I have an open secret about fine-tuning models.

**I predict most enterprises will independently rediscover this by the end of the year.**

First, some context. I’m an early adopter:

- I’ve been using **GitHub Copilot** since July 2021.
- I started using **ChatGPT** on November 30, 2022.
- I experimented heavily with fine-tuning models in 2023 and 2024. For most practical use cases, it was a waste of time and money.
- I’ve been using **Claude Code** since February 2025.

Before that, I spent years building ML infrastructure on cross-functional R&D teams:

- At Magic Leap from 2016 to 2020, we built sparse maps, dense maps, and object recognition pipelines.
- At Waymo from 2020 to 2022, we built labeling pipelines and planner evaluation test sets.

That work took teams of data scientists, researchers, infra engineers, and months of coordination.

**Today, Tinker by ThinkingMachines is a literal game changer.**

It’s an easy-to-use API that any solid engineer can learn quickly. One person can now do in a weekend what used to take entire teams weeks or months.

The real advantage was never fine-tuning. It’s proprietary data.

Tinker makes that data usable. Queryable. Composable. Iterative.

If you’re asking where to look for what’s next in AI right now, this is it: [Tinker](https://thinkingmachines.ai/tinker/

_The frontier isn’t bigger models. It’s turning your data into leverage._