ClaimVideo · 53:32 — 55:02

The thesis that cheap open-source models would close the gap on frontier models and capture most AI economic value has been decisively wrong; frontier models have captured roughly 90% of AI revenue even though open-source models may account for the majority of raw tokens consumed.

Gavin states that despite predictions open-source models would erode frontier revenue share, frontier models have actually captured the vast majority (~90%) of economic value this year, even though open-source models may handle most raw token volume. ✦ AI generated

Gavin Baker · BG2 Pod · 2026-06-11 · original ↗

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starts at this moment · 53:32

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“So, I just open it up to anyone around the table, what are your thoughts on whether or not, you know, have we challenged this thesis that cheap open-source tokens are going to always, you know, close the gap on these frontier models, or are they extending their leads?”

That has been decisively wrong. Probably more than 90%, and it may continue to be decisively wrong. Frontier might be 90% of the economic value. Open-source might be 80% of tokens.

verbatim transcript · starts at 53:32

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53:32>> That has been decisively wrong. Probably more than 90%, and it may continue to be decisively wrong. Frontier might be 90% of the economic value. Open-source >> might be 80% of tokens. Something that I think is very important on open source is that you know, I think there's this belief that it's bearish for AI. It's actually it may be very bearish for the frontier models. There's that bear

53:54case you talked about. It's actually really bullish for compute and hardware because if the frontier models are capturing less of the margin, then you're going to spend more on compute. So, the better open source does, the better it is for compute providers. >> And I yeah, I I will say it there is a very I would say between um spending time in the heart of like the West, Silicon

54:16Valley, and also spending time in Asia, there is like a very big um like a deep-seated belief in one versus the other, which is like if you spend a lot of time here, it's like all closed source, cloud, every all traffic is going to go, you know, by way of this direction. And then you spend time in Asia, you know, the the overwhelming belief is that we're going to find the right model to

54:40the right workload, and we're not going to overspend. >> Right. >> And I think, you know, I would say I would say the next year is probably going to be the most indicative of which way this falls um because I think I think the reason why uh closed source models have captured so much of the value is because um the models actually get the intention and actually carry through the work. And

55:05this is the first year where we actually had agents that actually carried out user intention from just answering a chatbot request to actually producing useful work. >> Right. >> Um now the the the level of this intelligent has scaled so rapidly, and we continue to push against like the most economically valuable tasks, which are coding and finance and all these like knowledge work tasks. But like for

55:29the long tail of tasks, if open source continues to maintain a 6-month lag, we might actually see a lot more open source used for you know, our everyday tasks that we might actually >> basically Jensen's argument, right? Jensen's argument is you're going to have model routing and we're just in a moment in time where the frontier models gain the advantage can do long-running tasks that open source models couldn't do it very well

▦ Anthropic ▦ OpenAI

Around this claim

Evidence · 1

Open source models just raise the floor of what people are interested in; data is most valuable at the frontier of model performance, not for capabilities that open models already handle.Oswald Nitski · 20VC · conf 90%

Counterpoint · 3

Open-source AI will diffuse economic value to everyone, just as open-source software did for the internet — preventing a future where a few companies capture all AI value.David Friedberg · All-In Podcast · conf 90%Open-source AI, like the open-source internet before it, will diffuse economic value to everyone rather than concentrating it in a few companies, and the real long game for China is to commoditize the knowledge economy so that their manufacturing and energy advantages become the dominant source of global value.David Freeberg · All-In Podcast · conf 85%Open source and decentralized AI training models, like BitTensor's subnet 62 Ridges AI, represent a disruptive orthogonal attack vector that could outcompete frontier labs by achieving near-frontier performance at a fraction of the cost through distributed contributions.Jason Calacanis and Chamath Palihapitiya · All-In Podcast · conf 80%

This moment responds to

supports → Open source models cannot displace frontier models like Anthropic's because frontier models drive measurably superior customer retention, revenue generation, and user engagement that cheaper open-source alternatives simply cannot match.Matt Murphy · 20VC rebuts → Closed frontier AI labs like OpenAI and Anthropic have built a business model that is really a 'toll road' — charging per use to justify massive valuations — and this model is not economically sustainable once cheaper open alternatives exist.Zeve Farman · The Cognitive Revolution explains mechanism → China's strategy is to keep its AI models open-source until they catch up to the frontier, and then switch them to closed-source to capture the value for themselves — mirroring exactly what Sam Altman did at OpenAI.David Sacks · All-In Podcast rebuts → China's strategy is to keep its AI models open-source until they catch up to the frontier, and then switch them to closed-source to capture the value for themselves — mirroring exactly what Sam Altman did at OpenAI.David Sacks · All-In Podcast