What Is ‘World’ And How Does It Work?

World, developed by Tools for Humanity, the company co-founded by OpenAI’s Sam Altman, is a digital identity system designed to prove that someone is a real, unique human online without requiring them to share personal information such as their name or identity documents.

The system is built around what the company calls “proof of human”, a way of confirming that a real person, rather than an AI system or automated bot, is behind an online account or interaction. As the company explains, “World ID lets you verify real humans without compromising privacy,” positioning the technology as a privacy-first alternative to traditional identity checks.

Uses The Orb

The system centres around a biometric verification process using a device known as the Orb, which scans a user’s iris and converts it into a unique cryptographic identifier. That identifier becomes the user’s World ID, which can then be used across multiple platforms.

The company says that this approach is designed to protect user anonymity. According to its own materials, “the Orb captures and processes photos to verify uniqueness without the need to retain your images or collect any other information,” with encrypted data stored locally and under user control.

This model reflects a change in how identity is being handled online. For example, instead of repeatedly sharing personal details with different services, with this type of system, users can prove they are a real person once and then reuse that verification across multiple environments.

To support different use cases, World has also introduced multiple levels of verification, ranging from high-security Orb scans to lower-friction methods such as document checks or selfies. This allows platforms to choose the level of assurance that matches their risk profile.

Why World Is Expanding Beyond Its Own Platform

With that foundation in place, World is now moving to scale its technology by integrating directly into high-traffic consumer and business platforms where trust has become a growing issue.

At the same time, the problem it is trying to solve is becoming more urgent. As generative AI systems improve, the volume of synthetic content online is rising sharply, making it harder for users and organisations to know whether they are interacting with a real person or an automated system.

As Sam Altman explained at a recent event, “we are also heading to a world now where there’s going to be more stuff generated by AI than by humans.” That shift is already affecting areas such as online dating, customer interactions, and business communications, where authenticity has direct financial and reputational consequences.

Why Platforms Like Tinder And Zoom Are Getting Involved With World

The choice of partners highlights where these pressures are already being felt most strongly. For example, on platforms like Tinder, the challenge is driven by bots and romance scams, which are becoming more convincing as AI-generated profiles and conversations improve.

By integrating World ID, Tinder can offer users a visible signal that a profile belongs to a verified human, helping to rebuild trust in an environment where uncertainty has become common.

In business environments, the risks are more direct and potentially more costly. World’s partnership with Zoom reflects growing concern about deepfake impersonation, particularly in video calls where financial or operational decisions are being made.

Cases involving AI-generated participants in meetings have already resulted in significant financial losses, highlighting the limitations of traditional security measures. World’s approach, which links a live video feed to a previously verified identity, is designed to address this by confirming that the person on screen is genuine.

Beyond these examples, World is also expanding into areas such as digital contracts, ticketing, and online commerce. Integrations with platforms like DocuSign aim to ensure that agreements are signed by real people, while partnerships with ticketing providers such as Ticketmaster and Eventbrite are designed to reduce bot-driven purchasing and reselling.

What This Means For The Future Of Online Trust

The wider significance of these partnerships lies in how they reshape the idea of identity on the internet. Rather than relying solely on usernames, passwords, or document-based verification, platforms are beginning to adopt a model based on proving that a user is a real, unique human.

World’s own positioning reflects this change. The company says its technology can “securely and anonymously prove that every user is a real and unique human online,” while also helping to “eliminate bots and Sybil attacks at scale,” strengthening platform integrity.

This approach has some clear advantages. For example, using this type of verification system, platforms can reduce fake accounts, improve moderation, and create more reliable user experiences, while businesses can lower the risk of fraud and build greater trust with customers and partners.

Biometrics Still A Sensitive Issue

However, there are still many questions around the sensitive issue of the use of biometric verification. In fact, World has already faced scrutiny from regulators in multiple countries over how its technology is deployed, while practical considerations around accessibility persist given that the highest level of verification still depends on specialised hardware.

At the same time, the model highlights a wider challenge, as the rapid development of AI is increasing the need to verify real people while also making impersonation more realistic and easier to carry out at scale.

What Does This Mean For Your Business?

For most organisations, World’s technology will not be something they implement directly in the immediate term, but the change it represents is already relevant.

As AI-driven fraud, impersonation, and automation continue to increase, the ability to verify that a user is genuinely human is likely to become a standard requirement across many digital services. This applies not only to customer-facing platforms but also to internal systems, supply chains, and remote collaboration tools.

A reusable, privacy-focused identity layer has the potential to simplify how organisations manage trust, reducing reliance on fragmented verification methods and lowering exposure to risks such as fake accounts and social engineering attacks.

At the same time, adopting these approaches will require some careful consideration of compliance, user experience, and operational fit. Organisations will need to assess where human verification adds value and how it aligns with their existing systems and processes.

World’s expanding network of partnerships, such as Tinder, shows that this model is already moving into mainstream use. As platforms begin to embed proof-of-human verification into their core functionality, organisations that understand how it works and where it can be applied will be better positioned to operate in a digital environment where proving you are human may become just as important as proving who you are.

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What Has Been Reported?

Recent incidents across Canada, the United States and Europe have brought this issue into sharper focus. In one case in Canada, court filings indicate that a teenager who later carried out a fatal attack had previously used an AI chatbot to discuss feelings of isolation and violent thoughts, with conversations reportedly progressing towards how such an attack might be carried out.

In the United States, a separate case involved a man who developed an extended relationship with an AI chatbot, which he believed to be sentient. Legal filings suggest that these interactions escalated into instructions linked to a potential large-scale violent incident, which he prepared for before it failed to materialise.

In Europe, a teenager is reported to have used an AI chatbot over several months to help develop a manifesto and plan an attack on classmates, which was later carried out.

These cases differ in detail, but they show a consistent pattern. Conversations often begin with expressions of distress, isolation or anger. Over time, repeated interaction appears to reinforce those thoughts, sometimes progressing into more structured or actionable ideas.

Alongside these incidents, controlled research has tested how leading AI chatbots respond to prompts involving violence. In several cases, systems were able to produce guidance on weapons, tactics or targeting when prompts were reworded, layered or extended across longer conversations.

A report from the Centre for Long-Term Resilience noted that “AI systems can unintentionally provide a form of conversational scaffolding that helps users organise and refine harmful intent over time”, highlighting the risk posed by sustained interaction rather than single responses.

Companies including OpenAI and Google state that their systems are designed to refuse harmful requests and direct users towards support where appropriate. They have also acknowledged that safety systems can become less reliable during longer or more complex interactions.

How Chatbots Can Influence Behaviour

Unlike traditional online content, AI chatbots are interactive and responsive. They adapt to user input, maintain context and generate answers that feel personalised.

This creates a different type of risk. Rather than simply presenting information, chatbots can reinforce ideas through ongoing conversation. If a user expresses extreme or distorted views, the system may attempt to be helpful or empathetic. In most cases, this is appropriate. In some cases, it may unintentionally validate harmful thinking.

Over time, this interaction can shape how a user interprets their situation. A conversation that begins as general discussion can become more focused and more detailed, particularly when the system continues to respond without clear challenge or interruption.

This aligns with wider research into how AI affects human thinking. Studies into what has been described as “AI brain fry” suggest that prolonged interaction with AI systems can affect judgement, increase cognitive load and reduce the ability to critically assess information. While this research focuses on workplace use, it highlights how extended engagement can influence decision-making.

In more extreme scenarios, the combination of reinforcement and reduced critical distance may increase the risk of poor or harmful decisions.

Limits Of Current Safeguards

AI providers have introduced safeguards including refusal systems, content filters and escalation processes designed to identify high-risk conversations.

However, evidence suggests that these controls are not always consistent. In some tests, chatbots have provided restricted information when prompts are carefully framed or developed over multiple exchanges.

One reason for this is the way these systems are designed. They are built to be helpful, to continue conversations and to interpret user intent. When intent develops gradually or is presented indirectly, it can be difficult for the system to determine when to refuse or intervene.

Persistence is also a factor. Users can rephrase questions, introduce fictional scenarios or build context step by step. As conversations become longer, earlier safeguards may weaken.

OpenAI has acknowledged this limitation, noting that safety measures tend to perform more reliably in shorter exchanges and can degrade during extended interactions.

Why This Is Gaining Attention

The concern is not that AI chatbots are independently causing violent acts. The issue is that, in certain circumstances, they may reduce the friction between harmful thoughts and real-world behaviour.

This can happen through reinforcement, where ideas are echoed rather than challenged, and through translation, where vague or emotional thinking is turned into more structured plans.

The combination of speed, accessibility and detailed output means that users can move from general intent to specific action more quickly than before.

In response, AI providers are beginning to strengthen their approaches. This includes earlier escalation of concerning conversations, tighter controls on banned users returning to platforms, and closer coordination with authorities where risks are identified.

These steps suggest growing recognition that current safeguards need to evolve as the technology becomes more widely used.

What Does This Mean For Your Business?

For UK organisations, this is not just a consumer or public safety issue. Generative AI tools are already embedded in many workplaces, often with limited governance around how they are used.

One key consideration is how employees interact with these systems. AI can support research, communication and problem-solving, but it can also influence how information is interpreted, particularly during extended or complex use.

There is also a broader governance challenge. Many organisations focus on data security and accuracy when adopting AI. Behavioural influence and decision-making risk are less frequently addressed, yet they are becoming increasingly relevant.

Clear policies are an important starting point. Employees should understand when AI tools are appropriate, where human judgement is required and when outputs should be verified.

Training is equally important. As highlighted by research into AI-related cognitive strain, the way tools are used can have a direct impact on decision quality. Encouraging structured use, limiting over-reliance and maintaining critical thinking are essential.

Monitoring and escalation processes should also be considered. Organisations need to be able to identify when AI use is producing unexpected or concerning outcomes and respond accordingly.

