Hello and welcome to Eye on AI. In this edition:<\/p>\n
Idealab\u2019s Bill Gross gives away 1.2 million AI-generated business ideas.<\/p>\n
Ex-OpenAI employee accused of planting bugs denied bail<\/a>.<\/p>\n OpenAI discloses more rogue AI incidents<\/a> involving financial, medical, and government data.<\/p>\n AI safety experts unhappy with Anthropic\u2019s proposed AI bill.<\/p>\n Anthropic pledges 5% of its shares to new public benefit fund.<\/p>\n Maybe neural networks aren\u2019t as hard to understand as we thought.<\/p>\n OpenAI\u2019s GPT-6 Astra aces new theoretical physics benchmark.<\/p>\n There\u2019s been a lot of concern, understandably, about AI eliminating jobs. So far, the great job apocalypse hasn\u2019t shown up. But there are hints out there that jobs are getting harder to find and that AI is part of the reason.<\/p>\n Bloomberg reported<\/a> this week that banks in the U.K. are slashing back-office jobs, thanks in part to AI Anthropic unveiled data<\/a> showing that as it has deployed AI throughout its fast-growing organization, who it hires has shifted. Far fewer junior employees are being hired in technical roles than previously. Since early 2025, hiring of junior technical employees has fallen from 27% of all new hires to 9%, the company said. Anthropic is also hiring a lot fewer administrative staff than a company growing at its pace typically would. If other companies follow this pattern as they adopt more AI, there are likely to be significantly fewer jobs in the economy.<\/p>\n While this may be worrying, a little-discussed feature of the AI revolution is how it could supercharge entrepreneurship\u2014and how a surge in new businesses could partly offset some of AI\u2019s negative impacts on employment. Yesterday, Bill Gross, the founder and CEO of Idealab and ProRata, unveiled a new project that highlights this potential.<\/p>\n Idealab was one of the first startup incubators when it was launched back in 1996. Since then, it has helped spawn some 175 companies. Over the years, Gross has developed 31 techniques\u2014he calls them \u201cmethodologies\u201d\u2014for generating business ideas. Really, these are creative prompts for thinking about idea generation. Gross breaks these down into three big categories: opportunities, which he says are things such as solving a business pain point, removing friction, removing waste, removing danger, and improving design, among others; enablers, such as new technologies, scientific advances, and new materials, processes, or changing cost curves that make solutions more feasible than before; and shifts, such as regulatory changes, as well as behavioral, economic, cultural, or geopolitical movements that make the timing for a particular business seem right.<\/p>\n Gross fed these 31 methodologies to an AI model along with some 17 million source documents that included news stories, research reports, expired patents, public company filings, and threads from online community discussion boards. In this way, Idealab\u2019s AI generated more than 1.2 million business ideas. And Gross has decided to give those ideas away for free.<\/p>\n \u201cI\u2019ve always wanted to have a positive impact with Idealab by starting many companies, but I can\u2019t start 1.2 million,\u201d Gross told me in an interview over the weekend. \u201cBut what about if I can empower entrepreneurship around the world way more than I\u2019ve been able to do in Pasadena [where Idealab is based]? So it just got me very excited that this could be a gift, taking my methodology and now AI\u2019s potential, to give it to others.\u201d<\/p>\n Unlocking trapped IP<\/p>\n Gross said he sees business as fundamentally a way to help solve problems, and he thinks AI can energize that process. \u201cI feel like so many people are talking about the hazards and dangers of AI,\u201d he told me. The positive use case for AI, he said, is often just reduced to \u201ccuring cancer.\u201d That would be an unalloyed good. But he thinks it actually sells AI\u2019s potential benefits short. \u201cThere\u2019s so many other problems that AI can actually address,\u201d he said.<\/p>\n Anyone can go to idealab.ai<\/a> and explore the 1.2 million ideas, querying them by industry, skills, individual interests and geography. People can upload their resume or Linkedin profile and get a personalized set of potential business ideas that might suit them. People who already have an idea, can upload a business plan and ask the AI engine to help refine it, or generate 100 neighboring ideas to help improve it.