Ben estimated that only around 500,000 people globally currently use any form of agentic coding workflow, out of 8 billion people.
Snapshot · God Mode Podcast
Ben estimated that only around 500,000 people globally currently use any form of agentic coding workflow, out of 8 billion people.
Where this was said
At 19:30 · chapter starts 17:40
Ben pulls in the macro framing from a recent Gavin Baker podcast appearance. Baker's argument is simple and stark: demand for AI tokens is growing roughly 10x over the next year, but data center commitments and supply-side build-out will only deliver around 3x more capacity. Something has to give — either labs cut pre-training compute to free up inference supply, or prices go up. Baker's view is that labs will not cut pre-training, because their investor stories are all built on reaching AGI, and you only reach AGI by continuing to pour resources into the next frontier model. Ben notes that both Fable and Claude are now at their sixth generation of models, converging in parallel. With only about 500,000 people globally using agentic coding workflows today against 8 billion potential users, the demand curve is almost incomprehensibly steep. The hosts' conclusion: the inference layer — companies like Cerebras, and the OpenRouter routing layer recently bid on by Stripe — is where the investment value will accumulate.
Gavin Baker argued on Invest Like the Best that AI token demand is growing 10x next year while supply is only growing 3x, implying prices must rise.
Investor Gavin Baker laid out a stark supply-demand mismatch for AI tokens: demand is growing 10x next year while supply grows only 3x. Frontier labs won't cut pre-training to free up capacity, so consumer prices are heading up.
Average AI intelligence will eventually be free, and frontier models will command a premium. The real investment opportunity isn't in the labs subsidising tokens with VC money — it's in the inference providers routing cheap models at scale.
Quickly forming opinions on how the Twitter algorithm and platform worked allowed the speaker to grow rapidly on the platform.
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The ImageNet dataset collected 15 million images to drive machine learning, becoming a cornerstone of the modern AI revolution.
A Stanford graduate student benchmarked human performance on the ImageNet 1,000-category challenge at roughly 4% error rate, a figure AI surpassed by 2016.
From the 2012 ImageNet breakthrough, it took only about 3–4 more years for AI algorithms to surpass human performance in naming 1,000 object categories.
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