News

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

A man and his sidekicks time travel through an infinite number of universes, arriving to unknown circumstances where futuristic drama inevitably lays in wait. Am I talking about Stuart Fails to Save the Universe on HBO Max, or legendary BBC series Doctor Who?

At face value, they clearly could be one and the same. But while The Big Bang Theory spinoff is just getting started with its debut 10 episodes (though season 2 has already been renewed), Doctor Who has been regenerating for decades since 1963... but is now stuck.

Not only has Russell T Davies — the man responsible for reviving the IP in the early 2000s — officially left, but the series has been put out to tender. This means that the broadcaster (BBC) asks independent production companies to pitch ideas and bid to make the program instead of using their own in-house team or current producer.

In short, absolutely anything could happen to Doctor Who over the next few years, including it being dropped completely. Yes, this is a worst case scenario, but you don't need the Doctor to tell you that nothing is impossible.

As we enter week six of Stuart Fails to Save the Universe airing on the small screen, something the cast told me has birthed a slightly unhinged idea: what if the two shows crossed over in an unexpected act of heroism?

"We're like four inept Doctor Who's," Lauren Lapkus (Denise) tells me. "The whole show has very heavy Doctor Who vibes." And you know what? She's right.

Let's start with the obvious. Doctor Who is actively looking for new production companies to pitch fresh ideas. Stuart Fails to Save the Universe is the new kid on the sci-fi block, armed with some of the best sitcom creators in the world and an overarching idea that could last for seasons upon seasons.

From those facts alone, it sounds like a conversation between HBO and BBC needs to happen. Add to this that the two distributors have worked together before — on Half Man, Industry and His Dark Materials, just to name a few — and we only have more solid reasoning.

Doctor Who has even been 'Americanised' before now thanks to the BBC's previous partnership on the show with Disney+. Was this the right fit? No, but audiences have certainly got a taste of what the Doctor can be.

Don't forget, Stuart Fails to Save the Universe is also steeped in classic sci-fi lore thanks to The Big Bang Theory. Take a look at how many references there were below and you'll realize both characters and creators had an inside-out understanding of the genre its built from.

So watching our new Doctor, whoever that ends up being, collide with Stuart and the gang doesn't seem too far-fetched to me. Even an alternate version of Stuart being the Doctor himself is something that I wouldn't question. Doctor Who obviously isn't a sitcom, but it's always been funny... so all of this should be enough to Chuck Lorre to come knocking.

Realistically, I think a singular crossover episodes is as much as we'd ever get here, but I'm daring to dream — mostly because as a Brit, I don't want to see Doctor Who have a neglected, unfitting end completely out of its own control.