driven decisions go wrong

The governor of the Reserve Bank of India (RBI) has told the country's banks to put artificial intelligence at the heart of their lending patterns.

The approach is based on the assumption that models trained on unconventional data can identify and leverage data points that manual underwriters would struggle to spot or justify easily.

This would, as per RBI governor Sanjay Malhotra, both strengthen the credit market and enable financial inclusion by adding a new class of borrowers previously overlooked by conventional methods.

Cautious AI optimism from one of the world's largest central banks

The Reserve Bank of India is not a small central bank by any measure, and remarks by its governor therefore can often shape not only domestic but global markets.

His position on unlocking credit markets for users with little or no financial history by adding raw compute that considers other signals such as cash flow, GST tax filings, utility payments, and one's digital footprint considerably changes the landscape in a part of the world where banks are traditionally more conservative than their global peers when it comes to lending.

Malhotra also noted that this is exactly the data that a first-time borrower, a gig worker, or a small enterprise without formal books lacks, even as AI enables better monitoring of debtors' financials.

“Predictive models can identify borrowers on the cusp of default early enough to counsel rather than merely recover,” he added. “Used well, AI may be the most powerful accelerator to financial inclusion.”

It is important to point out Malhotra wanted AI to be used as an assistant here - not that AI should overrule loan officers on applications they have already rejected.

His claim is narrower and arguably more persuasive: entire categories of borrowers are effectively invisible to conventional credit assessment because the paperwork it requires does not exist, and machine learning, over other data, can make those borrowers assessable to banks and vice versa.

Malhotra also cited concerns that most modern AI models are black boxes and do not always explain their reasoning fully or adequately, and that borrowers are generally entitled to know why they were turned down. This could help keep potential algorithmic bias in check and rationalize such decisions at a time when AI models are known to be vulnerable to data poisoning attacks.

This would require a human touch, especially when it comes to accountability for such decisions, rather than the complete hands-off approach that AI enthusiasts sometimes suggest is inevitable.

To this end, the conclusion of his speech may be the most telling of what India's central bank feels about an increased AI footprint in the banking industry: Malhotra said that the winners of the AI era will not be the fastest or heaviest adopters but the institutions that best understand what they deploy, own its outcomes, and keep customer trust, something he equated to the enduring capital of Indian banking.

I've often treated ChatGPT as though a poorly worded prompt might break it. I'd think about what I wanted to ask, rewrite it a couple of times, remove anything that sounded irrelevant, and essentially do half the chatbot's work in my head before ever submitting a prompt.

Recently, I've discovered that some people succeed with the complete inverse of that strategy. Instead of polishing every sentence, they simply talk, and keep talking. They switch on ChatGPT's advanced voice mode, start rambling, and let whatever happens to be bouncing around inside their head spill out for several minutes without interruption. I was curious to try out simply thinking out loud and letting ChatGPT sort through the mess afterward.

Rambling AI

The point was to say everything I was thinking, so I promised not to endlessly edit myself, even if I changed subjects halfway through a sentence. I turned on voice mode and told ChatGPT that I was going to simply talk through a range of topics for several minutes and not respond until I said so.

I began babbling. For the first minute or two, it felt strangely awkward. I kept catching myself trying to turn half-formed thoughts into proper sentences before I spoke them. Eventually I found a more comfortable rhythm.

I hit on a lot of topics in about ten minutes. I started with listing some vacation ideas, meandered into pros and cons of a new TV, then weighed how old my child should be before taking him to an action movie. That triggered brainstorming possible gift ideas for birthdays, and reminiscing about getting turned onto some of my favorite book series thanks to thoughtful gifts I'd received. Seen all at once, I felt like my mouth was a TV changing channels every ten seconds. Nonetheless, ChatGPT had a response in a few seconds.

"You covered quite a few topics, but they aren't as disconnected as they seem. Most of what you're thinking about revolves around spending time well, whether that's choosing a family trip you'll actually enjoy, buying gifts that feel personal or making everyday decisions without overthinking them. Rather than trying to solve each one separately, I think you're really looking for ways to make ordinary moments a little more meaningful."