<\/p>\n Existing organizations, such as companies or universities, can also search for business ideas across their own existing patent portfolio. Gross said that currently, a lot of IP in these large enterprises is essentially trapped, sitting there unused in any productive way. He thinks idealab.ai could free that IP\u2014allowing the formation of new business units or spin-out companies to exploit this hidden value. He said he tried it on Caltech\u2019s patent base, which is 9,978 patents, and found that those could be used to generate some 3,450 new businesses. He said that industrial conglomerate 3M<\/a> had some 21,000 patents that could be used to generate 1,549 new business ideas. \u201cI\u2019m really hoping that this will not only help entrepreneurs do things, but help big companies do things too,\u201d Gross said.<\/p>\n Gross\u2019s vision: 1 million new companies and 100 million new jobs<\/p>\n The AI model provides a rollout plan for the new business. If an entrepreneur needs money to help get the business off the ground, Gross said they can reach out to Idealab and the incubator will consider investing. And if an entrepreneur needs help to implement a business plan, Gross is planning to offer assistance there too. He has created what he calls a \u201cPeriodic Table of Business Functions\u201d that charts some 118 different jobs\u2014from customer discovery to sales to operations and legal\u2014that are required to set up and run a business. Idealab has created an agentic AI environment that will help entrepreneurs automate those tasks. Unlike the ideas, which Idealab is giving away for free, Gross is charging for these Periodic Table services, which he said will launch in 2027. It\u2019s not clear exactly what the cost will be, but Gross convinced Matthew Prince, the CEO of Cloudflare, to donate $100,000 worth of Cloudflare credits to any startup that builds on Idealab\u2019s platform.<\/p>\n Gross said he is hoping idealab.ai<\/a> will eventually catalyze 1 million new businesses and generate 100 million new jobs\u2014enough to offset the 100 million jobs that Goldman Sachs<\/a> once forecast that AI could eliminate from existing companies by 2030. (So far, there\u2019s not much sign the job picture is going to be as bleak as Goldman suggested). Gross said these new entrepreneurial jobs would be better jobs too. \u201cThese would be 100 million jobs that are much, much better than the 100 million jobs that were displaced, giving people more purpose and more excitement and more enthusiasm and more passion, and actually solving world problems,\u201d he said.<\/p>\n Whether or not Idealab can help create 100 million jobs, there\u2019s mounting evidence that AI may be helping to create a golden age of entrepreneurship. New business formation in the U.S. is near all-time highs, up some 11% since the pandemic ended in 2022, according to Census Bureau data. The situation is similar in other parts of the world. So the economic picture AI is helping to paint is more complex\u2014and perhaps less dystopian\u2014than the vision of fewer jobs and more concentrated wealth that has received the most media attention so far.<\/p>\n With that, here\u2019s more AI news.<\/p>\n Jeremy Kahn Before we get to the news, just a reminder to check out our new vodcast, Fortune AI Weekly. This week, Bea Nolan and I discuss our exclusive interview with Nvidia<\/a> CEO Jensen Huang, Anthropic\u2019s research on whether small models could self-replicate, and Anthropic\u2019s decision to ban users from being abusive to Claude. You can check out the vod here<\/a> on YouTube.<\/p>\n FORTUNE ON AI<\/p>\n Ex\u2013OpenAI engineer accused of bugging office stays in jail as prosecutors allege Saudi ties: \u2018Once he leaves, there\u2019s no getting him back\u2019<\/a>\u2014by Emily Forlini and Wen Shao<\/p>\n A rogue OpenAI agent robbed a crypto wallet to obtain funds to carry out a task<\/a>\u2014by Camila Grigera Na\u00f3n AI IN THE NEWS<\/p>\n China proposes new licensing regime for AI. Beijing is proposing new export controls on advanced AI, chip-design and other strategic technologies. The draft rules would restrict exports of leading AI models trained on sensitive Chinese data and advanced AI agents used in critical infrastructure, defence and government, while also covering technologies for designing and packaging advanced chips. Open-weight models would generally be exempt, but Beijing could still restrict transfers involving exceptionally large or top-performing models, potentially limiting overseas acquisitions or the movement of key personnel and intellectual property. The rules, partly prompted by Meta\u2019s attempted $2 billion acquisition of