Artificial Analysis has launched a new version of its Intelligence Index, a ranking system designed to show how well artificial intelligence models perform on demanding tasks, from writing software to conducting scientific research, as developers and businesses seek more reliable ways to compare increasingly powerful AI systems.
Artificial Analysis is an independent AI benchmarking and research company that evaluates and compares AI models on performance, speed, cost and other measures to help users and businesses choose the right systems.
The company, founded by Micah Hill-Smith and George Cameron, describes itself as an independent AI benchmarking organisation that evaluates models, AI agents, computing infrastructure and the chips used to run them.
The latest version, Intelligence Index v5, combines nine evaluations into a single score out of 100, with equal weighting for four categories: knowledge work, coding, science and general intelligence, each contributing 25% to the overall result.
For everyday AI users, the index offers a way to compare models beyond brand names and marketing claims, although the highest-ranked system may not necessarily be the best for every task.
For businesses, the rankings provide another tool for evaluating potential AI suppliers, particularly when performance must be weighed against operating costs and speed, with Artificial Analysis also publishing estimates of the cost and time required to complete benchmark tasks.
Rather than simply testing whether a chatbot can answer questions correctly, the index:
Tests AI beyond simple question-and-answer tasks.
Evaluates how well models modify software projects.
Measures their ability to automate business workflows.
Assesses performance in solving scientific problems.
One of the biggest changes is the use of private tests or answers withheld from models, which now account for 60% of the index, according to Artificial Analysis, a move intended to make it harder for AI developers to improve their scores by training models on questions that later appear in benchmark tests.
The developers clarify that the index is not a definitive measure of intelligence, however, and its results depend on the tests selected and the weighting assigned to them, meaning its rankings may not reflect how well a model performs in every real-world situation.
The launch reflects a wider challenge for the AI industry: as models improve, familiar tests can become too easy to distinguish the strongest systems, increasing the need for harder evaluations that better reflect how people and businesses actually use AI.
Depending on the side you find yourself on, artificial intelligence (AI) is emerging as a powerful tool, without doubt.
If you with the good guys, the tech is one of cybersecurity’s most powerful allies.
But a new global security crisis shows how the same technology that helps researchers uncover hidden software vulnerabilities can also help hackers exploit them.
Which essentially means that, for hackers, AI is becoming a powerful tool for identifying and exploiting security gaps in websites.
On October 21, 2026, many government websites and public services across Europe and elsewhere went offline as organizations scrambled to patch two critical security flaws in PHP, a programming language that powers nearly 70% of websites whose underlying technology can be identified.
Germany, France, Belgium, the Netherlands, Finland and the Czech Republic were among the countries affected. Canada and Australia also reported disruptions to government websites. The scale of the response underlined how a vulnerability in one widely used technology can ripple across the Internet.
The good news: AI can find what humans miss
Security researchers are increasingly using AI to analyze enormous codebases, trace complex program behavior and identify vulnerabilities that conventional reviews might overlook. In this latest episode, researchers associated with Calif and Google-owned cybersecurity company Wiz used AI-assisted techniques to help uncover critical PHP flaws.
The discovery demonstrates how AI can accelerate security research and help developers fix dangerous weaknesses before they become widespread disasters.
PHP developers released security updates, while cybersecurity agencies urged organizations to patch affected systems immediately or take vulnerable websites offline until they could be secured. In this context, a temporary shutdown is not necessarily evidence of a successful attack. It can be a deliberate defensive measure to protect sensitive information and essential services.
The bad news: hackers use AI, too
The same capabilities that help defenders understand complex code can help attackers analyze vulnerabilities, adapt exploit techniques and identify systems that remain unpatched.
Reuters reported signs of early exploitation attempts, adding urgency to the global response. Once a vulnerability becomes public, organizations face a race against time: security fixes may be available, but thousands of administrators still need to install and verify them.
AI does not automatically make the Internet safer or more dangerous. It accelerates what both sides can do. And because PHP is embedded in countless websites, the consequences of a single serious flaw can extend far beyond the original software.
The lesson: finding the bug is only the beginning
Organizations must patch promptly, monitor suspicious activity, maintain reliable backups and isolate vulnerable services when necessary. AI-assisted security testing should become a regular part of software development, not merely an emergency response.
This episode offers a glimpse of cybersecurity’s future: AI helping expose weaknesses that have survived for years, while attackers race to exploit them. The decisive advantage will belong to those who turn discovery into protection before the window for defence closes.
Your face may reveal more about your health than you realise. Now, artificial intelligence (AI) is learning to read the clues.
Could a single photograph of your face help predict whether you will survive cancer? It sounds like science fiction, but researchers are bringing the idea closer to reality with an AI system designed to estimate biological age and forecast cancer survival.
The technology, developed by researchers at Mass General Brigham, uses two AI models: one estimates how old a person appears biologically, while the other assesses the likelihood of survival based on facial photographs. The system was trained using more than 40 million images and evaluated on data from thousands of cancer patients.