I hadn't set out to have a conversation about priorities, but I could see where ChatGPT came up with that idea. I asked the AI to lay out some more of what I had been saying, and the chatbot chose to be a little snarky about it.

"Your brain appears to work like someone browsing with thirty tabs open. At first it seemed as though we were talking about holidays, televisions, books, recipes and birthday presents completely at random, but they all pointed toward the same goal. You seem to be looking for ways to make everyday life a little more enjoyable without making it more complicated. Once that became clear, the individual decisions started making much more sense."

Mining for golden ideas

The biggest lesson for me was that my carefully written prompts may have been removing useful information all along. When I edit myself before asking ChatGPT something, I naturally cut out the contradictions or any unfinished thoughts. Those are often exactly the details that explain what I really want. That's the only reason ChatGPT would have suggested I rethink how I shop based on my random anecdotes and ideas.

"I noticed you almost never describe gifts in terms of price. Whenever you become enthusiastic, you're talking about moments where someone feels understood or surprised. I think you'd enjoy shopping much more if you started with memories or interests instead of products. Several of the stories you mentioned already contain gift ideas that feel much more personal than searching for another gadget."

Talking naturally gives ChatGPT far more to work with. It hears the things you return to repeatedly, the subjects that excite you, and the ideas you quietly abandon halfway through. Those patterns rarely appear in a tidy two-sentence prompt because you've already edited them away.

I still think carefully written prompts are the best choice for a lot of ChatGPT tasks, especially when you want something specific. But when I'm trying to untangle my own thoughts, I may just ask ChatGPT to listen for a while and then help put everything in some order.

• Study finds young people are increasingly pessimistic about AI
• Nearly half say AI will have a negative impact on their careers
• They're also suspicious of leading AI figures and executives

Younger Americans are becoming increasingly distrustful not only of AI, but also the executives and evangelists who promote the technology, a new study has claimed.

A CNBC/Generation Labs survey of 1,088 Americans between the ages of 18 and 34 found the vast majority saying they distrusted the leaders of major companies investing in AI, including the likes of Elon Musk, Sam Altman and Mark Zuckerberg.

The reasons behind this distrust were fairly clear to spot - with nearly half (45%) of respondents saying AI would have a negative impact on their careers, compared to just 10% who believed the technology will help them.

AI villains

The study covered a range of wider concerns young people may have around the future, with worries around the economy and jobs among the biggest pressures, however the threat of AI was seemingly ever-present, with the technology increasingly influencing all areas of everyday life.

This expands to the figures becoming the poster children for AI - respondents were given nine key people in the AI industry and asked whether they trusted each of them to act responsibly on AI moving forward.

Perhaps unsurprisingly, every figure was marked negatively - with Palantir CEO Alex Karp scoring the worst, as 81% of respondents saying they do not trust him. Karp was followed by Palantir Chairman Peter Thiel (79%), Alphabet CEO Sundar Pichai (75%) and Anthropic CEO Dario Amodei (75%).

Other notable figures, including OpenAI CEO Sam Altman, Meta CEO Mark Zuckerberg, Nvidia CEO Jensen Huang, and SpaceX CEO Elon Musk all scored about the same 70% figure - but there was 'better' news for Microsoft CEO Satya Nadella, who recorded the "highest" score, with just 65% of respondents saying they don’t trust him.

Looking at technology's place in the wider world, 40% of Americans said they believed “the federal government” should establish rules for AI, with just over a third (36%) believing “an independent expert body” should do so - and just 8% said they think AI “shouldn’t be regulated.”

Keeping with the spirit of protests across the nation, nearly two-thirds (60%) of Americans believed data center construction “must be slowed,” with just 15% saying they wanted to be “speeding it up.”

Looking forward, the when asked about how they would rate the American economy, nearly 80% said they had a negative sentiment - and when asked about the future of the economy, 50% believed it “will get worse,” compared with just 22% who said it “will get better.”

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

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

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

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

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

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

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

The R&D disconnect

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

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

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

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

The pressure created by increased HMRC scrutiny

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

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

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

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

Establishing the conditions to claim with confidence

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

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

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

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This article was produced as part of TechRadar Pro Perspectives, our channel to feature the best and brightest minds in the technology industry today.