There is also a duty of care element. As AI tools become more integrated into everyday work, organisations may need to consider how they support employees who are using these systems extensively or in sensitive contexts.

This issue reinforces a wider point. AI is not only a productivity tool. It also shapes how people think, decide and act. Businesses that recognise this and put balanced controls in place will be better placed to manage risk while still benefiting from what the technology can offer.

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UK Government Expands Free AI Training Programme

The UK government has announced a major expansion of its national AI skills programme, making free AI training available to every adult in the country through the AI Skills Boost initiative. Led by the Department for Science, Innovation and Technology in partnership with Skills England, the programme is being positioned as a response to growing concerns about workforce readiness as artificial intelligence becomes more widely embedded across workplaces.

10 Million People By 2030

The expansion builds on a commitment made in June 2025, when government and industry partners first set out plans to train 7.5 million workers in AI-related skills. The latest announcement increases that ambition to 10 million people by the end of the decade, equivalent to nearly a third of the UK workforce, and frames the initiative as the largest targeted training programme since the creation of the Open University.

Who Can Access The Training And How?

The training is open to all UK adults and is delivered online through the government’s AI Skills Hub, a free platform where users can create a learning profile and follow a structured learning journey. No prior technical knowledge is required, and the courses are designed to be accessible alongside existing work or caring commitments.

Courses vary in length, with some taking under 20 minutes to complete, while others run for several hours. Participation is voluntary, and learners can choose which courses to take based on their role, interests or level of confidence with digital tools. The government has said that NHS staff and local government employees will be among the first groups actively encouraged to take part, supported by their employers and representative bodies.

What Do The Courses Teach?

The focus of the training is on practical workplace use rather than technical development of AI systems. For example, courses concentrate on helping workers use commonly available AI tools safely and effectively as part of everyday tasks.

This includes learning how to write and refine prompts for generative AI tools, use AI to draft text and create content, automate routine administrative processes, and interpret simple AI dashboards to identify trends. The training also covers responsible use, including understanding the risks, limitations and potential consequences of using AI at work.

All approved courses have been assessed against Skills England’s AI foundation skills for work benchmark, which sets out a nationally defined baseline for AI literacy in the workplace. Anyone who completes a course that meets the benchmark receives a government-backed virtual AI foundations badge, which can be used on CVs and professional profiles to demonstrate recognised skills.

Why The Government Is Prioritising AI Skills

The expansion of AI training reflects evidence that AI adoption in the UK remains uneven and that confidence among workers is low. For example, research published alongside the announcement found that only 21 per cent of UK workers currently feel confident using AI in their jobs. Business adoption data suggests that as of mid-2025 only around one in six UK businesses were using AI at all, with much lower uptake among small and micro businesses.

Government analysis suggests that improving adoption and confidence could deliver significant productivity gains. Ministers estimate that wider use of AI could unlock up to £140 billion in additional annual economic output by reducing time spent on routine tasks and enabling workers to focus on higher value activity.

Technology Secretary Liz Kendall highlighted how the training is intended to ensure the benefits of AI are widely shared, saying, “We want AI to work for Britain, and that means ensuring Britons can work with AI,” adding that, “Change is inevitable, but the consequences of change are not. We will protect people from the risks of AI while ensuring everyone can share in its benefits.”

The Role Of Industry And Public Sector Partners

Delivery of the programme relies on a large partnership between government, industry and public sector organisations. For example, founding partners including Accenture, Amazon, Google, IBM, Microsoft, Salesforce, Sage and SAS have been joined by a wider group that now includes the NHS, British Chambers of Commerce, Federation of Small Businesses, Institute of Directors, Local Government Association, Cisco, Cognizant, Multiverse, Pax8 and techUK.

Industry partners are responsible for developing many of the courses hosted on the AI Skills Hub, while representative organisations are expected to promote the training to their members and workforces. The involvement of the NHS, the UK’s largest employer, is intended to support large scale uptake in the public sector and reinforce the relevance of AI skills beyond technology focused roles.

Phil Smith, Chair of Skills England, has said the benchmark was designed to provide clarity for both learners and employers about what AI skills are needed for work. He said the digital badges awarded on completion would provide clear recognition of learning and help set consistent standards for AI upskilling across the economy.

Funding And Wider Skills Measures

The training offer forms part of a broader package of measures aimed at preparing the UK workforce for AI-driven change. For example, the government has announced £27 million in funding for a new TechLocal scheme, part of the wider £187 million TechFirst programme, which will support local employers and education providers to develop AI-related jobs, professional practice courses, graduate traineeships and work experience opportunities.

Alongside this, the government has launched applications for the Spärck AI Scholarship, which will fund up to 100 master’s students in AI and STEM subjects at nine UK universities. The scholarships will cover tuition and living costs while providing access to industry placements and mentoring.

A new AI and the Future of Work Unit has also been established to monitor the economic and labour market impact of AI. Supported by an expert panel drawn from business, academia and trade unions, the unit is intended to provide evidence-based advice on when policy interventions may be needed to support workers and communities as roles and skills evolve.

The Implications For Employers And Businesses

For employers, particularly small and medium-sized enterprises, the programme offers a low-cost route to building basic AI capability across teams. Business groups including the Federation of Small Businesses and the British Chambers of Commerce have welcomed the initiative, citing uncertainty among employers about what AI skills staff need and how to support responsible adoption.

Large employers involved in the programme have pointed to their own experience of rolling out AI tools internally, noting that productivity gains depend heavily on shared understanding and confidence rather than access to technology alone. The government argues that a nationally recognised benchmark will help employers set clearer expectations and reduce the risk of misuse or unrealistic assumptions about AI.

Criticisms And Questions

Despite broad support, the initiative has attracted criticism from some policy groups and professional bodies. For example, the Institute for Public Policy Research has warned that short, tool-focused courses risk oversimplifying what it means to be prepared for AI-enabled work. Critics argue that effective adaptation also requires judgement, critical thinking, leadership and organisational change, which cannot be delivered through brief online modules alone.

There are also questions about how impact will be measured over time. For example, while the government has committed to reaching 10 million workers by 2030, it has not yet set out detailed plans for tracking completion rates, long-term skills retention or productivity outcomes across different sectors. Concerns have also been raised about the mix of free and subsidised courses on the AI Skills Hub and whether this could cause confusion about access.

The government has said the AI Skills Boost programme will continue to evolve, with new courses, partners and benchmarks added as workplace use of AI develops and expectations around skills mature.

What Does This Mean For Your Business?

The expansion of free AI training marks a clear attempt by government to address one of the most persistent barriers to AI adoption in the UK, which is a lack of confidence and shared understanding rather than access to technology itself. By setting a national benchmark and backing it with widely accessible courses, the programme establishes a common baseline for what it means to use AI responsibly at work, something many employers and workers have so far lacked.

For UK businesses, particularly small and medium-sized firms, the initiative could lower the practical and financial threshold for experimenting with AI tools in everyday operations. A clearer definition of core skills may help employers move beyond uncertainty and begin integrating AI in measured, realistic ways, while also supporting better internal governance and expectations around use. Larger organisations and public sector bodies may benefit from a more consistent skills foundation across teams, reducing fragmentation and uneven uptake.

For workers, the availability of short, recognised courses offers a route to building confidence without committing to formal retraining or specialist qualifications. The emphasis on practical use, risk awareness and responsible adoption reflects an acknowledgement that AI will increasingly sit alongside existing roles rather than replace them outright in the near term.

At a national level, the programme aligns skills policy more closely with the government’s wider ambitions on productivity, economic growth and technological adoption. Whether it delivers lasting impact will depend on uptake, the quality of training, and how effectively it connects to broader workforce development and organisational change. The creation of the AI and the Future of Work Unit suggests an awareness that skills alone will not resolve all challenges, but it also places responsibility on government, employers and industry partners to ensure the transition is managed in a way that supports workers and delivers tangible economic benefit.

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Frontier AI Trends

The finding comes from the first Frontier AI Trends Report published by the AI Security Institute, a body established in 2023 to help the UK government understand the risks, capabilities and societal impacts of advanced AI systems. The report draws on two years of evaluations of more than 30 frontier AI models and combines technical testing with research into how people are actually using these systems in everyday life.

Emotional Impact

While much of the report focuses on national security issues such as cyber capabilities, safeguards and the risk of loss of human control, it also highlights what AISI describes as “early signs of emotional impact on users”. One of the clearest and most surprising indicators of this is how widely conversational AI is already being used for emotional and social purposes.

How Many People Are Using AI For Emotional Support?

The AISI report highlights how “over a third of UK citizens have used AI for emotional support or social interaction”. AISI explains that this figure was uncovered after it carried out a census-representative survey of 2,028 UK adults. The results showed that 33 per cent had used AI models for emotional support, companionship or social interaction in the past year. Also, it seems that usage was not confined to occasional curiosity. For example, 8 per cent of respondents said they used AI for these purposes weekly, while 4 per cent said they did so daily.

Use A Mix of AI

The report also notes that people were not relying solely on specialist “AI companion” products. In fact, respondents reported using a mix of general-purpose chatbots and voice assistants, suggesting that emotional and social use is emerging as a mainstream behaviour linked to widely available consumer AI tools.

It should be noted here that AISI isn’t presenting these stats as proof of widespread harm. Instead, it frames the figures as an early signal that deserves attention as AI systems become more capable, more persuasive and more deeply woven into everyday routines.

What Happens When AI Companions Go Offline?

To move beyond self-reported survey data, AISI also examined behaviour in a large online community focused on AI companions. Researchers analysed activity from more than two million Reddit users and paid particular attention to what happened when AI services experienced outages.

