As the AI coding era arrives, the way we build MVPs (Minimum Viable Products) needs to change too. In the past, implementing features was hard, so creating value with the bare minimum of functionality was what mattered. But now, thanks to AI tools, it has become all too easy to build far too many features. This piece emphasizes that this can actually confuse users and blur the essence of a product, delivering the message that you must boldly cut unnecessary features and focus on core value through close communication with your users.

1. The AI Era: A New Challenge in Building MVPs 🚀

Michael and Dalton discuss how MVPs should be built in the AI era, arguing that the conventional advice needs to be updated. In the past, creating value with minimal functionality was what mattered, but they point out that because AI has made implementing features so easy, it's actually causing problems. Dalton illustrates this through his own experience building a product (Standard DB). He implemented an enormous number of features in just two weeks, but ended up having to delete 80% of them.

"I just built and launched a product, and I had to delete 80% of the features I'd built from the MVP. This is a brand-new problem that didn't exist before."

In the past, adding features took so much time and money that 'feature creep' was something that took months to happen — but now 'feature creep' can occur in just two weeks.

"I feature-crept in two weeks. So you can see people building absurd, nonsensical products, because it's just so easy to keep adding features."

This means the developer's role as an 'editor' has now become more important than it was before.

2. Why Too Many Features Becomes Poison 🤯

When you build a product for yourself, it's easy to verify value during the MVP process, but when you build a product for someone else, the fact that you can add infinite features becomes dangerous. The brain thinks, "They'll need this feature too, and that feature too," and you end up wanting to provide every feature users want from the very start. But this is a big trap.

"Writing features is addictive. Talking to Codex is fun."

Dalton and Michael emphasize that right now is a time when the temptation not to talk to users and not to launch with a small product is stronger than ever. In the past, even when users presented a long list of features, developers knew they couldn't implement them all, so they tried to dig deep and figure out what users truly needed. But now, with AI tools, you can implement every single feature a user requests exactly as asked.

"Let me give you an extreme example. You talk to a user, take notes, feed them into Codex, and Codex will build every feature the user asked for."

But they warn that this is a very bad approach.

"That's a really bad thing. Don't do that. This is the counterintuitive part."

In the past, building what users wanted was hard, but now, thanks to AI, you can literally implement anything — so the risk of feature overload has grown exponentially. As a result, it's more important than ever for developers themselves to deeply understand the problem. If you don't truly understand the problem and just follow user requests, you can fall into the trap.

"Right now you have to work much harder to resist the temptation to build a far more complex and sprawling product than before."

Features should be added when users genuinely need them, and you shouldn't add them recklessly just because AI can make them easily.

3. Low-Quality Communication and a Noisy Market 🗣️

Just as AI has made it easier to build products, it's also made 'spam' easier, Michael says. In the past, the advice was that securing 100 passionate users was what mattered, but now you can easily use AI to send mass spam messages or buy leads to reach people. This ends up distorting the way many founders 'communicate with users'.

"Many founders are distorting the way they talk to users. It's spam."

They emphasize that this kind of mass spam is nothing but 'noise', and that truly listening through a small number of high-quality conversations is far more important.

"Truly listening through a small number of high-quality conversations is far better than spamming thousands of people."

For a startup to succeed, you need more than simply 'talking to users and building what they want'. Most users want their own business to succeed, but they don't know exactly how to make that happen. So developers need to figure out how to help their customers' businesses do better. By communicating more deeply with a small number of customers, the opportunity to make a real impact on their businesses grows enormously. This creates a virtuous cycle of 'word of mouth' rather than spam.

4. Dalton's Experience: Success Through Cutting Features 📈

Dalton uses his Standard DB experience as an example to drive this point home once more. After implementing many features, he held Zoom calls with 12 potential users. But many of them didn't understand the product. It was too complicated.

"It was so complicated that they didn't really understand what it was."

In the end, Dalton deleted 80% of the features. As soon as he did, users started signing up immediately — some even signed up during the call.

"The moment I deleted 80% of the features, they said, 'Yes, I'll sign up right now!' Some people signed up during the call."

This is highly counterintuitive. The fewer the features, the higher the adoption rate. Users had been overwhelmed by too much complexity. Dalton admits that while he had a grand vision for the product, users only cared about having their short-term problem solved.

5. The AI-Era Music Industry and Its Lessons 🎧

Michael compares the 1960s music industry with the present to offer insight into building MVPs in the AI era. In the 1960s, making a record was very hard. You had to sign with a record label, and studio costs were expensive. But now, you can make plenty of hit songs using just GarageBand, which comes pre-installed on a Mac. The tools used by the top professionals and the tools used by ordinary people have become the same.

But the demand for music isn't much different from the 1960s. People can't listen to more than 24 hours of music a day, and almost no one thinks, "I really want to hear a song made by some rural guy from Illinois that no one's ever heard of."

"No one is going to think, 'I really want to hear a song made by some rural guy from Illinois that no one's ever heard of.'"

This analogy applies directly to AI coding as well. AI tools are excellent, but they shine when used in the right hands. Anyone can easily make music, but not all of it becomes a hit. The democratization of AI tools will help more young talent enter the industry, but the number of people who actually succeed won't increase much, Michael analyzes.

6. Using AI Inside Companies and Differentiation Strategy 🏢

Michael says AI tools can create far greater synergy when used inside companies. Employees can use AI tools with clear goals and methods to handle their work efficiently while consuming fewer company resources. For example, using AI tools to make internal training videos is a different context from making a Hollywood blockbuster. Even if AI tools can't build the next Facebook, they can contribute to handling everyday work far more efficiently.

7. Differentiation and Core Value in the AI Era ✨

In conclusion, when building an MVP in the AI era, the fact that 'features have gotten cheap' paradoxically becomes a risk factor. So choice matters.

"You simply have to become more discerning."

In the past, developing products in public and sharing the process was good advice, but now AI tools have made it far too easy to generate 'nonsense' on LinkedIn or X (Twitter). Everything is filled with similar content, making it hard to differentiate.

"Codex makes it far too easy to generate nonsense about the product you're building on LinkedIn or X. And all of that content is exactly the same."

On the other hand, if you invest time in creating original content, you can build a following. For example, people who write unique posts about file systems steadily grow their followers. Paradoxically, the X accounts of people who work at AI labs actually provide the best information — because it's original content they wrote themselves.

Michael warns that a startup may appear to be getting positive feedback when it's actually experiencing a negative feedback loop. That is, you can use AI to spam many customers and get them to use your product, but in the end customers don't like the product and leave.

But this paradoxically provides a new opportunity. If all your competitors are spreading spam this way, then if you use AI tools carefully to deliver genuine value to people, you can gain a tremendous advantage.

"If you figure out how to use these tools carefully to create value for people, you can gain a tremendous advantage right now. A tremendous advantage."

Ultimately, this is an era where 'the people who are best at editing' shine. Those who strip away the unnecessary and focus on the essentials will stand out in the competition.

"When everyone else is doing the opposite, that's how you stand out from the crowd."

The video closes by emphasizing that instead of 150 features that confuse users, what matters is focusing on the two core features that make a business better or make life better.

In Closing

Building an MVP in the AI era is no longer just about implementing features quickly — deliberate decisions about what not to build, and deep communication to understand users' true needs, have become more important than ever. AI is a powerful tool, but using it wisely to focus on essential value will be the key to success. 🔑