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?
The thing people are skipping most is the uncomfortable pause before they build. Those five minutes of sitting with "does this problem actually hurt badly enough for someone to pay to fix it?" Most founders I talk to now move from idea to Cursor in under an hour. The idea never gets challenged.
AI removed the friction from building. But the most dangerous friction was never in the building. It was in the gap between "this feels like a good idea" and "I've convinced myself it is." That's where the real work happens — awkward conversations with potential customers, sitting with unclear feedback, waiting on data that tells you something you don't want to hear.
Those steps are still slow. And now that shipping is fast, slow feels wrong. So people skip it.
What I keep coming back to: the cost of a bad decision didn't change when AI arrived. Only the speed of making it did.
The stop-condition point is the one I keep forgetting too. With faster builds, I now want one cheap check before letting the next feature happen: what user behaviour would prove this is worth keeping? If I cannot name it, I am probably just making the wrong decision faster. A 10-minute user conversation usually beats another generated variant.
The step I see skipped is closing the loop after release. Shipping proves something was built—not that it solved the intended problem. With Agiloop (https://agiloop.ai), we connect production signals back to the original intent so the next iteration is based on evidence, not assumptions.
AI made everything faster. Disasters still happen, only faster. Before we were running on our own legs and now with AI we are in a high powered race car. Gets us there faster, but accidents can be a bigger mess, and if we go to the wrong destination, getting there faster just took us further from where we really wanted to be. AI is great, but it's just a tool. Without good guidance, the benefit is questionable at best.
Market is still adapting to AI. Its a new toy. Everything AI makes seems amazing, but once we realize building things was never an obstacle everything will stabilize again.
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.
For agency/content work, the skipped step is often agreeing on the acceptance test with the client. AI can turn a vague brief into 20 polished drafts before anyone names the one reader, action, and evidence of success. A small preflight—audience, job-to-be-done, and a kill/iterate condition—keeps speed from multiplying rework.
Yeah, I think the cost of a bad idea used to be the thing that forced people to think first. If it took three months to build, you'd at least talk to a few users before starting. Now it takes a weekend, so the thinking gets skipped.
The step i see skipped most is talking to real users before building. It feels slow next to shipping something, so people fake it by asking an AI what customers would want.
SIGNAL: AI made execution cheap, so the first thing that gets skipped is naming the decision before the build. Generating 20 variants feels like progress; committing to one does not.
GAP: Faster research and more competitor tabs don’t fix that — they fill the hole with data. You still don’t know which claim to counter or which price to test in the next ~2 weeks.
ACTION: Force the decision first (“What will we change in ~2 weeks if this is true?”), then only gather dated public evidence that feeds that action, and label uncertainty. Execution after.
Curious for others here: is the skipped step usually “talk to users,” or “write the kill criterion before you start”?
— Francisco / TrixellaIQ — competitive intelligence for D2C brands
https://trixellaiq.com (sample on the site)
One specific failure I've watched: AI makes generating options so cheap that it quietly replaces committing to one. Teams end up with 20 half-built variants instead of one validated thing, because generating felt like progress. The counterintuitive fix is spending judgment earlier — on the problem statement — not later on reviewing output.
Strong point: lowering execution cost makes a small validation loop more important, not less. Before shipping, define the riskiest assumption, run 5–10 conversations or a manual test, and set a stop rule; use AI to summarize objections, not manufacture certainty.
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I've seen people skip the boring step of watching a workflow before automating it. AI makes it easy to ship a polished fix for the problem as you imagine it. A short manual test with a few real users may feel slow, but it often reveals where an extra step or handoff really happens. Faster execution makes that check more important, not less.
The cost of a bad decision was never the build, so making the build cheap did not make the decision cheap. If anything it got more expensive, because a weekend project you cannot bring yourself to kill still eats the next six months of support, positioning and attention. The discipline I would add is a written kill criterion before you start: what has to be true in 30 days, and what you do if it isn't.