Chinese AI app Manus, underscore China\u2019s increasing use of export controls to protect strategic capabilities. Read more from the Financial Times here<\/a>. Investors question valuation of neocloud Nscale after series of setbacks. Nvidia-backed AI data center developer Nscale is facing investor resistance to the roughly $30 billion valuation it is seeking in a planned U.S. IPO, with some prospective investors arguing that $16 billion to $19 billion is more realistic. The pushback comes amid deteriorating sentiment toward AI infrastructure companies more widely, as higher borrowing costs and doubts about AI returns have hit peers such as CoreWeave and Nebius. Nscale, in particular, has suffered recent project delays, and Microsoft has also pulled out of a U.K data center deal with the company because of problems hooking the site up to the power grid. Nscale, which raised $3.36 billion in pre-IPO financing last month, has yet to begin formally marketing the offering as it works through SEC comments, and the valuation gap could delay or reshape plans to raise as much as $3 billion. Read more from Bloomberg News here<\/a>. EYE ON AI RESEARCH<\/p>\n Neural networks may be easier to interpret than we thought. Efforts to understand the inner workings of large neural networks by interpreting the activations of their neurons\u2014a method called \u201cmechanistic interpretability\u201d\u2014have, to date, been extremely difficult. A major problem is that researchers had found that the same neurons seemed to activate for different, unrelated inputs. For instance, a single neuron seems to respond to the word \u201ccat\u201d and the word \u201ccar.\u201d This \u201csemantic superposition,\u201d as researchers call it, makes mechanistic interpretability fraught. In fact, researchers often have to train a separate model\u2014called a sparse autoencoder\u2014to interpret the features represented by the larger model\u2019s activations and then conduct research on this smaller set of features, rather than trying to disentangle the confusing activations of the larger, primary model. Trying to understand the activations of the large model, especially in real time, was widely considered to be impossible. But some AI researchers aren\u2019t convinced. Google DeepMind\u2019s Neel Nanda said on X<\/a> that Cammarata\u2019s work doesn\u2019t change his core views. He and others argue that Cammarata focused on the strongest signals a neuron sends and overlooked its many weaker ones, where the model may still store facts in a muddled way. Others faulted Cammarata for publishing his findings in a blog, rather than a proper research paper (he says one is coming) and Cammarata himself acknowledged that parts of his argument are speculative.<\/p>\n AI CALENDAR<\/p>\n Oct. 20-22:\u00a0Nvidia GTC Berlin, Berlin, Germany.<\/p>\n Nov. 9-12: Web Summit, Lisbon, Portugal.<\/p>\n Nov. 16-17: Fortune 500 Innovation Forum, Detroit. Apply here<\/a> to attend.<\/p>\n Nov. 18-19: Slush, Helsinki, Finland.<\/p>\n Dec. 6-12:\u00a0Neural Information Processing Systems (Neurips) conference. Sydney, Australia.<\/p>\n Dec. 7-8:\u00a0Fortune Brainstorm AI, San Francisco. Apply here<\/a> to attend.<\/p>\n BRAIN FOOD<\/p>\n AI models are getting scarily good at math and physics. If we needed any more evidence that AI is getting incredibly good at math and physics, OpenAI\u2019s GPT-6 Astra model was recently assessed on a difficult new theoretical physics benchmark. The benchmark included 50 never-before-published research-level problems\u2014the sort PhD students and professors would work on. Astra scored 80%. But the model insisted that some answers marked as wrong were correct. When experts looked at the answer key, it turned out that the humans who had calculated the answers had made subtle errors and that Astra\u2019s solutions were, in fact, right. When these errors were corrected, Astra\u2019s score climbed to 98% in scenarios in which it had access to OpenAI\u2019s Codex harness. You can read the research about the new physics benchmark here<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"Hello and welcome to Eye on AI. 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jeremy.kahn@fortune.com<\/a>
@jeremyakahn<\/a><\/p>\n
OpenAI discloses four more rogue incidents involving financial, medical, and government data<\/a>\u2014by Emily Forlini
AI agents are going rogue\u2014the labs\u2019 own training choices are a big reason why<\/a>\u2014by Beatrice Nolan and Wen Shao
Exclusive: AI security company Gray Swan relaunches its red-teaming Arena contest for the era of AI agents<\/a>\u2014by Jeremy Kahn
