What Most Founders Get Wrong About Implementing AI

Every entrepreneur I speak with has the same gleam in their eye when they talk about AI. Many are conceived as AI-first, and they can’t wait to change the world. For others, they’ve seen the demos, read the case studies, and they’re ready to transform their business overnight. But in my conversations with founders, I keep encountering the same critical mistake: In their race to adopt AI while at the same time survive and capture market share, they’re making technical decisions today that they may likely live to regret as they will become their biggest growth inhibitor tomorrow.

As a field CTO at Google working with startups, I’ve watched brilliant companies hit a wall not because their vision was wrong, but because the shortcuts they took to get their first customers became the chains that prevented them from scaling. In other words, they wrote a check for technical debt that they could never satisfy.

Related: What Scaling My Startup Taught Me About Bad Tech Decisions

The sprint-to-survive paradox

Startups face a unique paradox. You’re born in the cloud with access to cutting-edge technologies. Yet in your urgent push to market, you make architectural decisions that create your own form of technical debt. Sometimes it’s even more insidious because it’s hidden behind modern frameworks and fresh code.

Think of it like building a race car while you’re already racing. Every shortcut you take, using duct tape instead of proper welds, jury-rigging connections, compounds into problems that will eventually force you to pit or crash.

A startup closes its first major customer by hardcoding features. They choose a database that’s quick to implement but won’t scale past 10,000 users. They build everything as a mini-monolith because it’s faster than designing microservices. Each decision makes perfect sense because it solves the most immediate problem, and you need to survive today to have tomorrow’s problems.

The true cost of shortcuts

However, technical debt works exactly like credit card debt. You’re moving quickly, trying to capture an opportunity without the resources to do it right. So you borrow against your future by choosing quick database solutions, hardcoding configurations, building tightly coupled applications and over-customizing for the sake of getting that one next customer.

Sometimes that’s exactly what you have to do. When an executive says, “We need this for one customer or we’re bankrupt tomorrow,” you make that decision with eyes wide open. But here’s what separates successful companies from those that implode: They have grown-up conversations about the real cost.

If you’re going to accumulate technical debt, document it and allocate future resources to pay it down. Set aside 10% of your next raise for addressing shortcuts. Don’t sweep it under the rug, as those are the problems that wake you up at 2:00 a.m. when systems fail.

Here’s a framework I use to gauge startup health: the 70-20-10 rule. Seventy percent of resources should focus on new features and customer needs. Twenty percent goes to maintaining what you’ve built — bug fixes, support, keeping systems running. Ten percent imagines the next major transformation.

So when that 20% maintenance bucket grows to 40 or 50% after just two years, that’s a red flag. You’re spending more time managing early decisions than innovating. That architecture you chose for your MVP is eating engineering hours. The unified specialty hosting provider that let you ship quickly now takes days to deploy simple changes, if you can make them at all.

Related: If You Want Your Business to Win in the Long-Term, Don’t Take Shortcuts

The AI reality check

Still, the biggest misconception among founders is that AI will solve undefined problems. Companies rush to implement AI because investors expect it, only to discover they lack the data infrastructure to support it.

Instead of asking, “How can I replace this person with an AI agent?” ask, “How can I make everyone 50% more capable?” AI is powerful, but it’s just another tool with specific requirements.

Startups have an advantage because they’re not dealing with decades of unstructured data. But if you don’t think about data structure from day one, you’re creating your own legacy problem. You need to build data collection and governance into your architecture from the beginning. Don’t wait until an enterprise customer demands it.

When to pay the piper

For resource-constrained startups, when do you invest in infrastructure versus features? Between inception and product-market fit, it’s about survival. Sometimes you have to be sloppy. Successful startups are like juvenile delinquents — they’ll break the rules but not the law.

The key is surviving long enough to reach the point where strategic infrastructure investments make sense. When you have enough cash and active customers to feel confident that you’ll still be around in 12 months, that’s when you can start thinking longer-term.

But if you wait too long, costs can spiral. I’ve seen startups that could have invested $100K in year two end up spending $2M by year four for the same improvements, while losing momentum to competitors who built thoughtfully from the start.

Building smart from day one

Even young companies can make smart infrastructure choices without breaking the bank. Serverless computing, managed databases, API-first architectures and containerization don’t cost more. They just require discipline.

By using cloud-native services, maintaining a clean separation of concerns, choosing open source over expensive licensed software and building with microservices in mind, even if starting with simpler architectures, startups maintain flexibility as they grow.

The winning startups aren’t just the ones with innovative features. They’re the ones whose systems let them fail fast, adjust quickly and make micro-changes without drowning in change control. They built for iteration from the beginning and plan for the future without overcommitting in the present.

Related: How to Build the Infrastructure Needed to Scale Your Company

Build for tomorrow, not just today

Don’t implement AI just to tell your board you have AI. Focus on your biggest challenges. What happens if you don’t solve them? AI should address real problems, not exist for its own sake.

Ask yourself: “If we grow 10x next year, will our architecture support that, or will we spend our Series A fixing what we built in our garage?”

The startup world celebrates speed, but there’s wisdom in knowing which corners to cut and which foundations to protect. Once you’ve made these mistakes a few times, it becomes easier to justify being thoughtful from the outset.

The question isn’t whether you can afford proper infrastructure. It’s whether you can afford not to.

Your future self — and future CTO — will thank you for the foundation you build today.

Every entrepreneur I speak with has the same gleam in their eye when they talk about AI. Many are conceived as AI-first, and they can’t wait to change the world. For others, they’ve seen the demos, read the case studies, and they’re ready to transform their business overnight. But in my conversations with founders, I keep encountering the same critical mistake: In their race to adopt AI while at the same time survive and capture market share, they’re making technical decisions today that they may likely live to regret as they will become their biggest growth inhibitor tomorrow.

As a field CTO at Google working with startups, I’ve watched brilliant companies hit a wall not because their vision was wrong, but because the shortcuts they took to get their first customers became the chains that prevented them from scaling. In other words, they wrote a check for technical debt that they could never satisfy.

Related: What Scaling My Startup Taught Me About Bad Tech Decisions