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.
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 think the Korean government did have a competition like this, I remember last year we got a bunch of models released at the same time to make the cut for the next stage. The models weren't anything to write home about, IMO.
Having it with clear hw requirements tiers is a nice differentiator. The only issue is that the benchmarks would 100% need to be closed, no other way around it. And then you have the issue of creating and curating good evals for every "stage" of the project. That's a hard task even for "honest" lab-internal evals. And you'd have to publish those evals after each round (for trust purposes), and start over for the next round. Doable, but it would cost a lot (probably more than the prize pools) and you'd have to keep doing this.
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.
> Even if the current approaches will continue to scale, this would be as if in the early days of computing, perhaps someone invented a bubble sort for sorting numbers (an n-squared algorithm), and the tech companies at the time decided they were going to build vast data centers to sort numbers and not bother to figure out that there's an n-log-n way of doing it <laughs>
...to which I have to say: yes, definitely! And he's right about open-source AI too.
Yes, there's a present shortage of usable frontier compute, but that doesn't establish that every proposed data center will earn a decent return, or that today’s hardware + model architecture will remain economically competitive, or more to Siegel's point, that algorithmic efficiency could not dramatically reduce compute requirements.
To borrow a real example from a prior boom, railroads were congested during the initial build-out while people simultaneously funded and built too many railroads for future demand. Likewise, Anthropic et al can be compute-starved now while the industry as a whole is overbuilding expensive, depreciating infrastructure.
The library analogy works (for me), but the uncomfortable part is that most "open" models are closer to receiving a compiled binary than receiving the library
No, I want govt to tax themmore. So far frontier AI companies produce negative value to near everyone (by sheer power cost increase it adds essentially tax to every other business) but themselves economy wise.
Yeah, wooho, new model found a bunch of bugs, now the bad guys can do it too so security expenses spiked! It's only good for shovel sellers.
Let me re-iterate the main lesson of decades of FOSS work: the advantages of open collaboration and knowledge-sharing are so enormous that FOSS software wins out eventually even if financial interest are stacked against it.
I fully agree with this article - please let's skip the chapter of closed and enshittified AI and go for the good stuff directly!
FOSS is the wrong analogy. Building frontier LLMs isn’t primarily an engineering discipline, it’s a scientific research program.
Of course we do have basically open source research programs, including most universities and big projects like CERN. But AI grew up in universities until it transpired that sufficient capital could only be found in the private sector.
It would be possible to make a decent publicly funded AI research program. But it would look more like the Manhattan or Apollo projects (which frontier labs already model themselves after) than some extra research grants for universities.
Listening to those pushing AI, why should we fund open source developers when we can reproduce their work with a few judiciously chosen LLM prompts? Perhaps the AI companies should fund FOSS so as to get more solutions to memorize but normals?
The private AI companies should be forced to release the models as open weights with a license (I.e no commercial use) due the high risk they present and the data they basically steal from everyone to train their models.
This should be the safety push not the regulatory capture that Dario is trying.
I must have missed the press release when Two Sigma open-sourced their algorithmic trading models so that the rest of us could make the best stock investments for our retirements. /s
“The economists need to start charting this out, if we are in a post scarcity world, how does everyone benefit from that?, obviously its not correct for just a few people or a few companies or even a few nations to be benefiting from this technology, it has to broadly accrue the benefits to everyone, but how is that gonna be done? We really need answers now. ” -Demmis Hassabis <Google Deep mind>
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[ 3.0 ms ] story [ 57.1 ms ] threadEvery 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.
Having it with clear hw requirements tiers is a nice differentiator. The only issue is that the benchmarks would 100% need to be closed, no other way around it. And then you have the issue of creating and curating good evals for every "stage" of the project. That's a hard task even for "honest" lab-internal evals. And you'd have to publish those evals after each round (for trust purposes), and start over for the next round. Doable, but it would cost a lot (probably more than the prize pools) and you'd have to keep doing this.
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.
Op-ed alt link: https://fortune.com/2026/07/03/open-source-ai-same-fight-as-...
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.
> Even if the current approaches will continue to scale, this would be as if in the early days of computing, perhaps someone invented a bubble sort for sorting numbers (an n-squared algorithm), and the tech companies at the time decided they were going to build vast data centers to sort numbers and not bother to figure out that there's an n-log-n way of doing it <laughs>
...to which I have to say: yes, definitely! And he's right about open-source AI too.
Ask Amodei how he feels about going to spaceman bad for compute that he couldn't find anywhere else in the market.
To borrow a real example from a prior boom, railroads were congested during the initial build-out while people simultaneously funded and built too many railroads for future demand. Likewise, Anthropic et al can be compute-starved now while the industry as a whole is overbuilding expensive, depreciating infrastructure.
Yeah, wooho, new model found a bunch of bugs, now the bad guys can do it too so security expenses spiked! It's only good for shovel sellers.
I fully agree with this article - please let's skip the chapter of closed and enshittified AI and go for the good stuff directly!
Of course we do have basically open source research programs, including most universities and big projects like CERN. But AI grew up in universities until it transpired that sufficient capital could only be found in the private sector.
It would be possible to make a decent publicly funded AI research program. But it would look more like the Manhattan or Apollo projects (which frontier labs already model themselves after) than some extra research grants for universities.
_LEAN_ FOSS, including the SDK then the computer languages too.
All computer languages with an ultra-complex syntax are excluded de facto.
Then there is the stability in time.
developer/vendor lock-in on software, planned obsolescence, are much more common in FOSS nowdays.
— Governments, companies, nonprofits should invest in free, open source.
Open weights + deterministic orchestration feels like the only sane long-term bet.