America is losing the open-weight aI race and Meta and Nvidia just admitted it
&imwidth=600&imheight=450&format=webp&quality=medium "America is losing the open-weight aI race and Meta and Nvidia just admitted it")
America is losing the open-weight aI race and Meta and Nvidia just admitted it Photograph: (Unsplash)
Meta released Muse Glimmer on Monday and Nvidia followed with Nemotron 3.5 Lightning a day later, both open-weight. The framing in the coverage is unusually candid: China is well ahead in open-weight models, and these are catch-up moves. Box chief executive Aaron Levie described the releases as planting ‘a very firm flag’ that America will have near-frontier open-source models.
Two of America's largest technology companies released open-weight AI models within a day of each other this week. The notable part is not the models. It is that the effort is being described, openly, as catching up to China.
What Happened
Meta unveiled Muse Glimmer on Monday — a 30-billion-parameter model under Apache 2.0 that runs on a single consumer GPU — alongside a Zuckerberg essay committing to open the weights of Muse Spark 1.2 as well.
Nvidia followed a day later with Nemotron 3.5 Lightning, an open mixture-of-experts model with 30 billion total parameters and roughly 3 billion active per token, built for the execution layer of long-running agents.
Box chief executive Aaron Levie, a signatory to an industry letter on open models, characterised the moment as planting ‘a very firm flag in the ground that America will have near-frontier open-source models.’
Read that phrasing carefully. Not the best open models. Near-frontier ones. And a flag planted is an intention stated, not a position held.
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Who Is Actually Ahead
The candour is warranted, because the open-weight field is currently led by Chinese laboratories and not by a small margin.
Moonshot AI released Kimi K3's complete weights on July 27 — 2.8 trillion parameters, the largest open-weight release in history, free to download and modify. Alibaba's Qwen3.8-Max carries 2.4 trillion parameters and claims a 16-day autonomous coding run. DeepSeek's V4 Flash reaches within two percentage points of Claude Opus 4.7 on coding benchmarks at roughly one per cent of the cost. ByteDance is reported to be pre-training a model of up to 10 trillion parameters.
In mid-July, six of the top ten models on independent leaderboards were Chinese, including all of the top five.
Against that, a 30-billion-parameter model from Meta and a 30-billion-parameter model from Nvidia are useful contributions rather than a change in the standings. Both companies still have to demonstrate there is an audience for their offerings in a market where developers have already built on Kimi, Qwen and DeepSeek.
Why It Matters Beyond Bragging Rights
Open weights determine which models the next generation of developers builds on, and that has consequences that compound.
A developer who downloads a model, fine-tunes it and ships a product on top of it has made a commitment that is expensive to reverse. Universities teach on what is available. Startups build on what is free. The ecosystem that forms around a model — the tooling, the tutorials, the fine-tunes, the accumulated institutional knowledge — is considerably more durable than any single benchmark result.
That is the position American open-weight releases are trying to contest, and the reason both Meta and Nvidia have commercial logic for doing so despite giving the models away. Meta earns through advertising on its own platforms and benefits from commoditising what rivals sell. Nvidia sells the hardware every open model runs on, so free capable models generate demand for exactly what it makes.
The Policy Fight Underneath
There is a regulatory dimension that gives the timing urgency.
On July 24, more than 20 US technology companies signed an open letter urging policymakers not to place early restrictions on open-weight AI — explicitly including models built through distillation.
That last clause is doing a lot of work. Three weeks earlier, White House science director Michael Kratsios had accused Moonshot AI of covertly distilling Anthropic's Fable model to build Kimi K3, with Treasury Secretary Scott Bessent warning that ‘open source is not open season on American IP’ and that sanctions were on the table.
So American technology companies are lobbying to protect the same practice their own government is threatening to sanction a Chinese laboratory over — because restricting distillation would constrain American open-weight efforts at precisely the moment they are trying to catch up. Zuckerberg's essay defends distillation explicitly, and Muse Glimmer is itself distilled from Meta's closed Muse Spark.
The uncomfortable position is that the tool America needs to compete in open weights is the tool America is accusing China of misusing.
