So, you’ve left the corporate world, and now it’s time to build your own startup. You’ve probably managed dev teams before, overseen product launches, maybe even helmed some fancy project management tools that made everything run like a well-oiled machine. You’ve done this before, right? Not exactly. When it’s your startup, everything changes—and, as I’ll explain, if you assume it’ll work the same way, you’re heading for a few surprises.

Startup founders often fall into a dangerous trap when starting a software project from scratch: thinking it’ll be just like building software inside an established company. Here’s why it’s not—and some advice on how to navigate the differences.

1. Switching from Product Manager to Teacher

In an established company, a software team already has two things that give them a serious edge: an existing market and a deep understanding of the business. They’re working within a proven model. Developers in that environment know what questions to ask, can fill in gaps intuitively, and likely understand why they’re building what they’re building.

At a startup, however, your devs are going to need a whole lot more context. They’re not working with familiar requirements—they’re working with your vision, which may be abstract at this stage. If your development team doesn’t understand why something matters, it’s a recipe for ambiguity and frustration on both sides.

Advice: Think of yourself less as a product manager and more as a teacher. Your job is to make sure they understand the core problems, not just the features. Teach them why each requirement matters, help them visualize the end-user, and create that shared language for decision-making. It might feel tedious, but it’s essential to avoid future misalignment and expensive rewrites.

2. Beware of Perfectionism — It’s the Budget Killer

In a large company, products with an existing user base often have to be polished. Features need to be rock-solid, invoices have to be perfect, and everything needs an audit trail. Startups, however, have a different goal: get an MVP in the hands of users fast. It’s a classic trap for first-time founders—focusing on “perfection” and “polish” before knowing if the business model even works.

Startup perfectionism is budget poison. It’s shocking how quickly adding “nice-to-have” features can chew through funding, especially if you’re paying a dev team to build things like automated invoicing or churn management before you’ve even proven people want what you’re selling.

Advice: Ruthlessly strip down your MVP. If a feature doesn’t help you validate your market, it goes on the “later” list. Keep the scope laser-focused on what helps you test your business assumptions. Let the non-essential features wait until you know you have customers who’ll use them.

3. Zen and the Art of the Startup Pivot

Building software for a startup means embracing one cold, hard truth: the business model will change. According to research, 93% of successful startups pivot at least once (and often more). Imagine being asked to go out and passionately sell something that you know might not look the same next year—or next month. It takes a level of zen acceptance that your original idea will likely morph, but that’s what keeps you flexible and ready to capture new opportunities.

For founders, that requires a mindset shift. You have to believe in your product, while also knowing you might be building the “wrong thing” in some way. The focus should be on preserving capital and brainpower for what’s next. The game is less about proving you’re right and more about staying adaptable.

Advice: Budget with pivots in mind. Set your burn rate assuming you’ll need to make big changes. Don’t let ego get in the way of listening to the market, and keep enough gas in the tank for at least one big strategic turn.

4. The Hard Work of Being Your Own “Internal Customer”

Here’s another big one. In a corporate environment, you have internal customers—departments or stakeholders with specific goals that align with the overall company mission. For a startup, the only customer you have is you. You don’t have a preexisting feedback loop from various departments, and you don’t have established success metrics. You have to create that from scratch.

Advice: Start by building an internal customer profile based on your target market, then use that to set clear goals and success criteria for your dev team. If you’re focused on, say, usability for early adopters, set KPIs around usability testing and build from there. By acting as your own “internal customer,” you’re setting a clear direction and saving your team from working in a vacuum.

5. Get Ready to Build AND Sell

Corporate software development often has the luxury of a separate, dedicated sales team to deliver the product to the right audience. As a startup founder, you’re both the builder and the seller. That means you’re not just iterating on software—you’re iterating on messaging, product-market fit, pricing, and maybe even distribution models.

Advice: Factor in time for sales-ready iteration in your dev cycle. As you build, keep track of how each release or update affects the user experience. Ask yourself if the changes make your pitch clearer or simpler and how they align with the current market’s needs. Ultimately, this approach will help you bridge the gap between building the product and ensuring it’s market-ready.

