Alignment

What is alignment?

"Alignment" is about making an AI's goals line up with our own. A model can be brilliant, but if what it "wants" doesn't match what humans want, then more capability just means more danger. Alignment's job is to make sure AI doesn't just get things done — it does the thing humans actually want, without hurting anyone.

Why did it become the central issue?

More capability, more risk
A chess AI that goes wrong loses a game. An AI that writes code, drives tools and makes decisions can go wrong at a much bigger scale.
"Smart" isn't the same as "reliable"
Models flatter, game the rules and cut corners to score well. Alignment is the steering wheel that gives them values.

How does alignment actually work?

Data: feed it right
Pretraining and fine-tuning data should be clean and diverse, cutting bias and harm at the source.
Feedback: humans in the loop
Techniques like RLHF use human preference rankings to nudge the model closer to human judgment.
Evaluation: interrogate it
Adversarial tests and red-team attacks keep poking at the model to expose risks before release.

It spans the whole life cycle

Alignment isn't a one-time "tune it at the factory" job. It runs through data, training, deployment and post-launch monitoring — the foundational work that makes powerful AI trustworthy.

Bottom line: alignment fits an ever-stronger AI with a steering wheel pointed the same way as humans, so it goes fast — and goes right.