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24-year-old fund manager's annual return is 24 times! His AI investment portfolio targets the "most scarce resource"
Former OpenAI researcher Leopold Aschenbrenner, through his fund, doubled $225 million to $5.5 billion. He pointed out that the bottleneck in AI development lies in electricity, not chips or models.
Former OpenAI safety researcher, 24-year-old German Leopold Aschenbrenner, took less than a year to turn his $225 million fund, Situational Awareness, into $5.5 billion. While Wall Street funds flooded into AI models and chip companies, he identified a overlooked blind spot: power. By precisely betting on infrastructure solutions to address AI’s electricity consumption, he achieved astonishing investment returns.
Leaving OpenAI to enter the AI investment market
After leaving OpenAI, Aschenbrenner wrote a 165-page report asserting that artificial general intelligence (AGI) will arrive sooner than most think, and that the ultimate winners won’t be the companies with the strongest AI models, but those that “control electricity.” To this end, he founded the hedge fund “Situational Awareness LP” and invested $875 million to acquire fuel cell company Bloom Energy.
This week, Bloom Energy announced a $2.8 billion gigawatt (GW) fuel cell deal with Oracle, causing its stock to surge 15% after hours, and the paper value of Aschenbrenner’s holdings instantly soared to nearly $2 billion.
Portfolio reveal: Long infrastructure, short traditional IT industry
The news indicates that his investments follow the “power first” logic:
The power-hungry monster behind computing: electricity consumption doubles annually
Aschenbrenner states that, looking back at 2022, training GPT-4’s compute cluster consumed about 10 megawatts (MW) of power, costing around $500 million. However, AI compute demand is expanding at roughly half a magnitude per year, meaning the largest training clusters’ power needs double every 12 to 18 months.
By 2024, the largest compute clusters will consume up to 100 MW—equivalent to running 100k high-end GPUs simultaneously. By 2026, leading training clusters will require up to 1 gigawatt (GW) of continuous power, comparable to a large nuclear reactor’s output.
Who controls electricity, controls AI’s future
He estimates that by 2028, AI training power consumption will soar to 10 GW—more than the total electricity generated by many U.S. states; by 2030, it could reach 100 GW, consuming 20% of the current U.S. total power generation. This is only for “training” models; when including the inference power used for public deployment, the electricity demand becomes even harder to imagine.
However, U.S. total power generation has only grown marginally by 5% over the past decade. Reports of transformer shortages and stalled data center construction prove that the grid cannot keep up. This is also why he dares to heavily back Bloom Energy: the real bottleneck in AI development isn’t chips or software, but whether humans can produce enough electricity.