I Tested 5 Startup Ideas in One Week Using AI. Here's What Survived.

The experiment

I had a backlog of startup ideas. You probably have one too. That notes app or Notion page where you dump every shower thought, every “what if,” every “someone should build this.”

Mine had 23 ideas in it. Some were months old, some were from last week. Most of them I had done zero research on. They just sat there, accumulating emotional weight, making me feel like I was “one good weekend” away from starting something.

So I decided to run an experiment. Pick 5 ideas from the list. Give each one a full day of structured validation using AI. See what survives.

Here is what happened.

The pitch: Meal prep companies spend hours every week planning menus that balance nutrition, cost, ingredient overlap, and customer preferences. An AI tool could do this in minutes.

What killed it: Founder-market fit. I have never worked in food service. I do not know meal prep company owners. I have no distribution into this market. The first question in the validation process asked about my unfair advantage, and I had nothing.

But the real kill shot came from the market research. The meal prep industry is fragmented into thousands of small local businesses. Most of them have 1-5 employees. Their tech budget is close to zero. The few that could pay for software are already locked into systems like GoPrep or Prep Perfect that include menu planning as a feature.

Time to kill: 2 hours. The founder-market fit question took 20 minutes. The market research confirmed it was dead.

Lesson: A real market with a real problem is not enough. You need a real path to reach those people, and a reason they would trust you over everyone else.

Idea 2: Browser extension that tracks price changes on B2B SaaS tools

The pitch: SaaS companies change pricing quietly. A browser extension that tracks pricing pages and alerts you when competitors change their plans, features, or pricing would be valuable for product managers and founders.

What killed it: The counter-argument exercise. I spent 30 minutes building the strongest case against this idea, and it was devastating.

First, pricing pages are easy to scrape manually. A virtual assistant could do this for $5/hour. Second, the people who care most about competitor pricing (product managers at large companies) already pay for tools like Klue, Crayon, or Kompyte that do competitive intelligence at scale. Third, the people who would use a free browser extension (indie founders) do not check competitor pricing frequently enough to justify installing anything.

The market was split between people who already overpay for enterprise tools and people who do not care enough to pay at all. No middle ground.

Time to kill: 3 hours. The counter-argument was strong enough on its own, but the competitive analysis made it airtight.

Lesson: “No one is doing this exact thing” is not validation. Ask WHY no one is doing it. Sometimes the answer is “because it is not a real business.”

Idea 3: Slack bot that summarizes long threads for managers

The pitch: Managers spend too much time reading Slack threads. A bot that summarizes threads over 20 messages and posts the summary in a separate channel would save hours per week.

What killed it: Customer conversations. Well, attempted customer conversations. I reached out to 6 managers I know personally and asked them about their Slack habits.

The responses were surprising. Four of them said they already ignore long threads and just ask someone for a summary in person. One said she uses ChatGPT to summarize by copy-pasting. One said his company moved to Loom for anything that would generate a long thread.

Nobody described this as a burning problem. It was a mild annoyance at best. When I asked “would you pay $10/month for this?” the answers ranged from “probably not” to “my company would never approve another Slack integration.”

The constraint analysis added another nail: Slack’s API has rate limits and message access restrictions that would make real-time summarization unreliable for large teams.

Time to kill: 1.5 days. The customer conversations took longer because I had to wait for replies, but they were worth every minute.

Lesson: There is a massive gap between “people complain about this” and “people will pay money to solve this.” Complaining is free. Opening a wallet requires real pain.

Idea 4: Open source alternative to Typeform for developers

The pitch: Typeform is beautiful but expensive and closed source. Developers who want customizable forms with logic, calculations, and API integrations would prefer an open source alternative they can self-host.

What killed it: Constraints. The technical constraint was manageable. The market constraint was the problem.

The validation research surfaced 8 existing open source form builders. Tally (free tier is generous), Heyflow, Formbricks, OpnForm, and several others. The space is crowded, and the existing options are already good enough for most use cases.

The financial constraint was worse. Open source form builders need a hosting/cloud offering to make money, which means competing directly with Typeform on infrastructure. The open source version attracts developers who self-host (and pay nothing). The cloud version competes with funded companies that have years of head start.

Time to kill: 4 hours. The competitive landscape was so crowded that the research almost did not need interpretation.

Lesson: “Open source alternative to X” is a strategy, not a business. It works when X is expensive and hated (like Elasticsearch/OpenSearch). It does not work when X has a generous free tier and 10 alternatives already exist.

Idea 5: AI skill that validates startup ideas (the survivor)

The pitch: An AI-powered process that walks founders through structured validation, from hard questions to market research to financial projections. Open source, runs inside Claude.

Why it survived: This one passed every question, but not comfortably.

Founder-market fit: I have been through startup validation myself, multiple times. I know the mistakes because I have made them. I use Claude daily. I understand the AI agent architecture. This was the first idea where my background was an obvious advantage.

Constraints: Technical constraints were low. The whole thing could be built as a Claude skill with no infrastructure. Financial constraints were irrelevant because the model is open source (no hosting costs, no subscription to manage). Market constraints were interesting: the audience (founders, builders, Claude users) hangs out in places I already know (Reddit, Hacker News, indie maker communities).

Counter-arguments: There were real ones. “AI validation is not real validation” was the strongest. And it is partially true. AI cannot replace talking to actual customers. But AI CAN replace the 90% of validation work that nobody does at all. Running market research, finding competitors, stress-testing assumptions, building financial models. Most founders skip all of this. An AI tool that does 80% of the work is better than the 0% that most people actually do.

Customer signal: I did not have formal customer conversations before building, but I had something adjacent. Years of reading r/startups and r/SideProject posts where founders ask “is this idea any good?” and get vague, unhelpful answers. The demand signal was everywhere. People wanted structured validation. They just did not have a good way to do it.

Why it scared me: Because it survived. When an idea passes validation, you lose the excuse not to build it. That backlog of 23 ideas was comfortable. It was potential energy, no risk. A validated idea demands action.

What I learned from the week

Speed matters more than depth. Two hours of structured validation beats two months of building and hoping. Every idea I killed in hours would have cost me weeks or months if I had started coding first.

The questions matter more than the answers. The 4 ideas that died were not killed by some brilliant insight. They were killed by obvious questions that I had never bothered to ask. “Is there an existing solution?” “Can I actually reach these people?” “Would someone pay for this?”

Your idea backlog is mostly dead weight. Of 5 ideas I was genuinely excited about, 4 died on contact with structured validation. I suspect the ratio would hold for the other 18 in my list. That is not depressing. That is liberating. It means you can stop carrying the mental weight of “what if” for ideas that were never going to work.

Validation is not about killing ideas. It is about finding the one that deserves your time. I went into this week expecting to find 2-3 viable ideas. I found one. And that one idea, the one that scared me, is the one I actually built.

The tool I used

The validation process I ran for each idea is now open source. It is an AI skill for Claude that runs the same structured validation: hard questions, market research with 11 parallel agents, competitive analysis, financial projections, and a go/no-go scorecard.

If you have your own backlog of ideas gathering dust, try running a few through it: github.com/ferdinandobons/startup-skill

The worst that happens is you kill some ideas that needed killing. The best that happens is you find the one worth building.