AI Is Only as Smart as the Context You Give It

AI has made intelligence abundant. Context is becoming the scarce resource.

Companies are racing to adopt better models, build AI agents and add intelligence to their products. But two companies can use the same AI and produce dramatically different outcomes.

The difference is often simple: what does the AI know before it answers?

The model didn't get smarter. The context got better.

Ask AI:

What's the best way to lower my monthly payments?

You'll get a reasonable answer.

Now tell it you have credit card debt, good credit, substantial home equity and care more about monthly cash flow than paying everything off as quickly as possible.

Same AI. Same basic question. Very different answer.

The model didn't get smarter. The context got better.

That's particularly important in financial services. Most financial decisions aren't universal. The right path depends on the person, their situation, what they're trying to accomplish and the tradeoffs they're willing to make. AI can reason through that complexity remarkably well. But first, it needs to understand it.

Customer data isn't necessarily customer context

Businesses already have enormous amounts of customer data. Clicks. Searches. Transactions. Email engagement. Applications. Purchases. Browsing behavior. Most of that data was collected to answer questions like:

Who is this person? What might they buy? How should we market to them?

Context answers a different question: What is this person trying to accomplish right now?

That's an important distinction. For most of the internet era, companies built data infrastructure to become better at targeting people. AI creates an opportunity to become better at understanding them.

Context needs structure

More data isn't necessarily better.

If someone wants help making a decision, you could ask them 100 questions. That's not intelligence. That's a terrible form.

The real challenge is knowing what you need to understand before giving an answer.

I think about that broadly through a simple framework:

Key takeaway (The intelligence is the same. The inputs are not):

Limited context: Goal → AI → Generic Answer

Rich context: Goal + Situation + Constraints + Preferences → AI → Relevant Guidance

The specific inputs will be different for every industry and every decision. Figuring out which ones actually matter is the hard part. And that's where domain expertise becomes incredibly valuable.

The model may not be the moat

This is what I find most interesting from a business-building perspective.

AI models will keep getting better. More companies will have access to them. Costs will fall. Capabilities that seem remarkable today will become commonplace. So simply having AI probably isn't much of a competitive advantage. But imagine two businesses using the same underlying model.

One knows that a consumer wants to lower their monthly payments.

The other understands the consumer's objective, financial situation, constraints and preferences.

Same intelligence. Different context. Different product.

Over time, that difference can compound. More interactions create more context. Better context can lead to better questions. Better questions can produce better guidance. That's the opportunity.

For most of the internet era, businesses competed to collect customer data so they could target people better. I think the AI era creates a much bigger opportunity:

Collect the right context so you can serve people better.

The smartest model in the world can't reason against information it doesn't have.

AI is only as smart as the context you give it.