“Open weights are nowhere near a sufficient solution”: Dario Amodei fires back on AI power

The New Stack · 2 months ago · 46 ● 3 sources

Anthropic CEO Dario Amodei argued that open-weights only shifts AI power to whoever controls the most compute and chips, not to truly independent developers. He cited California’s SB 53 definition of a frontier model as one trained using more than 10^26 FLOPs and requiring extra rules for developers earning over $500 million per year. He said regulation should be tiered (frontier vs off-frontier) and linked to safety testing, while still leaving room for smaller labs rather than enabling incumbents or banning open models wholesale.

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AI Security AI Policy Anthropic Open Source Models

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Teaching Everyone to Fish for Tokens

Interconnects · 2 months ago · 49

Nvidia’s investments in nearly open-source language models are framed as an effort to make the open-weight “token machines” ecosystem self-sustaining rather than dependent on closed competitors. Nvidia is reported to be spending $26 billion on this approach. Open models may either become more dependent on Nvidia’s financing or fork toward more efficiency and enterprise-specialized long-tail use, while revenue-share licensing and reduced interest in training full base models shape the ecosystem.

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Business Funding NVIDIA Open-Weight Models Open Source Models

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Fine-tune your own AI model using Unsloth Studio

YouTube · 2 months ago · 16

Unsloth Studio released a graphical interface that lets users fine-tune open-source AI models locally on their own machines through a point-and-click workflow.

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Open Source Models Fine-tuning

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X Open Sources Its Algorithm And Reveals What it Takes to go Viral

Trending Topics · 2 months ago · 4

X published the code for its “For You” timeline ranking, including the ranking engine and transformer-based feed scoring model, on GitHub under the Apache 2.0 licence. The codebase grew roughly 10 to 15 times larger than the prior release. X’s rankings are now more transparent and externally testable, with a new in-app “Under the Hood” tool that lets some users download label statistics and with parts of the system run outside the company.

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AI Evaluation Model Releases Open Source Models

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