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Etched Series C valuation hits $10.3bn with Sequoia leading, and a bigger round may follow. Etched's Series C valuation has landed at $10.3 billion after the AI chip startup closed a $300 million round led by Sequoia, co-founder and COO Robert Wachen confirmed. That is roughly double the $5 billion valuation the company carried in December, achieved in about seven months. Not bad for a firm that was, until recently, best known for the scepticism it attracted. Andreessen Horowitz, SK Hynix, Jane Street, and Diffusion Capital joined the round alongside earlier backers. The company says this is the highest valuation ever for a Sequoia-led Series C, a claim it has volunteered cheerfully and which, for now, nobody has publicly disputed. More investors than you might expect. The investor list has been building quietly for a while. When Etched emerged from stealth, it disclosed over $800 million raised across multiple unannounced rounds prior to this Series C, with earlier backers including Hudson River Trading, Jump Trading, Two Sigma, Ribbit Capital, Radical Ventures, Stanley Druckenmiller, Fei-Fei Li, and Arthur Mensch, among others. Total capital raised is now over $1 billion. Names like Andrej Karpathy, Geoffrey Hinton, and Peter Thiel also appear on the cap table. Wachen's explanation for that concentration of AI credibility is straightforward: 'These are all people who actually tried the hardware and are very excited about it.' Private demos in the company's office, apparently, do a lot of the work that press releases cannot. And if the Wall Street Journal's reporting is accurate, the fundraising is not done. Etched is reportedly in talks for a separate, larger round at approximately $20 billion valuation, led by existing investor Jane Street. That would be a separate transaction from the $300 million Series C, not a continuation of it, and would roughly quadruple the December valuation in under a year. Etched Series C valuation built on a specific technical bet. The company's pitch rests on a fairly specific architectural argument. Etched built two custom components targeting the two stages of AI inference: prefill, which processes the incoming prompt, and decode, which generates the output tokens. For prefill, Etched built a chip that operates at lower voltage than competing AI chips. Lower voltage means less heat, which allows more transistors to be packed in, and Wachen says the result is dramatically faster prefill performance. For decode, the company developed what it calls cluster scale memory: a proprietary interconnect that lets many chips share a single low-latency memory pool at high bandwidth. The claimed outcome is faster inference at lower cost per token. There is also a persistent misconception Wachen is eager to dispel. The systems are not locked to a single model type. They can run Mixture of Experts architectures like DeepSeek and Qwen, as well as non-transformer designs like Mamba, which uses a state-space model rather than the transformer backbone. The chips are sold as full rack systems, not standalone silicon. The first batch of chips was manufactured by TSMC, and Etched has reported $1 billion in booked orders. Client testing of the first full systems is under way, though mass production and delivery of rack systems is still ahead. From a garage server rack to a 10-megawatt facility. The company's operational footprint has grown considerably from its early days, when the founders ran chip-design tools on servers in an employee's garage, rebooted remotely by his wife when needed. Today, Etched employs 400 people across offices in San Jose and operates a 2-megawatt data centre on site. According to IndexBox, citing EE Times, the company has also opened a separate research and development facility in Milpitas, California, housing a 10-megawatt data centre, a lab, and a quick-turn surface-mount technology line. For context on the IP side, PitchBook's company profile shows Etched holds at least four active or pending patents related to tensor operations in AI models, with the first filings dated October 2024. The founders, CEO Gavin Uberti, Wachen, and CTO Chris Zhu, dropped out of Harvard in 2022 to launch the company before most of the industry had figured out that transformer-specific silicon was worth building. Google is now reportedly pursuing a similar concept with its Frozen v2 chip for Gemini, which suggests the original thesis is not looking quite as wacky as it once did. 'I think we still have to be humbled by what it will take to actually get to scale,' Wachen said. Fair enough. But getting to $10.3 billion before the product has fully shipped is, at minimum, a credible start. The next number to watch is whether that reported $20 billion round closes, and whether it does so before the rack systems reach mass production. Marcus Hale has been filing general news for the better part of fifteen years. He started at a regional evening paper, moved to a mid-sized digital outlet covering UK news, and spent three years as a general assignment reporter before going freelance. He has covered inquests, council elections, infrastructure announcements, and the kind of stories that sit on page five but matter on page one. He writes about public services, housing, local government, and the institutional stories that take six months to develop and thirty seconds to read. He prefers facts to angles and considers that unfashionable. Marcus lives in Bristol. He still reads the local paper and thinks that makes him an endangered species. July 27, 2026

