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Three rival AI CEOs did something nearly unheard of this weekend: Dario Amodei, Sam Altman, and Elon Musk all publicly agreed the industry needs to slow down. Trump's response, delivered Sunday from the sidelines of a golf tournament in Ireland, was immediate and blunt. "We're leading China in AI. We're the most sophisticated country in the world, and frankly, I want to keep it that way, because whoever wins AI wins," he told reporters, dismissing the safety concerns as "negative forces" pushing "things that won't happen."
Meanwhile, behind the public back-and-forth, something more concrete may be forming. Anthropic, OpenAI, and Google are reportedly in talks to build a joint, industry-led standards body to police AI development themselves, according to The Information.
Here’s what happened in AI today:
• Trump dismissed his own AI industry's safety warning within a day, saying "whoever wins AI wins."
• Anthropic, OpenAI, and Google are reportedly in talks to build their own joint AI safety standards body.
• House Speaker Mike Johnson rejected an AI moratorium too, proposing a meeting with platform providers instead.
…and a whole lot more that you can read about below.
• Trump Just Rejected His Own AI Chiefs' Warning
President Trump dismissed the rare, bipartisan-feeling call from Anthropic's Dario Amodei, OpenAI's Sam Altman, and xAI's Elon Musk to slow AI development, telling reporters Sunday in Ireland that the US can't afford to fall behind China. "We're leading China in AI. We're the most sophisticated country in the world, and frankly, I want to keep it that way, because whoever wins AI wins," Trump said. "We can put guardrails. We can do this and that. But I think you have a lot of negative forces that are bringing it up that shouldn't be bringing it up, and they're bringing up things that won't happen." David Sacks, the White House's AI and crypto czar, echoed the dismissal on X, telling the AI companies to "stop pretending you need anyone else's permission" and pushing back on the idea their safety push was "purely altruistic." House Speaker Mike Johnson separately rejected calls for an AI moratorium, proposing instead "a meeting of all the platform providers" he says he's already discussed with Trump. Democratic Congressman Pete Buttigieg called the response "deeply dangerous," saying Congress sitting on its hands while AI "is moving quickly" is exactly the wrong approach.
A weekend consensus among rivals, met with an immediate political rejection.
• The Three Biggest AI Labs May Be Building Their Own Watchdog
Anthropic, OpenAI, and Google are in talks to form an industry-led standards body to help police AI safety themselves, The Information reported today. The talks follow directly from Amodei's weekend proposal that AI companies accept independent, employee-level evaluator access and coordinate on shared safety standards, a commitment Anthropic made unilaterally and Altman said OpenAI would match. Separately today, reports indicate OpenAI has started blocking ads inside ChatGPT from competitors in AI image and audio generation, catching advertisers like Adobe off guard, a reminder that even as the safety conversation plays out publicly, the underlying competitive product decisions haven't slowed down at all.
Today's Job Board
Staff AI Engineer, Agent Architecture & Behavior | Artisan
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Build AI employees that take on real sales work.
AI Product Engineer, Full-Stack | Vimcal
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Build the world's fastest calendar for remote teams.
Energy Management System Engineer | Posh
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Build rapidly deployable energy solutions for commercial and industrial sites.
Fullstack Engineer | BoldVoice
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Build the speech and accent coaching app for non-native English speakers.
Senior Backend & Infrastructure Engineer | Scispot
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Build the data infrastructure platform used by biotech companies.
Senior Software Engineer | Curri
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Build the construction and industrial logistics platform.
Rust Engineer, Distributed Systems | Wasmer
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Build the operating system for edge computing.
Full Stack Engineer | Runway
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Build easier, more reliable mobile app releases for teams.
AI-First Fullstack Software Engineer | Soraban
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Build the AI tax workflow platform used by accounting firms.
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Today's Money Moves 💸
Mecka AI Nears a $500M Valuation as the Rush for Robot Training Data Intensifies
Mecka AI is closing in on a $500 million valuation in a Sequoia-led deal, the latest sign that data used to train physical robots has become one of the most competitive corners of AI funding.
Moonshot AI Targets $2 Billion in Annual Revenue
Kimi-maker Moonshot AI is targeting $2 billion in annual revenue, underscoring how fast Chinese AI labs are scaling commercially even as Western labs raise safety concerns about exactly this kind of rapid growth.
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Skill of the Day
Run a Real Coding Model on Your Own Laptop, for Free
A genuinely capable open-weight model, Qwen3.8-27B, dropped this week, and Unsloth's compressed versions let you run it locally instead of through an API. That means no per-prompt bill and nothing about your prompts or files ever leaving your machine.
Start by checking what your computer can actually handle. Unsloth counts your total RAM plus VRAM, or unified memory on a Mac, and 17GB or more is the sweet spot for a good experience. Install Unsloth, search for Qwen3.8-27B, and pick a "quant," a compressed version of the same 27-billion-parameter model. The smallest option, UD-IQ2_XXS, needs about 9GB. If your machine can handle more, UD-Q4_K_XL at roughly 18GB gives a noticeably better balance of quality and size. Download it, load it into a chat interface, and start prompting. If it runs rough, drop to a smaller quant. If you're already on the smallest one, a model like Gemma 12B is a reasonable fallback, just expect it to make more mistakes than the larger model would.
Prompt of the Day
The Local Model Setup Check
Before I download and run an open-weight model locally, help me figure out the right setup for my machine.
My computer's specs: [RAM, VRAM if you have a dedicated GPU, or unified memory if on a Mac] What I want to use it for: [coding, writing, general chat, etc.]
Tell me:
• Which quant size I should realistically start with, given my specs.
• What tradeoffs I'm accepting at that size versus a larger one.
• One sign it's time to downgrade to a smaller quant or a different model entirely.
• Whether my task is even a good fit for local, or better handled by a hosted model.
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