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A small business owner receives a call from a customer.

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

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

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

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

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

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

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

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

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

Security needs to be embedded, not bolted on

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

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

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

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

Trust is now measured by both people and machines

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

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

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

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

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

Trust and security are now competitive infrastructure

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

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

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

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

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

We've listed the best free website builders.

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There are plenty of ways to get rid of ads online, but ChatGPT has come up with an unusual approach to the bargain for its own ads. Free ChatGPT users can give up some of their messaging quota to make advertising inside ChatGPT disappear.

As OpenAI expands advertising on ChatGPT's Free tier (ads launched in the several countries, including the UK, this week), users can choose an Ads-Free version without upgrading to a paid subscription. The catch is that choosing it means accepting lower usage limits and potentially losing access to some features. It all depends on whether you value an uncluttered chatbot conversation or more opportunities to actually use it.

I was curious about what that trade actually amounted to in normal use. An ad appearing underneath a ChatGPT response sounded mildly irritating, but running out of useful interactions halfway through a conversation sounded considerably worse. OpenAI is evasive about precisely what the trade-off is, so I decided to experiment on my own.

Ad options

I wasn't going to just spam ChatGPT with meaningless prompts until it finally refused to answer. I came up with a fairly common use for the AI and decided to see if having the advertising toggle on or off made much of a difference.

To set up the experiment, I started a Free account, opened Settings and the Data menu where the Ads options live. You can see the choices above. ChatGPT encouraged signing up for a subscription to avoid ads, but the ad-free experience “with reduced usage” is right there.

I started with ads enabled and asked ChatGPT a bunch of questions about potential purchases, activities to do on the weekend, and other mundane queries, with plenty of follow-up questions as part of it.

The ads barely interfered with any of the conversation. OpenAI currently places ads below responses and keeps them visually separate from ChatGPT's answer, as you can see above. They're easy to ignore and never made an answer harder to read. It felt more like a sponsored Google result than an unskippable video in terms of annoyance levels.

Still, I never had to wonder whether ChatGPT itself was recommending the advertiser. That's good for OpenAI since it's a particularly awkward environment for advertising. When a chatbot is telling you what restaurant to visit, which laptop to buy or how to plan a vacation, even the suggestion that money could influence its answer would undermine the entire experience

The price of silence

The ad-free option felt pretty similar to the version with sponsored posts. You'd have to actually be watching for the ads to have much of an impact. OpenAI only says Ads-Free users receive fewer messages per day, may run into rate limits more often, and can lose access to tools like deep research or its image-making options. I noticed the reduced feature option, but multiple attempts to reach the ad-free limit had mixed results. It took nearly 40 messages to get a warning from ChatGPT in one instance, and another experiment crossed 60 messages without a peep from the chatbot.

The rate limit may matter a lot more for people using ChatGPT professionally. But when it comes to regular conversations that aren't as extensive, I didn't really feel constrained. Not to mention, even if you do hit the ad-free ceiling, you can always turn the ads back on to get more access.

My verdict? As much as I might prefer an ad-free ChatGPT conversation, I'd rather deal with the easily forgettable sponsored links than having to start all over with the toggle flipped back on. Slightly more clutter around the conversation beats possibly having to start over because I hit the rate limit.

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

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

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

How AI agents expand the attack surface

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

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

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

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

Why software-only defences keep falling short

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

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

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

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

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

The case for hardware-level security

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

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

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

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

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

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

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

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

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

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

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

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

Visibility is not the same as readiness

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

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

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

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

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

Confidence is becoming polarized

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

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

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

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

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

From unified data to unified decisions

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

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

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

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

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

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

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

AI can accelerate decisions - but foundations determine value

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

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

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

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

The new measure of resilience

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

That is the real 24-hour test.

