As many of you know, this weekend I appeared on the Lex Fridman Podcast with my friend Dylan Patel of SemiAnalysis to cover DeepSeek and the implications on the AI ecosystem. I recommend you check it out.

This post was tricky to pull together. I decided to share it anyways given the timeliness of the topic and other more exciting things I have to get to. The minor, thematic contradictions on motivations, costs, and trajectories are exactly indicative of why analysis and productionization of open-source AI is so hard. In that, it is a valuable lesson that building open-source AI will come with a lot of ups and downs, but now is the best time to do so.

The DeepSeek moment represents the end of the first chapter of AI's recent takeoff as told through the emergence of ChatGPT. It reminds us, that while substantial resources, coalitions, brands, and trends have been established, the narratives we have been championing are not set in stone. DeepSeek, especially with R1, resets all the narratives around open vs closed, US vs China, scaling and commoditization, etc. as we prep for yet another acceleration in the diffusion, progress, and adoption of AI.

Of all of these debates, the focus on open vs. closed AI models is the one least driven by economic factors and most driven by vibes. The open-source AI community is driven by a future vision where AI is not held by a few super-rich companies, a future where more people get to partake in the building of AI, a future where AI is safer, etc. These are ideals and building the tools and systems that make this vision a reality is a monumental challenge. Building strong AI models is far, far easier than building a sustainable open-source ecosystem around AI.

Building a better, truly open ecosystem for AI has been my life’s work in the last years and I obviously want it to flourish further, but the closer you are to the core of the current open-source ecosystem, the more you know that is not a given with costs of doing relevant AI training skyrocketing (look, I know DeepSeek had a very low compute cost, but these organizations don’t just fall out of the tree) and many regulatory bodies moving fast to get ahead of AI in a way that could unintentionally hamper the open. Yes, efficiency is getting better and costs will come down, as shown with DeepSeek V3, but training truly open models at the frontier isn’t much easier.

Building the future ecosystem of open

As a perfect case point, consider Meta. Meta, as a platform serving content to billions of users, is extremely well-positioned to use AI to make its services more engaging and more profitable for advertisers. The Llama project is not needed for that vision. Yes, it will be easier for them to integrate and optimize an AI that they train, but in a world where AI models are commoditized, what’s the point? The most compelling reasons for openly releasing the Llama models are not business reasons but rather ideological reasons. Mark Zuckerberg revisited this on the recent Meta earnings call:

I also just think in light of some of the recent news, the new competitor DeepSeek from China, I think it’s one of the things that we’re talking about is there’s going to be an open source standard globally. And I think for our kind of national advantage, it’s important that it’s an American standard. So we take that seriously and we want to build the AI system that people around the world are using and I think that if anything, some of the recent news has only strengthened our conviction that this is the right thing for us to be focused on.

The pro-America messaging from Zuckerberg long predates the new administration (especially given that all of Meta’s major apps are banned in China), even if the language is amplified now. This is purely an argument of “we are doing this because we should.”

This argument is extremely similar to that used by DeepSeek AI’s CEO Liang Wenfeng. In an interview translated by ChinaTalk, Wenfeng described the need for Chinese leadership in open-source AI (in addition to a clear commitment to keep releasing models openly).

Liang Wenfeng: Because we believe the most important thing now is to participate in the global innovation wave. For many years, Chinese companies are used to others doing technological innovation, while we focused on application monetization — but this isn’t inevitable. In this wave, our starting point is not to take advantage of the opportunity to make a quick profit, but rather to reach the technical frontier and drive the development of the entire ecosystem.…We believe that as the economy develops, China should gradually become a contributor instead of freeriding. In the past 30+ years of the IT wave, we basically didn’t participate in real technological innovation. We’re used to Moore’s Law falling out of the sky, lying at home waiting 18 months for better hardware and software to emerge. That’s how the Scaling Law is being treated.But in fact, this is something that has been created through the tireless efforts of generations of Western-led tech communities. It’s just because we weren’t previously involved in this process that we’ve ignored its existence.

The interview has many other comments making it clear that the way this will be done is by training powerful AI and releasing it for the world to use.

Both of these arguments, from Zuckerberg and Wenfeng, rely on the optimism that we, as a community of users of open AI models, will figure out how to create a valuable ecosystem around them. Right now, the vast majority of AI usage for applications comes through various API calls. Yes, some of this includes the usage of open-weight models like Llama and DeepSeek R1, but it does not give clear positive attribution to the fact that the model was open as a reason said the model was used.

The nationalistic comments regarding open-source AI are only likely to grow stronger as governments more deeply integrate with their leading AI companies.

One of the main arguments why American AI leaders believe that the AI ecosystem should be built on a Western foundation is the risk of China “poisoning the well” of our future computational infrastructure. To be very clear — there is absolutely no evidence of this to date, but it is a simple proposition that the Chinese Communist Party (CCP) could build ties to the leading Chinese AI laboratories and require them to train for specific behaviors or train in some sort of back door through model weights into American infrastructure.

America has been reeling with the potential of this sort of influence on TikTok. If AGI is to be a real thing that can be steered to ideological outcomes, a bill titled Protecting Americans from Foreign Adversary Controlled Applications Act (the bill banning TikTok and forcing a divestiture) will operate at entirely the wrong level of abstraction. American companies raced to host R1 in a competitive frenzy. This is how open-source works and it will be far easier to incentivize better open models from Western labs than it will be to ban companies from adopting Chinese technology.

As of the release of DeepSeek R1, Chinese AI companies didn’t have clear links to the government, but after said release, DeepSeek’s CEO met with the Chinese Premier Li Qiang (approximately second in command) to discuss their work.

AI is obviously far more in the radar of American leadership as a priority and has been for some time. This is a major advantage that the U.S. has in terms of a fast reaction to changing needs for open models.

In a recent Reddit AMA soon after his appearance on stage with Trump for the announcement of the Stargate project, CEO of OpenAI Sam Altman even acknowledged that their strategy “may be on the wrong side of history” here with respect to openly sharing AI components. OpenAI should get no credit until their actions change, but DeepSeek and a new government administration have made many forces re-evaluate their relationship to the open ecosystem.

The current imperative of open-source AI is to create feedback loops where open models become more useful than their closed counterparts. Given that AI is very expensive and slow to train, this cannot look like the accumulation of small security and reliability improvements like done with open-source software. There’s a chance that there is an algorithmic innovation that makes this possible, but for now, the solutions need to be more imaginative. Two examples I am currently interested in include:

* Feedback loops from d