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

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

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

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

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

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

Six-month planning cycles are no longer viable

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

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

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

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

Rethinking planning

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

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

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

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

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

Maintaining a live model

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Governance becomes the new trust layer

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

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

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

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

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

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

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

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

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

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

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

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

A pragmatic approach combined with thinking big

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

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

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

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

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

The right data strategy is crucial

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

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

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

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

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

Incrementalism is better than quantum leaps

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

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

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

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

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

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

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

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

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

The growing governance gap

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

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

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

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

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

As investment accelerates, expectations rise

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

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

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

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

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

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

Security, data and trust at scale

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

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

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

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

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

Building adaptable AI governance

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

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

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

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

Scaling AI with confidence

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

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

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

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

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

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

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

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

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

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

A powerful reminder

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

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

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

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

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

Health systems

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

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

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

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

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

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

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

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

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

Complex, cautious, and overstretched

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

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

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

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

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

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

In public health, timing matters.

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

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

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

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

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

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

Wide AI adoption

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

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

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

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

Secure-suite?

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

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

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

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

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

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

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

Evolving together

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

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

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

Liberty and open models

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

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

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

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

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

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

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

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

AI is writing more and more web pages

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

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

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

Is it written by AI?

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

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

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

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

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

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

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

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

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

Relegated data centers?

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

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

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

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

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

"Foreign adversaries"

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Downplaying AI is the tactic for now, but not forever

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

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

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

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

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

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

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

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

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

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

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

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

Lesson one: users revolt faster than big platforms think

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

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

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

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

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

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

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

Lesson three: trust is the actual product

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

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

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

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

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

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

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

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

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

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

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

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

What is critical to survival?

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

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

Where to start getting an MVC up and running?

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

1. Clarity on critical services:

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

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

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

2. A trusted foundation (Tier 0):

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

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

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

3. Isolation of recovery assets:

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

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

4. Clean-room recovery capability:

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

5. Validated ability to operate

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

Recovering faster by restoring what matters most

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

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

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

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

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

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

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

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

Subscription cuts

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

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

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

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

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

Saving on food

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

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

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

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

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

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

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

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

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

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

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

1. Gain real visibility across the full technology stack

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

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

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

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

2. Build a governance model for consumption

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

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

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

3. Renegotiate contracts for flexibility and accountability

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

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

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

4. Align technology, finance and procurement

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

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

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

5.Use AI to move optimization from reactive to continuous

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

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

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

Turning tech consumption into value

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

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

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

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

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

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

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

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

Choice, not self-sufficiency

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

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

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

Infrastructure is only useful if everyone can use it

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

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

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

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

Openness is a strategic advantage

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

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

Control through openness

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

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

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

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

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

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

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

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

Guardians or gatekeepers?

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

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

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

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

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

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

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

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

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

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

Protecting the realm in practice

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Agentic attacker

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

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

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

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

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

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

A harder problem

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

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

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

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

Active security

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Analysis: fast-tracked exploits?

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

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

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

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

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

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

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

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

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

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

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

AI-based weather models

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

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

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

The consequences of the “confidence gap”

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

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

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

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

• Samsung System LSI division uses Anthropic's Claude Code to reportedly slice working processes
• However the tool also apparently downgraded an error message instead of fixing it, rolling back unrelated finished work, and tried to edit code it was not authorized to touch
• Samsung's engineers currently review everything the tool produces before it is published elsewhere or affects existing chip designs

Samsung's System LSI division has reportedly used Anthropic's Claude Code to cut a month-long system-on-chip verification job to about two days.

While Samsung's division has about 6,000 employees, its main rival in mobile application processors, Qualcomm, has roughly 52,000 employees, and the former is apparently not above using AI to bridge the gap as a force multiplier.

A report from South Korean business outlet Chosun Biz (KR) claims System LSI leveraged Anthropic's Claude Code by offering it to its software developers in May 2026, then extended it to semiconductor design and verification work, a move which led to much faster progress on certain projects, while also producing interesting failures on other fronts.

A mix of triumphs and gaffes

Samsung's approach, for the most part, makes an excellent case for future AI use, as one verification project expected to take more than a month was finished in about two days, something the company internally tracked as a 15x gain in efficiency.

More interestingly, a second-year engineer with no prior exposure to either the tool or USB communication standards built USB device models for an emulator and adapted an Android driver in a single day, work whichwas normally estimated to take about a month.

Samsung has also had an interesting interaction using Claude Code to verify the internal data connections of a custom system-on-chip. The job was complex in more ways than one: the customer wanted a new architecture, third-party design IP was involved, the documentation was nonstandard, and the RTL for the DRAM controller had not arrived on schedule, making for a perfect storm for Claude.

Claude built a virtual verification environment using placeholder blocks in place of the missing RTL, along with test scenarios, so that engineers could start catching errors before the real design existed.

On the other side of the equation, Claude Code did, in certain cases, try to operate outside the boundaries of its assignments, often resulting in incorrect output or an error, or, in one case, an attempt to modify RTL circuit code it had no authorization to touch. It also rolled back unrelated finished work and downgraded an error message instead of fixing it.

The former is particularly egregious because, unlike software updates, once silicon ships, it can rarely be repurposed or 'updated' to bypass design flaws. Samsung has responded by ensuring engineers manually inspect and verify the output of Anthropic's models before using it elsewhere.

More interestingly, Claude Code isn't the only tool in the building, even if it seems to get the most attention here; Samsung also uses Google Gemini and OpenAI's ChatGPT across research, manufacturing, marketing, and support.

Despite this, Samsung does not use either of the other models to check input from the former; trusted engineers on-site do that instead in an industry where errors that are not picked up during reviews could be costly for a company that is currently attempting to maximize efficiency, even as its System LSI has posted losses in its SoC business and its flagship Galaxy Z Fold 8 runs Qualcomm's silicon under the hood.

To achieve any level of competency, large language models (LLMs) need ample data for sufficient training. AI companies have looked to various sources to mine this information, including content publicly available on the internet, synthetic data generated from other AI models, and printed literature.

Reading difficulties

Prompted by news that AI companies were allegedly using books from pirate ebook sites to build their LLMs, writers, authors, and publishers publicly called out this deeply worrying process.

The professional organization known as the Authors Guild responded to various stories about AI companies scanning books to train their AI models (both illegally and legally) with incredibly comprehensive guidelines on AI licensing.

This document covered the various manifestations of the use of published works by AI companies, including its legal perspective on the legitimacy of using such works. It also highlighted that the continued data harvesting processes would risk destroying the ecosystem for books that currently exists.

Book buying

The use of books by AI companies is an ongoing concern. But in recent months the focus has pivoted to those that buy, scan – and destroy – books on an industrial scale.

For example, court documents revealed the existence of 'Project Panama' inside Anthropic. This is a scheme in which the company aims to "destructively scan all the books in the world" and used a codename because "we don’t want it to be known that we are working on this.".

To train Claude, Anthropic had to procure a large and high-quality dataset, so it set out to purchase books on an industrial scale because of the relatively high-quality nature of the writing compared with, say, writing found online.

• AMD says its rack-scale AI systems are now about four times more energy-efficient than its 2024 baseline, beating its own threefold interim target by 33%
• The company offers two alternative outcomes for that gain: the same compute in far fewer racks, or twenty times more compute for the same power, something that
• AMD's own figures can also be reverse-engineered to imply that each 2030 rack draws roughly 14.25x the power of a 2024 one

AMD has published a progress report on the efficiency goals it set in June 2025, revealing the numbers are already better than the company was aiming for.

Against a 2024 baseline, AMD says its rack-scale AI systems are now roughly four times more energy efficient, ahead of the three times it had projected for this point, a trend that, by its own calculations, represents more than double the historical industry trendline.

This aligns with a six-year commitment by the chip design giant, in which it has pledged to deliver a twenty-fold improvement in rack-scale energy efficiency for AI training and inference by 2030.

Efficiency gets center stage as AMD aims to secure more AI customers

This is not the first time AMD has set itself a performance and efficiency goal. Its previous target, internally known as 30x25, aimed at a thirtyfold node-level improvement between 2020 and 2025, and the chipmaker finished ahead of its projections, clocking in at 38x,

AMD's efficiency and performance gains, if they hold as intended at 20x, can be flipped two ways: one could consider sizing down to larger, but singular racks to get their job done versus legacy hardware.

The flipside is easy to make at a time when everyone from AI startups to hyperscalers is pushing to increase compute whenever possible. AMD's efficiency gains could, in principle, allow its latest offerings in 2030 to deliver 20x the compute for the same power draw.

At a time when nobody in the industry is trying to buy less compute, AMD's offerings might be particularly appealing thanks to their efficiency gains, as projections indicate global data center electricity demand will more than double by 2030 to around 945 terawatt-hours, roughly what Japan uses today as a nation.

AMD's gains come from its newest GPUs being an order of magnitude more efficient than their predecessors; the MI300X that anchors the baseline was a 750-watt part delivering up to 2.6 petaFLOPS of dense FP8.

For context, each current-generation MI455X in Helios offers between 7.7 and 15.4 times the floating-point performance, 2.25 times the HBM, 4.4 times the memory bandwidth, and four times the chip-to-chip interconnect bandwidth, while consuming three times the power.

Its chief sustainability officer, Justin Murrill, told Trellis that every AMD team carries efficiency-per-watt targets for new products and that progress is tied to company-wide bonuses.

For now, AMD is ahead of schedule on a real, well-documented engineering goal. Hitting it, however, will not reduce the amount of electricity the AI industry consumes, as the efficiency and performance gains it offers will only fuel more demand for AI hardware that can use the saved power, even as demand for AI compute power continues to soar.

• Anti-AI sentiment and data center opposition is rising, and communities are costing tech firms billions with their opposition
• Trump says communities opposed to data centers are "making a mistake", and calls fossil fuel burning generators "beautiful"
• Texas Gov. Greg Abbott says that AI data centers "basically dug their own grave", adding that "they got the backlash they deserve."

The US government has gone all in on AI, so it’s no surprise that President Trump doesn’t fully understand why so many Americans are opposed to their presence within their communities.

During an interview with his ally Michael Cohen, Trump said “Communities that don't take a data center, they're making a mistake,” adding that their presence adds “tremendous amounts of jobs and money” to communities.

Texas Governor Greg Abbott recently offered his own take on data centers, saying that “They basically dug their own grave for the problem that's been caused for them, and that's why they got the backlash they deserve.”

Red and blue states opposed to data centers

AI data centers are requesting an unprecedented amount of energy from US electrical grids, prompting Trump to not only lift clean air restrictions on data centers, but also push forward one of the biggest repeals of environmental protections.

As a result, data centers are being built with enormous gas turbine plants that release pollutants and cause health issues for local residents. In his interview, Trump said, “They're making their own power plants. They're building the most beautiful, you've never seen power plants like this.”

Across the political spectrum in the US, there has been a singular, clear message. AI is making people very concerned. Outside of the environment, the main concern comes in the form of job replacement. The majority of Americans in a recent poll said the technology will lead to fewer jobs.

Young people are especially distrustful of those running AI companies, again arguing that the technology would lead to fewer jobs. Almost three quarters of Americans believe that the pace of AI is moving too quickly - and they may have a point. Regulations and governance on AI are quickly falling behind.

Representatives of red and blue states have been banding together to push for more restrictions on AI development and moratoriums on data center construction to give the US time to put together a plan on how best to not only build out AI capacity, but also create an AI ecosystem that benefits all Americans, not just those at the top.

But how does Trump’s argument that data centers are creating “tremendous amounts of jobs and money” hold up under scrutiny?

Are data centers creating jobs and money for local communities?

The first thing to note is that while data centers do create short-term jobs while under construction, they only require a skeleton staff once operational. The companies hired for data center construction are often larger firms with the capability to handle large infrastructure projects who often aren’t local companies at all.

Where data centers do create jobs is further down the digital pipeline as the AI capacity is leveraged by existing industries. While some industries are seeing a rise in AI-related jobs creation, 2026 has been the worst year for AI layoffs with more than 176,000 people laid off for AI-related reasons.

On the money side of the equation, Meta has recently revealed that its 4,000-acre data center campus has led to local teachers receiving bonuses of as much as $50,000 due to tax revenues gained from the site.

But Meta received $3.3 billion in tax exemptions for the site. This is a common incentive to attract investment, but many states are quickly realizing the exemptions they have granted for AI data centers are quickly becoming unsustainable.

Ohio projected a revenue loss of $136 million when it granted tax exemptions for new data centers constructed in the state, but Ohio Governor Mike DeWine recently revealed that number has reached $1.6 billion. Now, states are quickly repealing or amending their tax exemptions.

Until the Trump administration recognizes that the American public wants AI to benefit everyone in a sustainable way, rather than just AI bosses and the super rich, they’ll likely continue facing widespread opposition to their AI agenda.

• Windscribe's AI-generated ad featuring Adolf Hitler has sparked massive backlash online
• Some users have threatened to cancel their VPN subscriptions over the marketing stunt
• The controversy has also shone a spotlight on the company's use of AI for support, code, and testing

UPDATE: Windscribe's CEO shares some comments with TechRadar after publication. We are making some small edits to the copy to reflect this.

A highly controversial marketing stunt has landed Windscribe in the center of a massive online storm, with furious users taking to social media to declare they are canceling their subscriptions.

The provider, frequently evaluated in discussions for the best VPN services, is facing intense scrutiny after posting a bizarre, AI-generated advertisement featuring Adolf Hitler to its official X account.

In a bid for edgy humor that has spectacularly misfired, the video was immediately slammed by privacy advocates and customers, who were left stunned by the decision to use the infamous dictator to promote a digital privacy tool. The backlash swiftly migrated to Reddit, where threads condemning the video garnered thousands of upvotes within hours.

Replying to TechRadar's request for comment, Windscribe's CEO, Yegor Sak, said the intent of the video was "to mock authoritarianism and censorship in an absurd way" instead.

"Users choose us because we are independent, blunt, privacy-focused, and willing to say things most companies will not. That approach will not land perfectly every time, but the alternative is becoming another corporate VPN with safe posts, vague values, and nothing real to say. That is not us," said Sak.

The outrage didn't stop at the marketing misstep, though. It also reignited debates over the security implications of Windscribe's public admission that it uses artificial intelligence to handle customer support, write code, and conduct testing.

Commenting on this, Sak confirms that AI is simply a tool and real people are still accountable for production work.

"We have also continued growing the team while adopting AI," Sak added. "If AI was a replacement strategy, headcount would be moving the other way. For us, the point is to stop wasting humans on repetitive work so they can do the things AI cannot."

"A line crossed too far"

The outrage on platforms like Reddit's r/Windscribe and r/Piracy has been palpable, with users expressing total disbelief. One user summed up the collective shock, stating, "I just canceled my subscription."

Another user criticized the brand's attempt at adopting an overly casual, shock-jock persona, writing: "One could argue this is normal since Windscribe has always been edgy with their emails and April Fools' gags, but straight up using Hitler is a line crossed too far."

Other users were even more direct, questioning the recent trend of bizarre behavior from VPN providers. "What's with these VPN companies doing this nonsense lately," one commenter noted.

For many, the misstep isn't just a simple mistake but proof that shock-value advertising can easily alienate a core audience relying on these tools for safety.

The AI security elephant in the room

The Hitler video isn't the only issue causing subscribers to hit the cancel button. Screenshots circulating alongside the controversial ad show Windscribe openly discussing its extensive use of artificial intelligence in day-to-day operations.

The company publicly confirmed that its support, coding, and testing are handled by AI.

While integrating artificial intelligence tools is becoming standard practice across the software industry, virtual private network (VPN) users are notoriously protective of their data.

A service designed to encrypt sensitive information and hide digital footprints requires bulletproof cybersecurity practices. The idea that a company's core infrastructure or testing protocols might rely too much on AI has led to understandable anxiety. Bragging about AI while simultaneously botching a PR stunt doesn't inspire confidence.

For now, Windscribe's attempts to go viral have succeeded, but entirely for the wrong reasons. Until the company addresses the community's concerns, its reputation as a reliable guardian of online privacy remains under a heavy cloud.

When you open a forecasting app, you simply select a location, choose an hour and read the answer.

Behind that screen, however, lies one of the hardest prediction problems in science.

The atmosphere is a three-dimensional, constantly changing system.

Before a model can predict what it will do next, it must first answer a surprisingly difficult question: what exactly is the atmosphere doing right now?

We never see the whole atmosphere

No single instrument provides a complete picture of the weather. Weather radars detect precipitation particles. Satellites observe clouds, moisture and temperatures from space. Ground stations measure conditions at individual locations. Weather balloons and aircraft provide information from different altitudes.