The findings are striking. In a study involving 3,820 cancer patients, 64 of the 122 patients classified as being at the highest risk died within 90 days of their photographs being taken. Researchers also found that patients who appeared five years older than their actual age had a 21% higher risk of death over four years, while those who appeared younger had a 17% lower risk.
The underlying idea is that a face may carry subtle clues about the body’s overall condition. Biological ageing, accumulated illness and the effects of treatment could leave signals that AI can detect even when they escape the human eye.
If validated further, such technology could give doctors another tool for assessing patients, helping them make more informed decisions about treatment intensity, surgery or radiation therapy. A photograph is also relatively easy to obtain, potentially making this approach accessible in settings where sophisticated medical tests are harder to deliver.
But there is a catch. The model is not a crystal ball, and its predictions are not a diagnosis or a reliable countdown to death. Its training population also raises questions about whether the results will generalise across different ethnicities and healthcare settings. Researchers say further validation is needed before routine clinical use.
McKinsey is introducing an artificial intelligence (AI) avatar that plays the role of a client in some entry-level consulting interviews, as the elite advisory firm experiments with new ways to assess how candidates handle real-world business problems.
The Wall Street Journal reports the pilot, being rolled out this autumn for some undergraduate applicants in North America, involves a 10-minute conversation with a computer-generated client while human interviewers observe how candidates respond to questions and changing circumstances.
The avatar is designed to simulate the types of conversations consultants encounter when advising businesses and public-sector organisations, including clients who challenge recommendations or demand further explanations.
In one scenario, candidates may face a mayor seeking advice on a major development project, requiring them to explain their reasoning, respond to questions and adapt their recommendations as the discussion unfolds.
The exercise is intended to test candidates’ ability to think on their feet, communicate clearly and engage with clients, rather than simply deliver rehearsed answers to conventional interview questions.
Human interviewers remain responsible for evaluating candidates, with the AI avatar acting as a simulated client rather than independently scoring applicants.
The move reflects a broader shift in recruitment as employers explore ways to assess candidates through simulations of workplace situations, rather than relying exclusively on traditional interviews and academic credentials.
McKinsey already uses a gamified assessment called Solve to test problem-solving skills among applicants for many consulting positions, while its recruitment process can include personal experience interviews and business case exercises.
The firm has also developed AI tools to help candidates prepare for interviews, reflecting the growing role of artificial intelligence in its own work and the skills it expects future consultants to develop.
For years, the relationship between humans and artificial intelligence (AI) followed a straightforward pattern: people gave instructions, and machines responded, but as large language models (LLMs) have become more lifelike, empathetic and responsive, a new behaviour has emerged: sustained verbal abuse and cruelty directed at AI systems.
In late 2026, Anthropic became the first major AI company to explicitly prohibit “sustained and needless abusive or cruel behavior” towards its chatbot, Claude, marking a potential shift in how the industry approaches human interactions with AI.
While companies such as OpenAI, Google and Meta have policies restricting the use of AI to harm or harass people or generate hate speech, Anthropic’s approach extends the discussion to how users treat the AI models themselves and the dynamics of human-machine conversations.
This is not cruelty in the conventional sense, however, because AI systems do not experience emotions or psychological distress in the way humans do.
To understand why technology companies are beginning to set boundaries around how people interact with AI, two questions need to be examined:
What does abusive behaviour towards a large language model actually look like?
What effects can such behaviour have on the user, the AI model’s responses and the technology behind it?
Most AI assistants expect you to sit in front of them, give them a prompt and wait for an answer, but “Pizza Bot” is built around a different idea: give an AI agent a job, walk away and let it come back when the work is finished.
Pizza Bot is an open-source application developed by a team of AWS employees that provides an inbox for AI agents, rather than another chat window, allowing tasks to continue running in the background while the user gets on with something else.
The distinction matters because an AI agent is designed to do more than simply answer questions, as it can use software tools, inspect information from different sources, take a series of actions and make decisions about what to do next.
Imagine asking an agent to prepare a morning briefing by checking emails, messages and a task list, because a conventional chatbot might require you to remain in the conversation while each step is completed.
Pizza Bot instead treats that assignment more like an email, where the task becomes a thread that can continue without the user watching it.
How it works
The agent can work in the background, run scheduled tasks or respond to webhooks, which are automated signals from other software, and it can hand parts of a job to specialised agents.
Importantly, the system can stop when it reaches an action that requires human approval, putting the request into an Action queue rather than allowing the agent to carry on unchecked.
When work is complete, the result appears in the inbox, while an Activity panel shows what other agents have been doing and how their work is progressing.
Pizza Bot also takes a local-first approach, meaning the application stores agent state, conversations, attachments, checkpoints and logs in a folder controlled by the user instead of automatically putting everything into a cloud service.
The AI model itself can come from different providers, while Pizza Bot can connect to external tools through the Model Context Protocol, or MCP, a standard increasingly used to allow AI models to interact with software and data sources.
