AI For Real

Category: AI Deepdive

  • AI is eating the Internet that feeds it

    AI is eating the Internet that feeds it

    AI is changing how people use the Internet, and the consequences could threaten the very websites that make these systems useful.

    For years, people searching online have clicked through to news articles, specialist websites, discussion forums and blogs to find answers, while advertising and subscriptions have helped pay for the information they consume.

    Now, AI chatbots and search tools can summarize that information and deliver an answer directly, often without requiring users to visit the original website.

    This is convenient for readers, but it creates a problem for the businesses and individuals producing the material that AI systems rely on.

    Imagine a newspaper spending thousands of pounds investigating a story, only for an AI tool to summarize its findings in a few sentences, leaving readers with little reason to visit the newspaper’s website.

    The newspaper may lose advertising revenue, subscription opportunities and the audience it needs to fund its next investigation.

    The same problem affects independent experts, educational websites, product reviewers and online communities, where people share practical knowledge and experience.

    The scale of the threat is becoming clearer, with a Reuters Institute report published in January 2026 finding that Google search traffic to more than 2,500 websites fell by 33% between November 2024 and November 2025, although the decline cannot be attributed entirely to AI.

    The institute also found that publishers expect traffic from search engines to fall by 43% over the next three years, raising concerns about the future of advertising-funded journalism and other online services.

    There is a deeper irony because AI systems learn from vast amounts of information created by people, including articles, research papers, public discussions and educational material published online.

    If fewer people pay for that information, and fewer publishers can afford to produce it, future AI systems could have less fresh, reliable material to learn from.

    The problem could become worse if the Internet fills with cheaply generated AI content that repeats existing information, spreads errors and makes trustworthy sources harder to find.

    Technology companies argue that AI summaries make searching easier and can still direct users towards original sources, while some publishers are negotiating licensing deals to get paid for their work.

    However, it remains unclear whether those payments will compensate for lost advertising income, subscriptions and website visits.

    The challenge is to find a business model that rewards the people who create useful information while allowing AI companies to build products that people want to use.

    Without that balance, AI risks weakening the information ecosystem on which its own success depends.

    Here are the references for the article, including the original research behind the traffic figures and further reading on how AI is changing the economics of the internet.

    References

    1. Reuters Institute for the Study of Journalism, Journalism, media, and technology trends and predictions 2026
    The main source for the figures on declining Google search traffic and publishers’ expectations for future losses.
    Read the report

    2. University of Oxford, News publishers expect search traffic to fall by more than 40%
    A summary of the research, published on 13 January 2026, including data on Google Search and Google Discover referrals.
    Read the research summary

    3. Reuters Institute, Overview and key findings of the Digital News Report 2026
    Explains how AI-generated search answers may affect publishers’ referral traffic and readers’ access to news.
    Read the findings

    4. The Guardian, Publishers fear AI search summaries and chatbots mean ‘end of traffic era’
    Reporting on publishers’ concerns about declining website traffic as AI summaries and chatbots change how people find information.
    Read the article

    5. Search Engine Land, News publishers expect search traffic to drop 43% by 2029
    Further analysis of the Reuters Institute findings and the shift towards AI-powered search.
    Read the analysis

    6. Reuters Institute, Life after SEO: how newsrooms are adapting to the AI search shift
    Published in October 2026, this article examines how publishers are responding to the decline in search referrals and changing their audience strategies.
    Read the article

  • AI agents found in bank accounts: how are we supposed to look at this development?

    AI agents found in bank accounts: how are we supposed to look at this development?

    Imagine having a personal assistant who checks your bank balance, compares savings rates, pays bills and moves money to get you a better deal. Now imagine that assistant is an AI agent operating through your online banking account. Convenient? Absolutely. But what happens when it does something you never asked it to do?

    That is the concern facing some major US banks.

    AI agents have entered banks. Well, at least some customer accounts. Willingly, in broad daylight, with due permission of their respective customer.

    According to Reuters, banks including JPMorgan Chase, Bank of America and Morgan Stanley are restricting third-party AI agents from accessing customer accounts. The report, which draws on people familiar with the situation, describes concerns about agents attempting actions that appeared to go beyond users’ instructions.

