My guest today is Jonathan Siddharth, co-founder and CEO of Turing.

Jonathan incubated Turing in Foundation Capital’s Palo Alto office in 2018. Since then, it has grown into a multi-billion dollar company that powers nearly every frontier AI lab: OpenAI, Anthropic, Google, Meta, Microsoft, and others. If you’ve seen a breakthrough in how AI reasons or codes, odds are Turing had a hand in it.

Jonathan has a provocative thesis: within three years, every white-collar job, including the CEO’s, will be automated. In this episode, we talk about what it will take to reach artificial superintelligence, why this goal matters, and how the agentic era will fundamentally reshape work. We also dig into his founder journey: what he learned from his first startup Rover, how he built Turing from day one, and how his leadership style has evolved to emphasize speed, intensity, and staying in the details.

Jonathan has been at the edge of AI for years, and he has the rare ability to translate what’s happening at the frontier into lessons for builders today.

Hope you enjoy the conversation!

Chapters:

00:00 Cold open
00:02:06 Jonathan’s backstory: his experience at Stanford
00:06:37 Lessons from Rover
00:08:39 Early Turing: incubation at Foundation Capital and finding PMF
00:13:52 Why Turing took off
00:15:12 Evolving from developer cloud to AGI partner for frontier labs
00:16:49 How coding improved reasoning - and why Turing became essential
00:20:38 Founder lessons: building org speed and intensity
00:23:33 Why work-life balance is a false dichotomy
00:24:17 Daily standups, flat orgs, and Formula One culture
00:25:15 Confrontational energy and Frank Slootman’s influence
00:29:50 Positioning Turing as “Switzerland” in the AI arms race
00:34:32 The four pillars of superintelligence: multimodality, reasoning, tool use, coding
00:37:39 From copilots to agents: the 100x improvement
00:40:00 Why enterprise hasn’t had its “ChatGPT moment” yet
00:43:09 Jonathan’s thoughts on RL gyms, algorithmic techniques, and evals
00:46:32 The blurring line between model providers and AI apps
00:47:35 Why defensibility depends on proprietary data and evals
00:55:20 RL gyms: how enterprises train agents in simulated environments
00:57:39 Underhyped: $30T of white-collar work will be automated

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