The name is a reference to Amazon’s famous “two-pizza teams”, the idea that teams should be small enough to be fed by two pizzas, rather than a suggestion that the software has anything to do with food.
The concept
The bigger idea behind Pizza Bot is that AI agents may eventually become less like chatbots and more like digital colleagues that receive assignments, work independently and return with results.
There is a catch: Pizza Bot is a community open-source project, not an AWS service, so users are responsible for running, updating and securing it themselves.
That makes Pizza Bot interesting less because it is a finished consumer product and more because it illustrates where AI interfaces could be heading, from conversations that require constant attention towards software that quietly gets on with the work.
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.
Imagine you have a little digital butler living in your phone. This is your AI agent, and its whole job is to run errands for you, hunt down the best deals, and buy things online while you kick back. Sounds amazing, right?
Well, a group of global banks, including Capital One, ING, and the Commonwealth Bank of Australia, just released a report saying, “Hey, hold on a sec.” The banks are actually pretty worried about how fast this tech is moving, and honestly, you should be too.
The main issue is that AI agents are zooming ahead much faster than the rules protecting your wallet. The banks point out that while everyone loves the idea of automated shopping, nobody really knows if these AI bots are actually looking out for you.
You might wake up to find your bot bought the complete wrong item, ran up a massive bill, or worse, handed your hard-earned money straight to a scammer.
Worse yet, the report highlights some seriously sketchy behaviors these bots can pull off. Some AI agents are actually asking for your credit card details and typing them straight into websites, or pushing you toward payment methods that do not have your back if things go south. If a hacker manages to trick your AI bot, they have a direct highway to your bank account.
Tilly Norwood, the AI-generated actress at the centre of a growing Hollywood debate, has reignited controversy after appearing in an interview with NBC’s “Today” programme.
Norwood was questioned by NBC entertainment correspondent Chloe Melas about the backlash surrounding her creation and fears that artificial intelligence (AI) could eventually replace human actors, writers and other creative workers.
The interview has created a buzz because Tilly is no longer simply an experimental AI character appearing in social media clips, but is increasingly being presented as a potential performer with a place in the entertainment industry.
Tilly insisted that her purpose was to act as a creative tool rather than a threat to human performers, pushing back against the idea that her existence necessarily means fewer opportunities for actors.
The controversy is particularly sharp because Tilly has already been positioned for a feature-film role in “Misaligned”, a project being developed by Particle6, the company founded by comedian and former actor Eline van der Velden.
Van der Velden has said the intention is not to put Tilly into conventional live-action productions, but to develop AI-native films in which synthetic performers can operate alongside AI-generated environments and stories.
Human Performers Worried
Tilly has also argued that audiences should see AI as another creative medium, with her creator previously comparing the character to a new kind of paintbrush rather than a replacement for an actor.
But that reassurance has done little to settle the debate because the technology is developing precisely when performers are already worried about their jobs, likenesses and bargaining power.
The interview therefore touches a much bigger question than whether audiences like Tilly Norwood: it asks whether a computer-generated performer can eventually become a genuine entertainment star.
That question has become harder to dismiss as Particle6 develops “Misaligned” and says it is building a wider roster of AI-generated performers, while acknowledging that the technology could drive a major transition in creative jobs.
For now, Tilly remains a synthetic character rather than a human actor, but her growing media profile shows why she has become such a powerful symbol of Hollywood’s argument over AI.
The NBC appearance gives that debate another high-profile platform, and the uncomfortable irony is that an artificial actress is generating very real anxiety among the people whose jobs she is designed not to replace.
Amazon Web Services (AWS) has announced the expansion of its free Kiro program, giving students at 132 universities across 18 countries access to the AI-powered software engineering agent for one year.
The initiative aims to help students develop practical artificial intelligence (AI) and software-development skills before entering the workforce.
Key Features
Under the program, eligible students receive 1,000 Kiro credits every month without requiring a credit card. They also gain access to the latest AI models and Kiro Web, a zero-install version of the tool.
This expanded access is intended to overcome the limitations of conventional free AI tools, which often impose usage restrictions that can interrupt student projects.
Kiro uses a “spec-driven development” approach. Instead of simply generating code from prompts, students first define requirements, design decisions, and implementation steps. Kiro then helps generate code while students review the results and ensure they meet the original specifications. This encourages critical thinking, problem-solving, and responsible use of AI alongside technical skills.
Results and Global Expansion
The program began as a pilot in March 2026 involving 11 universities in two countries. Thousands of students subsequently used Kiro to develop projects, including an accessibility application for visually impaired users and a voice-enabled wellness platform connecting patients with medical professionals.
The latest expansion increases university participation twelvefold and extends the initiative across Asia Pacific, Europe, Latin America, and North America. Participating institutions include universities such as the National University of Singapore, Imperial College London, the University of Toronto, and the Massachusetts Institute of Technology.