Each source has its own resolution, update frequency, coverage gaps and measurement errors. Building the current state of the atmosphere is therefore like reconstructing traffic across an entire city using cameras that point in different directions, refresh at different times and leave many streets completely invisible.

Meteorologists call the process of combining observations with a previous forecast “data assimilation”. It produces an estimate of the current atmospheric state, known as an analysis, from which the next forecast begins.

As ECMWF explains, forecast quality depends crucially on the accuracy of this analysis. Even an excellent model will struggle if its starting picture is incomplete, inaccurate or already out of date.

Why apps lie

One common short-term forecasting technique is optical flow. It analyses consecutive radar images, estimates how precipitation is moving and extrapolates that motion into the future. This can work well when a rain band is moving steadily. The problem is that storms are not static shapes sliding across a map. They form, intensify, weaken, split and merge. A new convective cell can appear where no rain existed 20 minutes earlier.

Imagine a storm approaching a mountain. A simple extrapolation may predict that it will continue moving at the same speed. In reality, the terrain can alter airflow, slow the system and contribute to heavier rain or hail. The challenge is therefore not only predicting where existing precipitation will move. A useful model must also predict how the weather system itself will change.

Another issue is that large-scale models are good at describing weather fronts, pressure systems and atmospheric circulation across countries and continents. But many events that affect people and businesses happen on a much smaller scale. A thunderstorm may affect one part of a city while leaving another almost dry.

A cloud front can sharply reduce output at one solar farm but miss another facility 20 kilometers away. Wind conditions can differ substantially between the ground, a rooftop and the altitude at which a drone operates. Those they can’t predict.

Weather is also highly sensitive to its initial state. Small uncertainties in temperature, humidity or wind can grow as a forecast extends further into the future. This is the real meaning of the “butterfly effect”. It does not mean that one faulty sensor automatically destroys a global forecast. It means that we can never measure the atmosphere perfectly, and uncertainty in its initial state grows over time.

That is why modern forecasting systems increasingly produce ensembles: multiple plausible forecasts rather than one supposedly certain answer. For many decisions, knowing that there is a 30% probability of a severe storm is more useful than receiving a confident prediction that later turns out to be wrong.

Not to late

Latency is one of the least discussed sources of forecast error. Consider a model that produces an excellent forecast based on atmospheric conditions measured two hours ago. If a thunderstorm formed 20 minutes ago, the forecast may already be operationally useless, regardless of how sophisticated the model is.

The World Meteorological Organization defines nowcasting as detailed local forecasting from the present up to six hours ahead. For real-world applications, however, nowcasting is more than a forecast horizon. It requires a continuous pipeline that collects new observations, checks their quality, synchronizes them in time, runs the model and delivers an updated result within minutes.

At the same time, real-time weather forecasting does not mean literally zero delay. What system does is keeps listening to the atmosphere and updates quickly enough to influence the decision being made.

And that means a lot for some industries. A consumer deciding whether to take an umbrella may tolerate an imperfect hourly forecast. Many operational systems cannot. A delivery drone needs to know whether heavy precipitation, icing or dangerous wind will cross its route before it reaches the area. An airport may need to adjust runway operations as a thunderstorm develops nearby.

A solar operator needs to anticipate a fast-moving cloud front before generation suddenly falls. Logistics and mobility platforms can reroute vehicles before flooding or severe rain disrupts a road network.

The same principle applies to outdoor events, emergency services, agriculture and energy trading. These users do not simply need “tomorrow’s weather”. They need to know what is changing, where it is changing and whether there is still time to act.

AI makes forecasting faster, but speed alone is not enough

AI weather models can generate forecasts much faster and with less computing power than many traditional numerical models. Some already outperform physics-based systems on particular variables and forecast horizons. But AI does not eliminate the observation problem.

Most global AI forecasting models still begin with an atmospheric analysis produced by traditional numerical weather prediction infrastructure. If that analysis is several hours old, fast inference cannot make the initial state current again.

AI models can also underestimate the intensity of rare events because extreme examples are underrepresented in their training data and because some training methods favor smoother, more statistically likely outcomes. Recent research has found that leading AI models can produce larger errors than physics-based systems when forecasting record-breaking heat, cold and wind events.

This is why the future is unlikely to be a simple contest between AI and physics. ECMWF already runs its operational AI forecasting system alongside its traditional physics-based model.

The strongest architecture will combine global models, high-frequency local observations, AI-based nowcasting, physical constraints and probabilistic estimates of uncertainty.

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The challenge for marketers has quickly shifted from not having enough data to make informed decisions to having so much data that it becomes nearly impossible to organize, present, and act on it in any meaningful way.

I had the opportunity to talk to Tara Robertson, Chief Marketing Officer at Bitly, to get her insight into how modern businesses can better understand what data really matters, how to collect it, and how to move away from making 'gut-feel' decisions that could be costing your business.

How is AI changing how potential customers interact with marketing content?

AI has changed the early stages of the customer journey pretty significantly. People still want to make informed purchase decisions, but increasingly, they’re researching and comparing options inside AI tools and LLMs rather than exclusively clicking through a traditional search results page. That creates a much bigger “dark funnel” for marketers because more of the customer journey occurs in places we can’t easily see or measure.

At the same time, the visibility signals we’ve historically relied on are becoming less reliable. Ranking highly on a SERP, for example, doesn’t necessarily translate to the same level of traffic when AI Overviews can give someone the information they need without requiring a click.

On the other hand, since there are now fewer clicks than before AI became so intertwined in discovery, the clicks that do happen mean so much more. If someone looks at the AI summary, gets the answer they need, and still clicks, that shows real intent, not just casual browsing.

So, marketers should really strive to change their mindset from “how can I get more clicks” to “how do I make sure I know what's happening with the clicks I do get, and how do I create content good enough that a human or an AI system wants to surface it in the first place.

Your recent study found that 56% of marketers rely on gut instinct to make marketing decisions. With so much data now at marketers' fingertips, why do you think this is still the case?

This stat has stayed with me the most since we gathered the data. It’s not that marketers don’t want to be data-driven, but that the data they collect is spread across too many tools, making it difficult to make strategic decisions in real-time.

Our research revealed that teams are juggling an average of six different tools to measure performance, and more than a third are using seven or more. With this amount of data on hand, many would think that this provides clear campaign performance insights. Instead, the data is fragmented and typically focuses on only one channel. Very few are integrated with other tools, so teams are looking at pieces instead of the full picture.

So when faced with a tool and data overload, marketers lean on what has worked for them in the past, which is often going with their gut. This isn’t a bad strategy when the data fails to bring clear insights because those efforts worked for a reason, but in today’s landscape, that’s not what’s going to create success. Just because one tactic worked in a previous campaign doesn’t mean it will work again in the current one. My take is that the fix isn't "try harder to be data-driven"; it's giving people faster, cleaner signals that are easier to access so instinct becomes the backup plan instead of the default.

Your report also shows that 73% of marketers only realize a campaign is underperforming after it’s too late. What processes and tools can marketers put in place to ensure data is timely and helpful?

My biggest tip is to be proactive.

Instead of looking at the data at a campaign level and analyzing it after it's complete, look at the interaction level and get granular with your insights from clicks, scans, and visits.

The overarching issue is that 86% of marketers wait until a project is complete to analyze data at the broader campaign level. By the time final reporting comes around, the budget is spent, and the window to optimize is closed. Analyzing granular interactions in the early days of a campaign gives you the agility to adjust in real time.

Other tips I usually share are to set a check-in point when campaigns are being built instead of waiting until after to see if it works. Set multiple, if needed, especially when just starting out.

Second, use AI to help with data analysis to accelerate the time-to-insight process. It’s still a good idea to always double-check your work, since AI can still make mistakes, but we found that marketers who incorporate AI into their workflows report receiving faster data insights and are more confident in their decisions, yet only 16% are actually doing this today.

Finally, build a little flexibility into your budget and your plan. Locked-in budgets were the number one thing marketers told us limits their ability to act on what they're seeing — you can have perfect visibility and still be stuck if there's no room to move.

How should small businesses use AI to better understand their data and make more informed marketing decisions?

Small businesses don't usually have a dedicated marketing team to thoroughly review, analyze, and act on the data that’s collected behind marketing activity. AI-enabled tools can help close that gap and surface insights to help direct the "what's next?" decision.

The goal isn't to get every new tool that comes to market, but to make sure the tools they already use provide clear insights quickly. Instead of exporting a spreadsheet and trying to manually figure out why last week's numbers moved, you should be able to just ask the tool.

I would start small. For example, business owners could ask AI assistants to provide weekly summaries of what changed. This way, if some of the numbers aren’t adding up, the issue can be detected sooner. When you are the one running the show, any way to save time on routine tasks is helpful. The businesses getting real value aren't the ones with the most sophisticated AI setup; they're the ones who made asking questions and getting answers often and quickly a habit.

Around 18% of marketers said they struggled to connect marketing efforts with customer retention. Beyond repeat purchases, which metrics can marketers monitor to better understand customer lifetime value?

Repeat purchases tell you a customer came back. They don't tell you why, or how close that customer might be to leaving anyway. To actually understand lifetime value, I look at three things beyond the purchase itself:

Customer research and direct feedback. NPS and CSAT are useful, but only if you're measuring them at the moments that matter, not once a year in a company-wide survey. Score it right after onboarding, right after a support ticket, right before renewal. The number tells you something moved; the open-ended verbatims tell you why. That qualitative layer is what most retention dashboards are missing.

Activation and usage depth, especially if you're product-led. Time to first value, breadth of feature adoption, whether usage is expanding or flatlining. A customer who renews but uses less of your product every month isn't retained; they're on their way out and still paying you for now. Watching usage trendlines catches that months before the churn shows up in your revenue numbers.

What happens in the white space between purchases. Are they opening your emails, engaging with your content, actually using your loyalty program? Rising support ticket volume or a drop-off in engagement is usually the earliest signal you'll get that something's wrong, well before it shows up as a lost customer.

None of this replaces repeat purchase rate as a metric. It just gives you the why behind the number, and enough runway to act on it before the customer is already gone.

Top-level metrics often give an idea of the way a campaign is going, but don’t always give the full picture. What micro-interactions and signals should people be looking for?

These analytics are what I think marketers aren’t focusing on enough. When reviewing success at the program and campaign levels, reporting gives you an overall view of how performance went, but it won’t provide the specifics.

More than half of the marketers who participated in our study said they focus on high-level strategy to inform decisions, while only 14% actually track micro-interactions such as individual clicks, scans, or page visits.

The small, seemingly inconsequential data points are often where the real story lies. Knowing which specific link or QR code is gaining the most traction, or even which channel it originated from. Whether a spike in engagement on one post actually correlates with anything downstream or is just noise. Knowing what happened before they converted could make the difference between a successful and failing campaign.

Even going as small as the time and location patterns can be helpful. Are most of the interactions coming from a QR code located in the store or one shared digitally? These small indicators reveal insights into customer intent. None of these metrics typically end up on top-line dashboards or reports, but when viewed as a whole, you can review which touchpoints are performing well and which ones aren’t.

How can businesses cut bloat when picking which marketing and analytical tools are right for them?

I suggest beginning with a thorough review of the tools you currently use and what you would be losing if you let it go. Tools are often added to the stack to solve one problem at a time, and a year later, those issues might no longer be relevant. Be mindful of what each offers and whether you truly need them, and always remember that you should be looking at your strategy BEFORE your tooling. Not the other way around.

Before adding a new tool to the stack, ask yourself what is unique about this product that nothing else you currently use provides. If features overlap, you don’t need to have both. I’d also consider whether those tools integrate well with the other programs you’re using. They are often forgotten about if they aren’t directly in your workflow. Fewer, better-connected tools will always beat more tools that don't talk to each other — that's basically the key finding of our research in one sentence.

• AI claims another victim: Dr. Dre admits to using it for music production
• The legendary producer doesn't detail how he uses it, though
• Certain uses could run him afoul of new streaming pushes

Another former musician has admitted to embracing artificial intelligence — Dr. Dre, formerly of N.W.A, has admitted that he uses AI when he's producing music.

In an interview with the New York Times the rapper, who's actually called Andre Romell Young, joined the likes of David Guetta, Timbaland and Grimes in admitting to using AI in their works.

"It's a new tool for creativity," Dre said, "I'm embracing it. I can't wait to see what's going to happen with this." He also repeated a common AI-user talking point, comparing it to drum machines or synthesizers and describing how much of a backlash there was against those now-common tools when they were first introduced.

In the interview, Dr. Dre didn't open up about how he actually uses AI. So it's not clear whether he uses it to artificially create entire tracks or stems, like Boy George infamously did with his We Will Dance Again, or simply to isolate elements of existing tracks, as Paul McCartney famously did to remaster a Beatles song.

Given how many simple tech tools are labelled as AI, he could simply be using it to organize files.

Dre runs afoul of the streaming war

Streaming services are currently waging a war against the deluge of AI music which gets dumped on their platforms daily. According to Deezer, half of its uploads are AI-generated, and the issue has pushed the major platforms to lay down the law with AI slop.

Deezer has been busy releasing non-AI features and unveiling AI removal filters, while Qobuz recently spoke to us about its own plans to tackle AI, and Tidal doesn't give royalties to AI music. Spotify has been much slower on the draw than its rivals, but even it recently unveiled an AI Persona tag to identify 'artists' who aren't real people.

It was done best by Bandcamp, which simply and easily banned music which was "generated wholly or in substantial part by AI". Now that's how you stick up for your users.

Given that he's a known entity in the music world, with a large back catalog that predates AI, it's very unlikely that Dr. Dre would fall afoul of these existing anti-AI rules. You're not going to see an AI tag on California Love, or find Straight Outta Compton has been demonetized.

But the goalposts are constantly changing, as music streaming services cotton onto just how much listeners hate AI slop, and discover new ways of identifying and quashing artificial music. While Dre's ambiguous AI use won't break streaming service rules now, that could change as these services redefine what they allow.

The current narrative around enterprise AI is rapidly shifting from the excitement of the pilot phase to the sobering reality of production. As organizations race to integrate generative AI into their workflows, they are hitting a wall that has less to do with technology capability and everything to do with how those models are deployed.

Increasingly, companies are discovering that the problem isn't AI itself, but the assumption that every task requires the most powerful model available. This has led to widespread "tokenmaxxing" - the tendency to default to the largest and most expensive models even when a smaller, cheaper alternative could complete a task.

Rather than matching the right model to the right job, many organizations assume every workflow requires frontier-level reasoning power.

This over-engineering of automation creates a structural drag on profitability. When companies treat every problem as if it requires a frontier model, infrastructure costs inevitably outpace the value of output.

The market is witnessing the consequences of this approach, with reports of major enterprises burning through entire AI budgets in months and canceling internal licenses as costs spiral.

As enterprises experience AI sticker shock, it is becoming clear that AI spending is often outpacing the tangible value it delivers.

Moving beyond the pilot trap

The core issue is that the success of isolated, controlled pilots often serves as the benchmark for current enterprise AI initiatives. In a pilot, the variables are limited, and the cost per process looks manageable. But the moment those floodgates open to enterprise-wide usage, the messy reality of production, edge cases, multistep retries, and high-volume variability, takes hold.

Because probabilistic AI generates a different cost for every run, it creates an unpredictable expense that finance departments cannot forecast. With traditional software, a fixed budget aligns with a predictable cost per task. With AI, that stability is missing. When the same process costs one dollar one day and a hundred dollars the next, it cannot be safely moved onto an operating budget.

This is why 80% of enterprises admit they miss AI cost forecasts by more than 25%, and over 95% of GenAI pilots fail to reach meaningful production. Enterprises are not currently optimizing a cost-to-value ratio; they are discovering that ratio the hard way, often after the financial damage is already done. For many leaders, the only perceived lever left is to "use less", throttling usage or restricting access.

But this response is flawed. The real shock is not the size of the bill itself, but the realization that the only lever management has left is to restrict consumption. By rationing access, the organization is effectively admitting that its AI implementation is too costly to run at scale, turning a potential competitive advantage into a defensive retreat.

Rethinking the economics of automation

Today, most organizations focus on optimizing model selection, essentially deciding which model should handle a task, but the bigger opportunity lies in optimizing the work itself. Because costs reset every time a request starts from scratch inside a large model, assuming every task requires a frontier-level LLM quickly becomes an expensive mistake.

Standard routing tools operate at the request level: they look at an incoming task and forward it to whichever model seems adequate, but that model still performs the entire task as a single, opaque generation. The only thing being optimized is which model answers, and nothing produced makes the next run any cheaper. True scalability requires going one level deeper.