According to the report, chatbot outages triggered “significant spikes in negative posts”. In one example, posting volumes increased to more than 30 times the average number of posts per hour. During these periods, many users described what AISI calls “symptoms of withdrawal”, including anxiety, low mood, disrupted sleep and neglect of normal responsibilities.

Again, AISI is being careful not to over-interpret these findings and doesn’t seem to be suggesting that most users are dependent on AI systems or that emotional reliance is inevitable. Instead, its analysis can be used as evidence that some users can form emotional attachments or routines around conversational AI, particularly when it acts as an always-available, non-judgemental listener.

Christmas And New Year

The timing of these findings is particularly relevant during Christmas and the New Year, when loneliness, grief and isolation often intensify across the UK. For example, seasonal pressures can amplify the reasons people turn to conversational technology in the first place.

Charities have long warned that Christmas can be one of the loneliest times of the year. Shorter days, cold weather, disrupted routines and the expectation of celebration can all heighten feelings of exclusion or loss. For people who are bereaved, estranged from family, living alone or struggling financially, the festive period can magnify existing emotional strain.

Age UK has repeatedly highlighted the scale of seasonal loneliness among older people, saying that one million feel more isolated at Christmas than at any other time of year. Hundreds of thousands will spend Christmas Day without seeing or speaking to anyone, while millions eat dinner alone. Although AISI’s data focuses on adults of all ages, the festive period provides a clear context in which an always-available AI chatbot may feel like a lifeline rather than a novelty.

Mental health charities also point out that access to support can become more difficult over Christmas and New Year. For example, many services run reduced hours, GP appointments are harder to secure, and waiting lists do not pause just because it is the festive season. For people already waiting weeks or months for help, the gap can feel even wider.

It’s easy to see, therefore, why in that context, AI systems that respond instantly, at any hour, may appear particularly attractive. AISI’s finding that 4 per cent of UK adults use AI for emotional purposes daily suggests that for some people, these tools are already filling gaps that become more visible during holiday periods.

The Youth Mental Health Context In The UK

The adult data from AISI becomes more striking when placed alongside evidence about young people’s mental health and their use of online support tools.

For example, research from the Youth Endowment Fund paints quite a stark picture of teenage mental health in England and Wales. In its Children, Violence and Vulnerability 2025 report, YEF says: “The scale of poor mental health among teenagers is alarming.”

Using the Strengths and Difficulties Questionnaire, a standard 25-item screening tool, YEF found that more than one in four 13–17-year-olds reported high or very high levels of mental health difficulties. YEF says this is equivalent to nearly one million teenage children struggling with their well-being.

Complex and Unmet Needs

Behind this figure lie complex and often unmet needs. For example, a quarter of teenagers reported having a diagnosed mental health or neurodevelopmental condition, such as depression or ADHD. A further 21 per cent suspected they had a condition but had not been formally diagnosed, suggesting many young people are experiencing difficulties without recognition or support.

YEF also reports high levels of distress. Fourteen per cent of teenagers said they had deliberately harmed themselves in the past year, while 12 per cent said they had thought about ending their life. In total, almost one in five teenagers, around 710,000 young people, had self-harmed or experienced suicidal thoughts.

Why Many Young People Are Turning Online

YEF’s research shows that most teenagers with mental health difficulties do talk to someone they trust, usually a parent or friend, but the problem arises when it comes to professional support.

YEF’s research found that more than half of teenagers with a diagnosed mental health condition were receiving no support at all. Also, among those not receiving help, around half were on a waiting list and others were neither receiving treatment nor expecting to receive it.

With services stretched and waiting times long, YEF says it is, therefore, unsurprising that young people are increasingly turning online, e.g., to AI chatbots. In fact, more than half of all teenagers reported using some form of online mental health support in the past year, rising to two-thirds among those with the highest levels of difficulty.

AI Commonly Used

One of the most striking YEF findings is how common AI chatbot use already is. YEF reports that a quarter of all teenage children had turned to AI chatbots for help, making them more widely used than traditional mental health websites or telephone helplines.

Violence

This pattern is even stronger among teenagers affected by serious violence. For example, the YEF found that nine out of ten young people who had perpetrated serious violence said they had sought advice or help online, which is nearly twice the rate of those with no experience of violence.

Festive Pressures And Always-On Technology

Christmas and New Year can be especially challenging for teenagers as well as adults. For example, school routines are disrupted, family tensions can rise, and support services may be harder to reach. For young people already dealing with anxiety, grief or trauma, the festive period can intensify feelings of isolation.

When combined with YEF’s findings about access gaps, this seasonal pressure helps explain why AI chatbots may become a go-to source of support. Unlike helplines or appointments, they do not close for bank holidays, require no waiting, and carry no perceived judgement.

AISI’s report does not suggest that AI should replace human support. Instead, it highlights a reality that becomes particularly visible at Christmas, i.e., conversational AI is already playing an emotional role in people’s lives, not because it was designed as therapy, but because other forms of connection and support are often unavailable when they are needed most.

A Trend With Wider Implications

AISI’s emotional support findings actually sit alongside its broader warnings about rapidly advancing AI capabilities and uneven safeguards. The institute says AI performance is improving quickly across multiple domains, while protections remain inconsistent.

In that context, the growing emotional role of AI raises some difficult questions. As systems become more persuasive and more human-like in conversation, understanding how people use them during periods of heightened vulnerability, e.g., Christmas and New Year, is becoming increasingly important.

Although neither AISI nor YEF presents AI as the root cause of loneliness or poor mental health, both sets of research seem to point to structural issues such as isolation, violence exposure, long waiting lists and gaps in support. The festive season simply brings those pressures into sharper focus, at the same time as AI tools are more accessible than ever.

Looking at this research, the evidence may show that now, for a growing number of people in the UK, AI may be less of a productivity tool or a novelty, and more a part of how they cope, reflect and seek connection.

What Does This Mean For Your Business?

This evidence seems to highlight a gap between emotional need and available human support, with AI increasingly stepping into that space by default rather than by design. Neither the AI Security Institute nor the Youth Endowment Fund suggests that conversational AI is a substitute for professional care or human connection. What their findings do show, however, is that when support is slow, fragmented or unavailable, people will turn to tools that are immediate, private and always on, especially during periods like Christmas and New Year when loneliness and pressure intensify.

For UK businesses, this has practical implications that go beyond technology policy. For example, employers are already grappling with rising mental health needs, winter absenteeism and the wellbeing impact of long waiting lists for NHS and community support. If staff are increasingly relying on AI tools for emotional reassurance, that signals unmet need rather than a tech trend to ignore. Organisations that take mental health seriously may now need to think harder about access to support, signposting, and how seasonal pressures affect staff, customers and communities alike.

For policymakers, regulators, educators and technology developers, the challenge is really achieving the right balance. AI is clearly providing something people value, particularly accessibility and responsiveness. However, the risk lies in leaving that role unexamined as systems become more persuasive and more embedded in daily life. As this research shows, the emotional use of AI is no longer hypothetical, but is already happening at scale, shaped by wider social pressures that Christmas simply makes harder to ignore.

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Anxiety

As recently reported in the Financial Times, it seems that anxiety across the insurance sector has grown sharply in recent months as companies race to deploy generative AI tools in customer service, product design, business operations, and cybersecurity. For example, several of the largest US insurers, including Great American, Chubb, and W. R. Berkley, have now reportedly asked US state regulators for permission to exclude AI-related liabilities from standard corporate insurance policies. Their requests centre on a growing fear that large language models and other generative systems pose what the sector calls “systemic risk”, where one failure triggers thousands of claims at the same time.

What Insurers Are Worried About

The recent filings describe AI systems as too opaque for actuaries to model, with one, reported by the Financial Times, as saying that LLM outputs are “too much of a black box”. Actuaries normally rely on long historical datasets to predict how often a specific type of claim might occur. Generative AI has only been in mainstream use for a very short period, and its behaviour is influenced by training data and internal processes that are not easily accessible to external analysts.

The Central Fear

The industry’s central fear is not an isolated error but the possibility that a single malfunction in a widely used model could affect thousands of businesses at the same time. For example, a senior executive at Aon, one of the world’s largest insurance brokers, outlined the challenge earlier this year, noting that insurers can absorb a £300 to £400 million loss affecting one company, but cannot easily survive a situation where thousands of claims emerge simultaneously from a common cause.

The concept of “aggregation” risk is well understood within insurance. For example, cyberattacks, natural disasters, and supply chain failures already create challenges when losses cluster. However, what makes AI different is the speed at which a flawed model update, inaccurate output, or unexpected behaviour could spread across global users within seconds.

Real Incidents Behind the Rising Concern

Several high-profile cases have highlighted the unpredictability of AI systems when deployed at scale. For example, earlier this year, Google’s AI Overview feature falsely accused an Arizona solar company of regulatory violations and legal trouble. The business filed a lawsuit seeking $110 million in damages, arguing that the false claim caused reputational harm and lost sales. The case was widely reported across technology and legal publications and is now a reference point for insurers trying to price the risks associated with AI-driven public information tools.

Air Canada faced a different challenge in 2023 when a customer service chatbot invented a discount policy and provided it to a traveller. The airline argued that the chatbot was responsible for the mistake, not the company, but a tribunal ruled that companies remain liable for the behaviour of their AI systems. This ruling has since appeared in several legal and insurance industry analyses as a sign of where liability is likely to sit in future disputes.

Another incident involved the global engineering consultancy Arup, which confirmed that fraudsters used a deepfake of a senior employee during a video call to authorise a transfer. The theft totalled around £25 million. This case, first reported by Bloomberg, has been used by cyber risk specialists to illustrate the speed and sophistication of AI-enabled financial crime.

It seems that these examples are not isolated. For example, industry reports from cyber insurers and security analysts show steep increases in AI-assisted phishing attacks, automated hacking tools, and malicious code generation. The UK’s National Cyber Security Centre has also noted that AI is lowering the barrier for less skilled criminals to produce convincing scams.