How AI is changing careers in electronic trading and HFT. 23 July 2026 AI is changing careers in quant finance, and not just for your average quant in a bank. Electronic trading firms and market makers have spent years hoovering up elite talent, and AI is making them even more productive. It might also make that same talent more likely to leave... How do electronic trading firms use AI? AI is a broad field and quant firms have been using it for a long time. Machine learning tools like natural language processing (NLP) and computer vision have been used by quants for decades; they allow quants to look for specific signals in large datasets that tell them whether to buy or sell an asset. NLP, for example, can analyze millions of social media posts and search for specific keywords, grammar or other signals to extract broad consumer sentiment on a specific stock. Reinforcement learning, a technique where an AI model uses simulated trial and error to assess the optimal course of action, can be used to develop execution algorithms that make trades at the most efficient time. Similar techniques can be used to analyze post trade data in order to improve the efficiency of models going forward. They can also be used by quant risk teams to ascertain how much capital should be given to each trader. In 2026, however, AI is reaching a whole new level in electronic trading firms. Jane Street has encouraged traders to build trading strategies using Python since at least 2024. It says it's particularly fond of the PyTorch library (instead of other options like TensorFlow) as it allows traders to iterate through different strategies very quickly. Burgeoning languages like Mojo are also under consideration. As AI takes over, the nature of the work has not changed, but the volume of data and speed of analysis has gone parabolic. The GPUs facilitating the analysis have become more powerful, and large language models can ingest and structure data much more efficiently, meaning that that traditional machine learning methods can be applied to larger datasets for richer analysis. Many trading firms have massively grown their personal GPU clusters which facilitate their machine learning strategies. Jane Street seems to have doubled its number of GPUs in the past year and now has tens of thousands of them. Quadrature, a smaller AI trading firm paying millions per head, had ~20,000 GPUs in November, equivalent to roughly 11 chips per employee. XTX Markets has one of the most impressive clusters; open job listings say the market maker has "25,000 GPUs with 650 petabytes of usable storage." Trading firms have rapidly adopted AI tools while also building their own. At Hudson River Trading, staff are spending $1k a day on tokens and are being celebrated for it. Jane Street, meanwhile, has had difficulty adopting off-the-shelf coding tools because it uses OCaml, a relatively niche language; John Crepezzi, a Jane Street engineer who develops its agentic tools, said in a talk last year that there's more OCaml code in Jane Street than there is in the rest of the world combined. Trading firms have made massive investments in AI beyond their traditional business models by building out venture capital arms. Jane Street, for example, has a stake in Anthropic. Investments by quant firms aren't AI specific, though; Jump Trading and Susquehanna have both invested in prediction markets platforms like Kalshi and Polymarket. Which roles in trading firms are safest from AI? Which are most under threat? Annanay Kapila, a former high-frequency trader who now runs startup exchange QFEX, told eFinancialCareers Ltd. that "the best people will harness AI very well, become super productive and make tonnes of money." Roles in research, infrastructure and development are already starting to converge as firms want talent who can fluidly work across different domains rather than becoming siloed. Annanay said that "genuine, idiosyncratic human alpha will become more elevated and more productive." Traders who incorporate human-driven decision making are "probably the most protected" while purely systematic modelling roles are under threat. "[Claude] Fable has become really good at quant research," Kapila said. "It's able to spin up all the basic pieces of a trading strategy very quickly and identify a bunch of patterns." People who source data for their firms will also be safe, if not more valuable. "Getting access to data faster or getting access to data that other people don't have is going to become really important," Kapila said. Everyone has access to the Bloomberg terminal, but these professionals are focused on obtaining obscure data like "video footage outside French power plants checking how much smoke is going out of the chimney." In most industries, junior roles are being hurt the most by AI. This is not true for junior quants in market making firms, where the bar has always been high. "Unlike engineering, you can't make an impact as a junior unless you're really sharp," Kapila said. Junior quants use AI to speed up the process of modelling and writing trading algorithms, allowing them to test more ideas more quickly. As with any job that involves coding, experience with AI coding tools is fundamental. However, a senior quant recruiter, speaking anonymously, told eFinancialCareers Ltd. that "one of the biggest concerns for elite trading firms right now is candidates becoming too dependent on AI and losing their core coding and problem solving abilities." The talent war between trading firms and AI labs. The biggest impact on jobs in electronic trading firms is less about AI tools and more about the firms building them. AI labs like Anthropic and OpenAI have been open about their pursuit of talent from high-frequency trading firms. In 2024, $3m compensation packages were offered to HFT quants with just a few years of experience. Insiders say that there has been a significant brain drain from quant finance to AI, and that there hasn't been much traffic in the opposite direction. Today, that same pool of talent is still valuable. Anthropic openings for research engineers which request experience in quant finance (or at a rival AI lab) offer up to $850k in salary alone. You'll often get paid much more in equity, which can balloon in value as the labs continue to grow. One OpenAI employee told Business Insider last week that they own $50m in OpenAI stock despite working there for just three years due to its swelling valuation. For many candidates, the money isn't even the issue. Candidates eFinancialCareers Ltd. has spoken to have said they prefer working in AI labs because of the opportunity to feature in academic publications, and an alignment with the mission of reaching AGI. In quant funds, much of your best work will be kept proprietary. How does the work compare? Grant Stenger, an ex-Jane Street trader speaking to FT Alphaville earlier this year, said that "the core job is actually identical;" you'll use data to build a model, execute it within a constrained environment, and use feedback to enhance the model in future iterations. In terms of culture, quants say you tend to work fewer hours in finance as your work revolves around the markets, but AI lab staff say that the long hours are a good thing for them because of their commitment to the cause. Have a confidential story, tip, or comment you'd like to share? 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