AI, as amazing as it seems, is still a buggy experience at many real world tasks.

Your choice is whether to consider the bug a limitation or make it a feature.

Choosing the latter requires you to leave the comfort of determinism for the creative joy and frustration of predictions.

And to redesign your workflows

for a very different type of compute.

My first AI experiences flip-flopped between enlightening analysis and sycophantic lies.

Tinkering with it provided you a week of work from minutes of investment.

The closest to the feeling of using a credit card instead of saving up.

And as with credit cards, there's never a free lunch.

Initial AI-generated "answers" came with endless hours of verifying and adapting to your actual needs.

A lot has happened since, AI has gradually improved.

The first phase was all about scale.

Broad models fed everything on the internet. They had answers for everything but weren't always right

(as with the internet, by the way).

More data and more parameters gradually got them better, and AI got its normy a-ha moment with GPT-3.

The second phase went from scaled guessing to structured reasoning.

Models started to divide the work into steps,

to verify themselves and show their work.

The O1 model crossed the line from guessing to reasoning.

The third phase is where we are today.

Models and applications melt together in verticalized tools made for specific tasks and situations.

Users has increasingly embraced the bug,

with applications excelling from being integrated into workflows,

where AI leverages contexts, and test and verify their work.

This is truly exciting!

But it runs straight into a wall...