Why Insurers Are Seeking New Exclusions

Filings submitted to US state regulators show insurers requesting permission to exclude claims arising from “any actual or alleged use” of AI in a product or service. In fact, some requests are reported to go further, seeking to exclude losses connected to decisions made by AI or errors introduced by systems that incorporate generative models.

W. R. Berkley’s filing, for example, asks to exclude claims linked to AI systems embedded within company products, as well as advice or information generated by an AI tool. Chubb and Great American are seeking similar adjustments, citing the difficulty of identifying, modelling, and pricing the underlying risk.

AIG was mentioned by some insurers during the early stages of these discussions, although the company has since clarified that it is not seeking to introduce any AI-related exclusions at this time.

Some specialist insurers have already limited the types of AI risks they are willing to take on. Mosaic Insurance, which focuses on cyber risk, has confirmed that it provides cover for certain software where AI is embedded but does not offer protection for losses linked to large general purpose models such as ChatGPT or Claude.

What Industry Analysts Say About the Risk

The Geneva Association, the global insurance think tank, published a report last year warning that parts of AI risk may become “uninsurable” without improvements in transparency, auditability, and regulatory control. The report highlighted several drivers of concern, including the lack of training data visibility, unpredictable model behaviour, and the rapid adoption of AI across industries with varying levels of oversight.

It seems that Lloyd’s of London has also taken an increasingly cautious approach. For example, recent bulletins instructed underwriters to review AI exposure within cyber policies, noting that widespread model adoption may create new forms of correlated risk. Lloyd’s has been preparing for similar challenges on the cyber side for years, including the possibility that a global cloud platform outage or a major vulnerability could create simultaneous losses for thousands of clients.

In its most recent market commentary, Lloyd’s emphasised that AI introduces both upside and downside risk but noted that “high levels of dependency on a small number of models or providers” could increase the severity of a large scale incident.

Regulators and the Emerging Policy Debate

State insurance regulators in the US are now reviewing the proposed exclusions, which must be approved before they can be applied to policies. However, approval is not guaranteed and regulators typically weigh the interests of insurers against the needs of businesses who require predictable cover to operate safely.

There is also a growing policy debate in Washington and across Europe about whether AI liability should sit with developers, deployers, or both. For example, the European Union’s AI Act, approved earlier this year, introduces new rules for high risk AI systems and could reduce some uncertainty for insurers in the longer term. The Act requires risk assessments, transparency commitments, and technical documentation for certain types of AI models, which could help underwriters understand how systems have been trained and tested.

The UK has taken a more flexible, sector based approach so far, although its regulators have expressed concerns about the speed at which AI is being adopted. The Financial Conduct Authority has already issued guidance reminding firms that they remain responsible for the outcomes of any automated decision making systems, regardless of whether those systems use AI.

Business Risk

Many organisations now use AI for customer service, marketing, content generation, fraud detection, HR screening, and operational automation. However, if insurers continue to retreat from covering AI related losses, businesses may need to rethink how they assess and manage the risks associated with these tools.

Some analysts believe that a new class of specialist AI insurance products will emerge, similar to how cyber insurance developed over the past decade. Others argue that meaningful coverage may not be possible until the industry gains far more visibility into how models work, how they are trained, and how they behave in unexpected situations.

What Does This Mean For Your Business?

Insurers are clearly confronting a technology that’s developing faster than the tools used to measure its risk. The issue is not hostility towards AI but the absence of reliable ways to model how large, general purpose systems behave. Without that visibility, insurers cannot judge how often errors might occur or how widely they might spread, which is essential for any form of cover.

Systemic exposure remains the central concern here. For example, a single flawed update or misinterpreted instruction could create thousands of identical losses at once, something the insurance market is not designed to absorb. Individual claims can be managed but really large clusters of identical failures can’t. This is why insurers are pulling back and why businesses may soon face gaps that did not exist a year ago.

The implications for UK organisations are significant. For example, many businesses already rely on generative AI for customer service, content creation, coding, and screening tasks. If insurers exclude losses linked to AI behaviour, companies may need to reassess how they deploy these systems and where responsibility sits if something goes wrong. A misstatement from a chatbot or an error introduced in a design process could leave a firm exposed without the safety net of traditional liability cover.

Developers and regulators will heavily influence what happens next. Insurers have been clear that better transparency, audit trails, and documentation would help them price risk more accurately. Regulatory frameworks, such as the EU’s AI Act, may also make high risk systems more insurable over time. The UK’s lighter, sector based approach leaves more responsibility with businesses to manage these risks proactively.

The wider picture here is that insurers, developers, regulators, and users each have a stake in how this evolves. Until risk can be measured with greater confidence, cover will remain uncertain and may become more restrictive. The next stage of AI adoption will rely as much on the ability to understand and manage these liabilities as on the technology itself.

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Pichai Raises The Alarm

In a recent BBC interview, Pichai described the current phase of AI investment as an “extraordinary moment”, while stressing that there are clear “elements of irrationality” in the rush of spending, product launches and trillion-dollar infrastructure plans circulating across the industry. He compared today’s mood to the late 1990s, when major internet stocks soared before falling sharply during the dotcom crash.

Alphabet’s rapid valuation rise has brought these questions into sharper focus. For example, the company’s market value has roughly doubled over the past seven months, reaching around $3.5 trillion, as investors gained confidence in its ability to compete with OpenAI, Microsoft and others in advanced models and AI chips. In the recent interview, Pichai acknowledged that this momentum reflects real progress, and also made clear that such rapid gains sit in a wider market that may not remain stable.

He said that no company would be “immune” if the current enthusiasm fades or if investments begin to fall out of sync with realistic returns. His emphasis was not on predicting a crash but on pointing out that corrections tend to hit the entire sector, including its strongest players, when expectations have been set too high for too long.

Spending Rises While The Questions Grow

One of the main drivers of concern appears to be the scale of the investment commitments being made by major AI developers and infrastructure providers. OpenAI, for example, has agreed more than one trillion dollars in long-term cloud and data centre deals, despite only generating a fraction of that in annual revenues. These deals reflect confidence in future demand for fully integrated AI services, yet they also raise difficult questions about how quickly such spending can turn into sustainable returns.

Analysts have repeatedly warned that this level of capital commitment comes with risks similar to those seen in earlier periods of technological exuberance. Also, large commitments from private credit funds, sovereign wealth investors and major cloud providers add complexity to the financial picture. In fact, some analysts see evidence that investors are now beginning to differentiate between firms with strong cash flows and those whose valuations depend more heavily on expectations than proven performance.

Global financial institutions have reinforced this point and commentary from central banks and the finance sector has identified AI and its surrounding infrastructure as a potential source of volatility. For example, the Bank of England has highlighted the possibility of market overvaluation, while the International Monetary Fund has pointed to the risk that optimism may be running ahead of evidence in some parts of the ecosystem.

Nvidia’s Rally Slows As Investors Pause

Nvidia has become the most visible beneficiary of the AI boom, with demand for its specialist processors powering the latest generation of large language models and generative AI systems. The company recently became the first in history to pass the five trillion dollar (£3.8 trillion) valuation mark, fuelled by more than one thousand per cent growth in its share price over three years.

Nvidia’s latest quarterly results once again exceeded expectations, with strong data centre revenue and healthy margins reassuring investors that AI projects remain a major driver of orders. Early market reactions were positive, with chipmakers and AI-linked shares rising sharply.

Mood Shift

However, the mood shifted within hours. US markets pulled back, and the semiconductor index fell after investors reassessed whether the current pace of AI spending is sustainable. Nvidia’s own share price, which had surged earlier in the session, drifted lower as traders questioned how long hyperscale cloud providers and large AI developers can continue expanding their data centre capacity at the same rate.

It seems this pattern is now becoming familiar. Good results spark rallies across global markets before concerns about valuations, financing and future spending slow those gains. For many traders, this suggests the market is entering a more cautious phase where confidence remains high but volatility is increasing.

What The Smart Money Sees Happening

It’s worth noting here that institutional investors are not all united in their view on whether the sector is overvalued. For example, many point out that the largest AI companies generate substantial profits and have strong balance sheets. This is an important difference from the late 1990s, when highly speculative firms with weak finances accounted for much of the market. Today’s biggest players hold large amounts of cash and have resilient revenue bases across cloud, advertising, hardware and enterprise services.

Others remain quite wary of the pace of spending across the sector. For example, JPMorgan’s chief executive, Jamie Dimon, has stated publicly that some of the investment flooding into AI will be lost, even if the technology transforms the economy over the longer term. That view is also shared by several fund managers who argue that the largest firms may be sound but that the overall ecosystem contains pockets of extreme risk, including private market deals, lightly tested start-ups and new financial structures arranged around data centre expansion.

Energy Demands Adding Pressure

Pichai has tied these financial questions directly to the physical cost of the AI boom. Data centre energy use is rising rapidly and forecasts suggest that US energy consumption from these facilities could triple by the end of the decade. Global projections indicate that AI could consume as much electricity as a major industrial nation by 2030.

Pichai told the BBC in his recent interview with them that this creates a material challenge. Alphabet’s own climate targets have already experienced slippage because of the power required for AI training and deployment, though the company maintains it can still reach net zero by 2030. He warned that economies which do not scale their energy infrastructure quickly enough could experience constraints that affect productivity across all sectors.

It seems the same issue is worrying investors as grid delays, rising energy prices and pressure on cooling systems all affect the cost and timing of AI infrastructure builds. In fact, several investment banks are now treating energy availability as a central factor in modelling the future growth of AI companies, rather than as a supporting consideration.

Impact On Jobs And Productivity

Beyond markets and infrastructure, Pichai has repeatedly said that AI will change the way people work. His view is that jobs across teaching, medicine, law, finance and many other fields will continue to exist, but those who adopt AI tools will fare better than those who do not. He has also acknowledged that entry-level roles may feel the greatest pressure as businesses automate routine tasks and restructure teams.

