AI For Real

Author: Sorab Ghaswalla

  • 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.
  • Cloudflare’s “Pay Per Use” explained: could AI finally pay publishers for the content it uses?

    Cloudflare’s “Pay Per Use” explained: could AI finally pay publishers for the content it uses?

    There’s no doubt that artificial intelligence (AI) is changing the way people find information online.

    Instead of visiting a news website, reading an article and seeing its advertising, a user can increasingly ask an AI search engine or assistant a question and receive an answer that is built from information published by websites.

    That creates a problem for publishers: their journalism, reviews, research and other content may help an AI product answer a user’s question, but the user may never visit the publisher’s website.

    Cloudflare has now proposed a way to put a price on that use.

    It is called “Pay Per Use”, and unlike a traditional licensing agreement, it is designed to let AI companies pay publishers when their content is actually used rather than simply when it is crawled.

    What is Pay Per Use?

    The easiest way to understand Pay Per Use is to think of it as a meter for AI use of online content.

    An AI company says what kind of use it is willing to pay for and how much it will pay.

    For example, an AI search company could say: “We will pay when we use an article from a participating publisher as a source for an answer.”

    A shopping AI company could instead say: “We will pay when information from a participating product review helps us make a recommendation.”

    The publisher then decides whether to accept that offer.

    If it does, the AI company can access the publisher’s content and, when that content is subsequently used in the way covered by the programme, the AI company reports the use to Cloudflare.

    Cloudflare then handles the billing and pays the publisher.

    Cloudflare says publishers can see their reported uses and earnings through its dashboard.

    Why does Cloudflare want to pay for use rather than crawling?

    This is one of the most important parts of the idea.

    AI systems can crawl enormous amounts of information without necessarily using most of it.

    An AI search engine might download 1,000 articles while looking for information to answer questions, but ultimately use only 10 of them.

    Under a system that charges for every crawl, the AI company would pay for all 1,000 requests.

    Cloudflare argues that this is not necessarily the right way to measure value.

    Pay Per Use instead attempts to charge for what happens after the crawl.

    The AI company may access an article without paying simply because it is researching or indexing the page, but payment can be triggered later if the content is actually used in the defined way.

    That is the major difference between Cloudflare’s earlier Pay Per Crawl system and Pay Per Use.

    Pay Per Crawl charges for access to content, while Pay Per Use is designed to charge when that content creates value inside an AI product.

    What does this mean for publishers?

    For publishers, the attraction is relatively straightforward.

    Today, a publisher essentially has several choices when dealing with AI crawlers.

    It can allow them to access its content, block them, or negotiate an individual licensing agreement with an AI company.

    The problem with individual licensing is scale.

    A major newspaper might be able to negotiate a deal with a major AI company, but a small specialist publisher is unlikely to have the time or bargaining power to negotiate separate agreements with every AI service.

    Cloudflare is trying to create a common system in which the publisher does not have to build a separate technical or payment relationship with every AI company.

    A publisher would see an offer in its Cloudflare dashboard, decide whether to accept it and then monitor how much its content is being used.

    The publisher can also stop participating if the arrangement no longer makes sense.

    Cloudflare says publishers will eventually be able to have more control over pricing, including potentially charging different amounts for different types of use.

    That could become important because not every piece of content has the same commercial value.

    What does it mean for AI companies?

    The system is also designed to solve a problem for AI companies.

    An AI company that wants to use information from thousands or millions of websites would rather not negotiate a separate commercial and technical agreement with every publisher.

    Under Cloudflare’s model, the AI company integrates with Cloudflare once and can potentially connect with publishers that have opted into its programme.

    The AI company defines what counts as a paid use and sets the price.

    It then reports each use through an API.

    Cloudflare handles the rest of the payment process.

    For an AI company, that creates a potentially simpler way to obtain access to valuable material that might otherwise be blocked or placed behind a paywall.

    What does this mean for ordinary Internet users?

    Most users will not interact directly with Pay Per Use.

    You are unlikely to see a separate payment screen every time an AI assistant uses information from a newspaper article.

    Instead, the financial relationship is primarily between the AI company and the publisher.

    The potential benefit for users is indirect.

    If publishers can earn money when their journalism is used by AI systems, they may have a stronger financial incentive to continue producing the reporting, reviews, research and specialist information that AI systems depend on.

    At the same time, AI companies could gain access to more high-quality and frequently updated information.

    The cost of that access could ultimately become part of the economics of an AI product, although Cloudflare’s announcement does not establish that consumers will pay a specific additional fee.

    The biggest question: can AI companies be trusted to report every use?

    This is one of the most important technical questions surrounding the system.

