Startups & Funding

Etched hits $5B valuation with $1B in AI chip orders

Etched emerges from stealth with $1B in orders and a $5B valuation

Deep Dive

Etched, an Nvidia competitor, issued a progress report revealing $1 billion in contract orders for its frontier inference clusters—bundles of custom chips, racks, and software. The chips, manufactured by TSMC earlier this year, are currently being tested with customers. The startup claims these systems run inference faster, cheaper, and with better power efficiency than rivals. Etched has raised $800 million to date, including a $500 million round in December at a $5 billion post-money valuation. Notable investors include VentureTech Alliance, Jane Street, Hudson River Trading, Two Sigma, Ribbit Capital, and Stripes, plus angel investments from AI luminaries like Andrej Karpathy, Geoffrey Hinton, Fei-Fei Li, and billionaires Stanley Druckenmiller and Peter Thiel.

Despite today's success, Etched's journey began with rejection. In 2023, founders Gavin Uberti and Robert Wachen—both Harvard dropouts and Thiel Fellows—struggled to raise capital, nearly running out of cash. They pitched a 30-page memo arguing that AI would need specialized chips, but major investors passed. The fundraising environment has since flipped; investors now chase AI chip startups, especially those solving inference bottlenecks. Etched joins competitors like Cerebras and Groq, while hyperscalers Amazon, Google, and Microsoft build their own chips. Even OpenAI recently announced a custom chip for inference, highlighting the growing demand for specialized hardware.

Key Points
  • Booked $1 billion in orders for frontier inference clusters combining chips, racks, and software
  • Raised $800 million total, including a $500 million round at a $5 billion valuation led by Stripes
  • Investors include AI heavyweights Andrej Karpathy, Geoffrey Hinton, Fei-Fei Li, and billionaires Druckenmiller and Thiel

Why It Matters

Inference efficiency is critical; Etched's specialized chips could dramatically cut costs for AI companies serving millions of users

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