🤖 AI × Crypto🟢 Bullish

Why Leopold Aschenbrenner’s AI hedge fund is betting big on power companies and bitcoin miners to fuel the ‘superintelligence’ race

Fortune Crypto|Sharon Goldman|March 5, 2026 at 08:00 AM

Image via Fortune Crypto

🤖AI Summary

Leopold Aschenbrenner's hedge fund is making significant investments in power companies and bitcoin mining firms, viewing them as key infrastructure plays for the AGI development race. New filings reveal his strategy of betting billions on electricity and AI infrastructure companies that will fuel superintelligence development.

Key Takeaways

→Former OpenAI researcher Leopold Aschenbrenner is translating his AGI thesis into large-scale investment bets.
→His hedge fund is focusing heavily on power companies as critical infrastructure for AI development.
→Bitcoin mining companies are being targeted as key beneficiaries of the superintelligence race.
→The investment strategy centers on electricity and AI infrastructure as fundamental enablers of AGI.
→Filings show billions of dollars being deployed across these strategic sectors.

Mentioned Tokens

$BTC$72,531▲+1.9%

Let AI manage these →

Non-custodial · Your keys, always

Mentioned in AI

Companies

OpenAI→

#aschenbrenner #hedge-fund #agi #superintelligence #power-companies #bitcoin-mining #ai-infrastructure #openai #electricity #investment-strategy

Read Original →via Fortune Crypto

Act on this with AI

This article mentions $BTC.

Let your AI agent check your portfolio, get quotes, and propose trades — you review and approve from your device.

Connect Wallet to AI →How it works

AI × Crypto1h ago

IREN to expand processing capacity by 50%, prepares at-the-market offering

IREN announced plans to expand processing capacity by 50% through ordering over 50,000 Nvidia GPUs and filed for a potential $6 billion at-the-market share offering. The news caused the company's stock to decline in pre-market trading despite the significant capacity expansion plans.

AI × Crypto6h ago

A Multi-Dimensional Quality Scoring Framework for Decentralized LLM Inference with Proof of Quality

Researchers developed a multi-dimensional quality scoring framework for decentralized LLM inference networks that evaluates output quality across multiple dimensions including semantic quality and query-output alignment. The framework integrates with Proof of Quality (PoQ) mechanisms to provide better incentive alignment and defense against adversarial attacks in distributed AI compute networks.

AI × Crypto6h ago

Zero-Knowledge Federated Learning with Lattice-Based Hybrid Encryption for Quantum-Resilient Medical AI

Researchers introduce ZKFL-PQ, a quantum-resistant cryptographic protocol for federated learning in medical AI that combines zero-knowledge proofs, lattice-based encryption, and homomorphic encryption. The protocol achieves 100% rejection of malicious updates while maintaining model accuracy, addressing vulnerabilities from gradient inversion attacks and future quantum threats.