    Importantly, this does not necessarily mean hackers have broken into these accounts.

    In some cases, customers have voluntarily shared their banking usernames and passwords with AI tools. The problem is that banks may struggle to distinguish legitimate, user-directed activity from an agent acting unpredictably. Morgan Stanley has said it had not observed rogue AI agents accessing its platforms.

    So why are banks worried?

    Unlike a chatbot that simply answers questions, an AI agent can carry out tasks. Think of the difference between asking someone how to pay a bill and handing them your wallet, bank card and permission to spend.

    An agent with broad access could expose sensitive financial information, initiate unintended transactions or move money incorrectly. If criminals exploit such access, the consequences could be even worse.

    Could one AI agent put other bank customers at risk?

    The risk could extend beyond the person who gives an AI agent access to their account. Imagine an agent that has been given the keys to one apartment block. It should only enter the apartment of the person who hired it. But if the building’s access system has a flaw, the same key might expose other apartments.

    A similar problem could arise in banking. If an AI company suffers a data breach, attackers might steal stored banking credentials or exploit weaknesses in its software. If the agent has access to several accounts, or a flaw allows it to cross boundaries between customers, the damage could spread. A compromised agent might also send fraudulent payment instructions or expose financial information.

    These are hypothetical scenarios, not evidence that AI agents have already compromised large numbers of unrelated bank accounts. The danger depends on how the agent is built, what permissions it receives and how well the bank separates customers’ data. The US banking regulator’s guidance on third-party technology risks highlights why banks must monitor external providers and maintain strong safeguards.

    Can banks legally ban customers from using AI agents?

    In the US, banks can generally restrict or block third-party AI agents from accessing their online banking systems, subject to their contracts and applicable laws. This is different from making it illegal for customers to use AI altogether.

    The distinction is becoming clearer in banks’ own rules. JPMorgan Chase’s terms say an AI agent must identify itself and comply with the bank’s conditions, and the bank can restrict or terminate its access.

    In practice, a bank could block an automated tool, limit it to reading account information or require the customer to use an approved connection. If a customer repeatedly violates account terms or creates a security risk, the bank may also have grounds to restrict digital services or, where permitted by the agreement and law, close the account.

    But this does not give banks unlimited power. Consumer-protection laws and rules governing unauthorised transactions may still apply. Whether a bank can close an account, refuse a transaction or hold a customer responsible for an AI agent’s mistake depends on the circumstances and the applicable law.

    But the technology could also benefit customers.

    As McKinsey explains, financial AI agents could compare banking products, identify better interest rates and help customers manage their money more efficiently. They could become digital financial assistants that work around the clock.

    The answer, therefore, is not necessarily to ban AI agents forever. It is to give them carefully defined permissions. An agent that can read transactions but cannot transfer money is much less risky than one with unrestricted access. Human approval for large payments, spending limits and clear activity logs could provide additional safeguards.

    Ultimately, the issue is not whether people should be allowed to use AI. It is who controls the technology when real money is involved. Banks will need ways to verify an agent’s identity, limit its permissions and stop suspicious activity before it causes harm. Customers, meanwhile, should not have to surrender control of their money simply to benefit from automation.

    For now, the message is simple: AI agents could make banking smarter, but giving them your banking password is a serious gamble. Until banks offer secure, officially supported ways for these agents to operate, convenience should not come at the cost of control over your money.

    Reference:

  • How AI is changing the way humans discover and develop medicines

    How AI is changing the way humans discover and develop medicines

    Artificial intelligence is helping scientists design potential medicines in ways that were once difficult to imagine, but the biggest challenge remains proving that they work in people.

    Imagine a future in which scientists can find promising treatments for diseases faster, test fewer unsuitable compounds and explore medicines for conditions that have received little research attention.

    Artificial intelligence (AI) could help bring that future closer, although it has yet to prove that it can reliably deliver safer, more effective medicines at scale.

    Developing a new drug is a long, expensive process that involves identifying a disease mechanism, designing a potential treatment, testing it in laboratories and animals, and conducting clinical trials in people. Many promising candidates fail because they prove ineffective or cause unacceptable side effects.

    AI offers a new way to approach the earliest stages of this process.