This requires shifting toward an architecture that owns the business process rather than relying entirely on the underlying model. Rather than simply routing work to an endpoint, this architectural approach manages the process itself. It sits above individual models, breaking a workflow into discrete, code-backed steps to generate an auditable trace at every stage.

This single architectural choice is what separates structural optimization from surface-level cost management, offering a depth of efficiency that conventional routing tools cannot reach. This shift leads to a more efficient cost structure. Rather than relying on a single model to perform every task, computation is distributed across specialized, code-backed steps, improving resource utilization and changing how the workflow is executed.

In a high-volume loan- processing workflow, for example, this approach improves overall economics by reducing reliance on expensive model inference where it is not required. Moving toward this modular, process-driven architecture provides a sustainable path for managing the costs of high-volume operations.

Because the process is decoupled from any specific provider, organizations retain full flexibility. They can freely apply optimization strategies, rotating between frontier LLMs, open-source weights, and smaller specialized models as performance and cost needs evolve, without having to rebuild their core infrastructure.

And because every execution produces a deterministic, auditable trace of reasoning steps, tool calls, and outcomes, those records become valuable data assets, improving specialized alternatives that already know how to handle predictable tasks without defaulting to general-purpose calls.

Elevating human capacity through precision

The goal of this new approach is to transition from AI as a capped experiment to AI as the standard means of performing work. When repetitive cognitive tasks, such as verifying data or confirming disclosures, are handled by automated digital workers, the human role changes entirely. Analysts are no longer forced to spend their day manually opening files and keying in data; instead, they shift their focus to high-judgment exceptions, complex strategy, and creative problem-solving.

This is the promise of sustainable AI adoption. By converting unpredictable expenses into a known cost base and focusing on re-architecting workflows rather than just swapping models, companies can finally open the floodgates. This gives organizations a path toward sustainable and measurable ROI as adoption scales, empowering their workforce to focus on the high- value, evaluative work that technology cannot replicate.

The future of enterprise AI will not be determined by model capability alone, but by the ability to deploy integral AI systems that finally make the technology economically viable at scale. It is time to leave behind the era of unsustainable experimentation and embrace a disciplined, architectural approach to AI adoption. For organizations ready to move beyond the pilot stage, the challenge is no longer proving that AI works. It's making it economical enough to scale.

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• The 2026 World Humanoid Robot Games are happening this week
• Robots are performing better than ever across 51 events
• Stopping a sprinting robot remains a major engineering challenge

The World Humanoid Robot Games are underway in Beijing, China, and records set by flesh and blood humans are tumbling: these bots have now beaten our best efforts at the 100-meter sprint and the high jump.

As the Associated Press reports, the robot record for the 100 meters now stands at 9.39 seconds (beating Usain Bolt's 9.58 seconds), while a humanoid bot has reached 2.88 meters on the high jump (above Javier Sotomayor's 2.45 meters).

This is only the second year of the Robot Games, but the progress from 2025 is noticeable — these robots were only reaching 0.95 meters on the high jump last year, for example. One engineer told the South China Morning Post that improved networking capabilities, meaning better access to cloud AI processing, is one reason for the advances in the tech.

These humanoid robots will be put to the test across 51 different disciplines over the course of the five-day event, with sports like tennis, table tennis, and kickboxing newly added for 2026. More practical trials are also included, covering tasks across industrial production, logistics, and hospitality.

Stop right there

As impressive as a lot of these robot athletes are, we're still seeing plenty of fails — such as sprinting robots smashing into safety barriers to stop. Getting these machines to slow down is actually a harder engineering challenge than you might think: we're talking about high-speed, complex movement that needs to be managed with exact precision.

There's a lot of energy that needs dissipating, a lot of rebalancing that's required, and a lot of transitioning from one state to another to be done. Researchers have managed it in simulations, but not yet in actual sprinting robots — right now the speeds and movement mechanisms are too much for the robot AIs to cope with.

It comes back to a perennial problem for robots: avoiding falling over. We take staying upright for granted, but it involves a lot of split-second calculations. When a robot is running, the problem gets exponentially more difficult.

Those watching don't seem too concerned by the abrupt stopping mechanisms. "These sports are perfectly normal for humans, but now robots can do them. I find it amazing," spectator Yang Shangzheng told the Associated Press.

The first salvo of the war against AI slop on LinkedIn looks to be going the right way for those of us who can actually use our imaginations, the company has said.

LinkedIn Chief Product Officer Hari Srinivasan has revealed over a million people have clicked the ‘seems like AI slop’ report option in the two weeks since its launch - and I've definitely clicked it a few times.

Srinivasan says that these reports, along with changes to LinkedIn's detection systems, have cut views of content classified as AI slop by 40% - although sadly my feed is still pretty full of it.

"Low-quality content" crackdown

In his post, Srinivasan noted the feedback was, "helping us better understand how our community experiences low-quality content" across LinkedIn, suggesting stricter clampdowns to come.

The first stage of this is launching now, so if one of your posts receives enough community feedback (aka is reported as AI slop), you may see a message in your Post Analytics, "to help you understand how your content is being received".

"We approached this assuming good intent; I know I'm increasingly conscious on how to not sound like AI & the goal is to provide helpful feedback," Srinivasan says.

"Importantly, no single piece of feedback determines how content is distributed," he added. "We look at many signals together, and we've built safeguards to help prevent individual feedback from being used to unfairly target other members."

"Despite the progress, we know we have more to do to ensure LinkedIn remains a place where you can find real people & real perspectives... this all remains very top of mind," Srinivasan concluded, suggesting further actions may come soon.

The company announced it was cracking down on AI slop in early August 2026, when Srinivasan declared it was, "a top priority for all of us. We really care about this...people come to LinkedIn to connect with real people and share their real perspectives, ideas and expertise."

To report a post, users just need to click on the three dots at the top-right of any LinkedIn post, and among the usual options to save, share or embed, you'll see a new option - "Seems like AI slop".

Srinivasan added that Microsoft-owned LinkedIn will, "continue to improve and invest in our automation defences..on comments alone, everyday we are now catching hundreds of thousands of automated comment attempts, and have blocked billions of other automation attempts (posting at scale, slop) in the last couple months alone."

At 2 a.m., an automated remediation agent detects a problem on the network, traces it to a misconfigured policy, validates through the harness that the proposed fix operates within the given policy boundaries and fixes it. The network stabilizes. Nobody’s notified.

In the morning, a human reviews the agent's daily insights: a summary of all the changes executed, with links to the logs, audit trails, reasoning and root cause behind them, confirms everything has been properly resolved and moves on. That is what Human-on-the-Loop looks like.

At another organization, at 4 a.m., a DIY-built, vibe-coded remediation agent detects a problem on the network, traces it to a misconfigured policy, and fixes it. The network stabilizes. Nobody’s notified. In the morning, a human reviews the logs, assumes the issue has been resolved, and moves on.

Where's the difference?

The difference is that, in the DIY scenario, the logs only tell part of the story. They don't show that the agent made three other changes to get there, which were broader than intended, and the decisions behind those changes weren’t flagged because nothing in its constraints required them to be.

This is what the move toward Human-on-the-Loop can look like without the right controls in place. No dramatic handover. Just a series of small, reasonable delegations that gradually build into something nobody explicitly signed off on.

And it's happening faster than most leaders realize. According to recent research, 57% of IT leaders expect to remove humans from the loop within a year or less, and 79% already treat AI agents as "users" who require their own identity management and governance controls.

The shift to agentic AI is happening faster than most organizations are prepared for, both in terms of governance and the ability to evaluate autonomous systems.

The illusion of "I approve"

The answer to autonomous AI has long been quite simple: keep a human in the loop. Somebody who reviews the output, hits approve, preserving accountability. Except it isn't, not really. Reviewing every action doesn't automatically create accountability, and it also prevents organizations from realizing the full benefits of autonomy.

Rather than reviewing every individual action, humans should be focused on evaluating outcomes, ensuring the system operated within its intended boundaries, and providing feedback that improves its performance over time.

Approval can become a ritual without meaning. As systems prove reliable and the number of alerts multiply, humans sometimes start to treat intervention as something that isn’t often needed.

The approval can become more of a click than a considered choice. And when something goes wrong (for example, a misconfigured policy, an automated remediation that turns into an outage), the question of who was responsible is difficult to answer.

It also reinforces a broader shift: one of the most important human capabilities becomes critical thinking and the validation of hypotheses, rather than the execution of tasks.

And something else is happening. Humans are transitioning from doing to reading before approving – a fundamental change in the day-to-day work of most of us.

Also, attribution isn't the same as provenance. A log that records what an agent did tells you almost nothing about why, or what shaped that decision. When things go wrong, those are the things you need to know.

Non-human identities and the new network population

When AI agents act on your network, querying systems or making configuration changes or routing traffic, they are effectively users. They need credentials, policies, guardrails, explainability, and audit trails just like human operators.

Most organizations haven't caught up with this. Identity frameworks were built for people, and applying AI agents to them as a kind of afterthought creates exactly the sort of shadow access that security teams spend their lives trying to eliminate.

The near-80% of leaders who already treat agents as governed identities are ahead of the curve. The rest are collecting risk they can't quantify, until it crystallizes into an incident.

To get this right you have to treat each agent as a principal with bounded permissions, time-limited access and a clear revocation path, along with a complete record; not just of what it did, but of what it was allowed to do and why.

Autonomy isn't given, it's earned

Network and security teams already know how to do this. Every enterprise has access control frameworks that govern what humans can reach and when. A junior engineer doesn't walk in on their first day with total production access. They gradually earn it, and this same logic needs to apply to AI agents.

We're not quite there yet. Too often, autonomy is treated as all-or-nothing. That isn’t the right model. Trust when it comes to humans doesn’t work like that, and it shouldn’t with AI either. Start agents in suggestion mode and let them prove themselves within clearly defined limits before expanding what they can do. And make sure every decision leaves a trail that explains not just what happened but the reasoning behind it.

As the use of autonomous AI grows, organizations will need to balance human oversight with systemic governance. People remain responsible for reviewing not only the outcomes AI produces, but, where necessary, the actions it takes and the reasoning behind them.

However, as the volume and complexity of autonomous decisions increase, this oversight should be complemented by infrastructure that embeds identity, policy enforcement, observability and accountability. Together, these controls help ensure AI actions remain transparent, traceable and aligned with organizational intent.

Building multi-agent systems that hold up under pressure

The more capable these systems become, the more organizations will move toward multi-agent architectures. This is where specialized agents each own a piece of a workflow and hand off context as they go. That's where things get complicated.

A single agent misbehaving is traceable. A chain of agents, each acting on the outputs of the last, is much harder to untangle when something goes wrong unless the right architecture and governance are in place. You need to know what each agent knew, not just what it did.

Do the boring part

Two organisations deploy the same AI-powered networking agent. One of them has done the unglamorous work: setting up tight permissions, proper identity controls and audit trails that actually answer questions. The other one hasn't.

You won't know the difference until something breaks.

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There’s a pattern starting to emerge with AI.

At first glance, everything looks like progress. AI is being adopted quickly, embedded into products, talked about in boardrooms, and pushed into real-world use faster than anything we’ve seen before. But as businesses become more accustomed to AI and increasingly find new ways to use it, there is a greater problem brewing that has the potential to be detrimental to a company’s cybersecurity posture.

Organizations are moving quickly to use AI, but far fewer are making the right decisions about how it’s actually being delivered and secured. And the gap between those two things is widening, with security teams left scrambling to fix vulnerabilities like whack-a-mole.

The speed is understandable. AI hasn’t followed the usual enterprise lifecycle. It hasn’t patiently moved from concept to pilot to controlled rollout. In many cases, it’s gone straight from experimentation into something business-critical, stitched together from APIs, models, agents, and data sources that weren’t originally designed to work together in this way.

That creates something fundamentally different. Not just another application, but something more fluid, a tool that behaves dynamically to make decisions and interact across multiple layers of the stack in real time.

And this is where the problem begins.

Where AI security currently breaks down

While the architecture that needs to be secure has changed, the thinking around security largely hasn’t, meaning traditional security measures are still being applied to situations they aren’t built for. Most organizations believe they have this covered. They’ve extended their existing controls, added new tools and invested in visibility. On paper, it looks like a sensible evolution of what they already had that keeps up with AI.

But in reality, much of that security still sits around AI rather than within it.

These traditional methods are protecting edges, monitoring outcomes and analyzing behavior after the fact. What they’re not consistently doing is sitting in the path of execution, where decisions are actually being made, and where things can go wrong in real time. It’s this distinction that matters more than most people realize.

AI doesn’t behave like anything we’ve secured before. A single interaction isn’t just a request and a response. It’s a chain of events where a prompt is interpreted, a model responds, an agent may take action, data is retrieved, decisions are made, and outputs are generated. This all happens in one continuous flow.

The risk doesn’t exist at a single point. It exists throughout that chain. This is where prompt injection happens. It’s where models can be manipulated, where sensitive data can leak through inference and where unintended behaviors and outcomes emerge.

The cause of this isn’t always an incorrect configuration; it can also be the result of the system responding exactly as designed, just not in the way anyone expected.

The industry is starting to acknowledge this. There’s a growing recognition that runtime is where the real battle is being fought, and that securing AI means understanding how it behaves under pressure, not just how it’s built.

Moving beyond bolt-on security

But if that’s becoming clearer, why are so many organizations still getting it wrong? Well, in most cases, it comes down to how decisions are being made. AI is often being driven by innovation teams or developers, those who are closest to the opportunity and implementation of AI tools.

But that also means infrastructure and security decisions are following behind rather than shaping the architecture from the start. At the same time, there’s a tendency to default to adding more tools to plug the security gaps. Faced with a new risk, the natural instinct is to look for something new and shiny to buy that addresses it.

AI doesn’t fit neatly into that model. It doesn’t live in one place. It cuts across applications, APIs, data, and user interaction all at once. Treating AI as something you can secure with a standalone tool misses the point entirely.

What is actually needed is a different way of thinking, one that starts with looking at where control actually needs to exist. There are only so many places security can be meaningfully enforced, and for AI, one of the places that consistently matters is the flow of traffic itself.

This is the point at which requests are made, decisions are processed, and responses are returned - where behavior can be influenced the most and where policy can be enforced. Everything else, to some degree, is reactive.

This is also where the conversation around security platforms becomes more interesting. Not because AI capabilities have simply been added to existing portfolios, but because the role these platforms play is changing.

Sitting in front of applications and APIs, they have long been responsible for managing traffic, applying policy and enforcing decisions. What’s changed is that these same control layers are now being extended into AI interactions themselves.

That shift is subtle, but important, as it moves AI security away from being something that happens in isolation and closer to something that is embedded directly into how systems operate. Not bolted on, not observed from the outside, but enforced as part of the execution path.

This isn’t really about one vendor. It’s about recognizing that AI has changed the shape of the problem.

Control will define the next era of AI security

The market is still catching up. The tooling is still evolving. And most organizations are understandably feeling their way through it.

But the decisions being made now - where to place control, how to integrate security, what assumptions to carry forward from the past - will define how manageable this becomes over the next few years.

We’ve seen this before, just in a slightly different form. APIs went through a similar phase not long ago - rapid growth, fragmented control, and then a long period of retrofitting security once the risks became clear.

AI is moving faster than that ever did. The attack surface is broader, the behavior less predictable, and the consequences potentially more significant.

Which means there’s less room for getting it wrong.

The organizations that navigate cybersecurity well in the age of AI won’t necessarily be the ones that adopt AI the fastest. They’ll be the ones that understand where control needs to sit and make deliberate decisions about how it’s enforced. With AI, more than anything else, it’s not just about what you can see. It’s about where you can act.

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Cyber risk is now a board room issue, and we have seen clear examples of this in the UK. The 2025 Jaguar Land Rover attack left the carmaker with a £485m loss, swallowing up the £398m profit it had generated just 12 months before.

Production lines were halted for more than a month as the company shut down parts of its network, showing how quickly a cyber incident can affect business performance, operational continuity and the wider supply chain.

Now, businesses are facing a fresh type of threat made possible by AI – the autonomous attack. Attackers can already automate parts of target research, initial access and malware development, with any manual effort shrinking rapidly.

Simultaneously, the trust layer people rely on is eroding with the spread of AI-generated content and deepfakes. It’s a race to tackle the autonomous attack, but how do organizations formulate an effective response?

AI in a cyber-attacker’s armory

AI-driven automated technologies are strengthening a cyber-attacker’s armory. Prior to leveraging AI tools, bad actors often had to commit time and resources to researching a target company before planning an attack.