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

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

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

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

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

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

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

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

The hidden costs of AI agents

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

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

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

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

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

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

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

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

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

Getting costs (and risks) under control

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

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

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

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

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

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

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

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

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

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

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

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

Sovereign AI

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

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

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

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

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

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

Open source

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

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

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

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

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

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

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

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

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

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

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

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

A global response to AI

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

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

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

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

Stop thinking about governance as compliance

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

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

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

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

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

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

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

The trust advantage

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

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

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

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

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

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

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

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

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

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

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

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

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• In the first three months of 2026, $130 billion worth of AI data centers have been blocked or delayed across the US
• For the whole of 2025, the amount was only $156 billion
• Now AI firms are filing lawsuits to push back against the pushback

Increased strain on local power grids and water systems, noise and air pollution filling the surrounding area, and dubious economic promises are among the reasons citizens have taken a stand against AI data center development in their areas. And their side is winning time and again.

In just the first three months of 2026, local activism has resulted in $130 billion worth of projects being blocked or delayed across the US. For the whole of 2025, the publicly reported figure was only $156 billion.

As you might imagine, AI hyperscalers aren’t pleased with this.

Developers have taken to suing local authorities behind the bans and restrictions; meanwhile, there are reports that private intelligence firms are creating and selling dossiers on AI and data center critics to help companies and AI regulators tackle this growing movement.

And AI companies are already finding victory. Hill County, Texas, was one of the first counties in the state to impose a one-year ban on data center development in rural areas. However, the data center’s developer sued, and the law was rescinded.

The developers’ arguments predominantly stem from questioning what powers local leaders have in deciding how and where data centers are rolled out. As such, it’s unclear whether these lawsuits would hold up as well against statewide bans and moratoriums like the one announced just over a week ago by Texas Governor Greg Abbott — which has paused construction of around 1,800 data centers until audits on their impacts on local communities, water systems, and Texas’ power grid can be properly assessed.

It’s also unclear if AI true believers in the federal government will take a stand against states’ AI data center restrictions. President Donald Trump has called data centers a “tremendous thing”; though, despite the growing divide between Republican and Democrat voters, they share a bipartisan distaste for AI data centers, which could present a challenge for leaders who support them when it’s time for re-election.

Can AI data centers ever win?

Whether AI companies succeed in the courts or not, it doesn't seem like they’ll win in the court of public opinion for a little while longer, since data centers apparently have serious immediate downsides and relatively few upsides.

On the positive side, construction workers can see an uptick in revenue while the project is being built; maybe a few local workers will receive long-term employment to help manage the facility, and they can also bring in high tax revenue relative to their size.

But construction will eventually end; long-term employment on-site tends to be relatively limited, as a lot of management can be handled remotely, and tax revenues require profits.

On that front, AI data centers are generally enjoying solid financial performance. However, fears over the AI boom being somewhat bubbly continue, with concerns that the promised ongoing returns of data centers may end sooner rather than later.

In a worst-case collapse scenario, these buildings could become defunct, meaning local residents could face years of issues and never see any kind of reward.

These issues, conversely, can be very immediate and pretty major in some cases.

Meta contaminated Cheyenne’s water system with bacteria; data centers are said to have electricity bills for the general public of $23 billion, and a Michigan data center is in trouble for emitting incredible noise pollution 24/7, described by one resident: “You’ve seen movies and stuff where they have somebody in a cell torturing them with sound. And that’s basically what it is."

Many understand the enormous possible economic and scientific advantages of AI and the need for it to be supported and developed in their country — 55% of Americans don’t oppose the construction of new data centers in general, according to a June 2026 IPSOS poll. However, 59% of respondents would actively oppose a center being built within 10 miles of their home.

As AI excitement fails to die down, we’ll undoubtedly see even more data center plans pop up, and see just as many communities oppose them.

• China launched a fully autonomous vulnerability hunting attack against Taiwan
• The attack leveraged eight open-source AI models to hunt for new attack vectors
• The attack hit Taiwan government accounts, personnel records, the nuclear safety agency, and more

A first-of-its-kind cyberattack using autonomous AI has been spotted attacking Taiwan, and it compromised 85 government accounts and stole over 2,500 personnel records before moving on to hit the country’s nuclear safety agency and at least seven energy companies.

The attack used eight open-source AI models to build a hacking program that was able to independently conduct reconnaissance and intrusion, and was able to chain vulnerabilities and change tactics whenever it was blocked.

