AI Knows the Answer. Your Expert Knows When It’s Wrong.

Here’s something that doesn’t get enough attention in conversations about AI. Sometimes the most dangerous answer is the one that sounds completely reasonable.

It’s well written. It’s confident. It includes supporting information. Everything looks right.

Except it isn’t.

And the only person who notices is someone who’s been doing the job for 20 years.

Think about a physician reviewing an AI-generated recommendation.

The medical information might be accurate.

The studies might be legitimate.

The treatment might even be appropriate for most patients.

But this particular patient has three other conditions, takes six medications, and had a complication five years ago that changes everything.

An experienced physician recognizes the problem immediately.

The AI may not.

Or consider a financial analyst reviewing a company’s numbers.

The calculations are correct.

The assumptions seem reasonable.

The projections look great.

But a senior investment professional notices something subtle about the business model that makes the entire forecast questionable.

That’s judgment.

And it’s why I think we’re entering a particularly interesting phase of AI development.

For years, we’ve focused on getting models to produce better answers.

Now we’re spending more time figuring out how to determine whether those answers are actually good.

That might sound like the same problem.

It isn’t.

Evaluating complex work often requires as much expertise as performing it.

Sometimes more.

In legal AI, for example, Harvey has explored having experienced lawyers compare outputs, identify which they prefer, and explain why.

That feedback can help evaluate models and, through appropriate training methods, improve future performance.

The key isn’t simply telling the AI that an answer was wrong.

It’s understanding what made it wrong.

Did it overlook an important fact?

Misinterpret a requirement?

Make an unreasonable assumption?

Reach the right conclusion using flawed logic?

Or fail to recognize that the situation required human intervention?

Those distinctions matter.

Especially when AI moves beyond drafting emails and starts supporting decisions involving money, safety, legal obligations, or people’s health.

I’ve noticed something similar throughout my career.

The best executives aren’t necessarily the people who have an immediate answer to every question.

They’re often the ones who can recognize a bad recommendation before everyone else realizes there’s a problem.

They know which assumptions to challenge.

Which numbers deserve another look.

Which risks are being underestimated.

That expertise is enormously valuable.

And it creates a fascinating possibility.

What if we could use the judgment of experienced professionals to help AI become better at recognizing its own limitations?

Not simply more knowledgeable.

More reliable.

More aware of uncertainty.

Better at identifying situations where the obvious answer isn’t necessarily the right one.

That’s a different standard for intelligence.

And I suspect it will become increasingly important as AI takes on more complicated work.

Because in the real world, knowing the answer is useful.

Knowing when the answer is wrong can save you a fortune.

Sometimes it can save a life.

 

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