The Product Bottleneck

For decades, the bottleneck in product development was engineering. You'd have more ideas than you could build. More features requested than engineers available. The constraint was always: can we build this fast enough? AI changed that equation. When it's increasingly easy to go from a clearly written spec to working code, the constraint shifts upstream. The new bottleneck is deciding what to build—writing that clear spec, reading the signals correctly, making the right call on what matters. I've seen this shift firsthand. At Pangea.ai, we reduced specification time by 75%—from 4 weeks to 1 week—using AI-assisted prototyping. But that speed only matters if you're building the right thing. The time saved on coding gets wasted if you're coding the wrong feature. The fastest teams aren't the ones with the most engineers. They're the ones with the clearest product thinking.

Here's a number that should get your attention: the engineer-to-PM ratio is trending from 8:1 toward 2:1—and in some cases, 1:1. What does this mean practically? The old model: One product manager writes specs for eight engineers. The PM is the bottleneck for decisions, but engineers are the bottleneck for execution. You hire more engineers to move faster. The new model: Engineering velocity increases faster than product decision-making capacity. Now one PM can't feed enough clear specs to the engineering team. The PM becomes the constraint. The implication: Companies need more product capacity. Not necessarily more product managers in the traditional sense—but more people who can do the product work: talking to users, reading signals, writing clear specs, making fast decisions. This is why engineers who can also shape product are moving fastest. They don't wait for specs. They talk to users, develop intuition for what to build next, and iterate. The feedback loop is tight because there's no handoff.

Here's the uncomfortable truth: pattern recognition is increasingly commoditized. AI is getting very good at spotting patterns—in data, in user behavior, in market trends. If your competitive advantage is recognizing patterns that happened before, you're competing with machines. What AI struggles with: signals. Signals are emerging, ambiguous, contextual. They require judgment. A pattern says "this happened before." A signal says "this might be happening now—and here's what it could mean." Reading signals means: - Hearing what users don't say, not just what they do say - Noticing market shifts before they become obvious - Understanding why a trend is happening, not just that it's happening - Making calls with incomplete information At Pangea.ai, we achieved 60% operational efficiency gains not by spotting patterns in data, but by reading signals from users and the market. We saw that the hiring landscape was shifting toward AI-native workflows before it was obvious. That signal—not a pattern—drove our product decisions. The people who will thrive in the AI era are signal readers, not pattern matchers.

The fastest-moving people in startups today share a common profile: they can shape what to build AND ship it themselves. They're not waiting for someone else to write the spec. They're not waiting for someone else to talk to customers. They're not waiting for someone else to make the decision. They do it all. Signal → Spec → Ship → Learn → Repeat. This isn't about being a "full-stack" anything. It's about eliminating handoffs that slow things down. When you can go from customer conversation to shipped feature without waiting for someone else at each step, your velocity compounds. At AlphaPipe, I built European operations from 0 to 30 people by operating this way. I didn't wait for product decisions to flow down from headquarters. I was in the market, reading signals, making calls, and building systems. The operation scaled because decisions happened fast. The same principle applies to product. The operators who can read signals, scope correctly, and ship quickly are operating at a different speed than those who are waiting in line at each handoff.

If you're in product and you can't ship, you're in trouble. The value of pure strategy—writing specs and waiting for others to build—is declining. Execution velocity matters more than ever. If you're in engineering and you can't shape product, you're also in trouble. The value of pure implementation—building exactly what specs say—is declining. Product intuition matters more than ever. The sweet spot is the combination: people who can read signals, scope the right thing, and ship it themselves. Here's how to get there: For product people: Get closer to the code. You don't need to become an engineer, but you need to understand what you're asking for. Use AI tools to prototype. Build something yourself, even if it's rough. Close the gap between spec and reality. For engineers: Get closer to users. Don't wait for specs to tell you what matters. Talk to customers. Develop intuition for what to build. Make product decisions, not just implementation decisions. For everyone: Learn to read signals. Practice interpreting ambiguous information. Make calls with incomplete data. The skill of reading signals—and being right more often than wrong—is the new competitive advantage.

There's another angle to this shift: companies are drowning in AI hype. Every CEO has read LinkedIn posts about agents, copilots, and AI transformation. They want "an agent" because someone told them they need one. But they don't know what that means or whether it solves their actual problem. 85% of AI projects fail. Not because the technology doesn't work—but because they're poorly scoped. Companies jump to solutions before understanding problems. They build what's trendy instead of what's needed. The skill that's becoming valuable: asking "why" before "how." A CEO says: "We need an agent for our sales team." The wrong response: Start building an agent. The right response: "What problem are you trying to solve? Why do you think an agent solves it? What would success look like?" Often the real need is simpler than "an agent." Maybe it's automated research. Maybe it's better CRM hygiene. Maybe it's a workflow that doesn't require AI at all. The people who can cut through hype, ask the right questions, and scope projects correctly are becoming essential. Not because they're skeptical of AI—but because they're focused on solving problems, not implementing buzzwords.