The intrusion took place over the course of four days, and was first exposed by The Financial Times on August 12, 2026. The FT article covered research performed by Dream, an Israeli AI and cyberdefense company that first identified the breach.

Autonomous AI attack

The attack was first uncovered during routine monitoring of cyber criminal activity. Dream found a 160MB online archive of 1,395 files. Further examination of the files revealed that the attack relied on Hermes and OpenClaw - two open-source AI agents.

As is typical of attacks relying on AI models, the hackers had framed the context of the intrusion as a routine cyber readiness test in order to bypass the built-in guardrails of the AI models.

The attack used multiple agents to hunt for new vulnerabilities and access points across the internet, providing the tool with multiple attack paths to take if one failed to gain entry.

AI agents have been quickly integrated into the attacks of cybercriminal organizations and state-sponsored threat actors alike, enhancing their abilities to launch highly complex attacks at scale. “This must be the basic assumption of every government around the globe,” said Amir Becker, Dream's chief strategy officer.

Dream did not tie the attack to any specific cybercriminal group, nor did it confirm the target of the attack, but said it had alerted a government in the “Asia-Pacific.” Documentation within the recovered archive contained Simplified Chinese, which is the official written language used in mainland China.

The archive also contained data collected from the targets, which was written in Traditional Chinese. This form of written Chinese is widely used in Taiwan, Hong Kong, and Macau.

Taiwan's Ministry of Digital Affairs has refused to comment on the breach, and the Chinese authorities have not responded to requests for comment.

China has long considered Taiwan to be a part of mainland China. Taiwan declared its independence following the end of the Chinese Civil War in 1949. A report from Taiwan’s National Security Bureau earlier this year revealed that the country was subject to 2.5 million Chinese cyberattacks per day in 2025.

Expert perspective on autonomous AI attack

Collin Hogue-Spears, senior director of solution management at Black Duck:

The agents ran the intrusion end to end and invented nothing new to run it with. Familiar identity and API failures opened every confirmed path into Taiwan's systems. Dream Research Labs documented up to eight subagents working concurrently across twelve waves, ranking attack paths, redirecting when a technique failed, and researching alternatives online before trying again.

What they found was exposed development endpoints, an API accepting authentication tokens with the signature check disabled, unauthenticated data APIs, and passwords built from employee ID numbers.

The framework also ran its own AI static analysis hunting unknown flaws, but Dream says it worked against two public single sign-on SDK sample projects, and none of those findings produced a confirmed exploit on the live systems. No zero-day appears anywhere in the report, but a nuclear safety regulator does.

In conventional web and identity logs, this reads as a security scan. The distinguishing signal is the sequence across systems, not any single request. The tell is not the request. It is what the same account does next, somewhere else.

Conventional scanners have tested thousands of endpoints at machine speed for twenty years, so raw coverage is not the change here. What Dream Research Labs describes is chaining: password spraying, then fresh SSO sessions, then access to routes an account had never touched, then the same suspected weakness retested until it held, then one identity surfacing across several connected applications.

Simplified Chinese in the operator's notes is one signal. Traditional Chinese in the stolen files is just Taiwan. Dream rested its China assessment on a code-switching observation, and only half of it points at the attacker. Per Chinese-language coverage of the report, Simplified characters appeared in the operators' internal communications and Traditional characters appeared in the exfiltrated data. The first describes the operator's working language. The second describes the victim, because that is what Taiwanese government files look like [Traditional Characters].

The evidence therefore supports a Chinese Mainland-language operator against a Taiwanese target, with a target profile consistent with mainland collection priorities. It does not name a group or establish state direction. The report also publishes no indicators, no hashes, and no victim confirmation; it does not identify the model, and its executive summary claims installed backdoors while its own attack chain says authentication blocked the web shell.

Security leaders must reject unsigned authentication tokens and prohibit the alg:none setting outright, and separately require reauthentication or multi-factor at any single sign-on boundary into a sensitive system. Dream describes two independent identity failures in Taiwan, and closing one leaves the other open. Provider guardrails cannot compensate for a password-only SSO bridge.