    Cloudflare’s model depends heavily on the AI company reporting when it uses content.

    The buyer reports information such as when the use happened, the URL involved and an event ID.

    Cloudflare says its system checks that a reported use relates to a publisher that has enrolled in the relevant programme.

    But the underlying usage report is still supplied by the buyer.

    That means the success of Pay Per Use will depend partly on whether AI companies reliably and completely report the uses covered by their agreements.

    Cloudflare is therefore not simply building a payment system.

    It is also trying to build a system for measuring and recording how AI systems use online content.

    That record could ultimately be almost as important to publishers as the money itself.

    What stage is Pay Per Use at now?

    This is not yet a fully mature, open marketplace for every publisher and every AI company.

    Cloudflare says Pay Per Use entered beta on 30 September 2026.

    During the beta, Cloudflare is working directly with participating AI companies and publishers to test whether both sides want to continue using the system.

    That means several important questions are still being tested.

    What comes next?

    Cloudflare’s longer-term ambition is to allow new AI buyers to join the system without having to create a completely new technical and payment arrangement with every publisher.

    The company also wants publishers to have more control over their offers, including the ability to accept or reject offers, potentially negotiate prices and price different types of use differently.

    Cloudflare is also working on giving publishers more information about how their content was used.

    That could eventually include the keywords that led to a citation, the topic of the user’s request or the AI product in which the content was used, while avoiding the exchange of personal information.

    In other words, the next stage is not simply making payments work.

    Will Pay Per Use solve the copyright problem?

    Probably not by itself.

    This distinction is important.

    Pay Per Use is primarily an economic and technical system, not a new copyright law.

    If an AI company pays a publisher for using an article in an AI-generated answer, that can create a commercial arrangement between the two parties.

    It does not automatically settle every question about whether the underlying copying, crawling, storage, training or generation was legally permitted.

    For example, AI training raises different copyright questions from using an article as a cited source in an answer.

    Cloudflare’s own system recognises this distinction by allowing programme terms to specify what an AI company may do with content, including restrictions relating to training.

    The bigger idea

    Cloudflare’s proposal is ultimately an attempt to change the economics of the Internet.

    Pay Per Use is Cloudflare’s attempt to create a new link in that chain:

    content → AI use → measurable value → payment to the publisher.

    Whether that becomes a major new business model for the internet is still an open question.

    For now, it is a beta experiment.

    But if Cloudflare can persuade enough AI companies to pay, enough publishers to participate and both sides to trust the usage data, Pay Per Use could provide something the web currently lacks: a standardised way for online content to earn money even when the person consuming it never visits the website that produced it.

    Cloudflare’s Pay Per Use announcement

  • 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.

  • Developing AI That Can Tell What You Mean Before You Even Mouth The Words

    Developing AI That Can Tell What You Mean Before You Even Mouth The Words

    Imagine telling an AI to do something and watching it make the wrong move.

    But before you can react with more words, the machine already knows something is off. It detects, from your brain activity, that its interpretation does not match what you intended and adjusts what it is doing! That may sound like AI can read your mind before you have expressed the thought.

    Korean researchers have developed an AI system that can use brain signals to detect when a machine has misunderstood a person’s intention.

    Well, it cannot read your thoughts in the science-fiction sense, but researchers at South Korea’s The Korea Advanced Institute of Science and Technology (KAIST) have taken an important step towards AI detecting when its understanding of your intention is wrong.

    The technology, developed by a KAIST team led by Professor Sang Wan Lee in collaboration with Microsoft Research Asia, is called Neural Value Alignment, or NVA. The basic idea is surprisingly simple: instead of making AI rely only on what people say or write, the researchers want it to also detect signals from the brain that reveal when something has gone wrong.

    Consider a familiar situation. You ask an AI assistant or robot to perform a task. It follows your instruction, but does something you did not actually intend. Normally, you have to correct it by saying something such as, “No, that’s not what I meant.” KAIST’s system is designed to detect that mismatch from brain activity, potentially allowing the machine to correct itself without waiting for the person to explain.

    This is possible because the brain reacts when reality does not match expectations. The researchers used electroencephalography, or EEG, to record electrical activity from people’s brains while they interacted with an AI system.

    They focused on two types of signals. One indicates that the result was different from what a person expected. The other indicates that the situation itself has not developed as the person expected. The technical names are reward prediction error and state prediction error, but the important point is that both can provide clues about what a person thinks should be happening.

    The researchers found that these signals could be distinguished in brain activity. They then used them together to help an AI work out two separate things: whether it had chosen the wrong action, and whether it had misunderstood the person’s goal in the first place. That distinction matters because the same action can sometimes serve different purposes, while different actions can achieve the same goal.