Conclusion

Building software as a startup founder requires a whole different toolkit than you may be used to. You’re part-teacher, part-salesperson, part-zen master, and always the chief budget officer. By recognizing the unique mindset shifts and traps of startup software development, you’re positioning yourself—and your team—for the best chance of success. Focus on creating clarity for your team, set ruthless priorities, embrace change, and never lose sight of the fact that the first version is just the beginning. In the startup world, adaptability isn’t just a skill—it’s the entire game.

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I help companies turn their technical ideas into reality.

CEO @Sourcetoad and @OnDeck

Founder of Thankscrate and Data and Sons

Author of Herding Cats and Coders

Fan of squash, whiskey, aggressive inline, and temperamental British sports cars.

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The State of AI-Coded Software, May 2025

I’ll probably regret writing this. At the very least, I’ll cringe reading it in a few months. But here we are.

Lately, we’ve been getting a wave of client requests asking us to evaluate software they built using AI tools. These aren’t engineers. These are business folks using increasingly powerful AI products to try and build functioning systems. And to be completely honest, the results are both impressive and a bit alarming.

People are building whole applications on their own. Backends, frontends, user interfaces, even some database logic. Sometimes they even look good. These are smart people who don’t know how to code but have managed to produce working systems.

The problems show up immediately when we start reviewing the internals. The code is usually a mess. In many cases, it would be extremely difficult to maintain or extend. And if you need to move that code from the platform it was created in to a cloud provider like AWS, you’re going to hit a wall. These platforms wrap everything in layers of scaffolding that make portability a nightmare.

Security is worse. We’ve found plaintext credentials scattered across files. We’ve seen SQL injection vulnerabilities that shouldn’t even be possible in modern frameworks. We’ve seen structural issues that would get flagged in a freshman CS class.

Despite all that, what people are creating are legitimate prototypes. They’re functional. They run. But when you’ve put a few weeks into building something, and you show it to a software engineer, it’s hard to hear that your shiny new thing needs to be rebuilt from scratch.

I want to be clear. I am not anti-AI. Almost everyone at my company uses AI tools every day. We use Copilot, Cursor, ChatGPT, Claude, and more. We build out frontends with tools like v0 and Lovable. These tools have changed how we work.

Some of our engineers report productivity improvements of 30 to 40 percent. That’s not a rounding error. That is a major shift. But they are still writing the code. They are reviewing it. They are checking for performance, clarity, security, and maintainability. They are not letting the tools decide architecture. They are using AI like they use autocomplete or linters, but with more power behind it.

This is also where expectations need to be adjusted. These systems will not save you 90 percent on development. They will not let you skip engineering altogether. But if they save you 30 percent, that’s a real gain. Imagine you’re building a house. The general contractor says it’s going to be $500,000. You tell them you already did the blueprints, filled out all the permits, and figured out the site plan using some AI tools. If they came back and said, “Alright, I’ll knock 30 percent off,” that would be the best deal of your life. That’s where we are today with AI-generated software. A solid start. A real value. Not a replacement.

For me personally, AI has made it fun to write code again. I haven’t been a working programmer in over a decade, and most modern toolchains are enough to scare me off. Now, with the right assistance, I can build something without getting stuck on Docker configs and dependency mismatches. That’s a big deal.

In the startup world, AI-first development is everywhere. Most of the current Y Combinator batch is using AI tools to write the bulk of their code. But those teams are highly technical. These are engineers using better tools, not tools replacing engineers.

So for non-developers using AI to build products, here are three things you should keep in mind:

  1. These tools are great for building prototypes. If you build something yourself, you will understand it better and will be a better partner to your engineering team. That matters.
  2. These tools can help you build usable frontend components. You probably won’t want to go live with them, but they can get you close enough to work with a real development team.
  3. If your app is small, non-critical, doesn’t store sensitive data, and lives entirely in its native platform, you can probably keep it running. That’s fine for internal use or personal projects.

One day, you’ll be able to speak an app into existence and deploy it with a voice command. It will be fast, secure, and beautiful. But today, you still need an experienced software engineer to check your work before you send real data through it. That’s just where we are right now.

The upside is huge. We can now get experts from other domains to build working prototypes and test ideas without needing an engineering team on day one. That’s powerful. But if your product is going to handle sensitive data or support real users, bring in someone who knows what they’re doing. Not because the AI is bad. Because the stakes are high.