Etched, the AI chip startup founded by three Harvard dropouts in 2022, has closed a $300 million Series C funding round at a $10.3 billion valuation, doubling its worth in roughly seven months as demand for specialized inference hardware surges. Co-founder and chief operating officer Robert Wachen confirmed the round to TechCrunch on July 23, 2026.
The financing lands at a pivotal moment for the AI hardware sector, which has become a central front in the global technology race covered in our latest AI developments. The round was led by Sequoia, with Andreessen Horowitz, SK Hynix, Jane Street, and Diffusion Capital also participating, alongside earlier investors.
A Roster of Prominent Backers
Etched has assembled an unusually high-profile group of supporters for a company still proving out its first products. Other backers include Peter Thiel, former OpenAI researcher Andrej Karpathy, Figma co-founder Dylan Field, and Replit chief executive Amjad Masad. The company said the round represents the highest valuation ever for a Sequoia-led Series C.
The raise marks a steep climb from December, when Etched was valued at $5 billion after raising a $500 million round. Reaching a $10.3 billion valuation in about seven months reflects investor conviction that purpose-built inference chips could capture a meaningful slice of a market currently dominated by Nvidia's general-purpose GPUs.
From Design to Silicon
Etched's central bet is that chips engineered specifically to run transformer models, the architecture behind systems like ChatGPT and Claude, can deliver far more efficient inference than the general-purpose graphics processors that powered the first wave of the AI boom. The company faced skepticism from the start, with critics questioning whether hardware tailored to a single architecture could remain relevant as model designs evolve.
Last month, Etched announced it had successfully manufactured its homegrown chips, that its first full systems were being tested by clients, and that it had already booked $1 billion worth of orders. The company sells complete systems rather than standalone chips, positioning itself as a full-stack inference provider.
Wachen pushed back on the perception that Etched's products can only run specific large language models. He told TechCrunch that the systems can run any AI model, including Mixture of Experts architectures such as DeepSeek and Qwen, which split tasks across specialized sub-models rather than relying on a single large network.
Why Inference Hardware Is Heating Up
The funding round reflects a broader shift in the AI hardware market. While the past two years were defined by a scramble for the training GPUs needed to build models, attention is increasingly turning to inference, the process of running those trained models to actually generate responses. Inference accounts for the vast majority of real-world compute costs as AI products scale to millions of users.
That has opened the door for startups promising specialized silicon that can run models more cheaply and with lower energy consumption than incumbent hardware. Etched is competing not only with Nvidia but with a growing field of challengers, including established semiconductor giants and other venture-backed newcomers, all racing to capture a share of inference spending that analysts expect to balloon in the coming years.
A High Bar to Clear
Despite the strong fundraising, Etched still faces significant hurdles. Manufacturing custom chips at scale is enormously expensive and technically demanding, and the company must prove that its first systems deliver the performance and reliability its marketing promises. Competing with Nvidia also means contending with the market leader's deep software ecosystem, particularly its widely used CUDA platform, which has proven difficult to dislodge.
For now, investors are willing to pay up for the promise. A $10.3 billion valuation, $1 billion in orders, and backing from some of the most prominent names in technology signal that the market sees a real opportunity to reshape the silicon underpinning the AI economy.
Whether Etched can convert that promise into sustained advantage will depend on execution over the next two years, as its chips move from testing into widespread deployment.
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