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.
For years, one of the biggest fears surrounding artificial intelligence (AI) has been simple: if machines can do more of the work, what will people be left to do?
A recent move by Cognizant offers a different way of looking at the question.
The technology services company says it plans to hire 1,500 US college graduates and expand two new roles, called Frontier Certified Engineers and Frontier Business Operators, to a combined 15,000 people.
These are jobs that are emerging specifically because companies are trying to put AI to work.
The example is important because it shows that AI does not only replace existing tasks. It can also create demand for people who know how to build, manage, check and apply AI systems.
As businesses adopt these tools, they need employees who understand both the technology and the business problems it is supposed to solve.
Cognizant says one of its teams recently redesigned an account-management process for a food-service company using 17 AI agents, saving account managers around 11 hours a week.
That does not necessarily mean the account managers became unnecessary. Instead, some of their routine work was taken over by AI, potentially leaving more time for work that requires judgement, communication and decision-making.
But there is another side to the story. A company announcing thousands of new AI-related positions does not prove that AI will create enough jobs to replace every job it disrupts.
Some roles will almost certainly shrink or disappear, particularly where work consists largely of repetitive and predictable tasks. And not everyone whose job changes will automatically have the skills needed for the new positions.
That is why the more useful question for graduates may not be whether AI will take jobs or create jobs. It is how quickly the nature of work will change, and whether education and training can keep up.
Cognizant says it has already trained more than one million people through its global skilling programme and now aims to reach two million by 2030.
For graduates entering the workforce, the lesson is neither to fear AI nor to assume it will solve the jobs problem. The safer bet is to understand how AI works, learn how to use it, and develop the human skills that remain difficult to automate.
What do you have to say about this? Do write in the “Comments” section.
OpenAI has launched “Daybreak for Frontline Defenders”, a global cybersecurity initiative designed to help organizations protect essential services from increasingly sophisticated AI-enabled cyberattacks.
The initiative represents a $1 billion commitment to subsidized access to AI-powered cybersecurity tools, training, technical assistance, and partnerships.
The program focuses particularly on organizations that often have significant security responsibilities but limited resources. These include water and wastewater utilities, electric-grid operators, state and local governments, community and regional banks, nonprofits, and open-source software maintainers.
OpenAI says its tools can assist defenders with reviewing legacy code, investigating suspicious activity, identifying and validating vulnerabilities, prioritizing risks, and developing and testing security fixes.
In simple terms, OpenAI’s Daybreak program is an effort to help organizations that protect essential services, such as electricity, water, local governments, banks, and nonprofits defend themselves against cyberattacks using AI.
These organizations may not have the same cybersecurity resources as large corporations, so OpenAI plans to provide subsidized access to AI-powered security tools, training, and technical support. Daybreak is about using AI to help the people defending critical systems become faster and more capable at stopping cyber threats, while keeping human experts responsible for making decisions and verifying the results.
A major component of the initiative is Daybreak for America, which initially concentrates on US essential services before expanding to partner countries.
OpenAI says it intends to expand the model to partner countries in the coming weeks. Access is being rolled out progressively rather than being universally available worldwide from day one.
The initiative reflects OpenAI’s broader view that AI should strengthen cybersecurity defenses as AI-powered attacks become more capable. However, the program also highlights the importance of human oversight: Daybreak is designed around verification, monitoring, safeguards, and authorized defensive use.
An unidentified provider has released a powerful artificial intelligence (AI) coding model called “Ox Alpha” for free, leaving developers unable to determine who built it or how their code may be handled.
Ox Alpha appeared on OpenRouter, an online platform that provides access to multiple artificial intelligence models, on Thursday and is also available through OpenCode.
The model is described as a reasoning system designed for coding, sustained agentic work and production workloads, with a context window capable of processing roughly 1 million tokens.
That capacity allows the system to work with large software repositories and lengthy tasks, while its multimodal capabilities allow users to provide text, images and video alongside coding requests.
The model has attracted attention because its performance has reportedly compared favorably with leading commercial coding systems, despite being offered without a disclosed developer or established company behind it.
