Situational Awareness, summarized — and graded

What the essay argues

Leopold Aschenbrenner's 165-page essay is built on extrapolating orders-of-magnitude (OOM) trends. The chain of reasoning: compute keeps scaling, algorithms keep improving, models get "unhobbled" (tools, agents, reasoning) — and those combine to reach AI that can do the work of an AI researcher around 2027. That triggers an intelligence explosion (2027–29) and superintelligence in the 2030s, with national security inevitably taking over via a government "Project."

The key predictions

• AGI by 2027 — AI at the level of an automated AI researcher
• Intelligence explosion 2027–29 — AI automating AI research, compressing a decade into a year
• Superintelligence in the 2030s
• Compute scaling ~0.5 OOM/yr and trillion-dollar capex
• Open source fades; proprietary algorithms form a durable US moat
• A US government AGI project ("The Project") by 2027/28

How the predictions are holding up

The bottom line

The input curves Aschenbrenner bet on — compute, capex, raw capability — have largely held or exceeded expectations. His biggest miss is on diffusion: open source did not fade. The defining claim, AGI by 2027, resolves by January 2028.

Frequently asked questions

It's Leopold Aschenbrenner's June 2024 essay arguing AGI is plausible by 2027, followed by an intelligence explosion and superintelligence in the 2030s, driven by compute scaling, algorithmic gains, and 'unhobbling.'

AGI by 2027, an intelligence explosion 2027–29, superintelligence in the 2030s, continued compute scaling, trillion-dollar capex, open source fading, and a US government AGI project.

Partly. Its compute, capex, and capability predictions have largely held; its open-source prediction is wrong; and its defining AGI-2027 claim is still open, resolving by January 2028.