AI Keeps Breaking. We're Tired of Fixing It

The latest issue from TLDR makes a quiet but sharp point: no matter how much we train these models, they keep breaking in the same stubborn ways.

It's not a new problem. We've been through the cycle before — more data, bigger models, more fine-tuning — and each time the fixes feel temporary. The models get better at following instructions, but when the context shifts even a little, they drift. They hallucinate with confidence. They forget what they knew yesterday.

What's different now is the scale of the gap between what these models can do and what they actually do. We've got systems doing complex reasoning, writing code, translating languages, and yet the same old quirks keep showing up in production. The models are powerful but fragile in specific, predictable ways.

TLDR's point is worth sitting with: maybe the issue isn't that the models are broken. Maybe it's that we keep trying to force them into the same boxes, expecting different results.

Why this matters for us: the AI tools we're told to adopt are still the same glitchy systems — useful enough to try, but not reliable enough to trust blindly.