Over the past year, I’ve slowly come to a strong belief: in the post AI era, you either need to be a builder or a seller. Increasingly, the expectation is that people should be able to do both.

You were always more valuable if you could do both, even pre-AI. Think of early founding team members who had a broad set of capabilities with enough depth to execute. What’s changing now is that this is no longer just an advantage. It is becoming the expectation even within formalized roles at larger companies.

The shift is happening because AI is compressing the distance, both within and between implementation and distribution.

Today, product, design and engineering are closest to the build cycle, while sales, solutions architects, marketing and customer support are closest to the distribution cycle.

AI tools not only reduce the time required for work, they also reduce the skill required for large parts of it.

If you break work down, it has two components:

  • Mechanical / procedural skill
  • Taste / judgment

AI is getting very good at the first. Which means the second becomes even more valuable.

Let’s make this concrete. Imagine we’re designing how a partially fulfilled order should be handled in a food delivery app.

A customer orders five items. Right before the restaurant confirms, the kitchen marks one item as sold out. The rest of the order is already cooking. The PM has already decided the policy: silently refund or substitute the unavailable item without interrupting the customer.

The designer’s job is to communicate this on the order tracking screen so the customer trusts the change without being alarmed. The work breaks down into two parts.

Mechanical skill: This is what AI tools are already great at with a one-shot prompt.

  1. Generate the order tracking screen states showing the removed item, updated total and revised order state.
  2. Write the copy: “One item was sold out and refunded. New total: $24.50.”

Judgment skill: This is where domain context and personal point of view matter.

  1. Visual weight. It can’t look like an error because nothing actually went wrong. But it also can’t be so quiet that the customer later thinks there was a billing mistake.
  2. Show the math vs. just the total. One optimizes for transparency, the other for simplicity.
  3. Information hierarchy. The customer opened this screen to check their delivery ETA, not to read a change log. Don’t hijack their attention.

The mechanical implementation not only used to take longer in the pre-AI era, it also required someone who knew design and knew how to use the tools to generate the artefacts. Today, much of that same work can be done by someone with little design knowledge using AI. A PM who was previously a 4/10 in design can now become a 7/10 by letting AI handle the mechanical work. The same idea can be extrapolated across engineering, design and business.

This means a single builder persona becomes much easier to achieve. You can realistically be a 7/10 across multiple disciplines. The game now is how quickly you can climb the judgment ladder in each of them.

But why is selling still different?

If AI is collapsing disciplines within building, why wouldn’t it collapse selling as well? It may well happen eventually, but there’s a reason it hasn’t today.

Going back to first principles, selling is about influencing another human’s emotions and logic. Building, on the other hand, involves working with machines. That distinction is still tangible enough for selling to remain its own skill.

To become more valuable, you’ll increasingly need to do both. Building becomes the baseline. Selling is what compounds it.