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.
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.
That's what people said about operating systems, and databases, and compilers, and so many other big complicated categories of software that over time became increasingly dominated by OSS
OSS only dominates for software that is commoditized and the published computer science research for that software domain is close to the frontier.
OSS struggles at being relevant when software is non-commodity e.g. office suites. In software domains like databases where the state-of-the-art computer science research is often unpublished, OSS struggles to be relevant at the higher end of the market on technical merits.
When deciding what should be OSS, it is useful to consider the preconditions that have made it successful.
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.
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.
Number one expense for SMB is healthcare, providing a nationalized healthcare service would likely unlock trillions in value (imagine what Americans would do if they got $200-500 more per paycheck?).
Instead we are forced to watch some of the wealthiest companies on the planet burn money for fun because apparently the government is "wasteful."
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.
No. If it was let to the markets we wouldn’t even have WWW or EMAIL now.
> 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…
This comes across as corporate bootlicking and I refuse to elaborate to keep with HN rules.
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.
That's what people said about operating systems, and databases, and compilers, and so many other big complicated categories of software that over time became increasingly dominated by OSS
OSS only dominates for software that is commoditized and the published computer science research for that software domain is close to the frontier.
OSS struggles at being relevant when software is non-commodity e.g. office suites. In software domains like databases where the state-of-the-art computer science research is often unpublished, OSS struggles to be relevant at the higher end of the market on technical merits.
When deciding what should be OSS, it is useful to consider the preconditions that have made it successful.
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-...
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.
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.
I would add supporting literacy programs to that list. Unfortunately, many Americans struggle to read beyond the sixth grade level.
Redistribution can only get you so far. Creating new wealth is more sustainable.
So why did we stop doing that in favour of winner takes all weath centralisation?
AI destroys wealth, so what is that?
> Redistribution can only get you so far.
That "so far" being a middle class the envy of the rest of the world which the US threw away to create a new class of oligarch.
"Back at the tail-end of that era, in the early 1960s, America’s richest faced a 91 percent tax rate on income in the top tax bracket."
https://inequality.org/article/tax-the-rich-we-did-that-once...
Number one expense for SMB is healthcare, providing a nationalized healthcare service would likely unlock trillions in value (imagine what Americans would do if they got $200-500 more per paycheck?).
Instead we are forced to watch some of the wealthiest companies on the planet burn money for fun because apparently the government is "wasteful."
What a crock of shit.
16 replies →
Well because you said that, we're going to add public investment in crypto too, everyone gets an NFT with their taxes this year.
[dead]