Exclusive: Daytona, a startup building the \u2018agentic cloud,\u2019 valued at $1 billion in latest funding round<\/a>\u2014by Jeremy Kahn<\/p>\n
Anthropic\u2019s proposed AI bill criticized by AI safety researchers. Anthropic said it would\u00a0 revise parts of a draft federal AI safety bill the company has been circulating on Capitol Hill after details of the bill leaked and drew criticism from AI safety advocates. Many of these safety experts said Anthropic\u2019s bill was weaker than the bipartisan FRONTIER Act, which itself has drawn criticism from some safety advocates for pre-empting some state-level AI laws and for relying too heavily on standards and rules the AI companies themselves develop. The Anthropic proposal would allow the Commerce Department to seek a court order blocking a dangerous model only under narrowly defined circumstances, establish independent model evaluations, and preempt some state AI safety laws\u2014but critics say it lacks adequate standards for evaluators and sets the bar for government intervention too high. California state Senator Scott Wiener and other safety advocates attacked the proposal, while former White House AI czar David Sacks criticized it from the opposite direction, arguing its definition of frontier AI would subject too many startups to regulation. Anthropic acknowledged that its language on state-law preemption was insufficiently precise and said it would revisit risk thresholds and develop stronger requirements for independent evaluators. You can read more here<\/a> in The Next Web.
Anthropic to give 5% of its shares to new public benefit fund. Anthropic has pledged 5% of its shares\u2014currently worth about $48 billion\u2014to a new public benefit fund designed to address societal impacts of AI, with additional shares to be committed as the company\u2019s valuation rises. The fund could pursue initiatives ranging from AI safety and biomedical research to mitigating job losses, including potentially making large charitable donations or even direct payments to people who lose their jobs. Anthropic may be seeking to counter growing public ire about AI and AI companies ahead of a planned IPO that could value it at roughly $2 trillion. The move would bring the proportion of Anthropic stock pledged to public causes to about 20% and comes amid broader Silicon Valley debate over whether the public should receive a direct financial stake in the enormous wealth potentially created by AI. You can read more from the Financial Times here<\/a>.<\/p>\n
Hacker used AI agents to attack more than 100 major companies. A Chinese-speaking hacker used commercially available AI agents to autonomously break into websites belonging to more than 100 companies, including Deere, SharkNinja and Cummins, Reuters reported<\/a>, citing a report from cybersecurity researcher Snorre Fagerland of NorForge. The hacker, apparently using a combination of agents based on DeepSeek\u2019s AI model and Anthropic\u2019s Claude Code, accessed through the open-source Cairn platform, attempted more than 1,200 intrusions with minimal human instruction. Most of the successful hacks took about 23 hours and cost an estimated $50 apiece in AI tokens, Fagerland told Reuters. The campaign provides striking evidence that AI agents can now execute sophisticated cyberattacks largely without human supervision, potentially allowing a single attacker to conduct attacks at a scale that would previously have required a large team of hackers.<\/p>\n
But now Nick Cammarata, a researcher at the interpretability startup Goodfire, has penned a blog<\/a> in which he argues that superposition is mostly wrong. He says the apparent mess came largely from crude analysis tools and outdated training methods. The famous \u201ccat and car\u201d neuron, he argues, actually detects a shape the two share. When he checked Alibaba\u2019s open-source Qwen3-32B model, he found about 90% of neurons had one main job.
Cammarata\u2019s essay went viral among AI researchers and other industry watchers. If Cammarata is correct, then mechanistic interpretation may be more effective than many had assumed. And that in turn is critical because mechanistic interpretability is one of the few reliable ways we have to monitor what AI models are \u201cthinking,\u201d especially as other methods, such as monitoring a model\u2019s \u201cchain of thought,\u201d or the reasoning steps it explicitly writes down on a digital scratchpad while it is working on a task, are becoming increasingly unreliable. (More powerful models can choose not to write down all their reasoning steps or even lie about their reasoning.)<\/p>\n