Many users praised analyses claiming a shift to cheaper AI models will boost infrastructure margins and token demand via Jevons Paradox, calling the points excellent and expressing optimism about future adoption and growth.
Based on 24 visible X reactions from 130 accounts; directional sample.
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@GavinSBaker Great Post, makes perfect sense. The more advanced frontier models and opensource counterparts get within the next 12-24 months, the more applications for „good enough“ opensource models arise with better cost/perf ratio + certain enterprise data need sovereign/local solutions
@GavinSBaker @sarthakgh @Creditisoxygen Completely agree with Gavin
@GavinSBaker Great point
@GavinSBaker Excellent point
The mega bull case for AI infrastructure would be *if* market share shifted away from certain frontier labs with 90%+ inference margins toward cheaper models, whether open-source or closed. It would increase the ROI on AI spend for end customers by increasing intelligence per dollar, which would drive incremental token demand. Margin dollars would effectively get redistributed from the frontier labs to AI infrastructure providers. The infra winners would be those with the lowest per token cost and the winners at the model layer would be those with the highest token efficiency. There are many reasons Jensen is so focused on open source, but this is likely the most important one as I think he is probably less worried about a monopsony these days. Lower margin % at the model layer = more margin $ at the infra layer all else equal. With SpaceX and Meta being vertically integrated and possessing the #3 and #4 models respectively it is more possible than ever. Note that Grok 4.5 is ahead of Fable for some useful tasks at a much lower cost, so ranking them #3 is conservative. This is not happening yet. Cheap, mostly open source tokens are likely the majority of volume today but the majority of economic value is still accruing to the most intelligent models. Might change though. We will see.
Cheaper tokens trigger Jevons Paradox, driving massive demand shift to infrastructure.
@GavinSBaker Great Post, makes perfect sense. The more advanced frontier models and opensource counterparts get within the next 12-24 months, the more applications for „good enough“ opensource models arise with better cost/perf ratio + certain enterprise data need sovereign/local solutions
The mega bull case for AI infrastructure would be *if* market share shifted away from certain frontier labs with 90%+ inference margins toward cheaper models, whether open-source or closed. It would increase the ROI on AI spend for end customers by increasing intelligence per dollar, which would drive incremental token demand. Margin dollars would effectively get redistributed from the frontier labs to AI infrastructure providers. The infra winners would be those with the lowest per token cost and the winners at the model layer would be those with the highest token efficiency. There are many reasons Jensen is so focused on open source, but this is likely the most important one as I think he is probably less worried about a monopsony these days. Lower margin % at the model layer = more margin $ at the infra layer all else equal. With SpaceX and Meta being vertically integrated and possessing the #3 and #4 models respectively it is more possible than ever. Note that Grok 4.5 is ahead of Fable for some useful tasks at a much lower cost, so ranking them #3 is conservative. This is not happening yet. Cheap, mostly open source tokens are likely the majority of volume today but the majority of economic value is still accruing to the most intelligent models. Might change though. We will see.
Jevons Paradox is about to hit AI harder than almost any industry we have seen before. People once thought faster internet would simply let us load the same websites more quickly. That was not even close to what happened. Faster connections created video streaming, cloud software, online gaming, video calls, social media, and entire businesses that could not exist on slow internet. Every increase in speed created new reasons to use more bandwidth. AI will work the same way. Today, we mostly use models for chat, coding, search, writing, and a few business workflows. But once intelligence becomes cheap enough, fast enough, and reliable enough, it will be built into every process that involves a decision. The biggest AI workloads probably do not exist yet. They are waiting for the cost of intelligence to fall. It will create millions of new tasks that are currently too slow, too expensive, or simply impossible.
🫡 https://twitter.com/GavinSBaker/status/2076369936251851091
Many users praised analyses claiming a shift to cheaper AI models will boost infrastructure margins and token demand via Jevons Paradox, calling the points excellent and expressing optimism about future adoption and growth.
Based on 24 visible X reactions from 130 accounts; directional sample.
Ask a question below.
Published answers will appear here.
Jevons Paradox is about to hit AI harder than almost any industry we have seen before. People once thought faster internet would simply let us load the same websites more quickly. That was not even close to what happened. Faster connections created video streaming, cloud software, online gaming, video calls, social media, and entire businesses that could not exist on slow internet. Every increase in speed created new reasons to use more bandwidth. AI will work the same way. Today, we mostly use models for chat, coding, search, writing, and a few business workflows. But once intelligence becomes cheap enough, fast enough, and reliable enough, it will be built into every process that involves a decision. The biggest AI workloads probably do not exist yet. They are waiting for the cost of intelligence to fall. It will create millions of new tasks that are currently too slow, too expensive, or simply impossible.