When Building Gets Cheap, Product Matters More

This is really the other half of the vibe coding conversation.

Every company suddenly wants an AI strategy.

That usually means a few predictable things happen in very quick succession:

• someone says "we need to be doing more with AI"
• a few demos get built
• everybody gets excited for a week
• then the whole thing slams into a wall called reality

Not because the models are useless.

Not because nobody knows how to prompt.

Mostly because "we should add AI" is not actually a product strategy.

The New Bottleneck

For a long time, one of the big constraints in software was just making the thing.

You needed engineering time, design time, planning time, and enough energy to drag an idea across the finish line. That friction was annoying, but it had one weird advantage: it forced some teams to think a little harder before they built random nonsense.

Now the cost of bringing an idea to life is dropping.

That part is real. You can prototype faster. You can explore more directions. You can get rough internal tools in front of people quickly. That is a huge win.

But when implementation gets cheaper, a different bottleneck takes over:

Deciding what is actually worth building.

That is product work.

Just Because We Can Does Not Mean We Should

This is the sentence I want printed out and taped to a lot of conference room walls.

Just because we now have the ability to bring things to life does not mean we should.

Not every feature needs AI.

Not every workflow benefits from a chatbot.

Not every dashboard needs a "summarize with AI" button bolted onto the top like parsley on bad pasta.

Sometimes the best AI strategy is:

• fix the broken process
• clean up the data
• simplify the workflow
• automate one boring step
• leave the rest alone

That is not anti-AI. That is just having standards.

Why Companies Keep Hitting The Wall

The wall shows up when a company moves from demo energy to operational reality.

The prototype looked good in a meeting. The generated copy sounded smart. The chatbot answered six canned questions correctly. The internal tool kind of worked if nobody clicked too fast and nobody asked it anything weird.

Then real life enters the chat.

Now you have to answer things like:

• who exactly is this for
• what job is it helping them do
• what data does it rely on
• how often is it wrong
• what happens when it is wrong
• who owns the output
• how do we evaluate quality over time
• what is the fallback when it fails
• does it save time or just create a new review chore

This is where a shocking number of "AI initiatives" reveal themselves to be expensive improvisation.

The issue is usually not that nobody can build. The issue is that nobody made the call on whether the thing should exist, what good looks like, or how the business will absorb the new failure modes.

Product Management Is Not Getting Less Important

If anything, product management and vision are getting more important than ever.

When teams can make almost anything, the advantage shifts toward the people who can:

• identify the right problem
• frame the user need clearly
• choose the right level of ambition
• define success in a way you can measure
• protect the team from shiny-object roadmaps
• say "no" early and often

That is the real leverage now.

The PM role also gets more technical in AI-first teams, but I do not think the takeaway is "PMs are dead." I think the takeaway is that weak product thinking gets exposed much faster.

You can no longer hide behind a slow delivery cycle and pretend ambiguity is strategy.

If the team can prototype three directions this week, then someone has to be able to explain why direction two matters and why directions one and three are a waste of everybody's time.

Vision Starts Looking Like Taste

This is also why I think vision is becoming a bigger differentiator.

Not "vision" in the fake LinkedIn sense where someone says they are reimagining the future of work and then ships an AI meeting summary nobody asked for.

I mean actual product taste.

The ability to look at ten possible things you could build and know which one solves a real problem, which one creates noise, and which one will quietly become a maintenance nightmare six months later.

That kind of judgment matters more when the cost of making the wrong thing drops.

Because when it is easy to build, it also becomes easy to flood your company, your product, and your users with low-value junk.

The New PM Skill Stack

I do think the PM toolkit is changing.

A strong PM in an AI-heavy team should probably be able to:

• prototype ideas without waiting on a full handoff
• understand the rough tradeoffs between model choices
• write clear acceptance criteria and eval-style checks
• spot where human review is required
• work closely enough with engineering to understand failure modes

But even that is secondary to the core job:

Good product people create clarity.

They reduce confusion. They sharpen scope. They protect the team from building seductive garbage. They connect a technical capability to an actual user problem and a business outcome.

Without that, "going AI" becomes a very expensive way to make more things nobody needed.

My Short Version

AI makes it easier to build.

That does not make product less important. It makes product more important, because the bottleneck moves upstream.

The winners are probably not the teams that generate the most features. They are the teams that know what not to build, where AI actually helps, and how to turn a capability into something people trust and want to keep using.

That is not vibes.

That is judgment.

Closing

We are heading into a phase where more teams can bring more ideas to life faster than ever.

Cool. That part rules.

Now comes the harder question: do you have the product vision to point that power at the right problems, or are you just accelerating your way into a larger pile of AI-shaped clutter?

That is the real test.

Written from home, where "we should add AI" is still not accepted as a complete product requirement.