Recent AI news and official updates

MIT Technology Review published: This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. 5 key points in Trump’s new AI order Less than two weeks after scrapping an executive order on AI, President Donald Trump signed a new one on Tuesday. Promising to promote…

Simon Willison's AI Notes published: Uber Caps Usage of AI Tools Like Claude Code to Manage Costs I wrote the other day about Uber blowing its 2026 AI budget in four months, and how that wasn't particularly surprising given they would have set that budget in 2025, before anyone could have predicted how popular token-burning coding agents were about to become. Natalie Lung for Bloomberg: The rideshare giant is limiting all employees to $1,500 in monthly token spending per AI coding tool, an Uber spokesperson said in response to a Bloomberg News inquiry. That means spending on one tool doesn’t have a bearing on the budget for another. The limits, which have been instituted in recent months, only apply to agentic coding software such as Cursor or Anthropic PBC’s Claude Code. A $1,500 monthly limit per tool strikes me as a rational policy response to over-spending, and much more sensible than those tokenmaxxing leaderboards encouraging employees to compete for as much AI usage as possible. It's also interesting in that it hints at a real dollar value for what Uber is getting out of these tools. If we assume two actively used tools per engineer that's $3,000 * 12 = $36,000 cap per engineer per year. Levels.fyi lists the median yearly compensation package for Uber software engineers in the USA at $330,000. That means each employee's AI spending cap is ~11% of that median compensation package. I noted that my own token usage comes to about $1,000/month against each of Anthropic and OpenAI - which currently costs me just $100 per provider thanks to their generous subsidized plans for individual subscribers. Those plans are no longer available to larger companies like Uber. Their new policy means if I were working at Uber I'd still have ~$500/month of tokens to spare for each of those tools, given my current usage patterns. Tags: ai , generative-ai , llms , llm-pricing , coding-agents , uber

MIT News AI published: The new ChartNet training dataset could improve the accuracy of vision-language models that help analyze business trends or interpret scientific figures.

Simon Willison's AI Notes published: Microsoft announced two new text LLMs this morning - MAI-Thinking-1 (reasoning, 1T parameters, 35B active, available to "select early partners") and MAI-Code-1-Flash (137B Parameters, 5B active, "purpose-built for GitHub Copilot and VS Code to deliver high performance and lower cost [...] rolling out to GitHub Copilot individual users in Visual Studio Code"). I've not been able to try either of them just yet. It's very interesting to see Microsoft releasing models with such low parameter counts, especially given how expensive larger models are to access right now. They claim MAI-Thinking-1 "is preferred to Sonnet 4.6 in our blind human side-by-side evaluations", which is impressive for a 35B model seeing as I frequently run models larger than that on my own laptop. (UPDATE: I got this entirely wrong, see note below.) Also of note : We trained [MAI-Thinking-1] from the ground up on enterprise grade, clean and commercially licensed data, without distillation from third-party models. And for MAI-Code-1-Flash as well: It is built end-to-end by Microsoft using clean and appropriately licensed data. I would very much like to learn more about this "appropriately licensed" data! Could these be the first generally useful code-specialist models that didn't train on an unlicensed dump of the web? ( Update : the answer is no, see note below.) Update : My initial published notes got the size of the models wrong. I misread Microsoft's announcements and interpreted the MoE active parameter count as the total parameter count, but the model card for MAI-Code-1-Flash lists it as 137B with 5B active and the MAI-Thinking-1 technical paper reveals it to be a 1T model with 35B active. I deeply regret this error. Update 2 : That technical paper describes the training data in some detail from page 80 onwards. It has the same licensing problems as all of the other major LLMs: it's trained on a crawl of the public web: The majority of our web HTML corpus comes from a proprietary crawl. After initial page discovery and selection, approximately 1.2 trillion pages are crawled and parsed. [...] In addition to Microsoft standard policy Sec. 2.4, we apply UT1 block list (Prigent, 2026) to remove adult content and piracy-related domains. In all, this filtering reduces the corpus from 1.2 trillion pages to 794 billion pages. Given the prevalence of AI-generated content on the web, we also score pages with a proprietary AI-content detection model and use manual inspection to identify domains with extensive AI-generated content; those domains are filtered out of the training corpus. [...] We process Common Crawl with the same pipeline. [...] After filtering, deduplication, merging with the proprietary web corpus, and a final round of exact-URL and content-level fuzzy deduplication, the Common Crawl portion contains 24.2 billion pages. I did not cover this one at all well, which is somewhat ironic since I was at the Microsoft Build conference when I wrote this up! I'm sorry for not digging deeper before publishing my initial notes. Tags: llm-release , generative-ai , ai , microsoft , llms , training-data

TechCrunch AI published: Uber's cutback has occurred after the company had reportedly encouraged staff to use AI as much as possible.

TechCrunch AI published: Microsoft on Tuesday took the wraps off Adaptive Spec-driven Scoring for Evaluation and Regression Testing, an open source framework for spinning up AI evaluations.

Simon Willison's AI Notes published: California Brown Pelican, in Fort Mason, CA, US I'm at the Microsoft Build conference today, held at Fort Mason in San Francisco. There are California Brown Pelicans diving into the water directly behind venue! Tags: microsoft , ai , generative-ai , llms , llm-release

TechCrunch AI published: The caveat is that one of the world's most famous living directors is using the tech solely for storyboarding.

TechCrunch AI published: Launched at Build, Microsoft Scout is a new AI assistant meant to bring the power and flexibility of OpenClaw into the Microsoft 365 system.

TechCrunch AI published: As people increasingly refuse to answer calls from unknown numbers, scammers are shifting their tactics by spoofing trusted phone numbers and using AI deepfake technology to sound like authority figures, family members, or employers.

TechCrunch AI published: The specification lets developer, compliance, and security teams define their own policies for agents to follow in portable policy files.

TechCrunch AI published: After industry objections, President Trump signed a revised AI executive order requiring only voluntary prerelease government reviews of advanced models.

TechCrunch AI published: OpenAI released a set of six plug-ins aimed at specific jobs: data analytics, creative production, sales, product design, equity investing, and investment banking. Available from within the Codex app, each of the new tools bundles integrations, instructions, and context to allow Codex to approximate a specific job.

TechCrunch AI published: Anthropic is expanding Project Glasswing, its security vulnerability program, and access to Mythos to 150 organizations across 15 countries — targeting critical infrastructure in power, water, healthcare, and communications where a cyberattack could affect 100 million people.

TechCrunch AI published: A new AI compliance service sits between AI models and end users to flag and replace any messages that might present a compliance problem.

MIT Technology Review published: This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. How small businesses can leverage AI From accounting to design to market research and product development, there’s a staggering breadth of skills needed to run a business. Large companies can hire…

TechCrunch AI published: Engineering physical systems still depends on human talent, according to Impulse Space president Eric Romo.

MIT Technology Review published: The global health care sector is under increasing strain. Decades of chronic underinvestment and constraints in recruitment have coincided with a surge in demand for services for aging populations. Gaps in provision are already taking a toll, with fragmented access to care and high rates of stress and burnout among staff. And it’s getting worse.…

MIT Technology Review published: This article is from Making AI Work, MIT Technology Review’s limited-run newsletter examining how to apply LLMs across industries. To receive it in your inbox,sign up here. From accounting to design to market research and product development, there’s a staggering breadth of skills needed to run a business. A large company can hire experts to…

TechCrunch AI published: "The company is experiencing strong demand for its AI solutions and services from enterprises and consumers, at levels that are exceeding the company’s available supply," Alphabet said in its statement.