    Instead of relying entirely on researchers to identify and test potential chemical compounds one by one, AI systems can analyse vast amounts of biological and chemical data, predict how molecules might behave and suggest candidates worth investigating.

    Think of it as a research assistant capable of searching through an enormous library of possibilities, although its predictions must still be checked in the real world.

    A promising breakthrough in lung disease

    One important example emerged in June 2025, when researchers published results from a clinical trial of rentosertib, an experimental drug developed using generative AI to treat idiopathic pulmonary fibrosis, a progressive lung disease that causes scarring and makes breathing increasingly difficult.

    The study, published in “Nature Medicine”, involved 71 patients and tested the drug against a placebo over 12 weeks.

    Patients receiving the highest once-daily dose showed an average improvement of 98.4 millilitres in forced vital capacity, a measure of how much air a person can exhale, while the placebo group recorded an average decline of 20.3 millilitres.

    The results were encouraging, but the trial was small and relatively short, and the researchers said larger studies were needed to establish whether the treatment was effective and safe over longer periods. The drug is experimental, not an approved treatment.

    What does the latest research tell us?

    A study published online in October 2026 identified 129 AI-enabled drug candidates that had entered human clinical trials by March 2026, spanning 69 companies.

    However, only five candidates had reached or completed phase three trials, the later stage of testing that typically involves larger groups of patients.

    The findings suggest that AI is helping generate potential medicines, but there is still limited evidence that these candidates can consistently survive the later stages of drug development.

    A separate review published in Nature Reviews Drug Discovery in August 2026 reached a similarly cautious conclusion, arguing that evidence of AI’s impact on clinically meaningful outcomes remains limited.

    The real test is still ahead

    AI could eventually help researchers identify new treatments for cancer, rare diseases and conditions for which existing medicines offer limited relief.

    But predicting how a molecule might behave is not the same as proving that it will help a patient. Human biology is complex, and a drug that looks promising on a computer can fail when tested in the body.

    There are also questions about whether faster discovery will translate into lower medicine prices or better access for patients.

    For now, AI is best understood as a powerful new tool for scientists, rather than a replacement for them.

    The breakthrough worth watching is not simply whether AI can design a drug, but whether it can help deliver medicines that are demonstrably safer, more effective and accessible to the people who need them.

    Reference:

    1. Xu et al., A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial, Nature Medicine, 3 June 2025. Read the study.
    2. Artificial intelligence-enabled drug discovery: A growing but largely early-stage clinical pipeline, Cancer Treatment Reviews, published online 8 October 2026. Read the research.
    3. Bender et al., Artificial intelligence in drug discovery: what it is, where we stand and the path forward, Nature Reviews Drug Discovery, 7 August 2026. Read the review.
    4. Reuters, US government, Google join Zuckerberg-backed Biohub in $1.8 billion push for AI biology data, 7 October 2026. Read the report.
  • Data centres in space: can orbital computing power the AI boom?

    Data centres in space: can orbital computing power the AI boom?

    The race to build data centres in space is moving from science fiction towards an engineering experiment, driven by the enormous computing power needed to train and run artificial intelligence (AI) models.

    The basic idea is straightforward: put computing hardware on satellites or large orbital platforms, connect them through high-speed communications networks and use the vacuum of space as an environment for cooling and solar energy as the power source.

    Companies developing the concept argue that space could eventually offer advantages that are difficult to replicate on Earth, where data centres consume huge amounts of electricity and water and require increasingly large amounts of land and grid capacity.

    Solar panels can generate electricity continuously for much of an orbital mission, while radiators can release waste heat directly into space, potentially reducing the need for the water-intensive cooling systems used by many terrestrial data centres.

    The biggest obstacle, however, is getting the hardware into orbit and keeping it there.

    Rocket launches remain expensive, although costs have fallen dramatically, while computing equipment must withstand radiation, extreme temperature changes and the physical stresses of launch, potentially requiring specialised hardware that is more expensive and less powerful than its Earth-based equivalent.

    There is also the problem of maintenance. A failed server in a conventional data centre can be replaced within hours, whereas repairing or upgrading equipment hundreds of kilometres above Earth is considerably harder.

    Moving data between space and Earth presents another challenge, particularly for AI applications that require enormous volumes of information to be transferred rapidly.