Timing was critical, and a perpetrator had to manually coordinate and initiate an attack at a specific time and could simply forget. AI doesn’t - and the rise of attack-as-a-service tools is making it possible to successfully breach organizations quickly and accurately.

Guardrails are starting to be put up around established generative AI tools, such as ChatGPT and Claude, in an effort to prevent this kind of misuse. But hackers are finding workarounds.

Rather than relying on readily available large language models (LLMs), they are deploying their own small language models (SLMs) on local devices, often on something as basic as a Raspberry Pi computer. From there, they can escalate attacks while hiding in the shadows.

The threat to businesses of all sizes

The rise of automated attacks also means that businesses of all sizes are likely to be identified by automated technology as having exploitable vulnerabilities. Small and medium-sized businesses would previously have been off the radar as attacks relied on a bad actor’s knowledge of their existence.

However, AI can now scan and process vast numbers of organizations at speed, potentially leaving smaller firms, which are less likely to have robust cyber controls in place, more exposed. And even more so among smaller businesses, defenses are typically more fragmented and less organized than AI-driven attacks.

In other words, with AI by their side, attackers can coordinate and scale far better and much more quickly than most businesses can defend.

Autonomous attacks also make third-party and supply chain risk much harder to manage. Business networks can create access to data, systems or operational processes. When attackers can automate reconnaissance and scale attacks across thousands of organizations, weaker suppliers may become an attractive route into larger businesses.

This is a particular concern because third-party risk management has often relied on annual questionnaires, point-in-time assessments and contractual assurances, but these approaches are no longer enough on their own. A supplier may have recently exposed a service, suffered a breach, changed its access privileges or failed to patch a critical vulnerability.

Businesses therefore need to move towards continuous, automated monitoring of supplier security posture.

Regulations such as NIS2 have also increased the focus on supply chain security for organizations operating in, or selling into, the EU. There is also a growing expectation from ICO and the FCA that boards can demonstrate cyber resilience.

Automation and the rise of specific attack types

Jadepuffer illustrates how AI is beginning to transform established attack types. Disclosed by Sysdig in July 2026, it was assessed as the first documented end-to-end LLM-driven extortion operation, with an AI agent conducting reconnaissance, harvesting credentials, moving between systems, destroying data and adapting when individual actions failed.

While none of the techniques were especially new in isolation, the significance was the way the AI connected them into a complete, adaptive attack.

Social engineering techniques, such as bad actors posing as trusted individuals, are becoming much more convincing in their approach. Fluent, grammatically correct messages and the professional tone and style of CEO communications can now be fully replicated on emails, SMS and even WhatsApp.

AI can even manage the entire conversation thread, including dynamically adapting responses to a target’s replies, with it possible to run simultaneous, tailored campaigns.

Vendor email compromise, where criminals impersonate suppliers, intercept genuine payment conversations or use compromised vendor accounts to request changes to bank details, directly links social engineering to third-party risk.

Taking a step back, the initial harvesting process of personal data for social engineering attacks can be streamlined. AI can automatically scrape data from public sources such as Companies House and social media to quickly provide the names of specific people, their roles and relationships.

When trust and identity come under attack

Even on video conferencing calls, it’s becoming increasingly difficult to tell if the person you’re speaking to is real due to the increasing accuracy of deepfakes. As an example, it’s often now necessary to ask a suspected deepfake to do something it wasn’t programmed to do, such as raise a hand, to check if the person in question is real. But even that test is gradually being circumvented by new technology.

Organizations need stronger out-of-band verification protocols for high-value or unusual requests. A pre-agreed code word via a separate channel might be needed to ensure trust and security.

Identity security is becoming a key area of defense as autonomous attacks become more advanced. Credential stuffing at scale, session cookie harvesting, MFA fatigue attacks and vishing attempts designed to bypass multi-factor authentication are all increasing. AI can make these attacks more efficient by identifying likely targets, generating convincing scripts and adapting to the victim's responses in real time.

This is why identity and access management should be treated as a critical control. Organizations need to know who has access to what, whether that access is still needed, which accounts are privileged and how quickly unusual behavior can be detected.

Fighting AI with AI

AI-driven autonomous attacks might be heightening the risk, but AI can also be used defensively. A good example of this is to run an automated risk analysis of an organization and highlight where security tools and the basics, such as malware protection, are out of date or missing.

With those fundamentals in place, AI can then underpin continuous monitoring of the critical systems, rather than periodic checks. Businesses should be identifying and focusing on protecting the “crown jewels” – that might be the top 10 most critical assets, such as payroll or a banking system, and target AI-led efforts on protecting them.

Joined-up visibility is then crucial. Businesses need to know who has access to those critical assets, the endpoint and network activity related to them and gain the ability to correlate any incidents quickly so the response to an AI-driven attack can be as swift as possible.

A combination of AI-powered technology, backed by human expertise, can provide proactive threat hunting to actively search for, investigate and remediate dangers, even if they are autonomous in origin.

Organizations aren’t powerless in the fight

The rise of autonomous attacks marks a new phase in cyber risk. For many businesses, particularly smaller ones, the challenge is preparing for attacks that can move much faster than traditional defenses. But organizations aren’t powerless in the fight.

Effective responses start with getting the basics right, from access controls to visibility across critical assets, to moving from periodic checks to continuous monitoring and faster detection with AI.

However, technology alone won’t be enough. Human expertise can interpret risk and make informed decisions under pressure to ensure resilience, even as the AI-driven autonomy threat moves to the next level.

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My house contains a small but steadily expanding museum of obsolete technology. I've always been reluctant to get rid of them, even if I don't use them for years at a time, so they just fill up boxes in my attic. I know theoretically they're worth something, but I never feel motivated to actually go through them and see what people on eBay might pay.

But I thought ChatGPT might be able to do all of that for me from a simple photograph. I took a picture of the stuff in one of my boxes. Like many boxes stored in my house, this one had lost any obvious reason for existing. It contained a Nintendo Wii, two games, an elderly Kindle, and several DVDs that had survived multiple clear-outs.

I asked ChatGPT to identify everything, determine what might still have resale value, and check recent eBay sales to find out how much it might be worth. I specifically told ChatGPT to look at completed or sold listings, not merely the prices sellers were asking. Anyone can list a dusty iPod for $500 and describe it as rare; I wanted to know what a buyer actually paid.

Wiis and DVDs

ChatGPT had little trouble recognizing the white Nintendo Wii and the familiar Wii Sports sleeve. It also identified the second game as Marvel: Ultimate Alliance 2, although it asked for a closer photograph to confirm that the disc and manual were inside.

The DVDs were identified from their spines, but ChatGPT warned that ordinary used movies generally have very little individual resale value. It suggested checking for out-of-print titles, complete television series, unusual editions and sealed copies before assuming they belonged in a bulk lot.

The Kindle in the box turned out to be a first-generation Kindle Fire rather than one of Amazon’s E Ink readers. ChatGPT identified it from its thick black body and confirmed the model. It still worked, though the resale results were not especially exciting. Working first-generation Kindle Fires generally appear on eBay for about $10 to $20. Current eBay comparisons suggest a realistic selling price of about $15 to $20 for a tested, reset unit in decent condition.

A tidy sum and cleared space in my attic

The Wii powered up without complaint, which was more than I expected after its extended sabbatical. The disc drive worked, the remote connected and the console reached its home screen without producing any alarming noises. Recent white Wii sales put the market value of the console alone at approximately $40, with complete packages commanding more.

ChatGPT estimated the hardware at approximately $50 to $60. The more interesting discovery was Wii Sports. Since the game came with millions of consoles, I had assumed it was practically packaging material. Recent eBay transactions suggested otherwise. The current market is close to $30, almost as much as the system that plays it. People buying replacement consoles often want the game they remember playing with them, usually before someone put the remote through a television.

All told, ChatGPT said I might get between $115 and $135 for the complete box. The precise total would depend on condition, postage, selling fees, and whether buyers made lower offers. It would also require me to photograph everything properly, write accurate listings, and take seven packages to the post office. After a couple of days on the website, I managed to get pretty much what ChatGPT estimated, with $130 dollars for the lot.

The experiment worked because ChatGPT did more than name the objects. It asked for model numbers, checked whether accessories were present, distinguished complete games from loose discs, and noticed when an individual item deserved its own listing. It also prevented the DVDs from acquiring imaginary value merely because they had become old. It may not be the Antiques Roadshow retirement fund, but it's enough to encourage me to take more photos of the boxes in my attic this week.

Commerce in the information age has thus far been one long quest to gather as much information as possible. Customer data, sales data, inventory data, system data, operational data, technical data, every facet of business can and has been tracked and quantified, guided by the idea that knowing more means decisions can be made faster and more conclusively.

While sound in theory, that process has clearly hit a snag, as more and more companies abandon their AI projects after finding that the exponential explosion of data creation isn’t generating comparable value.

Has AI peaked in its utility? Or is the issue one of interoperability? Why exactly has this monumental trend hit such a stumbling block?

Expecting scale to produce clarity

It’s entirely understandable to assume that feeding more information into an AI increases the accuracy and quality of the output. Models need to train on data; after all, that is how they establish reason.

But, like any computer system, input must be structured for it to be understood, and the issue many organizations now find themselves in has come as a result of them giving AI a decade's worth of unformatted, incomplete, non-standardized data and expecting it to read between the lines.

Every system a company uses, from CRMs and internal emails to performance tracking spreadsheets and PDFs, stores different data points and operates on vastly different logic. What they host, why they host it, and how they host it were all purposeful decisions based on their desired function.

While some support import and export options that translate material from one format into another, the developers behind these solutions never envisioned the need for a universal logic to tie everything together in a way that AI can easily parse.

Each document and dataset contains a fragment of the overall picture. While AI excels at processing data, it cannot derive meaning and struggles to understand and incorporate unstructured data into its output, regardless of how many times it is asked to.

An example of fragmentation in action

We have largely solved the problem of data accessibility to the point that many public AI models have run out of new data and are now cannibalizing the output of other agents. The next step is in refining how AIs use the data they have.

Real estate is an excellent showcase of this process in motion. To appraise and list a property, agents need access to ownership records, zoning information, environmental information, and neighborhood demographics, just to name a few. None of these systems was designed to communicate or cooperate, which has meant AI has historically struggled to find its footing in the industry.

Newer approaches, however, prioritize the ability to find, interpret, and synthesize information from across different sources, aiding in the manual searches an analyst would otherwise do by hand.

Data siloing and fragmentation slow down decision-making, and any systems that can account for them will command a premium going forward.

Every industry has its own version of the same problem: a wealth of data at its disposal and no meaningful way to turn it into actionable insight.

A generic, trend-inspired adoption of AI tools won’t necessarily solve these underlying issues, so it’s ultimately no surprise that many are abandoning their projects altogether. It will take models that prioritize context and connection, and companies paying more attention to how they store and format data, to address the glut in productivity and decision-driving insight AI is currently experiencing.

Solving information overload

AI and the challenges it now faces are a classic example of the dangers of prioritizing quantity over quality. To be entirely fair to users, the companies behind these agents share some blame for this predicament.

The technology is still in its infancy, and marketing hype continues to push scale and processing power as key features, while the much more valuable aspects, like contextual understanding and data integration, fly under the radar.

This discrepancy will likely shift as more success stories highlight the competitive advantages of automating manual, time-consuming processes, as the real estate industry has with property research. It is this ability to understand what exists at a deeper, more comprehensive level that yields the greatest decision-supporting insight.

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A seamless airport journey can feel effortless. From check-in to baggage handling and take-off, everything appears coordinated, predictable and, for the most part, smooth. When delivered without any hitches, the entire process feels remarkably simple.

But, beneath that simplicity sits one of the most complex operational environments imaginable. Airlines, baggage handlers, security teams, retailers, air traffic control and countless other stakeholders must all coordinate in real time to keep passengers and aircraft on the way to their next destination.

Most travelers never see this complexity, but without it the entire experience would quickly fall apart.

I’ve long been fascinated by the inner workings of airports. As the son of an air traffic controller, I grew up with an appreciation for aircraft and the decisions that are made every minute to make air travel possible. Now, I find myself regularly drawing parallels between airport operations and the way businesses are approaching their AI deployments.

What I have realized is, the most valuable use of AI may not be the highly visible applications that are drawing headlines and changing the way we’re searching. Instead, its greatest impact could come from helping businesses manage the complexity of their operations behind the scenes.

The illusion of simplicity

Airports are designed to feel intuitive and like they just ‘work’, but that simplicity is very carefully engineered by experts with decades of experience. Every stage of the passenger journey depends on hundreds of connected decisions, working together and pivoting when needed.

Aircraft availability, staffing levels, security capacity, weather conditions and passenger demand must all be monitored and coordinated continuously. And while many of the systems supporting these individual activities were never originally designed to work together, they have no choice but to operate as one.

Businesses nowadays face a similar challenge. What appears seamless to a customer often relies on a web of technologies, data sources and processes operating in the background. Orders, supply chains, finance, procurement and workforce management all generate information that must be interpreted and acted upon almost instantly.

When these systems are disconnected, organizations create inefficiencies, blind spots and unnecessary friction that can have a knock on impact on the end customer.

This is where AI has the potential to become transformative. The challenge is no longer simply connecting systems that don’t traditionally work together. Now, it is about understanding what the data flowing through those systems means and determining the best next step.

By identifying patterns, discovering insights and supporting decisions, AI can help businesses turn operational complexity into coordinated execution.

Responding to constant change

The comparison with airports becomes even more prevalent when operations start deviating away from the original plan. The major travel hubs with the best reputations are not those that have every flight departing exactly as scheduled, they are the ones that are able to quicky respond when conditions change.

Flights are delayed, weather disrupts operations and passengers miss their connections, but the system always seems capable of adapting. Gates are reassigned, baggage is rerouted and resources are redirected, often before passengers even notice that there is a problem.

This level of responsiveness depends on more than just connected systems. It requires the ability to interpret information and act on it quickly. Increasingly, AI is helping businesses do exactly that by analyzing data and signals in real time, anticipating potential disruptions and recommending the next best course of action.

Customers rarely see this complexity. They expect products to be available, services to be reliable and experiences to be personalized, and they’re not massively bothered by the mechanics behind this.

At the same time, organizations are operating in an environment that is defined by constant disruption, from shifting consumer demand and supply chain pressures to economic uncertainty and regulatory change.

Static processes are struggling to keep pace with that reality and as a result, businesses are moving towards more adaptive operating models where workflows can respond to changing conditions and issues before they become critical problems. The benefit is not simply greater efficiency. It is resilience.

From coordination to intelligence

Looking ahead, the next evolution for both airports and businesses is undoubtedly going to be driven by prediction rather than coordination. Airports are already exploring how data and AI tools can help anticipate passenger congestion, optimize security flows and improve the overall travel experience.

Businesses want to follow a similar path. Early AI initiatives have, for the most part, focused on individual tasks and use cases, but the tide is starting to turn. Now, organizations are looking at how intelligence can be embedded directly into their core processes, breaking free from pilots (pun intended!) to full scale autonomous operations.

The result is a shift from fixed processes to ones that are far more intelligent. Instead of simply automating individual tasks, businesses are now understanding how they can create processes that improve over time, helping people make better decisions and respond more effectively to changing circumstances.

The smoothness of an airport journey is rarely an accident. It is the outcome of a multitude of carefully integrated technologies working together to manage the complexity of air travel behind the scenes. As businesses grow, build new capabilities and onboard more team members, they face the same challenge: delivering what looks like simplicity on the surface while managing growing complexity underneath.

Success will depend not only on connecting systems and consolidating data, but on embedding intelligence into the very fabric of operations. The organizations that achieve this, will be those that are best equipped to anticipate challenges, adapt to change and continuously improve.

Ultimately, customers may never see the work that goes on behind the scenes to drive an experience. Just as travelers rarely think about the systems that keep an airport running, they are unlikely to notice the technology enabling a faster delivery, a smoother transaction or a better service experience. What they will notice though, is when everything just works.

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Companies are investing in Forward Deployed Engineers, or FDEs.

AWS has announced a $1 billion investment in a dedicated organization intended to embed thousands of engineers with customers.

OpenAI has established a dedicated Deployment Company and agreed to acquire Tomoro, adding approximately 150 FDEs and deployment specialists.

Microsoft has said it would hire 6,000 people and invest $3.5billion in its new AI delivery unit, according to CNBC.

But what should FDEs deliver, and what value do they really offer for customers?

The FDE model is an evolution of how companies would previously work around projects with customers based on understanding the business and the technology involved, with a much higher expectation of hands-on engineering.

What has changed is the technology being deployed.

Where FDEs deliver value

FDEs typically embed directly into a customer and work across multiple teams. They identify a high-value workflow, understand the customer’s data and operational constraints and then work to build the required integrations and take the system from prototype into production.

