Michael Seibel's MVP advice is perfect for SaaS but needs one critical addition for AI. Learn the AI MVP validation framework that saves founders $200K+ in rebuilds.
Y Combinator's Michael Seibel has given some pretty good best startup advice. In his How to Plan an MVP talk, he tells founders: "Launch something bad quickly."
For traditional software startups, this advice is perfect. It created Airbnb, Stripe, and Twitch.
For AI startups, it's incomplete.
After five years building Hal9 and helping dozens of founders launch AI products, I've watched this pattern repeat: A founder follows Seibel's advice perfectly—talks to users, builds fast, launches in weeks. Three months later, they're stuck with a prototype that looks done but is missing 100% of the infrastructure that makes AI products actually work.
They thought they were 80% done. They were 20% done.
Here's the critical addition AI founders need: The thing that looks done isn't done. AI products have a hidden backend complexity that makes the prototype-to-product gap 10x bigger than traditional software.
Michael Seibel uses three perfect examples of "launch something bad quickly":
Airbnb's first version: No payments. No map view. You had to exchange money with the host in person.
Stripe's first version: No bank deals. Almost no features. The founders would come to your office and integrate it for you.
Twitch's first version: Only one channel (Justin's life). Extremely low-res video. No video games.
These are billion-dollar companies that started with products "most people would say is pretty shitty."
But here's what's critical: The thing users saw was the thing that mattered.
Airbnb's first version let people book a room. The frontend was the product. Stripe's first version let developers accept payments. The frontend was the product. Twitch's first version let people watch live video. The frontend was the product.
With AI, that's not true anymore.
A frustrated founder came to us after spending three months building with Lovable, following YC's playbook perfectly.
She'd built fast. Shown it to users. Got feedback. But features that worked one day mysteriously stopped working the next.
We helped her rebuild a real, working MVP in just under 30 days.
Here's what happened: She'd built 80% of a beautiful user interface. But she was missing 100% of the infrastructure that makes AI products actually work—connecting to real data sources, cleaning data correctly, handling the AI logic and edge cases, making it work reliably across different inputs.
Y Combinator's CEO Garry Tan recently celebrated that " for about a quarter of the current YC startups, 95% of their code was written by AI ."
That sounds amazing until you realize what it means: these founders used AI coding tools to generate frontend code fast, then discovered they only built the part users can see—not the part that makes it actually work.
The difference between a prototype and a product is the backend. And for AI, that backend is exponentially more complex than traditional software.
A founder wanted to pay us $200K to build their AI MVP.
We scoped it out. Told them what they actually needed for the first 30 days. Their reaction? It felt too small. Unpolished. Maybe even embarrassing.
They had the money. They wanted to skip right to the "perfect" version.
Here's what we told them: "Don't spend the money. Your MVP is supposed to feel uncomfortable. But more importantly—don't build anything until you know people will pay for it.
But AI founders often skip this because they're so excited about the technology.
The founder listened. They built the MVP now at a fraction of the cost, after validating that people actually want what they're building.
The lesson: Money doesn't solve validation. The more capital you have, the more tempting it is to skip talking to users and just build. But for AI products especially, building the wrong thing perfectly is exponentially more expensive than building the right thing badly.
Another founder was about to spend $500K acquiring an AI company to "skip the build phase."
We looked under the hood. The technology was basic—the kind of thing we'd build in a 30-day MVP. It looked impressive in demos but would break the moment you tried to use it with real data at scale.
He called off the acquisition. Saved himself nearly half a million dollars.
The lesson: In traditional software, if it works in the demo, it basically works. In AI, if it works in the demo, you've built maybe 20% of the actual product. Before you buy, build, or bet on AI technology, stress-test it with real data, edge cases, and scale.
AI founders often iterate on the wrong layer.
They iterate on the frontend—adding features, changing the UI, tweaking the user flow. They don't realize the problem is three layers deeper in how they're processing data, handling context, or managing AI responses.
One founder spent three months iterating on their UI while the actual problem was that their data pipeline was fundamentally broken. They were polishing a feature that would never work right because the foundation was wrong.
The lesson: When your AI product isn't working, the fix is rarely in the UI. Before you iterate on features or UI, validate that your data pipeline, AI logic, and infrastructure are sound.
Talk to users before building.
Before you write any code, validate the problem exists and people will pay to solve it. Use ChatGPT or Claude to simulate what your solution might do. Talk to potential customers. Show them mockups. Test if anyone will commit to using this before it exists.
One fintech founder came to us with just a Wix website capturing emails. Just a product page with screenshots. No actual product.
That was smart. They validated demand before spending a dollar on development.
Build something users can interact with to validate you're solving the right problem. Use Lovable or Replit to create a frontend that looks real. Behind the scenes, manually pull levers. Send form data to a Google Sheet. Fake the AI responses until you understand what users actually need.
Critical distinction: You're not launching a product. You're launching a validation experiment. This is your "something bad" that you launch quickly.
That fintech founder? Within a week of working with us, they had a prototype integrated into their website collecting real behavior data.
This is the extra step we add for launching AI apps.
Build what users don't see but what makes everything actually work: Connect to real data sources. Build proper data cleaning and processing. Implement the actual AI logic. Test data infrastructure that actually makes prototype work.
Within 30 days of starting, that fintech founder had tested the backend AI infrastructure, and had a fully functional MVP they could charge for. They converted their first paying customer that same month.
This is where AI ≠ traditional software. Airbnb didn't need this stage—their "bad" MVP was just a simpler version of the final product. AI's "bad" MVP is often fundamentally different infrastructure from what you need to ship.
We've all heard the saying "don't fall in love with your MVP."
But for AI, there's an additional trap: founders fall in love with the demo, not realizing the demo is built on fundamentally unproven infrastructure.
A bad Airbnb MVP meant manually handling payments. Annoying, but fixable.
A bad AI MVP often means you chose the wrong approach to data processing, the wrong way to handle context, the wrong infrastructure to actually get the AI app to work. Fixing it doesn't mean adding features—it means rebuilding the foundation.
The cost of getting it wrong is 10x higher.
Here's what we've seen work across dozens of AI products:
Verdio: Validated the carbon credit calculation problem first. Built a prototype in two weeks. Then built proper infrastructure. Now they have paying customers.
Proofbound: Validated that businesses wanted AI-generated books. Built a prototype fast. Then built the actual AI pipeline. Under 30 days to first paying customer.
MoneyHaven: Validated the cultural savings model problem. Prototyped the experience. Built real conversational AI infrastructure. 30 days to validated MVP.
The pattern: Validate → Prototype → Test Infrastructure.
Here's the thing: 'launch something bad' means the UI can be rough. That's fine. But you need to validate that the backend logic actually works—even if you're doing it manually at first. Don't spend months building infrastructure until you've proven your approach actually solves the problem.
Test the thing users don't see, but that makes everything actually work.
Watch real founders who followed this staged approach, or schedule a call and let's talk about your AI product idea—before you spend money building the wrong thing.