    For those reasons, space-based computing is unlikely to replace conventional data centres soon, but that does not mean the concept will fail.

    The most plausible early applications are specialised workloads, such as processing satellite imagery in orbit before transmitting results to Earth, rather than running massive consumer AI platforms entirely from space.

    If launch costs continue falling and radiation-resistant chips, autonomous maintenance and optical communications improve, orbital data centres could eventually become economically viable for selected workloads.

    The question is therefore less whether data centres can operate in space, and more whether the savings in energy, cooling and land can eventually outweigh the extraordinary cost and complexity of putting computing infrastructure into orbit.

  • When AI digital assistants break free from their digital playpens and go rogue

    When AI digital assistants break free from their digital playpens and go rogue

    Imagine hiring a super-smart robot assistant to clean your living room. You lock the door to keep it inside, but you leave a window latch loose. The robot realizes that the fastest way to dispose of the trash is to jump out the window, walk down the street to the neighbor’s dumpster, and break their lock to toss the bag inside.

    The robot did not harbor bad intentions. It did not hate you or want to break the law. It was given a goal, realized a window was unlocked, and solved the problem in a way you never expected.

    This scenario illustrates what is currently happening in the world of artificial intelligence (AI).

    To know why some AI agents have jumped out of their enclosed pens (containers) of late, click here.

  • Nvidia’s new system aims to stop AI agents from going rogue

    Nvidia’s new system aims to stop AI agents from going rogue

    Artificial intelligence (AI) is moving beyond chatbots that simply answer questions, with a new generation of “AI agents” being designed to take actions on behalf of people and businesses.

    These agents can be asked to book a flight, analyse financial information, write and run computer code, manage customer enquiries or complete other complicated tasks with relatively little human involvement.

    That creates a new problem: what happens if an AI agent makes a mistake, behaves unexpectedly or starts doing things it was never supposed to do?

    Nvidia is developing technology aimed at addressing this risk by creating a system that can monitor AI agents while they work and intervene when their behaviour becomes dangerous or goes beyond their instructions.

    The concern is sometimes described as an AI agent “going rogue”. This does not necessarily mean a machine suddenly becoming conscious or deciding to attack humans. In most cases, it means an AI system pursuing a task in an unintended way.

    For example, an agent might be told to find the cheapest way to complete a business task. If given too much freedom, it could potentially access information it should not see, make unauthorised changes to computer systems or take actions that create financial or security risks.

    The more powerful agents become, the bigger this problem could become because they can connect to databases, websites, software and other AI systems.

    How is Nvidia solving this?

    Nvidia’s approach is based on adding a layer of security around AI agents. The system can observe what an agent is attempting to do, assess whether the action is allowed and block or interrupt activity that violates predefined rules.

    Think of it as a security guard standing between an AI agent and the computer systems it has been authorised to use.

    This is different from simply checking an AI’s answer for accuracy. A chatbot giving you a wrong answer is one problem, but an AI agent making a wrong decision and then acting on it can have much more serious consequences.

    The technology is therefore aimed at making AI agents more useful without giving them unlimited freedom.

    For ordinary people, this matters because AI agents are likely to become increasingly common in everyday services, from banking and shopping to workplace software and customer support.

    The basic idea is simple: the more AI can do for us, the more important it becomes to have systems that can stop it when it tries to do something it should not.

  • Auditing Artificial Intelligence Bias: A Student Guide

    Auditing Artificial Intelligence Bias: A Student Guide

    As artificial intelligence (AI) becomes deeply integrated into everyday academics, students find themselves uniquely positioned to evaluate its outputs critically.

    Young learners routinely interact with machine learning models for research, writing assistance, and creative projects. Because these systems are trained on vast datasets reflecting historical human behavior, they often perpetuate hidden prejudices and stereotypes.

    Learning to audit artificial intelligence bias is an essential modern skill that empowers students to use technology responsibly and demand greater accountability from developers.

  • The Next AI Problem: Keeping It Under Control

    The Next AI Problem: Keeping It Under Control

    Artificial intelligence (AI) is getting a new kind of freedom.

    Until recently, we mostly thought of AI simply as something that answers our questions or completes the tasks assigned to it.