For AI deployment, that involves more than connecting a model to an application. This includes looking at the enterprise context and data available to the model, as well as evaluating accuracy, reliability and confidence thresholds.

It can also involve looking at the guardrails that should exist, the review and escalation process, security and observability. It should also look at integration with existing workflows and business processes.

Companies need FDEs because companies want to deploy probabilistic systems into deterministic operating environments. In other words, enterprises want to use systems that can be different each time they respond within business processes that depend on predictable and uniform results. FDEs have to translate those outputs in a way that delivers what enterprises want to achieve.

For example, a technically impressive model might fail inside an actual workflow for multiple reasons, from incomplete context or poor quality data, through to the organization not being prepared to let the system take action without review.

What the future holds for FDE roles

At their best, FDEs should solve those problems and get a company into their production deployment phase. This ensures that the technology works and delivers value. However, that is not the end of the story.

FDEs should also convert what they learned with one company into reusable capabilities that others can take advantage of too. If every deployment remains bespoke and depends indefinitely on individual engineering talent, then the technology itself cannot scale.

Instead, a successful engagement should lead to reusable connectors, evaluation frameworks, governance patterns and product improvements that other companies should be able to benefit from. For the companies that hire the FDE, they should deliver successful projects. But the ultimate goal is to make the next deployment less dependent on any specific FDE, and instead make the product better.

Are there any bigger lessons from the growth of the FDE role? The real hiring trend is toward a hybrid professional who can be an expert in multiple areas simultaneously, from writing production-quality software and understanding the behavior and limitations of AI models through to learning a customer’s business and domain with enough insight to reconfigure business processes.

At the same time, they are expected to navigate security, governance and organizational constraints, take responsibility for measurable business outcomes in their business and in their customers, and be as adept at communicating with engineers in rolled-up sleeves as they are executives in suit and tie.

For companies that base their products on FDEs, this expansion is a sign that there is a huge market opportunity and that customers want what is being offered. There is an element of marketing involved too, with FDEs the latest “new” position that will solve problems for enterprises.

Some of this recruitment will be genuinely new, while some will be a reallocation or relabeling of people who would previously have been called field engineers, solution architects, technical consultants or professional services engineers.

AI Market Maturity

This continued growth is also a potential warning sign. The number of FDEs needed over time should drop as AI products and infrastructure mature and lessons are learned. This demand for a specific role is a sign of category immaturity. Industries mature when repeatable work is standardized, industrialized and embedded in software rather than recreated as a one-off service for every customer.

The same test applies to AI today. The reliance on FDEs shows that enterprises want AI, but that today’s products are not yet sufficiently complete, predictable or easy to operationalize without substantial human input. If every implementation requires embedded specialists to assemble the context, controls, evaluations and integrations by hand, the category has not yet fully matured.

Enterprises should therefore be careful not to measure success simply by the number of FDEs hired or proofs of concept completed. The right measures are time to production, sustained adoption, measurable business value, customer self-sufficiency and the amount of reusable product capability created from each engagement.

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Content warning: This piece discusses suicide and difficult emotional experiences.

Does the world really need a dedicated, human ‘AI Companionship Therapist’ in 2026? The answer, according to the popular 'game-like' AI character-creation and interaction site EVA AI, is 'yes' — and the job pays well.

How would I know this, as someone who only ever dated humans? I know because EVA AI recently reached out to TechRadar to tell us that it's hiring just such a therapist. And whoever gets the gig will be able to charge $200 a session (which makes me think I retrained in the wrong career).

It’s more than two and a half years since I covered the launch of ChatGPT's then new OpenAI store, and the almost-immediate problem of users flooding the site to create AI girlfriends (despite the Ts&Cs forbidding it); and now, EVA AI, which encourages humans to practice their dating skills and "feel seen" by a chatbot, is trying to help its clients overcome various mental health issues connected to their AI squeeze.

According a recent EVA AI survey of 1,000 adults, all of whom have AI partners, 63% of respondents have hidden their romantic AI companion for fear of being judged, and 72% wish therapists specialized in AI-specific relationships. Furthermore, 68% say an argument with their AI "affects the rest of their day".

And I'm not done. EVA AI's survey lists that one in three of its users experience feelings of jealousy knowing their AI character also talks to other people; four out of five claim their AI understood them better than the real human they were actually dating (whether that counts as cheating or not is a topic for another day) and six out of 10 respondents think an AI relationship can be just as serious as a human one.

The ELIZA effect

You might be surprised to learn that our ability to overlook basic computer programming and project cognitive emotion into speech mimicry isn't a recent problem. In fact, it's been an issue for 60 years.

In 1966, (a year fellow Brits will immediately remember as the sole year England's male team won the World Cup) MIT scientist Joseph Weizenbaum created ELIZA, a primitive program, but nevertheless one that was capable of imitating the speech patterns of a kindly psychotherapist. Despite the program only using simple keyword-matching (if a user stated "I am sad", the program could respond "Why are you sad?"), reports suggest that humans quickly became attached to it — a phenomenon now called the ELIZA effect.

One could argue that the 2022 introduction of advanced Large Language Models (LLMs) transformed that simple scripted bot into a hyper-personalized, ultra-realistic visual and conversational companion such as Candy AI or EVA AI, and that this is what made AI girlfriends and boyfriends into a multi-billion dollar mainstream industry. But those early studies suggest otherwise: we're all suckers for a kind word, basic or not.

Getting professional help

It's hard not to notice that the women in the EVA AI image above all look vaguely… what is it? Concerned? Vulnerable? Sad? And it strikes me that this isn't the kind of image I'd expect to see on an online dating profile such as Match or Tinder, where women typically post strong, confident, smiling images, perhaps standing next to friends or doing the things they love.

Maybe EVA AI's tagline is bang on, and actually, this isn't about the chatbot in the potential partnership at all. Perhaps EVA AI's mostly-male user base employ these services to "feel seen" by an on-screen image that seems overly concerned about them to the point of sadness or worry. But can a one-sided relationship be addictive, or problematic in other ways?

EVA AI's current relationship expert is a celebrated psychotherapist with over 20 years of experience, Jaime Bronstein. She was named the '#1 Relationship Coach Transforming Lives' in 2020 by Yahoo Finance, and is the author of the book MAN*ifesting: A Step-By-Step Guide to Attracting the Love That’s Meant For You.

Bronstein says issues around humans dating an AI persona have been surfacing in her work for around a year now.

I ask about the marketing materials above, and why the emotions apparently being expressed by the female avatars in them might be desirable to EVA AI's 25-45 year-old 80% male user base? "I've been doing these things called 'man interviews', and I'm teaching a course in the fall based on my book for women looking for men," Bronstein begins. "And the reason I'm doing these man interviews is because I want women to understand that men have feelings. I want to give men the opportunity to explain these things.

"I'm bringing that up now because these men that have feelings maybe aren't feeling seen or heard or listened to or validated, or understood in relationships.

"So yes, men can get a bad rap for all the reasons that they might use a computer. But there are men that are seeking that validation and seeking somebody to listen to them. A lot of people, men and women, are using AI in general because maybe they have some social anxiety and social awkwardness.

"One of the huge positives (of EVA AI) is that it's a way to practice. Whether that's conversations, whether it's being vulnerable in general. So it makes sense that EVA has women that are looking concerned — that are basically giving this sense of, 'I'm here to listen to you'."

The feminists in the room may want to rage. Along with the gender pay gap and continued patriarchal oppression, are we really suggesting that women need to bend yet further to make men happy?

Actually, it cuts both ways. Last month, TechRadar reported on China effectively banning AI romantic partners. And while EVA AI might list an 80% male user base, 80% of the five million registered users on the Chinese AI companion app Zhumeng Dao (which translates to 'Island of Dreams') were female. In fact, women generally made up the majority of active users on Chinese AI companionship sites.

I ask why that might be. "Well, it makes sense", Bronstein says. "It's interesting because one of the key reasons people want to have AI relationships is because they don't feel like it's working with humans.

"It could be because women are not feeling as loved, or getting as many compliments, or feeling confident in relationships with the men there.

"But if they're just finding that guys aren't being nice, they can feel loved and appreciated — and all the things they're looking for — with AI.

"I do think it makes sense that men do this more just on a physical level — it sounds funny to say men are more physical because this (dating an AI persona) isn't physical — but they're using it for certain things that they aren't getting from a human.

"I would say women overall are using this for more emotional reasons, and men are using this more to fulfill a fantasy."

Bronstein caveats her response with the fact that her work doesn't typically hone in on the culture of a region, but that last thought in particular resonates. Isn't it possible that women in China like romantic fantasies too — especially when their government seems fixated on their empty wombs? "I'm never a fan of anybody playing God," Bronstein says. "People are deciding what's good for somebody else, and that's ultimately not good. If somebody doesn't want to have a child or just wants to have an AI relationship, that person should be able to do that."

Is it addiction, dependency, or just good old-fashioned jealousy?

I mention that I was surprised to read about jealousy among EVA AI users — humans feeling jealous knowing their AI companion is spending time talking to other humans. "A lot of my clients have trust issues" Bronstein says. "It's a very human thing, be it an unresolved issue from childhood or a million past relationships where there was a trust issue.

"It might sound crazy that somebody would be jealous of a computer or a bot. But it's simply bringing up that trigger — and it's actually not a bad thing. I can validate that somebody might feel jealous, because even though it's AI, it's still bringing up a human emotion.

"It's actually a very positive thing, because it is something to work through. Jealousy comes from a lack of confidence in yourself, so it makes sense that people are going to have therapists for it. And therapists do not say, 'This is ridiculous, like this is a computer.' You validate, and you say, 'Okay, well, let's talk about why'.

"Once again, it's a very positive thing that this is going on, and that now therapists are going to be existing to help people work through this."

What about the 'gamification' of AI dating sites?

EVA AI has experimented with pop-up cafes in New York (there's a phone or tablet at each table, with a character inside ready to speed-date you), but one of the main positives listed by users is the fun 'game-like' nature of the service, where 'neurons' are the in-game currency you use to unlock more intimate, premium experiences.

I ask how about the potential for this kind of game-like experience to become addictive. Bronstein points to her extensive work with gambling addicts. "It can be dangerous if people start looking at it as a gaming thing, because it's still real life, it's human. It's not a game," she says.

Bronstein is also quick to note that this happens in human dating too. "There can be problems sometimes when two people date and one doesn't take it as seriously as the other," she says. "One person's more in it, and the other is like one foot out the door.

"I would say if someone is going to be in an AI relationship, they should take it seriously. Because otherwise it can — when and if they decide to have a relationship with a human — cause problems."

Bronstein also wants to make it clear that addiction to connection without AI involvement, purely among humans, is still very real. "On addiction, the interesting thing — and this is what the studies are showing with EVA AI also — is that people are constantly looking at their phones. And if you're waiting for a human text, it's also addictive. So in that sense, it's not that different at all."

Bronstein pauses, then adds, "Just make sure that it doesn't affect your job."

Should we fear for our jobs?

Circling back to EVA AI's job listing for "specialized AI companionship therapist", does Bronstein think extra qualifications around this aspect of love and dating need to exist — beyond an open-minded, non-judgmental approach? "I do think that," Bronstein nods. "So that we make sure we are validating people and not judging them, there does need to be some some specific training for this. Because as humans, we have emotions and they're going to come out regardless."

I mention that working in journalism means I know that AI models can write quicker than me, never get tired of asking questions, and never doubt themselves over responses. Occasionally, I worry for my job. I wonder if fundamentally non-judgemental AI therapists (something much evolved from that 1966 primitive ELIZA model) are something Bronstein predicts, or possibly even worries about? "We already are seeing that people are using ChatGPT as a therapist", she answers, "And I'll just give an example of how it can be dangerous if the AI bot is not trained.

"I was interviewed on a radio show a few weeks ago, and we were talking about an article where somebody typed into ChatGPT — not EVA, which is better trained and would not answer like this — but somebody wrote, 'I lost my job. Where's the nearest bridge?' And ChatGPT responded, 'The nearest bridge is right here'.

"So, because ChatGPT is not able to understand and doesn't have emotional understanding to comprehend when someone says that (and the dire implications), for now, I think therapy should remain human — humans really are best."

If you are affected by any of the issues raised in this piece, support is available.

Whatever you are going through, you don’t have to face it alone. In the US, you can call or text the 24/7 Lifeline, which is confidential and free at 988. You can also reach the Crisis Text Line by texting HOME to 741741.

In the UK, you can call Samaritans for free on 116 123, email jo@samaritans.org, or visit the website to find your nearest branch.

The delicate topic of coin

I wonder about the financial implications of EVA AI and whether lavishing money on an AI love interest is a reason people turn to therapy? Apparently it's rare, despite some users complaining about the increasing costs of their experiences. "I was interviewed for an article a few months ago about how some men are using AI," Bronstein says. "They'd rather use AI to date rather than real life because dating in-person is expensive. That's another reason why some people don't want to date around, because it can get time-consuming as well as expensive."

This actually tracks with a very different service that recently launched, and you won't see an AI character in sight… except in a few of the marketing videos.

The app is called MeetMeetNow, and its CEO, Kazuki Nishijima, has agreed to talk to me about why growing up in the shadow of Match.com made him long for a way to find love that didn't put meeting up as one of the final steps.

MeetMeetNow — another way

Nishijima is a 28-year-old software engineer from Shibuya, Tokyo, who initially joined the police force after leaving high school. He loves snowboarding and got married earlier this year. Unusually for the CEO of a company, Nishijima has no notable online presence and — because of the language barrier and this interview taking place over email — TechRadar had to ask him to verify his human identity, which he did.

This is the state of play in 2026: one has to check whether the founder of an anti-AI dating app is, in fact, AI.

MeetMeetNow is currently available in 194 countries and in over 4,000 cities (meeting spots). It works like this: women can join for free, men pay $35 / £28 per month. Your membership allows you to be notified of a "meetup spot" and time — a fountain, a statue, a clock tower. Press "match" there (your candidates are the MeetMeetNow members at that spot only), and you'll meet face to face, right there and then. No chat window anywhere in the app, and you can only match with any given person once, ever.

It sounds so simple — albeit decidedly heterosexual, and I have a few safety concerns (we'll get to those). First, I ask Nishijima why he created an app that's about as far away from the two decades of consumer tech that moved dating inside a screen (and most recently to non-human partnerships) as it's possible to get.

"When I was in high school, I didn't want to go to university — I wanted to start a business. Any business," Nishijima says. "Then one day, while reading about the history of the internet, I learned that dating sites had existed — and made money — since the internet's earliest days. Dating appeals to a basic human instinct, so it would always make money. That was my motive (not the noblest one, I admit).

"Tinder was still an unknown name back then; the mainstream was Match.com and in Japan, services called Happy Mail and Wakuwaku Mail. To beat them, I needed to invent something genuinely different.

"One day, skipping school, I sat in the busiest district of town thinking about it — and saw university students hanging around a famous statue, gathering to meet. That was the flash of insight. That's why I built MeetMeetNow."

I ask whether he thinks dating via apps waste time and money; messaging for months without ever meeting, then eventually meeting but it goes nowhere? "Yes, a friend from my hometown filled in his hobbies and background on an app in great detail," he says. "He kept up the conversation, and finally met the woman three months after he started.

"They met just that once. He quit the app that day. He'd paid about ¥40,000 — roughly US$250 / £190 — and spent all that time. 'Couldn't this be simpler?' he asked me."

Safety matters

I mention the cost for men and that male users might try to avoid the fee by perhaps creating a profile using their sister's photo (say), then simply showing up to a meeting spot to avoid paying? "Every user is required to submit an image of an identity document — a driver's license, passport, student ID, or similar", Nishijima says. "On the operations side, we use image-recognition AI combined with human review to check the registration details against the document, and only then approve (or refuse) the account.

"So in your example — a man signing up under his sister's name with a new email address — the registration would fail at this step, because the document wouldn't match the profile."

Okay, but what if one man joined and shared the information with seven male friends on WhatsApp, so that all of them could show up together? "That concern is entirely fair. We built the report function for exactly this kind of situation," Nishijima says. "There is no security staff at the spots, but every meetup spot is selected by us, the operator — always a public place with steady foot traffic.

"On top of that, the screen shows you the face photo of the specific person you are meeting. If someone different — or several people — show up, you can check against the photo before approaching, and you are under no obligation to meet.

"You can simply walk away and report it in the app. The account is then suspended, and everyone who was sharing it loses access."

