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
