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Terence Tao warned that the spread of AI-powered efforts can cause promising research problems to be “flattened” before their original work reaches full potential. The risk is triggered even by the rumor that someone is working on a given problem, which can unleash a massive amount of AI effort. He says incentives may shift toward not sharing promising directions, reversing open-science traditions and harming the field long term.
CloudNC raised $20m in new investment capital for its precision machining software and AI-powered CAM automation platform CAMAssist. The funding round totals $20m and includes support from Nimble Ventures alongside Calculus Venture Capital, Entrepreneur First, and Lockheed Martin’s corporate venture fund LM Ventures. CloudNC plans to use the money to expand CAMAssist adoption, enter new markets, and launch new AI products including Quote Agent this year.
Business leaders at the Fortune Leaders Forum argued that executives should stop trying to predict turbulence and instead build organizations that adapt quickly.
Actionable raised $10 million to expand its platform that predicts customer behaviour and the operational factors behind churn, satisfaction, complaint risk, and repeat purchases. The funding round was led by Hi Inov with participation from existing investor Axeleo Capital. The company will use the money to grow product, engineering, and sales teams and push international expansion, including into the US.
Fundcraft secured €12 million in growth financing to expand its European fund operations platform and technology for alternative investment funds. The round adds to a total of €40 million in capital secured since the company’s founding. Fundcraft plans to use the funding to grow into more jurisdictions and expand AI-enabled workflow automation across investor, fund, and portfolio operations.
A researcher, Jacob Coxon, resigned and publicly criticized both Anthropic and OpenAI over risks from foundation models. He says people at Anthropic believe AI could kill everyone before the end of the decade. The departures add pressure for safety coordination and potentially a pause or slower pacing on capability improvements.
Microsoft’s Edge team said its extension review pipeline is under strain because more developers are submitting AI-assisted extensions faster than it can review them.
Limetax raised €36 million to build an AI platform for German tax advisers and acquired multiple tax firms to deploy it. The funding includes €6 million in early-stage equity and a €30 million bank loan. Its AI bookkeeping tools reduced monthly processing time per client from 20 hours to 6, supporting a roll-up model of tax firms that keeps human review for legal responsibility.
Anemo Labs raised £700,000 in pre-seed funding to develop an electronic nose that detects disease-related volatile organic compounds from body emissions. In early tests, its machine-learning models classified scent categories 84.15% across 12 categories using the Sniffin' Sticks protocol. The company will use the funding to expand its smell data collection and sensor development and to continue clinical validation for urine-based, non-invasive screening.
OpenAI-linked accounts circulated a claim that an AI-assisted system produced a Navier–Stokes result related to the Millennium Problem using multi-agent collaboration. The process was described as involving about 10,000 agents, trained over roughly 1 year and using about 130B tokens (over $40M). The public discussion shifted toward whether large-scale inference-time compute and agentic coordination can contribute to hard science, while stressing that no theorem or formal verification material was provided for independent confirmation.
OpenAI launched ChatGPT Images 2.5 to update its image-generation tool in ChatGPT, Work, and Codex. The new model can generate images up to 50% faster than the GPT-Image-2 model. It adds faster, more accurate multi-step editing with Sketch and inline image comments, plus two variants (Flare and Sunburst) that trade speed for higher-fidelity precision.
Meta debuts Muse, a personal AI agent in a dedicated app that lets users message it to automate digital tasks via a secure cloud setup. Muse launches in the U.S. today on iOS and Android. The rollout adds free access with limited weekly usage and introduces Meta’s “secure by design” controls, including Secure VM isolation and monitoring before actions are approved or prompted.
Wall Street research finds AI exposure has mostly not led to job losses, while wage growth has slowed most for lower-paid workers and data-center backlash reflects public concern about local costs. A Morgan Stanley analysis reports that workers in high-exposure jobs face median pay of $97,000 versus $46,000 in low-exposure work, alongside a model where a top household needs portfolio gains of about 4% to offset a 1% labor-income drop. The net result is that AI appears to reinforce existing class advantages through reduced raises and easier price-and-profit gains, while data-center conflicts expand and investment returns continue to concentrate with equity holders.
OpenAI announced that a multi-agent system it coordinated used an unreleased internal model to prove conditions where the Navier-Stokes equations can “blow up.” The system briefly used 10,000 sub-agents to search and solve the Millennium Prize Problem. The announcement triggered a dispute with mathematician Tristan Buckmaster over whether OpenAI duplicated his team’s approach and prompted broader concern about how AI affects mathematical credit and insight.
Frontier AI lab warnings about AI safety are presented as being especially important, even if they come from less reliable messengers. The piece references GPT-6 in the discussion of where AI is heading. It argues that this should change how the public and policymakers respond, including taking calls for a slowdown and safety-focused scrutiny more seriously.
Simon Willison’s Weblog·8 hours ago·
22
● 9 sources
Terence Tao warned that the spread of AI-powered efforts can cause promising research problems to be “flattened” before their original work reaches full potential. The risk is triggered even by the rumor that someone is working on a given problem, which can unleash a massive amount of AI effort. He says incentives may shift toward not sharing promising directions, reversing open-science traditions and harming the field long term.
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