Currently, only 5 companies in the world have access to frontier AI training compute and are also pursuing development of AGI (Google DeepMind, OpenAI, Anthropic, xAI, and Meta). This will still hold in 2026 for Google and OpenAI, and plausibly also for Anthropic, Meta, and xAI.

Stance towards trying to develop AGI can change, but the frontier AI training compute barrier is increasingly insurmountable for any company that doesn't already have impressive AI development accomplishments. In 2024, frontier compute was 100K H100s, and that cost about $5-7bn (it was still possible to use legacy air cooling infrastructure with H100s). In 2025, that's 100K chips in GB200 NVL72 racks, which costs $7-11bn. In 2026, OpenAI's Stargate Abilene sets the lower bound at 400K chips in NVL72 racks (GB200 or possibly GB300), which is a 1 GW datacenter campus that costs $35-45bn (though you can continue building out the 2025 [...]

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Outline:

(02:34) RLVR and Large World Size

(05:20) 2026

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Narrated by TYPE III AUDIO.