The Engineer Who Went Long on Everything
In 2010, a former Intel software lead walked out of Stanford GSB and into the first Felicis Ventures fund meeting with $41 million to deploy. The fund was small. The bets were not. Fifteen years later, Sundeep Peechu is sitting on a $900 million fund - the firm's tenth - and the portfolio behind him looks like someone curated the defining companies of two technological eras.
The thing about Peechu is he never stopped being an engineer. He coded at Sarvega before Intel bought it. He thinks in systems, in infrastructure, in what happens when the underlying layer shifts. When he backed Plaid in the early days, he wasn't betting on a fintech app. He was betting on the plumbing that every fintech app would eventually need. Same with Supabase. Same with Weights and Biases. Same with the dozens of companies in his portfolio that most people didn't hear about until they were already massive.
The goals for tech should be people in 2070 saying 'I'd rather be a peasant today than a king 50 years ago.'
- Sundeep PeechuThe Long Game
Here is what Peechu keeps saying, in tweets and on panels and in interviews: most people misunderstand how venture returns work. They picture a quick flip, a hot IPO, a three-year payday. But when he talks about Adyen and Shopify - two companies he uses as proof of something - he points to year nine, when Adyen had $80 million in revenue and Shopify had $150 million. By most metrics, those were good but not legendary numbers. Then they compounded. Nearly all of the legendary status accrued in the second decade.
That's not a philosophy that sounds exciting at a cocktail party. It's the kind of thing that separates the funds that matter from the funds that make noise. Peechu has been at Felicis since the first external fund. He wasn't chasing headlines. He was building a body of work.
His own career has operated the same way. Born in India, he made it to IIT Madras - a school that accepts a fraction of a percent of applicants - then to the University of Illinois for a master's in computer science, then to a startup in Chicago building XML security infrastructure. His parents lived apart for over a decade so he could receive a better education. That's not trivia. It's context. When Peechu writes a check to a first-generation founder building something improbable, he's not performing solidarity. He knows the arithmetic personally.
From Sarvega to Felicis: The Intel Years That Weren't Wasted
Most venture origin stories skip the middle. Peechu's middle is instructive. After Sarvega got acquired by Intel in 2005, he stayed. Spent years in product and technical lead roles inside one of the world's biggest technology companies. He watched how enterprise software actually gets adopted - slowly, with procurement cycles and IT committees and integration nightmares. He watched how infrastructure decisions made at a big company ripple out for a decade.
Then he went to Stanford, did the MBA, and came out on the other side convinced that the founders who understand the full stack - product, distribution, infrastructure, enterprise sales dynamics - are the ones who build durable companies. That's the lens he brings to Felicis. Not just "is this product exciting?" but "does this person understand every layer of the problem they're solving?"
In five years, I'd be shocked if people say 'I'm investing in AI' - it is just going to be the foundational layer of almost everything.
- Sundeep Peechu, on AI as a categoryThe AI Thesis He's Been Running Since Before It Was Fashionable
Felicis started investing in AI companies before "AI investing" was a competitive category. Runway. Poolside. Weights and Biases. Deep Infra. Flower Labs. Each investment has a logic: infrastructure before applications, tools before outcomes, the layer that everyone else will eventually depend on.
In September 2024, Peechu published a thesis that cut through a lot of noise: AI is catalyzing a long-awaited enterprise software promise. Not because AI is clever. Because it's shifting the market from SaaS, where you pay for access to software, to AI co-pilots and autopilots, where you pay for outcomes. That shift, he argued, increases market sizes by 10x or more for the startups that get it right. Traditional SaaS has a ceiling. AI-native software doesn't have one yet.
After surveying more than 40 Chief Information Security Officers, he made another call that got attention: AI security will be bigger than cloud security. Not a prediction for the distant future. A structural observation about what enterprises will need to spend money on as AI becomes critical infrastructure.