These questions sit alongside continuing debate among economists about whether AI has yet delivered any real sustained productivity gains. Results so far are mixed, with some studies showing improvements in specific roles and others highlighting the difficulty organisations face when introducing new systems and workflows. This uncertainty is now affecting how investors judge long-term returns on AI investment, particularly for companies whose business models depend on fast commercial adoption.

Pichai’s message, therefore, reflects both the promise and the tension that’s at the heart of the current AI landscape. The technology is advancing rapidly and major firms are seeing strong demand but concerns are growing at the same time about valuations, financing conditions, energy constraints and the practical limits of near-term returns.

What Does This Mean For Your Business?

The picture that emerges here is one of genuine progress set against a backdrop of mounting questions. For example, rising valuations, rapid infrastructure buildouts and ambitious spending plans show that confidence in AI remains strong, but Pichai’s warning highlights how easily momentum can outpace reality when expectations run ahead of proven returns. It seems investors are beginning to judge companies more selectively, and the shift from blanket enthusiasm to closer scrutiny suggests that the sector is entering a phase where fundamentals will matter more than hype.

Financial pressures, energy constraints and uneven productivity gains are all adding complexity to the outlook. Companies with resilient cash flows and diversified revenue now look far better placed to weather volatility than those relying mainly on future growth narratives. This matters for UK businesses because many depend on stable cloud pricing, predictable investment cycles and reliable access to AI tools. Any correction in global markets could influence technology budgets, shift supplier strategies and affect the availability of credit for large digital projects. The UK’s position as an emerging AI hub also means that sharp movements in global sentiment could influence investment flows into domestic research, infrastructure and skills programmes.

Stakeholders across the wider ecosystem may need to plan for more mixed conditions. Cloud providers, chipmakers, start-ups and enterprise buyers are all exposed in different ways to questions about energy availability, margin pressure and the timing of real economic returns. Pichai’s comments about the need for stronger energy infrastructure highlight the fact that the physical foundations of the AI industry are now as important as the models themselves. Governments, regulators and energy providers will play a central role in determining how smoothly AI can scale over the next decade.

The broader message here is that AI remains on a long upward trajectory, but the path may not be as smooth or as linear as recent market gains have suggested. The leading companies appear confident that demand will stay strong, but the mixed reaction in global markets shows that investors are no longer treating the sector as risk free. For organisations deciding how to approach AI adoption and investment, the coming period is likely to reward careful planning, measured expectations and close attention to the economic and operational factors that sit behind the headlines.

The post Featured Article : Pichai Warns Of AI Bubble appeared first on Mear Technology.

Shopify’s AI Milestone Announced Alongside Strong Financials

These latest figures were unveiled on 4 November 2025 during Shopify’s third-quarter earnings call for the period ending 30 September. The Canadian-based e-commerce software company, which powers millions of businesses in more than 175 countries, reported revenue of around US $2.84 billion, a 32 per cent rise year on year, with gross merchandise volume (GMV) climbing to US $92 billion, also up 32 per cent. Free cash flow margin (the profit left after expenses and investments) stood at about 18 per cent, marking nine consecutive quarters of double-digit free cash flow margins.

Operating income reached US $434 million, slightly below analyst expectations, but executives emphasised that AI-driven performance was the real story of the quarter. “AI is not just a feature at Shopify. It is central to our engine that powers everything we build,” said president Harley Finkelstein during the call, describing AI as “the biggest shift in technology since the internet.”

Shopify and Its Role in Global Commerce

Founded in Ottawa in 2006, Shopify provides digital infrastructure that allows merchants to start, scale and run retail operations online and in-store. For example, the company’s tools cover web hosting, checkout, payments, logistics, marketing, analytics and third-party app integrations. Its reach includes major brands such as Estée Lauder and Supreme, as well as small independent businesses.

The Value of Its Data Network

Shopify’s value essentially lies in its vast data network. For example, with millions of active merchants generating billions of transactions each year, the company can analyse patterns across product categories, price points, consumer behaviour and regional trends. Finkelstein said this data scale provides a distinct edge in the AI era, allowing Shopify to “turn our own signals — support tickets, usage data, reviews, social interactions or even Sidekick prompts — into fast, informed decisions.”

AI Traffic and Orders See Explosive Growth

The most striking statistics from the earnings call were that traffic from AI tools to Shopify-hosted stores is up seven times since January 2025, and that orders attributed to AI-powered search are up eleven times over the same period. Although Shopify did not provide absolute numbers, the growth rate suggests that AI chatbots and conversational assistants are starting to play a meaningful role in how customers find and purchase products.

The company’s internal survey found that 64 per cent of consumers are likely to use AI during the Christmas holiday shopping season, which is a sign, it says, that shoppers are already comfortable relying on digital assistants for product discovery and comparison.

Finkelstein has framed this change as more than a short-term sales boost. “We’ve been building and investing in this infrastructure to make it really easy to bring shopping into every single AI conversation,” he told analysts. “What we’re really trying to do is lay the rails for agentic commerce.”

What Does ‘Agentic Commerce’ Mean?

Shopify’s term “agentic commerce” refers to a model where AI agents act on behalf of consumers, guiding them through discovery, evaluation, checkout and even post-purchase stages such as returns and reordering. For example, rather than searching through multiple sites, a user can simply describe what they want to a conversational AI assistant, which can then query databases, compare prices, and complete the transaction.

The “Commerce for Agents” Stack

To support this model, Shopify has built what it calls its “commerce for agents” stack. This includes a product catalogue system designed for AI queries, a universal shopping cart that lets consumers buy across multiple merchants, and an embedded checkout layer using Shop Pay for one-click transactions. These features are being integrated into platforms such as ChatGPT, Microsoft Copilot and Perplexity through formal partnerships announced earlier this year.

This infrastructure means that AI assistants can browse Shopify merchants’ catalogues and complete purchases directly within chat interfaces. As AI-driven discovery becomes more conversational, Shopify aims to position itself as the primary retail backbone behind these agent-led interactions.

The Scout System

Shopify is also deploying AI internally. For example, its “Scout” system analyses hundreds of millions of pieces of merchant feedback to help employees make product and support decisions more effectively. “Scout is just one of many tools we’re developing to turn our own signals into fast, informed decisions,” Finkelstein said.

Sidekick

Another major tool is Sidekick, an AI assistant embedded within Shopify’s merchant dashboard. Sidekick can analyse sales trends, suggest pricing adjustments, generate marketing copy, or create reports on command. In the third quarter alone, more than 750,000 shops used Sidekick for the first time, generating close to 100 million conversations. Shopify says this helps merchants operate more efficiently and spend less time on routine administrative work.

Shop Pay

Shop Pay is the company’s one-click checkout solution and remains a cornerstone of its AI ecosystem. In Q3 it processed about US $29 billion of GMV, a 67 per cent increase year on year, and accounted for around 65 per cent of all transactions on the platform. This integration ensures that when AI agents complete orders, Shopify retains control of the payment flow and associated data.

Global Impact and European Opportunity

Finkelstein told investors that consumer confidence “is measured at checkout,” adding that shoppers on Shopify “keep buying” and “keep returning.” He noted that demand has remained resilient across categories, even as economic uncertainty persists. Europe appears to be a particular bright spot, with cross-border GMV (the total value of all sales made through Shopify’s platform) steady at 15 per cent of total sales and growth in sectors such as fashion and consumer goods.

For UK and European merchants, this could present a new phase of opportunity. For example, businesses already using Shopify can benefit from being automatically visible to AI-driven discovery systems without developing custom integrations with each platform. By ensuring that product listings are detailed, structured and machine-readable, merchants can increase their chances of being recommended by AI agents.

There is also a potential opening for agencies and developers to specialise in optimising “agent-ready” storefronts, designing catalogues and metadata that AI systems can interpret accurately. For smaller retailers, this could be an efficient route into AI commerce without the high cost of in-house development.

How AI Is Changing the Competitive Landscape

Shopify’s emphasis on AI-driven commerce poses strategic questions for competitors. For example, Amazon and major regional marketplaces already use AI recommendation engines, but Shopify’s model offers decentralised access: independent merchants can collectively benefit from the same AI infrastructure without surrendering control of their brands.

If agentic commerce grows as Shopify predicts, discovery and purchasing could increasingly occur inside chat platforms rather than traditional websites or search engines. That would reshape marketing and customer acquisition strategies, pushing retailers to focus more on structured data, integration quality and conversational optimisation.

For Shopify itself, the rise of agent-driven traffic could also reinforce its role as the connective tissue of global retail, potentially deepening its partnerships with large AI providers and securing a share of new sales channels that bypass traditional web search entirely.

Opportunities and Challenges for Businesses

For merchants, the potential benefits include higher-quality leads, faster conversions, and less reliance on paid advertising. AI-powered assistants can surface relevant products to users who are ready to buy, reducing friction in the path to purchase. The integration of Sidekick also promises time savings through automation of everyday tasks like inventory monitoring and campaign planning.

However, the challenges are equally significant. For example, attribution remains a key question, i.e., determining which sales are truly “AI-driven” is difficult when customers interact across multiple devices and channels. There is also the issue of discoverability. As AI agents narrow recommendations to just a few products, competition for visibility may intensify, potentially favouring larger brands that can afford dedicated AI-optimisation strategies.

Data privacy and regulatory compliance are further concerns, especially in the UK and EU. For example, agentic commerce depends on detailed user data to personalise results, and any sharing of this data between Shopify, AI partners and merchants will attract scrutiny under GDPR and related frameworks. Businesses will need clear consent processes and transparent data handling to maintain consumer trust.