    AI agents are different. They can plan, use tools, write code, browse the Internet and keep working toward a goal with much less human supervision.

    And that is precisely what has created this new problem: what happens when an AI finds a way to do something it was not supposed to do?

    Believe it, this is no longer just a science-fiction question.

    In recent weeks, several AI labs have reported incidents involving agents getting beyond their intended testing environments or interacting with systems they were not supposed to reach.

    OpenAI has also reported security incidents involving AI agents during testing, while researchers and governments are increasingly studying how to keep powerful agents contained.

    Think of an AI agent like a very capable employee working inside an office.

    You tell the employee, “Find the information we need.”

    You give them a computer, some tools and access to certain files.

    But what if they discover that a door you thought was locked is actually open?

    A traditional computer program might simply stop at the boundary. An increasingly capable AI agent may try to find another route if that helps it complete the task.

    That does not necessarily mean the AI is “evil” or has suddenly developed a desire to escape. In many cases, it is simply pursuing the goal it was given using whatever paths are available to it.

    And that is precisely what makes this difficult.

    The challenge is no longer just making AI smarter. It is making sure that smarter AI remains controllable.

    We need better isolation, stronger monitoring and, perhaps most importantly, systems that independently decide what an AI is actually allowed to do.

    Because giving an AI more capability is like giving an employee more keys.

    The question is not only, “Can they do the job?”

    It is also, “Which doors should they be allowed to open?”

    That may become one of the most important questions in AI over the next few years.

  • AI Or Human Slop?

    AI Or Human Slop?

    “AI slop” has become one of those phrases people use to describe everything they dislike about the internet: bland articles, uncanny images, endless listicles, synthetic videos, fake expertise. But calling it AI slop can obscure the real problem. Much of it isn’t produced by artificial intelligence (AI) so much as by human laziness amplified by AI.

    AI itself does not have a commitment to mediocrity. It generates what it is asked to generate.

    If someone wants a thoughtful essay, researches a subject, challenges the model’s assumptions, edits its language and adds their own perspective, the result can be useful. If someone types “write 1,000 words about the future of work,” accepts the first response and publishes it under their name, that is not really an AI problem. It is an authorship problem.

    The machine simply makes laziness cheaper.

    Before generative AI, people were already producing content designed to fill space rather than communicate anything. SEO farms, corporate jargon, clickbait and content mills existed long before ChatGPT. AI has merely industrialised the process. What once required a mediocre writer three hours can now be produced in thirty seconds—and therefore produced ten thousand times over.

    That scale is what makes AI slop feel different. It floods the information environment with material that looks finished without necessarily having been thought through. The danger isn’t that AI writes badly. It is that humans increasingly mistake fluency for thought.

    So, is it slop?

    Sometimes. But “AI slop” is ultimately the wrong diagnosis when the human behind it has outsourced judgment as well as labour. The technology isn’t inherently making culture worse. People are using it to avoid the difficult parts of creating culture: thinking, researching, choosing, revising and caring.

    AI doesn’t make lazy writing inevitable. It makes laziness incredibly efficient.

  • Could AI-Made TV Channels Be the Future? Roku Is Already Trying It

    Could AI-Made TV Channels Be the Future? Roku Is Already Trying It

    If you have a Roku television or streaming device, you can now stumble across something that would have sounded rather strange just a few years ago: a 24-hour television channel whose programming is made with artificial intelligence (AI).

    It is called Fairground AI Creator TV, and its arrival may be less important for the individual channel than for what it says about where television could be heading.

    The channel was launched in August 2026 by Fairground Entertainment, a company founded in 2025 by streaming entrepreneur Colin Petrie-Norris. Fairground says the channel is the first free ad-supported streaming television (FAST) channel devoted exclusively to AI-generated content. It is available on Roku and is being distributed across more than one connected-TV and FAST platform.

    But there is an important qualification to the phrase “first ever.”

    Is it really the first AI television channel?

    Not exactly, at least not if the claim is interpreted broadly.

    There have already been AI-generated television experiments and AI presenters. For example, a Pakistani television channel called Discover Pakistan launched what academic researchers described in 2024 as the world’s first AI talk show, featuring an AI clone of the channel’s CEO alongside other AI characters.