    The research is still a long way from an AI that casually reads people’s minds. The experiment relies on EEG equipment to measure brain signals, and the researchers are demonstrating a scientific method for detecting mismatches between human intentions and AI behaviour. It is not a consumer product that can currently be plugged into ChatGPT, a phone or a home robot.

    The next stage is to develop this idea for more natural cooperation between people and machines. KAIST says the approach could eventually be applied to physical robots in homes and factories, autonomous vehicles, medical robots and rehabilitation systems. The team’s ongoing work with Microsoft Research Asia is aimed at developing brain-computer technologies that allow humans and AI to communicate and collaborate more naturally.

    There is no announcement that this particular technology is being released as a commercial product. For now, it remains a research advance. But its significance is clear: the future of AI may not depend only on machines understanding what we say. It may also depend on machines becoming better at recognising when what they have understood is not what we actually meant.

    Reference

    https://pubmed.ncbi.nlm.nih.gov/42636135

  • How Money And Speed Are Affecting AI Safety

    How Money And Speed Are Affecting AI Safety

    Artificial intelligence (AI) is developing very quickly, but this growth is also creating concerns about safety and responsibility.

    My latest newsletter, “The Disappearing Watchdogs: How Commercial Pressure May Be Reshaping AI Accountability,” explains how business pressure may be affecting the way AI companies handle safety and ethics.

    Some major AI companies have reduced or reorganized teams that were specifically responsible for AI safety, alignment, and risk. These teams were created to identify possible dangers and make sure AI systems were developed responsibly. In some cases, safety responsibilities have been moved into regular engineering and product teams.

    All of this can create a conflict between speed and safety. AI companies face strong competition and pressure to release new products quickly. Engineers and product teams may therefore focus more on meeting deadlines and competing with other companies. When the same teams are responsible for both developing a product and checking its risks, independent safety checks may become weaker.

    So What It Means To You

    AI systems can be used to create scams, spread fake information, influence public opinion, and make decisions that affect areas such as employment, loans, and insurance. Without strong safety measures, these risks could become harder to control.

    AI safety should not depend only on companies policing themselves. Independent testing, auditing, benchmarking, and red-team exercises could provide additional protection.

    Summing up: there’s an important challenge before the AI industry: AI companies need to balance innovation and business growth with public safety. Developing AI quickly is valuable, but safety and ethical responsibility should remain an essential part of the process.

  • When Even Disasters Become AI Content: Nepal Floods Expose The New Age Of Fake Reality

    When Even Disasters Become AI Content: Nepal Floods Expose The New Age Of Fake Reality

    AI-generated images, recycled disaster footage and misleading captions are reportedly flooding social media alongside genuine scenes from Nepal’s catastrophe. The disturbing lesson is: natural disasters themselves are becoming raw material for synthetic misinformation.

    A disaster happens. Within minutes, the Internet fills with images.

    Buildings disappear beneath torrents of muddy water. Bridges buckle. Helicopters hover over devastated valleys. People run for their lives. Entire towns appear to be swallowed by floodwaters.

    The instinctive reaction is to believe what we see. That instinct is now becoming distinctly dangerous.

    In the aftermath of the catastrophic flash floods that struck the Nepal-Tibet border on August 26, social media users began sharing dramatic photographs and videos claiming to show the destruction. But fact-checkers have now found that some of the most striking material was not from Nepal at all.

    Some of it, as it turns out, was generated by artificial intelligence (AI).

    Some was real footage from completely different disasters.

    And some of it was genuine footage wrapped in a false narrative.

    That combination may be more consequential than any individual fake image.

    The problem is no longer simply that someone can manufacture a photograph. The problem is that, during a genuine emergency, the Internet can now become a chaotic mixture of reality, recycled reality and synthetic reality — all circulating under the same headline.

    And don’t blame AI for it. It’s the man behind the machine who chooses to weaponise the technology against truth.

    The Flood Was Real. Some Of The Images Weren’t

    A massive flash flood struck the Himalayan region near the Nepal-China border after what scientists and satellite imagery indicate was a glacier and rock collapse.

    And there is genuine footage of the destruction.

    That is precisely what makes the fake material so effective.

    According to some media reports, AAP FactCheck examined several posts circulating after the disaster and found a striking pattern. One Facebook post showed supposedly dramatic before-and-after images of a flooded settlement. The images were not photographs of Nepal: AI analysis indicated they had been generated using an OpenAI image generator, while Meta had also labelled them as AI content. AAP reported that OpenAI’s SynthID detection found invisible AI watermarks in the images.