They must also monitor route diversity per source, per session, per account, and per device rather than by request rate alone, because a distributed set of agents spreads requests across addresses and sessions that no single volume threshold catches. If your detection assumes one attacker at one address working one path at a time, you have modeled the wrong shape.

Your thresholds were built for one attacker on one path. This was eight, in parallel. And they must ask two questions of any AI attack disclosure before acting on it: which model ran the operation, and what can we hunt on tomorrow morning?"

Via United24

• The Department of War announced an expanded counter-drone marketplace
• It's built by four-year-old startup Kaizen Laboratories under a $15 million prototype contract
• The platform sees defense vendors including Anduril, DroneShield, AeroVironment, and SMARTSHOOTER listing products for sale to approved buyers

The US Department of War has announced an expanded version of the counter-unmanned aerial system marketplace run by Joint Interagency Task Force 401, its coordination hub for counter-drone work.

A report by DroneFront found the platform is built and operated by Kaizen Laboratories, a four-year-old software company in New York, under a $15 million prototype contract.

The announcement is an impressive milestone in a procurement experiment that has been running behind closed doors since February 2026, with access available only to vetted buyers.

AI-assisted digital procurement with safeguards

The inventory available to the Department of War's list of buyers is not visible to third parties, and for those who do have access, it is heavily tiered based on export-control status.

Buyers are manually approved by JIATF-401 admins and granted access based on classification levels; eligible buyers include federal procurement officers, military commands, pre-cleared allies, and defense ministries from partner nations.

Vendors on the platform are also subject to a detailed vetting process that involves business verification, product evaluation, and compliance with export controls before they are allowed to publish anything.

This essentially fits with what a digital weapons catalog is, but with considerably more firepower under the hood than a typical gun shop might carry, and where mistakes or even the display of classified data could have catastrophic security ramifications.

For example, if data about the effectiveness or limitations of certain counter-drone hardware were made public, it could undermine its usefulness in certain theaters of war.

Australia, Poland, South Korea, the United Kingdom and Romania have been publicly named as partner nations, and Army Secretary Dan Driscoll has said state and local law enforcement agencies are being onboarded as well.

JIATF-401 reported $13 million in marketplace purchases as of April. By early August, task force spokesperson Lt. Col. Adam Scher put the figure at $21 million. That shows subsequent sales of about $8 million over the next 4 months, or about $2 million per day.

For a platform that was contracted out to be built at $15 million, one could argue that the sales are moving slower than expected, but that might be the point here: Kaizen CEO Nikhil Reddy has said the platform remains in a slow-rollout phase, with a limited set of orders being used to validate that the ordering and adjudication process works before wider release.

The more consequential piece of the platform, however, is one that has yet to be released: an AI-assisted planning layer that lets a user enter a threat profile, a budget, an operational readiness state and an environment, and then, in Reddy's own description, scans the products in the marketplace agentically and returns a recommended capability configuration across the kill chain. Kaizen said in early August that this was expected to go operational within days.

Given how dynamic the modern battlefield is and how quickly demand shifts among multiple players and allies, having a more centralized yet relatively secure platform to issue orders and conduct threat assessments is a key addition to the Pentagon's arsenal as it continues to support a plethora of allies and domestic partners alike. Whether its website at cuas.mil, which already has restricted access from certain countries, will meet those needs in the future remains to be seen, but it does seem to be off to a promising start.

• Linus Torvalds called Linux 7.2-rc7 part of a "new normal" of oversized release candidates
• Torvalds attributes the volume of fixes to reviews by various AI tools, a week after calling rc6 the biggest in years by commit count
• The growth is in late-cycle bug fixes rather than features, and Torvalds credits AI review rather than AI authorship, with humans still writing and submitting the patches

Modern coding approaches dictate that Release Candidates should be expected to shrink as patches and code fixes get increasingly smaller as the software enters what many call a "stabilization phase".

Linux 7.2-rc7 did not do that, and neither did rc6 before it - and announcing the seventh candidate on August 9, 2026 Linus Torvalds wrote he could not say he was thrilled about the size of it, but that it is what it is: the new normal, with a lot of fixes, many of them the result of review by various AI tools.

