They already invest in open-source AI, but nothing is truly free. Commercial AI will usually dominate because devs are paid to make it their primary effort. Goodwill and part-time contributions cannot reliably compete with livelihood and profit incentives.
Just because a software is closed-source doesn't mean the knowledge can't be shared. You don't need to see the underlying code to explain to someone architectural patterns or best practices.
The library analogy in the scenario would hold true if LLM providers refused to answer any questions about RL or Transformers.
I am a big proponent of open-source open-weight models, but mostly because I think it's just a better product. We've seen that they are much cheaper to train and operate. Frontier intelligence might not be needed for most tasks. Just let the market decide. My bet is that LLMs will become analogous to programming languages, and big labs will make their money by fine-tuning models for very specific use cases or by deploying them for customers.
Title was: I argued with the father of open source for 2 years. Now the AI fight is the same — only bigger
Op-ed alt link: https://fortune.com/2026/07/03/open-source-ai-same-fight-as-...
I'd rather the US fund universal childcare, medicare for all, and free school lunches than give a cent to subsidize a technology the American public absolute hates.
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ABSLOUTELY NOT.
this is like saying "gov should invest in pyramid schem, because everyone is doing it". or btc. or web3 pictures of monkeys.
what i expect the gov to do is to add a 999% tax or tarif on top of GPUs bougth for AI, after the first 100mi that company spends on it each year.
“Tax payers should fund my hobby”
Fixed the title for you
We really need to band together to fund / sponsor targeted inducement prizes (a la Nobel laureate Michael Kremer) for open models.
Every 6-12 months, give out $200K to the first model to hit a min threshold on a set of ~5-10 hard benchmarks (+ perhaps one secret benchmark) using a total of 16GB / 32GB / 64GB / 128GB of VRAM (at a min context length of 200K), then move the threshold up. Quantization etc. is dealers choice, it just needs to nail the benchmark on a reference machine by using exactly that much VRAM (no mapping to RAM / disk etc.)
You could crowdsource the funding, and cross subsidize by adding targeted prizes focused on corporate needs (the classic one is PDF processing benchmarks), and say that 25% of each corporate prize funding also flows into the general prize pool.
For a lot of these open-source model companies, it's less about the $s (though $200K is nothing to sneeze at), it's the clear recognition that helps their model efforts stand out, gain usage etc.