    Another widely circulated video combined those fabricated images with dramatic footage of bridges apparently being overwhelmed by floodwater. YouTube had flagged the video’s audio and visuals as altered or AI-generated, and SynthID analysis also indicated AI generation.

    Then came another category of deception.

    A video shared as footage of the Nepal disaster was actually a 2021 mudslide in Atami, Japan. A reverse-image search traced the footage to reporting about that disaster. The video had simply been repurposed and presented as something happening in Nepal. AAP reported that the misleading post had accumulated more than 360,000 views.

    Another clip presented as the Nepal catastrophe was actually footage from a deadly mudslide in Uttarakhand, India, in 2025.

    In other words, the misinformation was not one thing.

    It was a Frankenstein’s monster of AI-generated material, old disaster footage and real events stripped of their original context.

    AI is Moving Into Disaster Zones

    For years, fake news powered by manipulated photographs and recycled videos was largely associated with politics, celebrity scandals and conflict.

    Natural disasters were different.

    A flood, earthquake, wildfire or landslide already provided spectacular images. There seemed to be little reason to manufacture them.

    That assumption is now obsolete.

    Generative AI (gen-AI) has made the creation of convincing disaster imagery cheap, fast and accessible. A person does not need a camera crew, a helicopter or access to the disaster zone. A text prompt can produce an apparently catastrophic scene in seconds.

    And the Nepal episode shows why that matters.

    The goal does not necessarily have to be sophisticated geopolitical manipulation. Sometimes the motivation may simply be clicks.

    A dramatic disaster photograph attracts attention. Attention generates followers. Followers can eventually be monetised through advertising, affiliate links, engagement farming or other forms of online traffic.

    The tragedy becomes content.

    The more shocking the image, the more valuable it can become.

    The Disaster Becomes a Template?

    There is something particularly bleak about this development.

    A natural disaster is one of the few moments when large numbers of people turn to strangers online for information.

    Families look for images of affected towns. Travellers look for information about roads and airports.

    People abroad search for signs that friends and relatives are safe.

    Aid organisations monitor the situation.

    Journalists look for eyewitness material. Governments attempt to communicate evacuation and rescue information.

    In that environment, false images do not merely pollute an abstract information ecosystem. They can interfere with the way people understand an unfolding emergency.

    Recent research is beginning to examine exactly this problem. A 2026 study of AI-generated videos depicting real-world crises notes that modern video generators can fabricate realistic depictions of wars, disasters and public emergencies, creating significant misinformation risks.

    Researchers also warn that the behaviour of detection systems can change when synthetic material is altered and redistributed through social networks.

    Another recent study argues that misinformation during disasters should be evaluated not only by whether a claim is false, but also by how believable and harmful that false claim could be.

    That is an important distinction.

    A ridiculous fake photograph may be harmless. A convincing fake showing a supposedly destroyed bridge, a stranded population or a false evacuation area is something else entirely.

    The Technology to Fight Back Exists

    Platforms and AI companies are developing systems to identify synthetic material.

    Google’s SynthID, for example, embeds invisible digital watermarks into AI-generated images, audio, text and video. Google says the markers are designed to remain detectable even after common modifications such as cropping, filtering, changes in frame rate and compression.

    Meta has also said it uses industry-standard signals and disclosures to label AI-generated material on Facebook, Instagram and Threads. But the company acknowledges an important limitation: not all AI-generated content can currently be detected, and invisible markers can sometimes be removed.

    But that caveat is crucial. Detection is not authentication.

    And no AI detector should become the sole basis for deciding whether a piece of disaster footage is genuine.

    The old tools still matter.

    • Reverse-image searches.
    • Checking the earliest known upload.
    • Looking for the original location.
    • Comparing weather and geography.
    • Checking satellite imagery.
    • Finding local news reports.
    • Examining whether landmarks actually exist where the video claims they do.

    And, above all, asking a very simple question:

    Who first posted this, and when?

    The new question for journalists should also be:

    “Is this picture real, and does it show what somebody says it shows?”

    Those are two very different questions.

    Nepal Is a Real World Case Study

    The most important lesson from the Nepal flood is therefore not that AI can create fake flood pictures.

    We already knew that.

    The real warning is that the practice is becoming normalised.

    AI-generated disaster imagery can now appear alongside recycled footage and genuine eyewitness material within hours of a catastrophe.

    And the incentive structure of social media encourages precisely the kind of content that performs best: dramatic, emotional, frightening and instantly understandable.

    A person sitting thousands of kilometres away from a disaster can now manufacture a scene that looks as though it was captured at the centre of it.