A week earlier, opening the rc6 message, Torvalds had been blunter still, calling that release candidate huge and reckoning it the biggest rc6 in years by commit count.

An easier, detailed AI-enabled review process?

Torvalds is not describing AI writing for the Linux kernel; that still requires human intervention. The tools being used are review and analysis agents pointed at existing code, and they produce actionable bug reports. Humans then write the fixes, submit them through the normal subsystem maintainer process, and the creator of Linux, as the maintainer, pulls the code.

Phoronix, which closely tracks the cycle, describes the current environment as one in which AI and LLM coding and review agents keep kernel activity at an all-time high. It has resulted in a much larger-than-normal Release Candidate this late in the cycle; RC7 comes packed with a plethora of bug fixes that would otherwise have been in play much earlier in past cycles.

By one count of the RC7 pull, more than 400 fixes came in from upwards of 230 contributors, a volume more typical of an early merge window than a closing candidate.

Torvalds noted in the accompanying message that nothing looked particularly scary, and that the majority of the fixes were spread across drivers, filesystems, core networking, and architecture code, which was relatively small, making a potential release by the following weekend possible.

The AI approach doesn't have Mr. Torvalds particularly keen, but he has subtly acknowledged its advantages when it comes to code review, and it could bring a different issue into play: the maintainer model, which involves a few trusted individuals to read and test code before it is eventually released to the masses may not be too keen to have the equivalent of an AI-powered firehose of fixes and new information to deal with indefinitely.

For now, it seems larger updates are the new norm, for better or worse, and AI will be powering an increasingly large share of code review in an ecosystem that drives most of the world's servers, smartphones and tablets, as well as a sizeable portion of its personal computers.

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

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

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

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

The PC is no longer just a tool

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

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

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

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

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

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

Why where AI runs matters

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

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

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

Technology is only part of the answer

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

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

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

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

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

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

Most of them will not exist in five years.

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

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

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

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

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

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

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

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

We've run this experiment twice.

The past and the future

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

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

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

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

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

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

The example of cloud

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

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

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

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

It has already happened once inside this stack

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

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

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

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

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

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

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

The same consolidation is coming for AI

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

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

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

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

Better software, worse odds

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

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

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

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

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

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

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

The delusion of special

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

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

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

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

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

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

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

Your toughest competitor is your customer

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

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

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

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

Know which company you are

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

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

So what is?

The good news, and who gets it

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

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

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

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

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

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• Ahrefs has launched Letaido, a new agent-powered marketing tool
• Letaido will have native access to Ahrefs data
• It promises to help save users hours every week

Ahrefs, one of the biggest digital marketing, SEO, and AEO platforms, has announced the launch of Letaido, its agent-powered marketing workspace.

The new tool promises to help teams move beyond AI chatbots to plan and execute multi-step tasks, build bespoke dashboards and reports, and automate repetitive workflows. It will also work behind the scenes to continuously monitor websites and competitors.

According to a press release distributed by Ahrefs earlier this week, Letaido will benefit from native access to Ahrefs data, helping marketers and agencies automate content marketing, SEO, and marketing research without any need to manage custom integrations of APIs.

The new tool will also seamlessly connect with widely used marketing platforms with secure integrations, built-in hosting, and always-on infrastructure, supporting workflows that keep on running, even when the instructions have stopped being given.

How will Letaido help marketers?

According to data from BCG’s 2026 global CMO survey, only 32% of CMOs have rebuilt how marketing operates, and 42% admit they use GenAI exclusively as an assistant for individual tasks. Ahrefs promises that Letaido will help close the gap between the first phase of generative AI, which operated largely in silos, and AI that can operate across your full marketing stack, helping marketing teams and agencies connect and plan multi-step tasks.

"Marketing teams have spent years accepting that getting to an insight means clicking through reports, exporting CSVs, filtering data, and manually cross-referencing information across several tools," said Ross Simmonds, CEO and founder of Foundation. "With Letaido, those hours can now go toward the work that actually matters: thinking, strategy, and creativity. Our team has seen keyword research and bottom-of-funnel content audits, work that used to take a full 40-hour week, now take just 60 minutes."