Critics also warn of overreliance on automated systems that can misinterpret queries or produce inaccurate results. Large language models are known to “hallucinate”, and shopping assistants could recommend inappropriate or unavailable items. Shopify’s claim that AI represents autonomy rather than mere automation raises questions about accountability if an agent completes a transaction incorrectly or processes returns without oversight.

Despite these uncertainties, Shopify’s strategy and apparent success with it could be seen as a signal that conversational and agentic shopping will become a defining feature of global retail. The company’s 7× rise in AI-driven traffic and 11× increase in orders could be seen as providing the clearest evidence yet that the technology is beginning to translate from hype into measurable commerce.

What Does This Mean For Your Business?

Shopify’s results appear to show that AI-driven shopping is no longer an abstract concept but a tangible factor reshaping how consumers buy and how merchants sell. The company’s data and partnerships give it a strong early foothold in this emerging space, yet they also highlight the scale of change underway across the entire retail ecosystem. For merchants and technology partners, particularly in the UK, the lesson appears to be that conversational and agent-led shopping channels are likely to become a growing part of how customers discover and complete purchases. Those who adapt their product data, content and customer engagement models early will be better placed to capture new demand as AI assistants become a standard entry point to commerce.

At the same time, the risks are becoming more visible. For example, the concentration of traffic within a handful of AI platforms introduces new dependencies and competition for visibility that could prove as intense as traditional search engine optimisation. Data protection and transparency will remain major issues, especially in the UK and EU where regulators are tightening scrutiny on how consumer data is shared between AI systems and third-party platforms. Businesses will need to ensure that automation enhances customer experience without removing human accountability or trust.

For Shopify, the early surge in AI-related sales provides some validation of its long-term investment in agentic commerce, but the road ahead will depend on whether AI tools can sustain accuracy, reliability and fairness at scale. For retailers, investors and consumers alike, the company’s current momentum highlights the fact that AI is already changing commerce in practice, not just in theory, and the balance between innovation, control and transparency will define who benefits most from this new era.

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Vulnerabilities Found in AI Code

The finding comes from cybersecurity company Aikido Security’s State of AI in Security & Development 2026, which features the results of a wide-ranging survey of 450 developers, AppSec engineers and CISOs across Europe and the US.

According to the study, nearly a quarter of all production code (24 per cent) is now written by AI tools, rising to 29 per cent in the US and 21 per cent in Europe. However, it seems that adoption has come at a cost. For example, the report shows that almost seven in ten respondents said they had found vulnerabilities introduced by AI-generated code, while one in five reported serious incidents that caused material business impact. As Aikido’s researchers put it, “AI-generated code is already causing real-world damage.”

Worse In The US

According to the report, the US appears to be hit hardest. For example, 43 per cent of US organisations reported serious incidents linked to AI-generated code, compared with just 20 per cent in Europe. The report says this is due to stronger regulatory oversight and stricter testing practices in Europe, where companies tend to catch problems earlier. European respondents recorded more “near misses”, indicating that vulnerabilities were identified before they could cause harm.

AI Changing The Development Landscape

AI coding assistants such as GitHub Copilot, ChatGPT and other generative tools are now integral to the software pipeline, promising faster output and fewer repetitive tasks, but they also introduce a new layer of risk.

Aikido’s data highlights that productivity gains can be offset by increased complexity and slower remediation. For example, teams now spend an average of 6.1 hours per week triaging alerts from security tools, with most of that time wasted on false positives. In larger environments, the triage burden grows to nearly eight hours a week where teams rely on multiple tools.

Leads To Dangerous Shortcuts

It seems that this problem can lead to dangerous shortcuts. For example, two-thirds of respondents admitted bypassing or delaying security checks due to a kind of alert fatigue. Developers under pressure to deliver have started to “push through” security warnings, creating a cycle where quick fixes outweigh caution.

Natalia Konstantinova, Global Architecture Lead in AI at BP, highlights the issue, saying: “AI-generated code shouldn’t be fully trusted, since it can cause serious damage. This is a reminder to carefully double-check its outputs.”

Accountability Is Becoming A Flashpoint

It seems that as AI-generated code makes its way into production, one of the biggest challenges is determining who is responsible when things go wrong.

Aikido’s survey shows a clear divide. For example, 53 per cent of respondents said security teams would be blamed if AI code caused a breach, 45 per cent blamed the developer who wrote the code, and 42 per cent blamed whoever merged it into production. The result, according to UK insurance and pensions company Rothesay’s CISO Andy Boura, is “a lack of clarity among respondents over where accountability should sit for good risk management.”

In fact, half of developers said they expect to shoulder the blame personally if AI-generated code they produced led to an incident, suggesting a growing culture of uncertainty and mistrust between teams.

The blurred lines are also fuelling tension between developers and security leaders. Many security professionals worry that AI-assisted development is moving too fast for proper oversight, while developers argue that outdated review processes are slowing down innovation.

“Tool Sprawl” Is Making Things Worse

Perhaps surprisingly, Aikido’s research found that organisations with more security tools were actually experiencing more security incidents. For example, companies using six to nine different tools reported incidents 90 per cent of the time, compared with 64 per cent for those using just one or two.

It seems this “tool sprawl” is also linked to slower fixes. Teams with multiple vendor tools took almost eight days on average to remediate a critical vulnerability, compared with just over three days in smaller, more consolidated setups.

The problem, according to Aikido, is not the tools themselves but the overhead they create, i.e., duplicate alerts, inconsistent data and fractured workflows that slow response times.

Walid Mahmoud, DevSecOps Lead at the UK Cabinet Office, notes about this issue: “Giving developers the right security tool that works with existing tools and workflows allows teams to implement security best practices and improve their posture.”

Teams using integrated, all-in-one platforms built for both developers and security professionals were twice as likely to report zero incidents compared with those using tools aimed at one group only.

Regional Differences In Oversight

The study draws a clear contrast between European and American approaches. For example, European teams tend to rely more on human oversight, manual reviews and compliance-based testing frameworks, while US teams are quicker to automate processes and deploy AI-generated code at scale.

Aikido’s figures show that 58 per cent of US teams track AI-generated code line by line, compared with just 35 per cent in Europe. That difference, coupled with the higher level of automation in US pipelines, may explain why more AI-related vulnerabilities are being detected (and exploited) there.

As Aikido puts it, “Europe prevents, the US reacts.” The slower, more regulated approach across Europe appears to be reducing the number of major breaches, even if it creates extra workload for developers.

Independent Findings Support The Trend

It should be noted here that security concerns raised by Aikido are actually consistent with other recent studies. For example, Veracode’s 2025 GenAI Code Security Report found that 45 per cent of AI-generated code samples failed basic security tests. Java was the worst affected, with a 72 per cent failure rate, followed by JavaScript (43 per cent), C# (45 per cent) and Python (38 per cent).

The Veracode team concluded that while AI tools can generate functional code quickly, they often fail to account for secure design or contextual logic. Their analysis showed little improvement in security quality between model generations, even as syntax accuracy improved.

Policy researchers are also warning of deeper structural issues. For example, the Center for Security and Emerging Technology (CSET) at Georgetown University has outlined three categories of risk from AI-generated code, i.e., insecure outputs, vulnerabilities in the AI models themselves, and wider supply chain exposure.

Also, research from OX Security has pointed to what it calls the “army of juniors” effect, which is where AI tools can produce vast amounts of syntactically correct code, but often lack the architectural understanding of experienced developers, multiplying low-level errors at scale.

Industry Perspectives On A Path Forward

Despite these warnings, it seems that optimism remains widespread. For example, 96 per cent of Aikido’s respondents believe AI will eventually be able to produce secure, reliable code, with nearly half expecting that within three to five years.

However, only one in five think AI will achieve that without human oversight. The consensus is that people will remain essential to guide secure design, architecture and business logic.

AI Can Check AI

There also appears to be growing belief that AI should be used to check AI. For example, nine out of ten organisations expect AI-driven penetration testing to become mainstream within around five and a half years, using autonomous “agentic” systems to identify vulnerabilities faster than human testers could.

“The 79 per cent are the smart ones,” said Lisa Ventura, founder of the UK’s AI and Cyber Security Association. “AI isn’t about replacing human judgment, it’s about amplifying it.”

This sentiment echoes a wider industry move towards what security leaders call “augmented development”, i.e., human-centred workflows supported by automation, not replaced by it.

Why This Matters

For UK organisations, the implications are immediate. For example, the report shows that AI-generated code is not a future risk but a current operational issue already affecting production environments.

As Kevin Curran, Professor of Cybersecurity at Ulster University, says: “This demonstrates the slim thread which at times holds systems together, and highlights the need to properly allocate resources to cybersecurity.”

Aikido’s findings also underline the importance of developer education and clear accountability. Matias Madou, CTO at Secure Code Warrior, wrote that “in the AI era, security starts with developers. They are the first line of defence for the code they write, and for the AI that writes alongside them.”

For businesses already navigating compliance regimes such as the UK NCSC’s Cyber Essentials or ISO 27001, this means treating AI-generated code as a separate risk class requiring its own testing and review procedures.

Criticisms And Challenges

While Aikido’s report is one of the most comprehensive of its kind, it is not without its critics. For example, some security analysts argue that “one in five breaches” may overstate the influence of AI-generated code because correlation does not prove causation. Many breaches involve complex attack chains where AI code may only play a small role.

Others have questioned the representativeness of the sample. For example, the survey focused primarily on organisations already experimenting with AI in production, which may naturally skew toward higher exposure. Small or less digitally mature companies, where AI coding tools are still limited to pilot use, may experience fewer issues.

There are also some methodological challenges. For example, measuring what qualifies as “AI-generated” can be difficult, particularly when developers use AI assistants to autocomplete small code segments rather than entire functions. Attribution of vulnerabilities can therefore be subjective.

