Ride the Models
Stay valuable in AI by playfully applying each new model to your own work and re-testing what it couldn't do before.
- Difficulty
- Easy
- Time to result
- ~ongoing to results
- Steps
- 3
- Confidence
- 85%
A career-survival practice for the AI era: don't try to out-predict AI, ride on top of it. Every time a new model ships, playfully apply it to whatever you actually care about, and keep 'turning over rocks' — re-testing tasks it couldn't do before, because it probably will be able to soon. The edge of AI isn't in San Francisco where it's built; it's wherever AI meets a real person doing real work, so you can be among the first to discover what a new model is useful for.
Origin
Dan Shipper's prescription for individuals worried about AI displacing their jobs, framed as 'ride the models.'
Core principles
- 01Riding the models extends your powers instead of leaving you behind
- 02The real edge of AI is where it meets a real human doing something real, not where it's built
- 03Curiosity and play uncover useful applications faster than fear or FOMO
- 04What a model can't do today it often can do in the next version — keep re-testing
How to run it
- 1
Try every new model on your own workflows
When a model ships, run your real tasks through it and ask how its new powers change what you can do, rather than ignoring it out of fear.
Pro tip If your employer restricts the latest models, experiment on your own time so you're not handicapped from riding them.
Watch out Ignoring new models because they scare you is a rational feeling but a losing strategy — it leaves your skills frozen while the frontier moves.
- 2
Lead with play, not FOMO
Apply the new model to something enjoyable you care about — inside or outside your job — because the best discoveries come from having fun, not from anxiety.
Pro tip Chase your 'moment of joy' — the first time AI does something for you that makes you say 'I can't believe it did this' — and let that pull you into building more.
Watch out FOMO-driven, joyless usage rarely surfaces the interesting, useful applications and burns you out.
- 3
Keep turning over rocks
Re-test tasks a model failed at before on each new release; capability that's absent today frequently appears in the next version.
Pro tip Treat every release as a chance to be one of the first people in the world to discover a new use — you don't need San Francisco access to find it.
In the wild
Shipper's own benchmark task was impossible for models one generation; when a new model shipped he 'turned the rock over again' and re-ran it, finding it now scored 60/100.
→ He caught a real capability jump precisely because he re-tested a previously-failing task on the new model.
Wanting a head of L&D, Shipper typed a loose hunch — someone from General Assembly who's now AI-pilled — into Codex and went to do something else. It surfaced the perfect candidate, who was even a Twitter follower.
→ He DM'd the person and had dinner with them; a task that would have taken ages happened by casually applying the model to real work.
Common mistakes
Ignoring new models out of fear
Avoiding AI because it threatens your job freezes your skills while the tools advance, which is exactly what leaves you behind and replaceable.
Never re-testing what failed before
Concluding 'AI can't do my job' from one model version and never turning the rock over again means you miss the version where it suddenly can — and miss being early to that discovery.
Is it for you?
Best for
Knowledge workers anxious about AI displacement who want a concrete, low-cost practice to stay ahead
Not ideal for
People seeking a fixed, one-time skill to learn — the whole point is a continuous habit, not a static competency
From the transcript
“the only thing you need to do is ride the models. And that means use them for whatever it is that you do”
“the way to ride the models is like not one specific thing cuz they're always changing, but it is to be curious and playful”
“keep turning over rocks”
“I think the edge of AI is wherever AI meets like a real human doing something”
From the episode
The AI paradox: More automation, more humans, more work
Dan Shipper