Remember when GPS first came out and suddenly everyone could get anywhere twice as fast? Great, right? Except for that guy who drove into a lake because his GPS told him to. Or the countless delivery trucks that got wedged under low bridges. Or my personal favorite: the tourists who kept driving into someone's private driveway thinking it was the entrance to a national park.
That's where we are with AI in product development right now. We've got this incredible acceleration technology, and we're using it to ship the wrong things faster than ever before. It's like strapping a rocket engine to a shopping cart and pointing it vaguely toward success.
Failing Fast → Failing At Warp Speed
Here's what's happening in product teams everywhere: AI tools are making it ridiculously easy to go from idea to shipped feature. Claude Code and Cursor write your code. Atlassian Rovo drafts your specs. Figma Make and Loveable spin up prototypes and polished UIs in minutes. Automated testing catches your bugs. We're achieving what would have been science fiction five years ago – near-instantaneous delivery.
And our customers? They're experiencing product whiplash. Features appearing and disappearing like quantum particles. Interfaces morphing weekly. Every interaction feels like a beta test because, well, it basically is.
The uncomfortable truth that Itamar Gilad and others keep pointing out: most product ideas fail. Studies show only about one in three tested ideas succeed, even in high-performing companies¹. Now imagine multiplying that failure rate by AI-powered velocity. We're not just failing fast anymore – we're failing at warp speed.
¹ Why Impact/Effort Prioritization Doesn't Work
The Real Bottleneck Was Never Code
Here's the thing: most product teams already skip proper validation. They don't talk to customers weekly. They don't test assumptions. They build based on executive hunches and competitor FOMO. Teresa Torres has been championing this better approach for years².
John Cutler calls these places "feature factories" – organizations that measure success by output rather than outcomes³. Now imagine giving a feature factory AI-powered delivery tools. It's like putting a Formula 1 engine in a car that's been driving in circles around a parking lot. You're not lost anymore – you're lost at 200mph. The fundamental problem isn't speed; it's that nobody checked if customers actually wanted to go where you're headed.
² Teresa Torres on Product Discovery
³ 12 Signs You're Working in a Feature Factory
The Navigation Partner We Actually Need
Marty Cagan predicts that "product discovery will become the main activity of product teams, and gen ai-based tools will automate most of the delivery."⁴ But here's the catch that Christina Wodtke points out: when AI does the synthesis for you, you skip an essential part of the process. "You don't inhabit the data," she warns. "Inhabiting the data isn't just about getting insights. It's about how the act of wrestling with raw information rewires your brain."⁵
This creates an interesting tension. We need AI to handle the volume and complexity of modern product discovery – processing hundreds of customer interviews, analyzing competitive landscapes, and identifying patterns across vast datasets. But we also need to stay close enough to the raw customer reality to develop real product intuition. It's tempting to let AI do all the heavy lifting, but that's exactly how we lose the feel for what our customers actually need.
The teams getting this right aren't asking "How can AI help us code faster?" They're asking "How can AI partner with us to build the right thing in the first place?" They understand that optimizing delivery when discovery is your actual constraint is like turbocharging the engine when your navigation system is broken – you'll just arrive at the wrong destination faster.
⁴ A Vision for Product Teams
⁵ AI Can Synthesize Data for You, But Should It?
Arriving at the Right Destination
So as you implement that shiny new AI-powered delivery pipeline, ask yourself: Is delivery actually your constraint? Or are you about to become really, really good at shipping the wrong things?
The data should sober us up. MIT found that 95% of AI pilots yield no business impact⁶. Not because the technology doesn't work, but because teams are accelerating in the wrong direction. Meanwhile, Atlassian's research reveals a stark reality: 68% of developers save over 10 hours weekly using AI, yet they don't feel more productive because organizational bottlenecks remain untouched⁷. The difference between those who see gains and those who don't? They're applying AI where it actually matters.
The winners in this new era won't be the teams that ship fastest. They'll be the teams that validate fastest, learn fastest, and pivot fastest when they discover they're wrong. That's where AI becomes a true partner – not in writing more code, but in understanding whether that code should exist at all.
But here's what separates the good from the great: feeding those learnings back into the system. Speed still matters – you need to ship to learn. But when AI helps you validate the right things to build AND helps you incorporate those insights back into your strategy, you create a compound effect. Each cycle makes your next decision exponentially better. That's the real competitive advantage.
Let's stop using our GPS to go faster in circles. Point it toward actual customer value first, then floor it. Your customers – and your future selves – will thank you.
⁶ 95% Companies Failing with AI: An MIT Nanda Report Misread by All
⁷ AI Productivity Gains Are Being Offset by Organizational Bottlenecks