    That changes the information environment around every future emergency.

    The next major earthquake, cyclone, wildfire, tsunami or flood will not merely produce a race for eyewitness footage. It will produce a race between reality and synthetic reality.

    The disturbing question is no longer whether people will use AI to fake disasters. They already are.

    The question is how quickly society can build a culture of verification strong enough to prevent those fakes from becoming the first version of reality that millions of people see.

    Because when the ground is shaking and the water is rising, misinformation is not merely an Internet nuisance.

    It can become part of the disaster.

    Reference:

    What do you think? Write in the Comments section.

  • When Thinking Harder Can Make It Worse For An AI Model

    When Thinking Harder Can Make It Worse For An AI Model

    For much of the past two years, one of the clearest ideas in frontier AI has been that reasoning models get better when they are given more time to think.

    The recipe sounds intuitive. Instead of asking a model to jump straight to an answer, let it generate a long chain of intermediate reasoning. Give it more test-time compute, and difficult problems become easier. In effect, the model gets to spend more time working things out before committing to an answer.

    But research emerging in 2026 is putting an important qualification on that idea: sometimes the model has already solved the problem, and then talks itself out of the correct answer.

    This phenomenon is increasingly being called “overthinking”.

    Why an LLM Overthinks

    A Google DeepMind study published in July examined the thought processes of open-source reasoning models including Qwen3 and distilled versions of DeepSeek-R1.

    Rather than simply counting how many tokens a model uses, the researchers broke its reasoning into smaller sub-thoughts and mapped how those thoughts connect. They identified patterns they call the “Explorer” and the “Late Landing”. Both point to over-exploration and over-verification as important drivers of unnecessary reasoning.

  • Duh. Someone’s Already Made A Claude Watermark Remover

    Duh. Someone’s Already Made A Claude Watermark Remover

    A new online tool, “Claude Watermark Remover”, has emerged just days after Anthropic introduced an invisible watermarking system for newer Claude models, highlighting a rapidly developing contest between AI transparency measures and tools designed to circumvent them.

    The website, claudewatermarkremover.app, allows users to paste Claude-generated text and receive a re-written version designed to break the statistical signature embedded in the original output.

    According to the site, the process uses a non-Claude model to substantially alter wording and sentence structure while attempting to preserve the original meaning. The service is advertised as free and requires no registration.

  • 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.

  • Cisco’s New Antares AI Models Could Make Software Security Faster And Cheaper

    Cisco’s New Antares AI Models Could Make Software Security Faster And Cheaper

    Finding a security vulnerability in a large codebase is a bit like searching for a leaking pipe inside a skyscraper. You know water is escaping somewhere, but figuring out which floor, which room, and which pipe is the real challenge.

    Cisco believes it has found a better way.

    The company has introduced Antares, a family of security small language models (SLMs) designed specifically for vulnerability localization. Instead of writing code or generating patches, these open-weight models focus on one important job. They help developers identify where a known security flaw is likely hiding inside a software repository.

    That distinction matters.

    (BTW: An open weight model is an AI model whose trained parameters (weights) are publicly available, allowing organizations to run, inspect, and often fine tune it on their own infrastructure without having to build the model from scratch.)

    Many organizations today rely on large general purpose AI models for coding assistance. While they are powerful, they can also be expensive to run and often require sending sensitive source code to cloud services.

    Antares takes a different approach. It is a smaller, specialized model that can run locally, keeping proprietary code inside an organization’s own environment while significantly reducing compute costs.

    Think of it this way. If a Swiss Army knife can do twenty different jobs reasonably well, Cisco’s Antares model is a precision screwdriver built for one task. It may not do everything, but it performs its intended job with remarkable efficiency.

    The Implications

    For businesses, this could mean faster security reviews, lower AI infrastructure costs, and fewer delays when responding to newly disclosed vulnerabilities. Security teams no longer have to sift through thousands of files manually before deciding where to focus their attention. The AI helps narrow the search, allowing human experts to spend their time verifying and fixing issues instead of hunting for them.

    Developers also benefit. Because the models are open weight, organizations can inspect, fine tune, and integrate them into their own development pipelines. That flexibility makes them attractive for enterprises that want greater control over their AI tools instead of relying entirely on closed commercial services.

    The two models—Antares-350M and Antares-1B—as open-weight models now available to the broader community on Hugging Face.

    Cisco’s announcement reflects a broader trend in enterprise AI. Bigger is not always better. Sometimes the smartest solution is a smaller model that excels at one critical task. In cybersecurity, where speed, privacy, and cost all matter, that focused approach may prove more valuable than another all purpose AI assistant.