OpenRouter lists the provider as “Stealth,” providing no public information that identifies the organization responsible for the model or explains where the underlying technology was developed.
The anonymity has fueled speculation that Ox Alpha could have been developed by a major Chinese artificial intelligence company, although no company has publicly claimed responsibility and the model’s origins remain unverified.
The uncertainty is particularly significant for developers using the model to process proprietary source code, because the identity and data-handling practices of the provider cannot be independently established.
SiliconANGLE reported that OpenRouter’s provider arrangements can involve retention of prompts and model outputs, making the destination of code submitted through an unidentified provider an important security consideration.
OpenCode has offered Ox Alpha free during an initial preview period, increasing access while also giving developers an opportunity to test its capabilities.
China has launched its first robot traffic police squad, marking a new phase in technology‑driven urban management.
According to Global Times, the squad debuted in a major city as part of efforts to integrate artificial intelligence and robotics into public service. These humanoid machines are designed to perform routine traffic duties such as directing vehicles, assisting pedestrians, monitoring violations, and providing information to citizens. Equipped with sensors, cameras, and AI‑driven recognition systems, the robots can identify license plates, detect traffic infractions, and relay data to command centers in real time.
The report highlights that the initiative is not merely experimental but part of a strategic modernization of policing and civic management.
Authorities emphasized that robot officers can operate continuously without fatigue, reducing human workload and enhancing efficiency.
While human officers remain essential for complex decision‑making and enforcement, the robot squad represents a symbol of China’s ambition to lead in AI‑powered governance. Analysts quoted in the article noted that this deployment reflects both technological confidence and a push to showcase how robotics can improve everyday urban life.
Robotics In the Rest of the World
Beyond China’s experiment, robotics is advancing rapidly across the globe. Industrial robotics remains the largest segment, with factories increasingly deploying collaborative robots (“cobots”) to work alongside humans in assembly, packaging, and logistics.
The International Federation of Robotics reported record installations in 2025, driven by demand in automotive, electronics, and e‑commerce sectors.
Meanwhile, humanoid robotics is transitioning from prototypes to commercialization. Companies such as Tesla (Optimus Gen 2), Boston Dynamics (Electric Atlas), and startups like 1X Technologies are preparing humanoid robots for industrial and household use.
These machines integrate multimodal perception—combining LiDAR, vision systems, and AI language models—to navigate complex environments and interact naturally with people. Analysts project humanoid shipments to grow at a 95% compound annual rate through 2030, potentially creating a trillion‑dollar market by mid‑century.
In healthcare, robots are assisting with surgery, rehabilitation, and elder care, addressing labor shortages in aging societies. Service robots are also expanding into hospitality, retail, and security, offering cost‑effective alternatives to human labor at operating costs as low as $2 per hour. Autonomous mobile robots (AMRs) are now common in warehouses, while agricultural robots are improving crop monitoring and harvesting efficiency.
Globally, robotics progress reflects a convergence of AI maturity, hardware reliability, and economic necessity. Nations are investing heavily in robotics to offset workforce declines, enhance productivity, and maintain competitiveness.
A new CBS News report highlights the growing risks posed by artificial intelligence “nudify” apps, particularly as students return to school.
These applications can use AI to transform ordinary photographs of fully clothed people into realistic-looking nude or sexually explicit images, often within seconds. The technology has raised serious concerns about privacy, harassment, exploitation, and the potential targeting of children and teenagers.
CBS News tested 70 AI photo-editing applications and found that 19 were capable of producing disturbing sexualized images from photographs.
The report also found that some of these apps were easily accessible through major app stores, with several having limited or no age restrictions. Research cited by CBS News indicates that about half of US teenagers have encountered AI-generated pornography, and some reported that the images involved themselves or people they knew.
Our Take
AI “nudify” apps are a serious threat to privacy, safety, and dignity, especially for young people. In our view, stronger safeguards and age restrictions are needed to prevent their misuse. Parents, schools, and technology companies should also educate children about digital consent, responsible AI use, and online safety.