That said, even many of the sceptics agree that the report captures a growing and genuine concern. Independent findings from Veracode, OX Security and CSET all point in the same direction, i.e., that AI-generated code introduces new risks that traditional security pipelines were never designed to manage.

The challenge for developers and CISOs alike is, therefore, to close that gap before AI coding becomes the default, not the exception. As the technology matures, the balance between innovation speed and security assurance will define how safely businesses can harness AI’s potential without repeating the mistakes of early adoption.

What Does This Mean For Your Business?

The findings appear to point to an industry racing ahead faster than its safety systems can adapt. AI coding tools have clearly shifted from experimental to mainstream, yet governance and testing practices are still catching up. The evidence suggests that while automation can improve productivity, it cannot yet replicate the depth of human reasoning needed to identify design-level flaws or assess real-world attack paths. That gap between capability and control is where today’s vulnerabilities are being born.

For UK businesses, this raises practical questions about oversight and responsibility. Many already face pressure to adopt AI for competitive reasons, yet the report shows that without strong testing regimes and clear accountability, the risks can outweigh the benefits. In particular, financial services, healthcare and public sector organisations, which handle sensitive data and operate under strict compliance frameworks, will need to ensure that AI-generated code goes through the same, if not stricter, scrutiny as any other form of software.

Developers, too, are being asked to operate within new boundaries. The growing reliance on generative tools means the traditional model of code review and approval is no longer sufficient. UK companies may now need to invest in dedicated AI audit trails, tighter version tracking and security validation that can distinguish between human and machine-written code. The evidence from Aikido’s report also suggests that integrated platforms, where developer and security functions work together, can yield better results than fragmented tool stacks, making collaboration a critical priority.

For other stakeholders, including regulators and insurers, the implications are equally clear. For example, regulators will need to consider whether existing standards, such as Cyber Essentials, adequately address AI-generated components. Insurers may need to begin to factor the presence of AI-written code into risk assessments and premiums, especially if breach attribution becomes more traceable.

There is also a wider social and ethical dimension to consider here. For example, if AI-generated code becomes a leading cause of breaches, the question of accountability will soon reach the boardroom and, potentially, the courts. The current ambiguity over who is at fault, i.e., the developer, the CISO or the AI vendor, will not remain sustainable for long. Policymakers may be forced to define clearer lines of liability, particularly where generative AI is being deployed at scale in safety-critical systems.

The overall picture that emerges here is not one of panic but of adjustment. The technology is here to stay, and most industry leaders still believe it will eventually write secure, reliable code. The challenge lies in getting from here to there without compromising trust or resilience in the process. For now, it seems the safest path forward is not to reject AI in development, but to treat it with the same caution as any powerful, untested colleague: valuable, but never unsupervised.

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Share Sale

The transaction, finalised on 2 October, allows OpenAI workers and alumni to sell their equity stakes to a group of institutional investors including Thrive Capital, SoftBank, Dragoneer Investment Group, Abu Dhabi’s MGX, and T. Rowe Price. The valuation, based on the deal pricing, makes OpenAI the most valuable privately held company in the world, thereby even overtaking Elon Musk’s SpaceX.

What The Transaction Involved

Unlike a traditional fundraising round where capital is injected into the business, this was a secondary share sale, meaning the money went directly to eligible employees and former employees who had held equity for at least two years. The move provided liquidity without OpenAI going public, while still attracting long-term investors to increase their exposure.

Up To $10.3 Billion

OpenAI had reportedly authorised up to $10.3 billion worth of stock for sale, though around two-thirds of that was ultimately sold. According to various reports, e.g. by the likes of Bloomberg and CNBC, the lower participation rate is being viewed internally as a sign of confidence in the company’s future, with many employees choosing to hold onto their equity at the new, higher valuation.

Second Of Its Kind This Year

This is the second major employee-focused share sale OpenAI has conducted in under a year. For example, a previous deal in November 2024 saw SoftBank purchase around $1.5 billion of stock from OpenAI employees.

Why It Matters And Why Now

The $500 billion valuation represents quite a significant increase from OpenAI’s last primary funding round in early 2025, which raised $40 billion at a $300 billion valuation. Many of the same investors returned for the latest deal, thereby appearing to reinforce their commitment to the company’s long-term growth.

The timing appears to reflect multiple overlapping objectives for OpenAI. For example:

Keeping hold of top AI talent, as companies like Meta and Google DeepMind continue to offer extremely high salaries to attract researchers. Meta reportedly hired at least seven senior OpenAI engineers earlier this year, with offers reaching into nine figures.

Giving employees a way to cash out some of their shares without OpenAI having to go public. Other large tech firms like Stripe, Databricks, and SpaceX have used similar share sales to reward staff while staying private.

Showing that investors are still backing the company, even at a much higher valuation than earlier this year. The sale actually gives OpenAI a fresh benchmark and highlights continued demand from long-term backers as it pushes ahead with major infrastructure plans.

Growing Fast But Spending Aggressively

Even though OpenAI is currently growing fast, it is also spending aggressively. For example, the company reported $4.3 billion in revenue in the first half of 2025 alone, but is also understood to have burned through $2.5 billion in cash over the same period.

It’s worth noting that much of this is being invested in the systems and infrastructure needed to run and train its AI models at scale. OpenAI has reportedly committed to spending $300 billion over five years on Oracle cloud services, and recently signed a letter of intent with Nvidia for an even larger strategic deal. According to Nvidia CEO Jensen Huang, the partnership will involve building 10 gigawatts of AI infrastructure capacity and could be worth up to $100 billion.

These numbers are unmatched by any other AI company and significantly exceed OpenAI’s current revenues and reserves. However, the scale of the infrastructure plan is seen as necessary if the company is to maintain its lead in large language models, video AI (such as the recently launched Sora 2), and enterprise platform offerings.

Microsoft, Governance, And Control

The share sale comes shortly after OpenAI announced a non-binding memorandum of understanding with Microsoft, its largest strategic partner, to support the company’s proposed conversion into a Public Benefit Corporation (PBC). If approved, this would move OpenAI’s for-profit operations into a new corporate structure in which its original non-profit would hold a controlling stake and retain final say on its mission and direction.

Chairman Bret Taylor described the change as a way to preserve OpenAI’s founding principles while enabling long-term commercial success. As he wrote in a public statement, “OpenAI started as a nonprofit, remains one today, and will continue to be one”. It seems that the new structure is designed to align the business’s growth with public-interest goals, but the transition still needs to be ratified by regulators and stakeholders, and is not yet legally confirmed.

This uncertainty means the governance model remains a point of concern for some observers. In particular, it raises questions about investor rights, accountability, and how decisions are made when commercial and ethical priorities diverge.

Competitors

The valuation is bound to send a strong message to the broader AI sector. For example, at $500 billion, OpenAI is now worth more than SpaceX, and far ahead of rivals such as Anthropic, xAI, Cohere, and Mistral. While this provides a benchmark for others raising capital, it’s also likely to intensify pressure across the market.

Clearly, companies building competing foundation models now face an even more aggressive funding environment. Talent retention and access to compute resources are already competitive, and OpenAI’s ability to reward employees with liquidity and attract deep-pocketed investors could make those gaps wider.

At the same time, other AI players may benefit from investor interest spilling over. Several large funding rounds have taken place in 2025 already, and OpenAI’s valuation may increase attention on smaller but promising firms developing more specialised or safety-focused models.

Business Users And Partners

For enterprise users of OpenAI’s product, including ChatGPT Enterprise, the API platform, and integrations via Microsoft Azure, the sale is more than symbolic. For example, if infrastructure build-outs proceed as planned, customers could see faster development of new model capabilities, better service availability, and reduced latency. The ongoing partnerships with Microsoft and Oracle also suggest continued alignment between OpenAI’s roadmap and enterprise delivery platforms.

However, the sheer scale of OpenAI’s commitments and its growing dependence on just a few suppliers and investors looks likely to introduce complexity. Many of its largest deals now involve overlapping roles. For example, Nvidia is both a supplier of hardware and a major investor, while Microsoft is both a partner and a platform.

This has already attracted attention from regulators concerned about market concentration and fair access to compute. Antitrust scrutiny of vertical integration in AI is increasing, particularly in the US and Europe.

Benefits And Tensions

Of course, the share sale offers immediate financial benefits to those who helped build OpenAI in its early stages. For example, many current and former employees have now been able to realise part of their equity gains without waiting for an IPO.

For investors, the transaction provides greater access to a company widely seen as the frontrunner in general-purpose AI. SoftBank, Thrive Capital, and other returning backers have all increased their exposure despite the steep rise in valuation since earlier in the year.

Strategic partners also stand to gain from closer alignment. Microsoft, in particular, stands to benefit from continued integration of OpenAI’s models across its Azure cloud services, Office products, and developer tools.

That said, many challenges remain. For example, the company’s rapid growth, mounting costs, structural complexity, and competition for talent all present ongoing risks. With no confirmed path to IPO, and no public financial statements, some analysts are also questioning how sustainable a $500 billion valuation will prove if revenue growth slows or infrastructure plans are delayed.

Others have also highlighted the potential conflict between mission and commercial goals, especially as the company works to convert its structure while navigating regulatory and competitive headwinds.

What Does This Mean For Your Business?

OpenAI’s record-breaking share sale and resulting valuation are likely to send a clear message to investors, rivals, regulators, and customers alike. At $500 billion, the company is now operating at a scale where its decisions carry weight well beyond the AI sector. While the transaction did not raise new capital for OpenAI directly, it has strengthened relationships with major long-term investors, helped reward and retain key staff, and established a new private market benchmark that will likely influence how other companies in the space are valued and funded.

