Every company says its data is a strategic asset. Then many of them send that data to the same handful of frontier models their competitors can call. The answers can be good. The intelligence still never becomes something the company owns.
In this episode of Talking AI, Matt Paige sits down with Manos Koukoumidis, cofounder and CEO of Oumi. Before Oumi, Manos led GenAI at Google Cloud and bootstrapped the Cloud PaLM effort that later moved into DeepMind as Gemini. He left to build what he calls pretty much the opposite: a platform that helps any enterprise create, deploy, and continuously improve its own specialized models.
His rule is simple. Rent what is generic. Build and own what differentiates you or what costs you the most. Manos explains where that rule holds, why a bank should use Claude Code for coding but own the model that processes its mortgages, and why he thinks owning your intelligence will look obvious two years from now.
In this episode, you’ll hear about:
- The three reasons to own your AI: differentiation, economics, and control
- Why frontier models used for narrow tasks are a Swiss Army knife you overpay for
- The early PaLM customer who asked for the model weights, and what that taught Manos
- Building a ChatGPT-style chatbot at Microsoft in 2016, years before transformers took over
- A decision tree for when to rent, route, fine-tune, or train
- Why Kirkland & Ellis decided to build its own legal AI
- A live demo: a fine-tuned Qwen model that beats Sonnet on a bank ticket classifier in about 20 minutes of human effort
- Turning production failures into the next training run in four clicks
- Why Oumi is open core, and the four assets an enterprise needs to avoid lock-in
- How a small AI-native team works: function collapse, design systems for Claude, and no code reviews for code you don’t understand
- Harness vs. model, and why you should not forget to train the brain
- The Three Prompts, a new segment on every Talking AI episode
Key Moments
- 00:00:00 — Intro: renting intelligence vs. owning it
- 00:01:16 — Quality, economics, and control: three reasons to own your AI
- 00:05:37 — Lessons from PostgreSQL and Linux
- 00:08:14 — The PaLM customer who asked for the weights
- 00:11:01 — Building a chatbot at Microsoft in 2016
- 00:13:09 — The executive decision tree: rent what is generic, own what differentiates
- 00:17:44 — Should Kirkland & Ellis use Harvey or build its own AI?
- 00:20:11 — Using AI to automate AI development
- 00:23:10 — Why Oumi went open core
- 00:25:10 — Demo: building a specialized model from a prompt
- 00:29:59 — The fine-tuned model passes Sonnet
- 00:33:45 — Deploying and turning production failures into training data
- 00:37:34 — One-click deployment, scale to zero, and the four assets you must own
- 00:39:01 — Open source vs. frontier labs: a false dichotomy
- 00:41:25 — Inside an AI-native org: function collapse at Oumi
- 00:46:56 — The downsides: code you don’t understand
- 00:48:50 — Harness vs. model: don’t forget the brain
- 00:50:41 — The Three Prompts
- 00:54:00 — Where to learn more about Oumi