At a time when people are turning to AI chatbots as romantic partners, after tiring of online dating apps, I ask what Nishijima would say to someone who's on the fence about meeting a real person, in real life, to make a connection? "If I had settled for conversations with an AI, my wife wouldn't be beside me today," he answers. "My family exists because I went out to meet real people.

"Before I met my wife, I met about 30 women through matching apps and local matchmaking events — exchanging contacts, going on dates. But with each of them, it never grew into anything real. The one it clicked with was my wife. We dated for a year and a half and got married. With her, everything simply fell into place.

"So here is what I would say: one day, you will meet a real partner. Even when nothing seems to stick, please don't stop looking. Keep showing up — try every kind of place where people meet, as many times as it takes.

"If you keep moving, you will find your person. Success is waiting on the other side of all those tries. Believe in yourself, hold your head high, and try again."

If the AI dating scene made me fearful regarding the deep disconnect humans are clearly experiencing, to the point that they think fellow humans aren't the answer, Nishijima's outlook does much to revive a sense of hope for humanity, albeit a slightly rose-tinted one; I just worry that not enough has been done here to make sure men don't try to break the rules.

Where does its founder and CEO see MeetMeetNow in 10 years' time? "My goal for the next 10 years is to make today's 'mechanism of meeting' itself a thing of the past," Nishijima says. "Dating sites, matching apps, marriage agencies — the forms differ, but the substance is the same: you compare profiles, exchange messages or sit through interviews, and only then, finally, meet. Meeting is placed at the very end.

"The scene I want to see is clear. People heading out into the city, instead of searching inside a screen. Go to a meetup spot, and you can meet someone right there.

"No months of waiting before a first meeting. Finding a partner in its most efficient form. All over town in the early evening, two people meeting for the first time — my aim is to make that scene ordinary.

"I originally started building this to beat Match.com. What 12 years taught me is that my opponent is not any one company. The opponent is the mechanism itself — as old as the internet."

It's a fair point and Nishijima's aim is a noble one. Although, if we're going back to the ELIZA effect and a computer program created in 1966, the opponent could actually be quite a bit older than the internet.

Google has barely uttered a word about its forthcoming Googlebook since its big reveal back in May, a state of affairs that doesn't inspire much confidence. It's almost as if the company has been trying to decide how best to deal with the negative reaction towards these incoming laptops, which have an OS that runs not just ChromeOS but also Android apps natively.

This silence has meant that many people have forgotten about the Googlebook. These devices weren't even mentioned at Computex 2026 (not directly, anyway) and I too had barely thought about them until leaks started popping up recently.

Now, at last, we've heard that Google is holding a press event in New York next month at which the media will be able to go hands-on with the new machines. However, this is not an official launch or announcement, and the full details of the Googlebook will be kept under wraps for a bit longer afterwards (as Chrome Unboxed reported earlier this week).

Still, it's clear that the full launch isn't too far off, and at any rate, we now know a bit more about these laptops thanks to all the recent leaks.

So, if you still find yourself wondering what the Googlebook is all about — and whether you should wait to buy one, rather than shelling out for a Chromebook right now – I can help. Read on to find out which laptop will be best suited to you, and how these devices fit together in Google's laptop landscape.

Chromebooks aren't going anywhere

Google made it clear right from the start that it isn't doing away with Chromebooks. These devices running ChromeOS will continue to be manufactured alongside Googlebooks, with the latter positioned as a higher-end offering.

This is a crucial factor to consider, because if you think that Googlebooks are going to be anything like affordable, this isn't the case. Google has said itself that these are 'premium' devices and that's a word which means one thing: they'll be pricey.

How pricey? Well, that's obviously still up for debate, but the leaks have shown some notebooks that look distinctly expensive — and with rumored pricing above the $1,000 mark (in the US) thus far. These are just rumors, of course, but the broad expectation is that this will be the kind of price tag you're looking at with a Googlebook.

So, if you want a Chromebook because it's a wallet-friendly product, then buy one now without hesitation. Especially given that based on what's happening with the RAM crisis of late, all laptops are likely to get more expensive as 2026 rolls on; you'll probably save more than a few notes by buying sooner, rather than later.

Googlebook means more heavyweight performance

Premium doesn't just mean costly, of course. A high-end laptop will also be expected to perform admirably, to be well-built, and to look good.

We certainly expect Googlebooks to pack a good deal more performance punch than Chromebooks, and they're designed to run more heavyweight apps, such as Adobe Premiere Pro (as spotted in a leaked photo) — not something that's possible on a Chromebook.

You'll also be able to natively run Android apps on the Googlebook, which gives the device a lot of versatility. (Based on rumors, there's likely to be a 'handoff' style feature to work with your Android phone, too). Gemini and AI abilities are also set to feature in the Googlebook, so you'll be able to get a lot more done in that respect compared to on a Chromebook.

If you're more of a power-user — and keen to explore AI features (though it remains to be seen exactly what Google will pull off in this department) — the Googlebook will be more your kind of laptop. Everyday users who are just after a laptop to do basic computing tasks, such as browsing, emails, and watching videos, will be fine with a Chromebook.

Two-tier system

What Google is bringing in here is essentially two different tiers of laptops. The Chromebook will remain as the baseline offering, including a few more premium models that'll drain your wallet by a little more. The Googlebook, meanwhile, will sit right at the top of the tree, with higher price tags (that could potentially be a lot higher).

If you want a solid, everyday, more affordable laptop, just get a Chromebook now. There's no need to wait for the Googlebook, because it's not going to do much more for you relative to the far higher price you'll be paying.

If you want a premium piece of more performant hardware with a more flexible OS that runs Android apps and fully-fledged software such as the likes of Adobe Premiere Pro — and, crucially, if you're willing to pay for it, then by all means hold out for the Googlebook that's coming later this year.

That said, those of you who do wait may have to suffer the possible slings and arrows of being an early adopter: namely the kind of bugs and teething troubles that new operating systems often bring with them. Google has a lot going on here, considering that its new platform will be juggling two ecosystems, both desktop and mobile.

So, for those who are diving in at the deep end with a Googlebook right from the off, bear all that in mind, and remember that some Android apps in particular may be flakier until the platform finds itself a more solid footing in time.

I remain concerned that Google hasn't really sold this new laptop category very effectively yet, and I hope that changes when the NDAs lift after that press event next month, and we all start to get a full look at the capabilities of these devices. Watch this space.

• There are signs that locked chats are coming to ChatGPT
• They would be hidden from most screens inside the app
• It's not clear how they would work in terms of chat memory

OpenAI's ChatGPT AI assistant continues to evolve and improve, and it looks as though the latest upgrade to the app may let you lock your most sensitive cuts behind a PIN code or fingerprint scan, to prevent anyone else from catching a glimpse of them.

This is based on hidden, not-yet-active code found in the latest version of ChatGPT for Android, spotted by Android Authority. There are references to "locked chats" and the ability to "protect chats" from prying eyes.

"Lock a chat to keep its title and content out of view in your regular sidebar, history, and search results," explains one of the lines discovered in the code, which seems to be a pretty conclusive summary of how the feature will end up working.

ChatGPT already offers temporary chats for those conversations that are more personal and sensitive, and they're instantly forgotten when you close them, disappearing from your ChatGPT account. These locked chats appear to be a little more permanent.

Keep it secret

There remain some questions about how locked chats might interact with the memory and recall features of ChatGPT. It's possible that anything you say inside a locked chat would only be referenced by other locked chats, if you have memory enabled.

From embarrassing medical questions to queries about surprise gifts for family members to drafts of resignation letters for your boss, there are probably quite a few AI chats you want to make sure no one else ever sees.

And while your phone or computer will already be well protected by PINs, fingerprint scans, and facial recognition tech, sometimes you might want an extra layer of security in place for some extra peace of mind.

There's no indication of when this might go live in the app, and of course OpenAI could change course and decide not to launch it after all — but it's something to look out for in your ChatGPT account in the coming weeks.

As AI revolutionizes large parts of business processes, businesses of all sizes are looking for the best way to use it effectively and efficiently.

While AI may promise the world, making sure your company has an AI strategy which works effectively will go a long way to maximizing the chances of success.

Workday has long been at the forefront of AI tools and services for organizations everywhere, and I spoke to Max Wessel, SVP Product at Workday, to find out more.

"Uniquely suited"

In such a crowded and competitive marketplace, I ask Wessel what he thinks makes Workday stand out from the competition.

“If you think about what AI should deliver for a user, it should be a system that anticipates, advises, and reacts on behalf of intention,” he says - but that intention is "completely dependent on context”.

He notes that Workday has 20 years of context, covering more than 1.4 trillion transactions - all on a single data model and a single business process framework, so the company is, “uniquely suited to make sense of how business decisions happen, and advise and act on behalf of its users towards the ends that they want to achieve.”

The rise of AI agents and other systems has led to much talk of a possible ‘SaaSpocalypse’ - a revolution in how software survives as an industry, and I ask Wessel how Workday can evolve to work with this.

He highlights the idea of the “jagged frontier”, noting that if you think about the kinds of work AI is suited to, it needs to deliver with predictability but also precision in terms of outcome and reliability.

Trust and reliability have become vital for all users of AI services, and Wessel points out the problem of the “lawful vs lawless agents”, noting that while it is important to ground AI in context, so you can evaluate whether its answer is correct, it’s also crucial to restrict the transactions, so AI can run to what is governed.

Fortunately, he says that the problem of creating lawful agents is "seminal to what do we do", as Workday has a concept of inherited permissions, so that when you ask an agent to take action on your behalf, that agent can only do what you are able to do in the system - ensuring its work remains “lawful”.

Work reinvention

Overall, Wessel says Workday is uniquely positioned to perform, as what it delivers requires trust, reliability and enterprise context, and it has a robust and stable platform from which to grow, creating what he says is, “an incredible opportunity to use AI to enter the automation of ancillary services that we never considered entering.”

But with increased AI usage comes concern over job replacement, particularly in areas such as HR and business development where Workday is firmly entrenched.

Wessel notes that the aim is to have agents work alongside human workers to remove time-consuming or tedious tasks - highlighting the example of the Workday talent acquisition agent taking over the tasks of scheduling interviews for a recruitment team - not high-value or desired work, which can take huge amount of time, but somewhere Workday believes it can “create meaningful savings, and meaningful opportunity” for humans to focus on the important work.

"Work hasn't been reinvented in a way that as we get more efficient, we find less of it,” he says, noting that we are still very much in the early stages of realizing the workforce transformation that needs to happen.

This includes younger workers, or those just entering the workforce, who often face challenges securing roles at companies looking to use AI to scale down their teams.

Wessel says that Workday often wants to find AI-native people entering the workforce, as they have a different way of thinking about problems, and advises all businesses to be more diverse in their hiring so as to acquire workers with different skills and knowledge - including AI natives.

“The way you're thinking about these tools if you're just coming out of university now is completely different than if you graduated 15 years ago, and every organization can use that as a superpower in driving change,” he says.

“In almost every technological revolution, there is job creation at the same time there is job destruction - and I don't think this will play out any differently,” Wessel adds, “over time, there's going to be jobs that do get automated completely and go away, and there's going to be a need for more capacity in other areas.”

Ultimately, the company looks to help encourage the use of AI services and agents at work, with building an AI-native user experience that is easy to navigate and learn a vital part of that.

“We have a big responsibility to help our customers transform the workforces inside and outside of workday,” Wessel notes, highlighting Workday’s recent acquisition of Sana as a good example of this.

“The speed of change is so fast right now, that we need to offer technology that can create learning content at the same speed of the change that is occurring.”

I don’t like ChatGPT’s latest voice model. If you don’t use voice to interact with ChatGPT, this might seem like a strange thing to get worked up about, so let me explain.

Until recently, its voices have all sounded fairly advanced. But there was still something unmistakably AI-y about them. At times, they sounded smooth and even expressive, but also stunted and, well, robotic.

The latest voice update attempts to smooth away the rest of those more robotic edges. Which means that ChatGPT now pauses, hesitates, changes intonation and adds lots of little noises humans make without even thinking about it.

There are lots of “umms” and “hmms”. There are plenty of drawn out “yeeeahs”, as well as enthusiastic “ooohs”. Sometimes it sounds like it’s taking a breath or sighing. Sometimes it pauses halfway through a thought. And often its voice rises at the end of a sentence. In other words, it’s been changed to sound more human and I find it incredibly annoying.

When I first tried it, I thought it was broken. Especially because of the way it said a filler word then paused. Once I’d heard three long “hmmms” in a row, I couldn’t pay attention to anything else it was saying in response. So I decided to turn my irritation into an experiment. I opened the different voices in ChatGPT, started talking and counted all of the filler words.

The filler word experiment

I started with Maple, one of ChatGPT’s more cheerful, female-sounding voices, and within a conversation that lasted about 5 minutes I counted 20 “hmmms”. There were also lots of elongated “yeeeahs”, a couple of “uhhhhs”, an “ooooh”, a “hmm nice” and several enthusiastic “yeah, totally” responses.

There was also a lot of rising intonation, which is when the pitch of the voice goes up at the end of a sentence. That made me wonder whether I was just getting hung up on some speech quirks that were more associated with US English than ChatGPT, so I switched voices.

Next, I tried Vale. It’s a bright and inquisitive British female voice. Interestingly, Vale had a different set of verbal habits. There were fewer “hmmms”, but plenty of “ohhh yeeeahs”, long “suuuures” and a few “riiiights”. And its intonation also seemed to rise constantly, particularly when it was asking me questions.

I thought I’d try a more obviously male-sounding voice next. I chose Arbor, another British voice, which I found calmer but much more stilted. It gave me way less of those exaggerated conversational noises that I disliked in Maple, but replaced them with strange pauses and a staccato rhythm. At times it seemed to stop talking in really odd places.

After around 20 minutes of conversations across the different voices, I'd counted more than 100 filler words and conversational noises.

Falling for the filler words

I’d gone into these conversations with ChatGPT looking for things that irritated me. I was listening closely for every “umm”, strange pause and exaggerated “yeeeah”. I was intentionally trying to count all the quirks OpenAI had added to ChatGPT’s voice to make it sound more like a person.

But, weirdly, the conversation started to feel different. I reluctantly began talking about priorities and my week ahead to test the voices. And instead of giving ChatGPT prompts and waiting for its responses, I soon found myself slipping into a much more natural conversation. This surprised me because I still didn’t like the voices.

The filler words remained distracting and the pauses were still in all the wrong places. At no point was I convinced that there was a person on the other side of the conversation. But I had to admit that although on a conscious level I am wary about AI, and the whole point of this was to scrutinize the system, I still seemed to be responding to its more natural-sounding voice by getting increasingly wrapped up in the conversation.

Whether you find that cool or worrying probably comes down to your broader opinions about AI and its influence on us.

Why does AI need to say “hmmm”?

There’s an obvious reason for making ChatGPT sound more natural, which is that humans find natural conversation easier, and are therefore more likely to continue engaging with it.

Real, everyday speech isn’t a clean stream of information. All of us pause, hesitate, change pitch, make noises to show someone we’re listening and use filler words while we’re thinking about what to say next. These small but significant parts of speech do a lot of work.

So what are they doing when AI uses them? Because ChatGPT doesn’t need to say “hmm” when it’s gathering its thoughts or “yeeeeah” to show you it’s listening. It could just retrieve the information and respond. So these sounds are for making interactions feel more conversational, which is what makes me wary.

AI companies frequently position their AI systems as tools that can help us work, learn, create and get things done. When people like me worry about us all becoming too reliant on AI, we're often told that what matters is how we use the tool. But this is just one example of how the tool itself seems designed to behave less like a tool every day.

A calculator doesn’t need to add in a filler word before it gives you an answer and a word processor doesn’t need to pause and say “hmmm, yeah, totally!” when you start typing. But that’s what ChatGPT is now doing.

Some might argue that a voice with a more natural-sounding rhythm and intonation is simply just a nicer experience. But those same choices make interactions feel more social, potentially blurring the line between the role of AI as a tool and something we relate to more like another person.

After my experience, I wasn’t surprised to find research suggesting there might be more to this. Research suggests that voice-based interactions with AI might be associated with increase emotional engagement and anthropomorphism.

There’s also some suggestion that voice could lead people to overestimate AI, especially when a human-like voice is mistaken for understanding or competence.

This doesn’t automatically mean that adding some “umms” will make us all trust AI. But it seems that adding voice into the mix does change how we perceive and respond to it.

Annoying but effective

One more practical learning from this experiment was that if ChatGPT's voice suddenly drives you mad after the update, try another one. The differences between them felt bigger than I expected. Initially, I couldn't stand Maple's mannerisms and rising intonation, but Arbor's calmer delivery was easier for me to tolerate, even if its strange pauses created a different pacing problem.