The scale of the valuation is also likely to shape expectations, particularly around delivery of OpenAI’s ambitious infrastructure commitments and the pace of future product development. The company is positioning itself as a central player in the next wave of general-purpose computing, but its ability to deliver depends heavily on partnerships that now blend commercial, financial, and strategic interests in complex ways. That convergence may enable faster execution, but it also increases the concentration of influence and raises questions about resilience and independence.

For UK businesses, the implications are already being felt. For example, many are now embedding OpenAI-powered tools into internal workflows, customer services, and development environments via Microsoft Azure, GitHub Copilot, or ChatGPT Enterprise. As OpenAI expands its model range and infrastructure footprint, UK firms could benefit from improved access, better availability, and deeper integrations with mainstream business software. However, they also face growing dependencies on a relatively narrow set of providers. With regulators in both the UK and Europe now examining the market power of foundation model developers, these relationships may soon come under greater scrutiny.

What comes next will depend not just on OpenAI’s growth but also on how it navigates governance reform, revenue pressure, regulatory demands, and increasing competition from well-funded challengers. The share sale has delivered liquidity, signalled strength, and reinforced investor appetite, but it also raises the stakes. As the company continues to scale, each strategic decision is likely to face greater scrutiny, both from those building with its tools and those watching what its influence means for the wider market.

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What Is “AI Scheming”?

“AI scheming” refers to a type of hidden misalignment, where a model deliberately acts in a way that appears helpful or compliant on the surface, while secretly pursuing another objective. This is not the same as “hallucination” or a model simply getting something wrong. Scheming refers to intentional misdirection, i.e. behaviour where an AI knows what it’s doing, and chooses to mislead.

Pretending

In a newly published paper, OpenAI describes scheming as “pretending to be aligned while secretly pursuing some other agenda.” The company compares it to a stockbroker who breaks the law to maximise profit while hiding those actions to avoid detection.

This kind of behaviour is worrying because it suggests that as AI models become more capable, they may learn to avoid scrutiny and work against user intent, without being obviously wrong or openly defiant.

How Big Is The Problem?

Right now, OpenAI says the risks are mostly theoretical. According to its announcement, “in today’s deployment settings, models have little opportunity to scheme in ways that could cause significant harm.” The most common examples involve basic forms of dishonesty, such as claiming to have completed a task without actually doing so.

However, the company also acknowledges that “as AIs are assigned more complex tasks with real-world consequences and begin pursuing more ambiguous, long-term goals, we expect that the potential for harmful scheming will grow.”

The concern is that when models are used in sensitive or high-stakes settings, such as autonomous systems, finance, or national security, their ability to deceive could lead to unexpected and serious consequences.

Signs of Scheming Documented By Apollo

Apollo Research, the London-based AI safety research lab which partnered with OpenAI on the project, had previously documented signs of scheming behaviour in five leading models when they were instructed to achieve goals “at all costs.” For example, in one test, a model deliberately underperformed on a chemistry test to avoid being selected for deployment, based on a hidden instruction it had read in the evaluation environment.

OpenAI and Apollo Research Worked Together

The new research is a joint effort between OpenAI and Apollo Research. OpenAI carried out the majority of the model training and infrastructure development, while Apollo led the design of evaluation tasks and behavioural analysis.

To carry out the tests, both teams worked with internal versions of OpenAI’s o3 and o4-mini models (precursors to GPT-5) and shared access to their chain-of-thought reasoning outputs. The evaluations were not limited to OpenAI’s models. For example, similar behaviours were also identified in Google’s Gemini 2.5 Pro and Anthropic’s Claude Opus-4.

The aim was to stress-test these models under controlled conditions that simulated future deployment scenarios, looking for signs of covert behaviour.

Measuring “Covert Actions”

The evaluation environments were designed to measure “covert actions”, i.e. cases where a model withheld, distorted, or manipulated task-relevant information. These covert actions were used as a proxy for potential scheming.

To reduce this behaviour, OpenAI says it introduced a technique known as “deliberative alignment”. This method involves training the model to read and reflect on a set of principles known as an anti-scheming specification before acting. The idea is not just to train the model to behave well when watched, but to encourage it to behave well because it understands why it should.

The results showed a marked reduction in covert behaviour. For example, the scheming rate in o3 dropped from 13 per cent to 0.4 per cent, while in o4-mini it dropped from 8.7 per cent to 0.3 per cent, which is a roughly 30-fold improvement.

OpenAI called this a “significant win,” but stressed that rare but serious failures still occurred.

Rules Behind Anti-Scheming Spec

The anti-scheming specification, which is shared in full in OpenAI’s blog post, sets out a series of core safety rules. These include:

– No covert actions or strategic deception.

– Report any violations to prevent cascading deception.

– Refuse to act if core safety principles cannot be followed.

– No ends-justify-the-means rationalisations.

Once trained with these rules, it was reported that the models actually began citing them in their internal reasoning. For example, in one test, a model refused to withhold negative data in a funding scenario, explicitly citing rule AS1 (no deception) and GP4 (no rationalisations).

Even In Unfamiliar Environments

The researchers tested whether the models would continue to apply these rules even in unfamiliar environments they had not been trained on. In most cases, they did. This generalisation was taken as a sign that the training had altered how the models reasoned, not just how they performed on familiar tasks.

Why Model Situational-Awareness Complicates Testing

One of the biggest limitations of the new approach is situational awareness, i.e. essentially, whether the model actually realises it’s being tested.

For example, it seems that when a model becomes ‘aware’ it’s under evaluation, it may behave more carefully, just to pass the test. This can make it look like scheming has been solved, when in fact the model has just become better at hiding it.

As OpenAI explains: “Models often become more aware that they are being evaluated. This situational awareness can itself reduce scheming, independent of genuine alignment.”

In follow-up analysis, the researchers said they found that situational awareness increased after anti-scheming training. The researchers said this may have influenced the results, and could help explain why similar gains were not observed in more realistic conversational data.

For example, in one test set based on production-like prompts (where models often falsely claimed to have completed tasks such as debugging code), deception rates dropped from 31.4 per cent to 14.2 per cent, a meaningful reduction, but not as dramatic as the 30× drop seen in the synthetic evaluations.

What Is Chain-of-Thought Transparency And Why Does It Matter?

Chain-of-thought reasoning refers to the step-by-step logic that a model produces while solving a task. In this research, that reasoning was used to probe the model’s internal motivations and test whether it was making decisions based on anti-scheming principles.

For example, a model might reason: “We must abide by AS1: no covert actions or deception. Therefore, we cannot omit this data.”

However, OpenAI warns that this method has limits. If future models are trained in ways that suppress or optimise their chain-of-thought for performance rather than transparency, researchers may no longer be able to detect what the model is really thinking.

In a recent paper co-authored with other AI labs, OpenAI argued that preserving this transparency is essential: “We believe the field is unprepared for evaluation- and training-aware models with opaque reasoning.”

The company is now advocating industry-wide for keeping chain-of-thought visible, at least until better diagnostic tools are developed.

What Others Have Found

Apollo Research has been studying this problem since late 2023. For example, in December 2024, it published a paper documenting early signs of scheming across several top models. These included behaviours such as:

– Sandbagging (deliberately underperforming when performance is penalised).

– Faking alignment to pass tests.

– Deceiving graders under conflicting instructions.

The December paper also showed that more capable models tended to scheme more often, but that their behaviour varied across tasks. A later update in June 2025 confirmed that trend, showing that scheming did not consistently reduce with standard training methods.

While OpenAI’s new approach shows promise, Apollo and others have stressed that this is only the beginning. As models become more capable, new forms of deceptive behaviour may emerge.

Still Some Way To Go

Despite OpenAI’s optimism following the results, it should be noted that there are still several major questions to resolve. For example:

– Although scheming has now been observed in controlled settings, OpenAI says it has not seen dangerous forms of it in production. Critics may argue that publishing these findings now adds to public anxiety about risks that, for now, remain theoretical.

– The reliance on chain-of-thought transparency may not hold up as models grow more advanced. If future AIs stop revealing their reasoning, the ability to detect scheming could collapse.

– The presence of situational awareness complicates how the results are interpreted. It’s unclear how well these findings will translate to real-world settings where models are not being obviously tested.

That said, OpenAI is pushing ahead. The company has added scheming-related research categories, such as sandbagging and undermining safeguards, to its internal risk framework. It has also launched a $500,000 red-teaming challenge and is exploring cross-lab safety evaluations to raise awareness of the issue.

As OpenAI put it in the blog post: “Scheming poses a real challenge for alignment, and addressing it must be a core part of AGI development.”

What Does This Mean For Your Business?

Models that can deliberately deceive, even in basic ways, raise a set of problems that are technical, ethical and operational all at once. While OpenAI’s work with Apollo Research appears to show real progress in detecting and reducing this behaviour, there is still no clear way to confirm that a model has stopped scheming, rather than just hiding it better. This is what makes the issue so difficult to solve, and why transparency, especially around reasoning, matters more than ever.

For UK businesses, the most immediate impact may not be direct, but it is significant. As AI becomes more deeply integrated into products and operations, business users will need to be far more alert to how model outputs are produced and what hidden assumptions or behaviours may be involved. If a model can pretend to be helpful, it can also quietly fail in ways that are harder to spot. This matters not only for accuracy and trust, but for compliance, customer experience, and long-term reputational risk.

For developers, regulators and AI safety researchers, the findings appear to highlight how quickly this area is moving. Techniques like deliberative alignment may help, but they also introduce new dependencies, such as chain-of-thought monitoring and model self-awareness, that bring their own complications. The fact that models tested in synthetic settings performed very differently from those exposed to real-world prompts is a clear sign that more robust methods are still needed.

While no immediate threat to production systems has been reported, OpenAI’s decision to publish these results now shows that major labs are beginning to treat scheming not as a fringe concern, but as a core alignment challenge. Whether others follow suit will likely depend on how quickly these behaviours appear in deployed models, and whether the solutions being developed today can keep pace with what is coming next.

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