Stop Building AI Features Nobody Asked For

The easiest way to waste six months in 2026 is to build an AI feature that gets a great demo reaction and a weak buying reaction.

People will tell you it’s cool.

They will not tell you it is urgent.

And unless you learn to hear the difference early, you can burn a lot of engineering time polishing curiosity instead of solving pain.

I think this is one of the biggest traps for builders right now, especially developer founders. AI makes prototyping fast, which feels like an advantage. It is an advantage. But it also makes it dangerously easy to ship the wrong thing at high speed. When build cost drops, discipline has to go up.

The market is rewarding faster validation, not just faster building

McKinsey’s latest work on AI-first venture building says AI is materially compressing venture timelines, reducing time to validation, and raising output per person and per dollar. It also makes a very important distinction that I think a lot of founders miss: the winners are not just using AI to generate more ideas or code faster. They are validating more ideas earlier and scaling the winners sooner.

That matters because most early-stage product mistakes are not engineering mistakes. They are market mistakes.

The classic version of this is already familiar. In CB Insights’ long-running startup failure analysis, lack of market need remains one of the biggest reasons companies fail. AI does not remove that risk. It actually makes it easier to hide for a while, because you can produce a lot of convincing surface area before you ever prove someone will pay.

That is the trap.

The harsh truth builders need to hear

A lot of AI features are built because the founder can imagine the future, not because the customer is desperate in the present.

That sounds harsh. It is also useful.

There are usually three reasons teams build AI features too early:

• the market expects an AI story
• the prototype looks impressive fast
• the founder wants to keep up with competitors

All three are understandable. None of them is evidence of demand.

For a builder, that is one of the harder mindset shifts: being able to build something quickly is not a reason to build it now. The real question is whether the workflow is painful enough, frequent enough, and expensive enough that a customer will change behavior or budget for it.

If the answer is vague, you are probably building a feature for applause.

My rule: sell the workflow before you automate the workflow

This is the cleanest principle I know for avoiding AI feature waste.

Do not ask:
Can we build this?

Ask:
Can we sell the outcome this workflow would create?

That subtle change does a lot of work. It forces you to think in commercial terms before you get seduced by technical possibility.

For developer tools, the workflow usually matters more than the model. Customers are not paying for “AI.” They are paying for:

• faster code review
• safer releases
• easier API integration
• less debugging
• better test coverage
• faster onboarding for new engineers
• fewer support escalations

That is where the demand lives.

The practical fix: run a 5-customer AI validation sprint

If I were running a devtools startup right now, this is the exact process I would use before shipping a serious AI feature.

Step 1: Name one painful workflow

Not a category.
Not a vision deck.
One painful workflow.

Examples:

• generating test cases for flaky endpoints
• writing migration notes during releases
• triaging production incidents
• summarizing code-review feedback for junior developers
• creating API integration snippets from messy internal systems

Good workflows have three traits:

• they happen often
• they are expensive or annoying
• they already make someone feel real pain

Step 2: Write the offer before writing the feature

This is where most teams skip the hard part.

Write one page that says:

• who this is for
• what painful workflow it fixes
• what measurable outcome improves
• how quickly value should show up
• what it costs

If you cannot explain the commercial value in plain language, the feature is still too fuzzy.

Step 3: Pre-sell to five target users

Do not ask, “Would you use this?”
That question creates fake optimism.

Ask for one of these instead:

• a paid pilot
• a design partnership with defined success criteria
• access to real workflow data
• a commitment to trial the feature when it reaches a specific outcome

The goal is not broad feedback.
The goal is proof of seriousness.

Step 4: Deliver manually or semi-manually first

This is the part builders hate and smart operators love.

If the workflow matters, do some of it manually first. Use prompt chains, scripts, internal tools, or even human-in-the-loop delivery. You will learn:

• where context breaks
• what “good enough” actually means
• where the user still wants control
• which output format people trust
• what edge cases kill the experience

That learning is worth a lot more than an elegant prototype built in isolation.

Step 5: Productize only what repeats

Once you see the same workflow, correction pattern, and desired outcome repeating across users, then build.

Not before.

Build the repeated value.
Not the imagined value.

A worked example

Imagine a small startup building for backend teams. The founder wants to launch an AI feature that “explains pull requests automatically.” It sounds useful. Maybe even obviously useful. But that is still too abstract.

The better move is to narrow it.

Pick one workflow:
help engineering managers review large PRs faster without missing risk

Now pre-sell that outcome:

• reduce review time on large PRs
• surface risky file changes
• summarize architectural tradeoffs
• generate a comment-ready explanation the manager can trust

Then run five pilots.
Maybe two customers care mostly about security-sensitive diffs.
Maybe one team wants onboarding context for newer reviewers.
Maybe another team does not need summaries at all — they need change-risk ranking.

Now the feature has shape.
Not because the founder guessed better.
Because the market taught the product what to become.

Where AI really helps in this process

This is the optimistic part.

AI is fantastic for speeding up validation work if you use it correctly. McKinsey is right that AI can help teams generate, test, and refine ideas faster, run mini marketing experiments, and pressure-test concepts before full commitment. That does not replace customer conversations. It makes the early learning loop cheaper and faster.

Use AI to:

• map competing products and positioning
• generate landing page variants for smoke tests
• synthesize interview notes
• build lightweight prototypes for pilot users
• summarize repeated objections
• cluster workflow patterns across early testers

That is real leverage.

What I would not do is confuse internal enthusiasm with external proof.

My practical take

One of the quiet business truths in the AI era is that it is now easier than ever to overbuild.

That means good founders need a stronger filter, not just faster hands.

The filter is simple:

• is the workflow painful?
• is the buyer specific?
• is the outcome measurable?
• has someone serious committed?
• did the same need repeat across multiple real users?

If the answer is no, slow down.
If the answer is yes, then build aggressively.

Because the real edge is not shipping more AI features.

It is shipping fewer, better ones — the ones customers were already trying to buy before the code was finished.