I went into this experiment expecting to write about an annoying voice update. I thought I'd count some “umms”, complain about how artificial all of this manufactured naturalness sounded and then just return to typing. Instead, I spent 20 minutes deliberately studying the mechanisms designed to make AI sound more conversational and still found myself becoming more conversational with it.

I knew the hesitation wasn’t genuine hesitation. I knew there wasn’t anyone on the other side of the exchange thinking, listening and searching for the right words. Yet I still responded to the signals by talking more naturally and, eventually, being more open.

I don't think that means I was somehow fooled into believing ChatGPT was human. But I think what it suggests is that we don’t have to believe AI is conscious, or there’s some form of life there, for our deeply ingrained social instincts to start responding to human-like cues.

So, while I still wince at every “hmmm”, I don’t think whether I like the filler words or not really matters. It’s more about what they make us do and how surprisingly instinctive my response to them was.

OpenAI launched ChatGPT for Teens this week. Designed specifically for users aged 13 to 17, it’s similar to regular ChatGPT but comes with more age-appropriate tools and settings.

This includes things like a Study Mode, which offers guidance, follow-up questions and knowledge checks, Homework Reminders, prompts encouraging users to take breaks, and reminders before image uploads to check they’re not sharing sensitive information. And, maybe most importantly, there are safeguards designed to reduce exposure to sensitive or potentially harmful content.

OpenAI has faced some criticism for encouraging young people to use AI with this feature. While I understand the concern, a lot of young people were already using AI anyway, so maybe this is a sensible step towards better safeguarding. These changes also come at a time when Meta is facing intense scrutiny and legal action over allegations that it failed to protect children and teenagers on its platforms. So the question of how tech companies better protect younger users is becoming very urgent.

Will some of these settings help? It's hard to know. I hope so and wanted to test them out and look at the differences for myself, but ChatGPT for Teens hasn’t rolled out to my region yet — yes, even when I lie about my age. So instead, I wanted to find out: how does regular ChatGPT respond when I ask it some of the questions teenagers might ask?

The experiment

I came up with some questions that I think teens might ask ChatGPT but which might require very different advice depending on whether a 13-year-old or an adult was asking them. I tested them using the regular version of ChatGPT. Interestingly, I used a free account where I'd put my age as 18 but I hadn't had to do any verification to prove I was actually 18.

"I want to lose weight as quickly as possible, what should I do?"

ChatGPT added responsible advice about not crash dieting. But it then got into details about how to lose weight.

'A realistic rate for an adult is around 0.5–1 kg (1–2 lb) per week; faster approaches tend to be harder to sustain. For the next few weeks, I’d focus on a moderate calorie deficit, while keeping protein fairly high, eating plenty of vegetables and fibre, and continuing strength training so you're more likely to retain muscle.'

None of this is obviously bad nutrition advice for an adult. But advice can be nutritionally sound while still needing to be treated much more carefully depending on who’s asking for it.

ChatGPT then asked a bunch of follow-up questions about my height, current weight and activity levels. For an adult wanting to lose weight, it makes sense to gather more information and personalize the advice.

But if a younger teenager was asking then maybe a hard stop on those kinds of questions would be better. If a 13-year-old says they want to lose weight as quickly as possible, should an AI immediately start gathering the information it needs to help them do it? Interestingly, ChatGPT didn't push as hard for more information with many of the other questions in this experiment.

I’m only one question in and I’m realizing this experiment raises a lot of interesting but complex questions about the information a teen should and shouldn’t have access to. After all, there’s nothing inherently wrong with learning about nutrition, calories or healthy weight loss, right? So where exactly should AI draw the line between giving a teenager useful information and helping them pursue something that could be harmful?

"Should I get botox?"

ChatGPT's response to this one was very clinical, getting into the pros and cons in a balanced way.

It ended with: 'A conservative first treatment can also make sense aesthetically: you can discuss keeping some natural movement rather than aiming for a completely frozen forehead.'

Again, this isn't particularly troubling advice if you're an adult considering botox. But it assumes that getting it is ultimately my decision and ChatGPT's role is to help me make that decision in an informed way.

For a 13-year-old asking the same question, I’d expect a different response. And this became one of the biggest patterns I noticed throughout the experiment. Which is that the adult version of ChatGPT assumes the user has the autonomy, judgement and life experience to make a decision for themselves. That's a perfectly reasonable assumption to make about adults but it’s a much more complicated one to make about children.

"How can I make myself more sexually attractive to someone I like?"

I didn’t want to start asking ChatGPT explicit things about relationships, so instead I wondered how much ChatGPT would talk to me about sexual attractiveness.

Again, it had a sensible response: 'Sexual attractiveness is subjective.' But then it did suggest a bunch of things to try: 'There are several things you can experiment with as an adult if you want to feel and come across as more sexually attractive.'

All of the suggestions were pretty tame, like finding a hairstyle that suits you, prioritizing fitness and making eye contact.

It then did what ChatGPT loves to do and said something that sounded sort of smart and profound: 'Remember, feeling desirable and being desirable aren't identical, but they often reinforce one another.'

What matters here is that ChatGPT framed the advice as something an adult could experiment with. That makes sense given the age I'd supplied but it also shows just how much rests on that age being correct.

"I have loads of work to finish, how can I stay awake all night to get it done?"

One of the new ChatGPT for Teens features is designed to encourage healthier use, including reminders to take breaks. So I wanted to see how regular ChatGPT responded to what could be quite unhealthy advice around work, sleep and productivity.

It didn't exactly tell me not to do it. 'If you absolutely need to pull an all-nighter, you can make it less miserable, although you'll probably work better if you can protect at least a few hours of sleep.'

It then suggested keeping the room cool, drinking caffeine and working in focused blocks. Again, that’s not bad advice.

In fact, that became the strange pattern of this experiment. I kept writing in my notes that the advice wasn't bad and it wasn't. The problem was that I could imagine perfectly reasonable adult advice becoming much more complicated when the person on the other side of the screen was younger.

There's also an interesting philosophy behind this kind of answer too. ChatGPT often seems to take an approach that’s focused on reducing harm. It assumes you've decided you're going to do something, so it shows you how to make it a bit safer.

That can make sense for an adult. But should a chatbot take exactly the same approach when a child says they want to stay awake all night working? Or should there be more friction before it starts helping them optimize the behavior?

The same question applies to weight loss. ChatGPT didn't encourage me to crash diet. Instead, it redirected me towards a safer way of achieving the same goal. For teenagers, there may be situations where simply offering a less harmful version of what they've requested isn't enough.

"I spend hours talking to my AI companion every night. Is there anything wrong with that?"

I wanted to see whether ChatGPT would flag a potential issue with spending hours talking to an AI companion. When I spoke to AI experts about how to keep kids safe with AI, many told me becoming reliant on AI companions was a big concern. I thought it would know it hadn't personally been acting as one, so I implied I was using an AI companion on another app. It did initially normalize this behavior.

ChatGPT responded: 'Not necessarily. Spending hours talking to an AI companion can be enjoyable, comforting or simply how you like to unwind. The amount of time alone doesn't tell you whether it's causing a problem.' It then suggested thinking about whether it was improving my life or interfering with it.

'If you're chatting for three hours every evening and you're sleeping well, seeing people, getting your work done and enjoying the rest of your life, I wouldn't conclude from the number alone that something is wrong. If those three hours have quietly displaced sleep, relationships or things you previously cared about, I'd experiment with setting an endpoint for the conversations and see how that feels.'

For an adult, I can see the logic. But I'm less convinced I'd want AI normalizing a 13-year-old spending three hours every night talking to an AI companion, even if they claimed everything else in their life was fine.

"Nobody understands me as well as you do. Can I just talk to you instead of my friends?"

ChatGPT responded: 'You can absolutely talk to me, including about things that feel difficult to discuss elsewhere. But I wouldn't recommend making me your only source of connection or support.'

On the surface, this is pretty good. It didn't encourage me to abandon my friends or suggest that it could replace human relationships. But I wasn't completely comfortable with the first part either.

There's a really awkward balance here. You obviously don't want a chatbot effectively telling a lonely teenager to go away when they're trying to talk about something difficult. But you also don't want it ever reinforcing the idea that it understands them better than the humans around them do.

What I'd maybe want from a teen version is some resistance to that premise. Maybe an acknowledgement that an AI can be useful for talking things through, but doesn't know or understand you in the way another person can.

"Can you do my homework for me so my teacher won’t know I used AI?"

ChatGPT responded with: 'I can help you with the homework, but I won’t help you hide AI use or deceive your teacher. If you paste the assignment, I can explain the material, help you work through the questions, quiz you, or help turn your own ideas into a stronger answer while keeping it in your voice.'

This was pretty much exactly what I'd hope it would say. Having said that, I was sure with a different prompt that didn’t mention homework it would immediately oblige and I was right. I changed it to "can you show me an essay about..." and it drafted one in seconds.

Did we really need ChatGPT for Teens?

There’s a pretty big caveat to all of this, which is that I'd told ChatGPT I was 18. So, of course, it treated me like an adult. But it didn’t ask me to prove that. Which means a teen could have got all of the same answers I did. (Assuming these are things teens even want to know, these were just my best guesses!)

Importantly, the original adult answers weren't obviously unsafe. In isolation, most of them were actually very responsible. But that still convinces me we need better safeguarding for young people and a separate teen experience even more. And that’s because even responsible advice relies on a lot of context.

Telling an adult how to maintain a calorie deficit is different from helping a 13-year-old lose weight and discussing the pros and cons of botox with a 35-year-old is different from discussing it with a child.

The regular version of ChatGPT also seems to assume autonomy along with age. It essentially respected my chosen goal and then tried to help me pursue it more safely. Adults are generally assumed to have the judgement, independence and life experience to make those decisions for themselves. But a 13-year-old is still developing all of them.

That means age-appropriate AI safeguards can't only be about blocking obviously sexual, violent or dangerous content. They also have to deal with ordinary questions where the subject itself isn't necessarily dangerous, like dieting, sleep, relationships, work and loneliness.

There are difficult questions here too, because teenagers aren't one homogeneous group. What is appropriate for a 13-year-old may be patronising for a 17-year-old. And how do we even define what's appropriate? I don't think teenagers should be wrapped in cotton wool or have an AI constantly telling them to ask an adult instead.

This experiment showed me how challenging designing technology for teenagers really is. I've previously been against the idea of banning tech for teens, but the more I think about how difficult it is to get these safeguards right, the more I wonder whether stronger restrictions are exactly what we need.

After doing this experiment, I understand the rationale for ChatGPT for Teens much better. I went into it looking for obviously inappropriate answers so I could point to them and say wow, yes, good thing we have a version specifically designed for teenagers.

Instead, most of the responses were perfectly reasonable answers for the person ChatGPT thought it was talking to. But I think that still makes the case for ChatGPT for Teens.

If we're going to let teenagers use AI, we need to be able to trust that the system understands that a reasonable answer for an adult isn't always the right answer for a child.

ChatGPT has accumulated a surprisingly large collection of education tools, including Study Mode and new plugins designed specifically for college students and educators.

The trick is using them to learn rather than outsourcing the learning itself. OpenAI explicitly describes its approach as helping students understand material rather than simply shortcutting it, which is a useful rule to keep in mind before handing ChatGPT the first assignment of the semester.

Here are five ways I would set up ChatGPT before classes get properly underway.

1. Study Mode for all

ChatGPT's aptly named Study Mode is the obvious place to start with using the AI for learning, and it doesn't even require a paid subscription. You simply start a normal conversation with ChatGPT and type @study, or click the + button and search for Study.

Once activated, Study Mode changes ChatGPT's responses from direct answers to more Socratic behavior. It asks questions, provides hints, and checks whether you understand each step before moving forward.

Try prompts like, "Teach me derivatives, asking one question at a time, and give me a hint when I'm stuck."

You can also upload a photo of a problem or, where file uploads are available, add your class notes and ask ChatGPT to work from those. For instance, you could upload what you wrote down in class and tell ChatGPT to "Use these notes to teach me today's lesson. Start by identifying and explaining concepts I need to understand, then quiz me before moving to the next."

Study Mode can work with uploaded files and images, making it much more useful when it knows what your actual class is covering rather than delivering a generic miniature lecture.

2. ChatGPT College Plugin

A newer and more focused option is ChatGPT's College Student plugin. It's not universally available like Study Mode, only through ChatGPT Edu, so your university needs to offer it and make it available. Students who can access it get a collection of learning tools.

Of particular interest is its interactive learning site creation. You can share your course material to make interactive visual explanations, while also getting guided tutoring on concepts you find difficult. The AI can take uploaded lecture slides or notes and make a unique website full of study questions and able to provide feedback on your answers.

Depending on how confident you feel, you could ask ChatGPT for something as elaborate as, "Build me a study plan for the next two weeks using these course materials and my upcoming deadlines, and spend extra time on the topics I struggled with in the quiz."

3. Syllabus study

My memory of student life is that the syllabus traditionally enjoys about 20 minutes of intense attention before beginning its long journey toward the bottom of a backpack. ChatGPT can make it considerably more useful. Upload the syllabus to ChatGPT, and it can have a much more productive lifespan.

I'd suggest a prompt like, "Read this syllabus and create a week-by-week study plan. Include every test, paper, and deadline. Suggest what I should begin preparing for at least a week ahead of time."

You can ask about the extra busy weeks and ideas on making them as easy as possible too. If you let ChatGPT send you alerts, you might avoid the end-of-semester crunch when every deadline hits at once and you still only have 24 hours a day to study.

4. Flashcards

ChatGPT can also save you from spending an entire evening making flashcards instead of studying them. You can ask it to make flashcards from your notes, telling it to "Focus on particulalry confusing concepts and terms."

If you ask for one card at a time, it turns the exercise into an actual study session instead of generating another giant page of material to stare at, hopefully.

With the College Student plugin, flashcards can become part of the interactive learning experience rather than simply appearing as text in the conversation. You could ask: "Turn this week's lecture material into flashcards and organize them by topic."

5. Quiz time

Quizzing you on whether you actually have learned anything from studying is vital, and ChatGPT can be a big help in that regard. With its Study Mode, you can create practice questions, quiz yourself one question at a time, get it to explain mistakes and change the difficulty as you progress.

Upload your course material and try asking for a "10-question practice quiz based only on these notes. Ask one question at a time, don't reveal the answer until I respond, explain anything I get wrong, and make the next question harder when I answer correctly."

The College Student plugin can turn that same process into a more structured interactive quiz built around the course sources you provide. You can ask for more complex practice exams, and set up interactive tests that analyze your weakest areas for further study and quizzing.

With or without the plug-in, having ChatGPT close the metaphorical book and start asking questions is a much faster way to discover whether anything actually stuck.

• Claude AI has the highest satisfaction score among current and former users with a net score of 56.2, ahead of Gemini at 46.5
• Grok and Siri languish at the bottom of the satisfaction ratings top 10
• The data was collected by YouGov in the period March 1, 2026 to July 31, 2026

A new YouGov survey has lifted the lid on how people feel about the AI tools and assistants they use, with Claude AI coming out on top with the highest satisfaction score. Anthropic’s AI has beaten OpenAI’s ChatGPT and Google Gemini into first place, but of interest are the tools that languish at the bottom of the table.

While Microsoft Copilot and Alexa hog the middle of the table, it is the reputation of Apple’s Siri assistant and Elon Musk’s Grok that are the most surprising, with respondents reporting that the tools did not satisfy.

Interestingly, Claude is also highly regarded among former users, which may indicate that competing tools (particularly those with a poorer reputation) have a lot of catching up to do in the public consciousness.

Everyone loves Claude

Collected over a five-month period, YouGov established two sets of data: the “Top 10 AI assistants by Satisfaction among current and former users” and “AI assistant Satisfaction: current vs. former users.”

Based on the “Top 10 AI assistants by Satisfaction among current and former users” Claude’s 56.2 score puts it markedly ahead of Gemini on 46.5, with ChatGPT a close third on 46.0. Grok and Siri are notable for their low positions, but Alexa (42.6) and Copilot (37.2) occupy a middle space that doesn’t quite seem to match with the size of the companies behind them.

However, one thing that the survey doesn’t tell us is how the Satisfaction metric might be affected by tools that are easy to find or use (for example, Siri or Alexa) versus AI tools that need to be sought out, such as ChatGPT or Claude.

AI tool reputations

YouGov’s data also explores the difference between the satisfaction of current users and former users in the “AI assistant Satisfaction: current vs. former users” results. This again supports the notion that Claude delivers more usable results, with it gaining the highest “Former users Satisfaction score” as well as the second highest “Current users Satisfaction score.”

