“No one is data ready. Absolutely no one is data ready.” —Yash Patil
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Yash Patil is the 23-year-old founder and CEO of Applied Compute, a $1.3 billion company helping businesses train custom AI models on their own data: smaller, cheaper, and purpose-built for the work they actually do. Before founding the company, Yash dropped out of Stanford and spent two years at OpenAI working on post-training infrastructure and Codex. He left with one core conviction: every company that runs its critical workflows on someone else’s model is building on shifting sand. Applied Compute is his answer to that problem, already serving customers including DoorDash, Cognition, and Mercor.
In our conversation, we explore:
Why “own or be owned” is becoming existential for any company that relies on frontier AI models
What it was like inside OpenAI the weekend the board fired, and then reinstated, its CEO
Why post-training is where competitive advantage is now being built, and what reinforcement learning with verifiable rewards actually is
Why evals have become the new production environment, and why companies will never share them with frontier providers
How a specialized model built for DoorDash outperformed frontier models on a narrow, high-value task
Why cost, not capability, is now the primary driver pushing companies toward custom models
Why Yash believes AI’s transformation of the economy will unfold over decades, and why near-term fears about mass job displacement are misplaced
Thank you to the partners who make this possible
Brex: The intelligent finance platform.
Guru: The AI source of truth for work.
Persona: Trusted identity verification for any use case.
Explore the episode
Timestamps
(00:00) Introduction
(03:50) Fable 5 and the case for owning your own models
(09:22) Why Applied Compute is betting on custom AI models
(12:30) Yash’s early influences and first projects
(17:42) His brief time building at Stanford
(19:29) Leaving Stanford for OpenAI
(25:58) Inside OpenAI during Sam Altman’s firing
(28:18) What Yash admires about Sam Altman
(29:43) Teaching models to reason
(35:39) The core insight behind Applied Compute
(39:40) How Applied Compute works with its customers
(45:55) Why model training never ends
(48:56) Why not every task needs a frontier model
(51:25) The culture and people of Applied Compute
(54:50) Applied Compute’s training infrastructure
(58:43) The coming compute crunch and other predictions
(1:03:48) Final meditations
Follow Yash Patil
Website: https://yashpatil.me
LinkedIn: https://www.linkedin.com/in/yash-s-patil
Resources and episode mentions
Books
The Design of Everyday Things: https://www.amazon.com/Design-Everyday-Things-Revised-Expanded/dp/0465050654
People
Brendan Foody on X: https://x.com/BrendanFoody
Luke Metz’s website: https://lukemetz.com
Barret Zoph on X: https://x.com/barret_zoph
John Schulman on X: https://x.com/johnschulman2
Ian Osborne on LinkedIn: https://www.linkedin.com/in/ian-osborne
Other resources
Applied Compute: https://www.appliedcompute.com
Claude Fable 5 and Claude Mythos 5: https://www.anthropic.com/news/claude-fable-5-mythos-5
Goldman Sachs: https://www.goldmansachs.com/
Sarah Chieng’s post on X: https://x.com/MilksandMatcha/status/1983587401688870972
Science Olympiad: https://www.soinc.org
Chai Discovery: https://www.chaidiscovery.com
TreeHacks: https://treehacks.com
Thiel Fellowship: https://thielfellowship.org
Building a hill-climbing machine: Launching seven new MAI models: https://microsoft.ai/news/building-a-hillclimbing-machine-launching-seven-new-mai-models
Kirkland & Ellis: https://www.kirkland.com
Palantir: https://www.palantir.com
Automating Merchant Onboarding at DoorDash: https://www.appliedcompute.com/case-studies/doordash
Composer: https://cursor.com/blog/composer
NVIDIA: https://www.nvidia.com
DeployCo: https://www.deployco.co
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