AI made execution cheap. Did it also make bad decisions cheaper ?
AI removed a lot of friction.
Code faster.
Design faster.
Write faster.
Research faster.
Launch faster.
That sounds great, and mostly it is.
But I think there’s a side effect we don’t talk about enough:
we can now execute the wrong thing much faster too.
Bad idea? Build it in a weekend.
Weak positioning? Generate 20 versions.
Wrong audience? Automate outreach to 5,000 people.
Bad process? Add AI and scale it.
The boring steps suddenly feel slow, so people start skipping them.
Understanding the problem.
Talking to users.
Questioning assumptions.
Thinking through tradeoffs.
Actually reviewing what AI produced.
AI can improve execution, but it doesn’t automatically improve judgment.
Sometimes it just gives bad judgment more leverage.
I’m noticing that the more execution gets automated, the more important deciding what deserves to be executed at all becomes.
Curious how others see this.
What important part of your work have you seen people skip more since AI made execution easier?
I think the bigger risk is that AI makes skipping validation feel productive. You can have a polished product, messaging, and outreach running before confirming that the underlying problem is actually worth solving.
I have found that talking to users and testing the problem manually first saves a lot of wasted execution later.
Exactly. The dangerous part is how productive it can look.
You can have a polished product, landing page and outreach running while still being wrong about the actual problem.
AI makes execution easier, but it makes validation more important, not less.
The step I see skipped is the first run on a clean machine. The feature gets written, the dirty local checkout is green, and the release goes out. The afternoon that used to be spent installing on a fresh Windows user (no cached tokens, no leftover files) is the first thing cut, because the code itself showed up in twenty minutes.
A gate that still catches the expensive misses: before a release, one scripted path on a clean profile. Open the app, do the one job the page promises, quit, reopen, and confirm the file is still there. If that path takes longer than writing the feature, the feature is not done. Faster execution did not make a broken first session cheaper to support.
That’s a good example of speed cutting out the boring checks first.
The feature may take 20 minutes now, but the failure still costs support time and user trust. Clean-environment testing feels even more important when building gets this fast.
The step I see skipped most is defining the stop condition before automation starts. Teams specify what the agent should do, but not what evidence should make it abstain, escalate, or do nothing. Once execution is cheap, a missing stop rule scales faster than a good prompt. I now treat 'when must this system refuse to act?' as part of the spec, alongside the happy path.
I like this framing a lot.
People define what the agent should do, but not when it should stop. Once automation scales, a missing stop condition can become more dangerous than a bad prompt.
“Do nothing” probably needs to be treated as a valid outcome more often.
User research is probably one of the first things people skip when AI makes everything else so fast. Faster execution doesn't help much if you're solving the wrong problem.
Exactly. User research starts feeling slow compared to building now.
But if execution gets 10x faster, validating the problem probably becomes more important, not less. Otherwise you just reach the wrong destination faster.
The skip I keep seeing is not the user call. People still interview. They let the model summarize the calls, then they build the summary. The sentence that contradicted the pitch never makes it into the ticket, so the "validated" idea is just the model's average of the notes.
Before any prompt I write one line this build is not allowed to become. If I can't write that line, the session is only a faster way to accept the model's defaults. Judgment did not get automated. It moved to a step nobody scheduled, because the output already looks finished.
This is a really interesting point.
Even when people do the research, AI can smooth out the uncomfortable parts of it. The one user comment that challenges the whole idea can disappear inside a neat summary.
The raw contradiction is often more valuable than the polished average.
The part I think gets missed is that AI made building cheaper but did not make owning anything cheaper. A bad idea still costs a weekend to build and then years to keep alive, answer support for, migrate, and eventually shut down. The weekend is the only part that got discounted. So the step people skip is not really validation, it is asking whether they want to still be maintaining this in two years, which used to be forced on them by how long the build took. The build was quietly doing that deciding, and nobody noticed until it stopped taking any time.
That’s a really good distinction. Building got cheaper, but ownership didn’t.
The maintenance, support, migrations and eventual cleanup are still there. I like the point that build time used to force some of that thinking naturally. Now you can skip straight past it.