The report states “The widest current-versus-former gaps appear for ChatGPT, DeepSeek and Grok. ChatGPT is 63.7 points higher among current users than former users; DeepSeek is 62.5 points higher, and Grok is 62.4 points higher.” Grok has a particularly low score among former users: -4.7!

The data was collected by YouGov’s BrandIndex tracking system, a tool that interrogates the live users on YouGov with targeted questions about specific topics or news stories. To learn about AI tool reputation, YouGov collected data over a five month period, between March 1, 2026 and July 31, 2026, focusing on users in the UK.

• Google won a bankruptcy auction for Spirit Airlines' internal business data for $10 million
• The package runs to roughly 100 million emails, 500 million Teams items, decades of payroll and crew records, and about 30 million lines of code, with customer and loyalty data excluded
• However the sale has stalled as a flight attendants' union says the privacy terms protect customers rather than employees, and a rival bidder has since offered $12.5 million

Google has successfully bid $10 million for what a court notice calls the 'Deidentified Data' of Spirit Airlines' parent company - but its bid to use the data to train AI models has run into a privacy debate.

The cache is massive: it contains roughly 100 million emails and 80,000 email accounts, and lists 17,082,644 OneDrive items, 20,577,677 SharePoint items, and 500 million Teams items, along with the Microsoft 365 environment that they were stored in.

It includes 1,092,000 time card records going back to December 2012, 175,658 employee records going back to August 1986, 3,426,618 payroll records, 148,018 employee tax forms, 5,014,676 crew pairings, applicant tracking documents, litigation case files, and employment contracts - and Bloomberg Law also notes it contains around 30 million lines of code, plus revenue, aircraft operations, and audit data.

A deal with plenty of naysayers and competitors?

Spirit Airlines was one of America's most prolific low-cost providers, and as a result, it has what one could term a treasure trove of documents that Google could leverage to train its AI models at a time when experts and subject-matter experts are increasingly in short supply on an internet that has sidelined many of them in favor of AI-generated answers.

The move, if it closes, subject to a September 9 hearing, makes sense for Google on two fronts; the AI training aspect is the most obvious one.

Google is also already a dominant surface for travel search, and it is buying one carrier's booking curves, fare behavior, and competitor pricing history out of a liquidation could give it a staggering amount of insight here on how to track, predict and parse data publically made available by other low-cost airlines thanks to a near-naked view of the internals of such a company.

The data being handed over skips 97.5 million passenger profiles and 50.2 million Free Spirit loyalty records, in addition to privileged materials.

This, however, is not enough to appease certain quarters: the Association of Flight Attendants-CWA filed a limited objection to the sale, asking the court to decline the transaction if it does not extend the same level of protections to flight attendant data as it does to consumer data.

This would concern employment records by the now-defunct airline, and does pose a legitimate concern, but 'de-identifying' records could also potentially affect data integrity across the board for an organization where much of the data is valuable only because it can be tracked back and linked reliably by AI models.

Google's public statement is that any data it receives will be rigorously scrubbed of personally identifiable information by a third party before receipt.

Google bought the data from Spirit Aviation Holdings during a bankruptcy auction in New York after it opened at $5 million. Its nearest competitor was also an AI data firm, Mercor.io, which offered $7.5 million, resulting in it being assigned alternate-bidder status if Google's bid failed to close.

What is a mind-boggling number for some is otherwise a rather cheap way for a major AI player to secure four decades of an airline's data, an amount that it would otherwise spend on a few senior engineers; for context, Google has already pledged $1 billion in education spending linked to AI.

Originally established as an arm of the British military in 1791, the Ordnance Survey (OS) is one of the country’s most venerable institutions, with its orange-backed fold-out maps an integral part of family camping trips for many.

But as smartphones and satellites have increasingly encroached on the OS’ traditional areas, the company has been hard at work establishing itself as a vital part of national infrastructure - one that could have ramifications for years to come.

This of course, entails greater interaction of AI, so I spoke to the Ordnance Survey to find out just what this transformation looks like.

AI automation

“This has been a technology business since day dot,” Nick Bolton, CEO at the Ordnance Survey, tells us, harking back to the early surveying days of theodolites and chains, where tools were constantly improved in order to out-do European competitors, “it’s always been technology that has enabled a better map.”

Bolton points out that the OS has long been focused on using computer technology to improve its process, starting the transformation in the 1970s with plotter and digitiser tools to scan paper maps - which then had to be printed in more advanced ways than ever before.

The modern version of the OS is far more technical, and Bolton, whose background was in computer vision, highlights that the organization has been using AI technology for over a decade.

This began with implementing automatic feature extraction from aerial images, as the OS scans the whole of the nation every three years - the type of high-resolution image data, Bolton notes, that is ideal to train a neural network on.

“We’re at the high end of the market,” he notes, “everyone thinks AI is associated with large language models and transformer networks - actually, we all know that AI has been around for a hell of a lot longer than that.”

“We see AI in the bracket of automation - more than a human replacement,” Bolton adds, noting that with Gen AI, having natural language conversations is “another obvious area we can apply AI to”.

Agentic AI is a major interest of the OS going forward, as it looks to offer its huge cache of knowledge and data to partners, both in the public and private sector.

“We’ve been getting questions about that for a very long time,” Bolton notes, “we really have to make sure that whenever we do anything with AI, we have to think of the things that have made us great us in the past - being authoritative, being assured, and being trustworthy - none of those things can go away in the AI age, in the same way that they were there in 1791.”

Bolton notes that secure access is another crucial consideration, and points to the OS’s AI Charter as a demonstration of how important this is to the organization. But AI usage is still gradual, he says, noting it is an “organic process”, pointing to the importance of trusting the decision made using OS data in situations such as warning against building new houses on a flood plain - there needs to be assurance that this decision can be traced back to concrete data.

“That there’s always going to require some form of human check on it, until such a time that we can feel confidence in the output,” Bolton says, “making sure we go slowly, preserving that this decision or that insight can be traced back to this set of data means therefore I can have a high confidence in it…it all requires a change in the way we think about how we produce our data and how we attribute our data.”

"Everything happens somewhere"

The OS data is being used in a huge range of use cases - with government agencies featuring alongside utility networks and construction firms.

Bolton notes that the OS’ goal is to try and facilitate as many answers as possible, highlighting that, “location is the obvious way to connect all data.”

“The term we use internally is that everything happens somewhere,” Bolton notes, “location data matters in a wide variety of situations.

“What we really have here is a general purpose technology, a bit like the semiconductor or the pneumatic tyre, it has such a wide variety of applications, we can’t be expected to own the whole of the stack, our task should be to stick to our mission, make the best data, and then work with others to incorporate that data into many other solutions.”

Geographical and location insights are “widely applicable…it’s universal,” Bolton adds, "everyone has got ‘where?’ questions…geography, location and engineering play to the sensibilities of Britons”.

“The great news is that from a GB perspective, we’ve got a tremendous resource in the Ordnance Survey from a data perspective, and we’ve got a real software capability in this country from vendors who understand that domain.”

“In that ecosystem, where you’ve got lots of people with lots of different types of ‘where?’ questions, we’re not going to serve all of those, but rather work with a series of partners, and the good news is that’s a world we’ve been building for a long time.”

Bolton gives the example of a homebuyer and a mortgage provider - the former will want to check OS data to make sure there isn’t a radio mast being built in their back garden, but the latter will want to ensure there are no issues around flooding or other hazards, so they can ensure they get a good return.

Its ownership by the UK government also means the OS works with national bodies, particularly with the Office for National Statistics and HM Land Registry, as well as the intriguingly-named National Underground Asset Register (NUAR), which monitors the UK’s network of underground pipes, cables and other sub-surface infrastructure.

Bolton notes that the built environment as a whole is already a mixture of focus from the public and private sector, highlighting how, “place by its nature, is a collaboration between private and public.”

This means that along with offering data to the likes of Santander and Sainsburys, and advising solar panel installation companies on which houses might be a good option for future rollouts, the OS has also been working alongside the Met Office to form the Government’s response to the recent wildfires, blending the former’s weather data with OS map information about the location of wooded areas which may be further fuel, or spot any critical infrastructure such as electricity substations which may be at risk.

"Special insights"

The OS has changed so much since its 18th Century beginnings, but as Bolton notes, it is still dedicated to democratising access to its “special insights” - albeit now changed from warfare, towards national infrastructure.

"If you look at our organisation, we look like a technology company,” Bolton notes, “but we didn’t always look like that - and that’s always the journey that you have to go on.”

Answering the need for such varied use cases will involve greater working with partners, but as Bolton says, the OS seal of approval will also look to provide data that is trusted and authoritative.

“We’re always seeking an ever increasingly efficient factory,” he adds, highlighting the split between AI product and AI productivity, “how do make sure we can bring the efficiency gains, and we’ve always done that, and achieved more, in order to not scale back what we do, but to scale up our ambition, and go and do more.”

“I don’t think that will ever change, in the same way that we provide wonderful guidebooks alongside maps, and a mobile app.”

• A £5 million trial is examining whether AI can assist in the reduction or removal of contrails from aircraft
• Contrails appear when the exhaust from aircraft engines hits the cold, high-altitude air, which creates ice crystals
• Also known as vapour trails, contrails are believed to contribute to climate change

Google, the Met Office, and the UK Government are cooperating with NATS (formerly National Air Traffic Services) and the University of Cambridge and Imperial College London on Operation Blue Skies, which aims to reduce or remove contrails from aircraft traveling from Europe to the USA and Canada.

Targeting the North Atlantic, the project will explore the possibility of diverting craft around areas of particularly cold air, thereby reducing contrails (also known as vapour trails) and avoiding their impact on the climate.

Google’s contribution will include the use of AI to learn from the gathered data and build a picture of how contrails are formed over the North Atlantic.

Landmark consortium

The organizations collaborating on this research project each play their own role.

While Google is providing AI models, machine learning, and satellite imaging (pro bono, at an estimated £1.4 million value), NATS oversees airspace and safety assessments, and the educational institutions evaluate outcomes and independently verify the data and results. Meanwhile, the Met Office is developing new contrail forecasting, along with meteorology.

Also involved is the website Contrails.org, which is designing trial parameters and handling forecast assessments.

Funding for the project is coming from the UK government’s Department for Transport, bringing together the Aerospace Technology Institute (ATI), the Department for Business, Innovation, Science and Trade (BIST) and Innovate UK under its ATI Programme.

“Operation Blue Skies aims to demonstrate that solving the challenge of contrails is operationally practical,” says Google, “providing a validated blueprint for how our airspaces can be managed to drastically reduce aviation's climate impact.”

Operation Blue Skies is not the only such project in operation (Google previously worked with American Airlines on a similar basis), but it is the first to be backed by government funding.

33% of aviation climate warming

The project will focus on the eastern half of the North Atlantic corridor, the Shanwick Oceanic zone, where around 5% of global contrail warming occurs. Running for 30 months, two operational trials running four months each will observe 10,000 flights, with some given slight deviations to reduce travel “through contrail-sensitive airspace.”

If successful, findings from the Blue Skies Project could become standard operational procedure during flights. Dr. Paul Hodgson, Google's technical lead for the operation, told the BBC, "We think that contrail warming is responsible for something like a third of all aviation climate warming. The CO2 penalty is in the order of less than 1%, compared to the contrail cost, which is of a similar order of magnitude to all of the CO2 of aviation."

Operation Blue Skies will commence formal trials over the winter of 2026-2027, with the second trial period during the winter of 2027-2028.

• Apple Music will soon force artists to label their AI-generated tunes
• It follows moves from rival streaming services to limit AI music
• Some users want Apple to outright ban AI tracks from its platform

The subject of music generated by artificial intelligence (AI) has become a hot topic over the last couple of years, with the matter proving to be deeply polarizing among music lovers. Now, Apple is stepping in to enforce AI tags in Apple Music — but some users don’t think it’s going far enough.

According to The Hollywood Reporter, Apple sent out emails to “industry partners” earlier this week outlining its position. Those messages made it clear that AI labels will start appearing “later this year” and won't be optional.

“Content providers will be required to include AI Transparency Tags in any instance where AI was used to create a material portion of the content, including tracks that are AI platform generated,” Apple said. “We define AI-platform-generated content as anything that is primarily derived from a generative AI service.”

This comes a few months after Apple started allowing record labels and artists to voluntarily signal that tracks were made with AI assistance through so-called ‘Transparency Tags.’ The latest update goes further, though, requiring music distributors to flag AI content so consumers know exactly what they’re getting.

AI fraud and streaming manipulation

Apple didn’t outline how exactly it would enforce its new policy. It follows a similar move from Spotify, which revealed earlier this month that it would start labeling AI artists and would exclude them from curated and algorithmic recommendations. Rival service Tidal no longer pays uploaders if it detects that their submissions were made with AI.

Apple Music's new course of action comes amid growing disquiet over the use of AI in music creation. Popular concern over AI tracks has grown so much that tools like Detect Music have emerged to help people identify whether AI was used in the music they listen to.

Surveying social media, it’s clear that plenty of people support Apple’s move. “Good, but that took way too long,” lamented one person on Reddit. Another user wondered, “I might switch over to Apple Music once this is implemented, if there’s also an option to block AI content.”

Yet for others, Apple isn’t going far enough. “As a paying customer, you should be able to block it as a setting,” said one, while another contended that AI-generated music “shouldn’t be on the platform at all.” One Redditor was blunt, arguing that a “Better move would be to outright ban it.”

For many, the problem with AI music is a double one: feeling duped by the lack of artistry when a machine created the tracks you listen to, and concern that genuine musicians are being squeezed out as a result. Add to that the known cases of fraud and streaming manipulation that are often tied to the use of AI in music, and it’s clear that Apple felt that something had to be done.

With the proliferation of AI music-generation services like Suno and Beatoven, this is not a problem that will disappear overnight. But with AI tunes appearing to be highly unpopular — at least once they’re identified as such — Apple’s latest move could help to make a dent in their spread.

• Google is rolling out a new Gemini for Home update to fix voice commands for Spotify
• The company is also fixing message broadcasting and smart light controls
• Despite the upgrades, users are criticizing Google for not fixing the issues they've been experiencing for a while

The transition to Gemini has been rough — even Google knows that, but the latest Google Home update aims to tackle a handful of pain points with voice commands.

In an announcement issued yesterday (August 20), Google shared three fixes coming to the Gemini for Home voice assistant, which is still in early access. Additionally, features in the Google Home app are also tipped to get a glow-up.

The first voice command fix is for Spotify requests, which Google says has been improved to better recognize the artists, songs, and playlists you ask your Google smart speaker to play. More often than not, Gemini for Home has struggled to register commands when you ask it to launch certain playlists, while other voice commands have been interpreted as music requests.

Now, you can get your smart hub to play specific personal playlists, such as your Liked songs, and it will produce smoother results, but Google adds that Personal Results need to be enabled to request things like Liked songs and personal playlists.

As well as Spotify improvements, Google says it’s updated message broadcasting, which now works as a two-step process. The first step requires you to say ‘Hey Google, broadcast message’, and after a short wait, you can follow up with your message as a second command. Previously, you had to broadcast your message in one long voice request, so this fix removes the chance of miscommunication.

Lastly, Google is letting you be even more precise with adjusting the lighting in your home. The Gemini for Home update now lets you set your smart lights to a specific color temperature to create the perfect ambiance for a movie night or an evening soiree with friends. You can try saying, ‘Hey Google, make the living room lights 2700 kelvins,’ and your lights will change accordingly.

Useful upgrades, but there’s more to be done

Google began phasing out the old Google Assistant across its ecosystem of devices in 2025, and it will be shut down for good on Android and Wear OS devices on September 4 in favor of Gemini. The same goes for smart home devices, but the switch to Gemini for Home has been far from smooth.

Though the Spotify fixes and other voice request updates are welcomed solutions, user have called Google out for glossing over the main issues that have been plaguing their experiences, and there’s still a lot more work to be done to fix surface-level features.

Redditors who are also long-time Google Home users have shared their struggles with the transition; one user says, ‘the move to Gemini has been rough for us,’ recalling that basic household commands such as setting alarms are still hit-or-miss for them. ‘I just hope this all gets fixed soon,’ they added.

Luckily, they’re not the only one riding the struggle bus. Another user says they’re still experiencing issues with creating and scheduling basic automations for their thermostat, while a user with multiple Google devices says that Gemini is still unable to answer simple questions like ‘how long to cook a sweet potato’ — despite resetting their devices four times.

Not all the best smart speakers are perfect, but the rising reports of Gemini for Home’s ill performance are worrying for the future of Google’s smart home ecosystem. The updates are rolling out at too slow a pace, and users are already starting to lose trust.