AI does not primarily have an intelligence problem.
It has a reward problem.
Reading The Alignment Problem by Brian Christian forced me to confront something uncomfortable:
Systems don’t become dangerous because they are intelligent.
They become dangerous because they optimize what we reward.
And what we reward is often a proxy.
We measure engagement instead of well-being.
We reward speed instead of wisdom.
We optimize dashboards instead of judgment.
Then we’re surprised when the system scales the distortion.
Alignment isn’t one problem.
It’s multiple failure modes:
- Measuring the wrong thing
- Hitting the metric without honoring the intent
- Working in training but failing in reality
- Optimizing in ways we didn’t foresee
- Learning from feedback loops we didn’t design
Here’s the uncomfortable part:
This isn’t primarily a technical gap.
It’s a responsibility gap.
If the organization rewards growth at all costs, the model will find a way.
If leadership rewards certainty over accuracy, systems will amplify confidence — not truth.
In an era where prediction is getting cheaper, judgment is getting more expensive.
And the real competitive advantage may not be intelligence.
It may be the discipline to ask:
What are we rewarding — and what might this system optimize that we don’t intend?
Before you deploy AI in your organization, ask yourself:
If this model became extremely good at the metric we chose,
would we actually be proud of the outcome?
Alignment begins there.
