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
00:01:16 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
Most companies don't have an AI problem. They have a prioritization problem. There are hundreds of places you could use AI, and the hard part is knowing which ones are worth the investment, which ones you're ready for, and what should come first.
GenROI uses AI-guided discovery to gather insights from across your whole organization, not just the few people who make it into a workshop. Our strategists then work with your leadership team to turn those insights into a prioritized AI roadmap weighted by business value, feasibility, and readiness.
Podden och tillhörande omslagsbild på den här sidan tillhör
HatchWorks. Innehållet i podden är skapat av HatchWorks och inte av,
eller tillsammans med, Poddtoppen.
