When a founder says "nobody else can make this call as well as I can," that's usually not a statement about the team's capability. It's a statement about the founder's inability to explain how they think. Those are very different problems with very different solutions.

A startup hires a senior product leader. Six to twelve months later, that person is gone. The internal narrative is some version of "it didn't work out". Maybe they weren’t experienced enough, maybe they weren't a culture fit, maybe they just couldn't hack it at this stage. The company goes back to market, finds someone new, and the cycle repeats. Everyone treats each failure as an isolated hiring mistake rather than a symptom of something structural.

I want to talk about what's actually happening here, because I don't think most companies are honest with themselves about it. The problem usually isn't the person. The problem is that the company has created conditions where no one could succeed, and then blames the individual when they don't.

When was the last time someone on your team told you that you were wrong about something important, and you changed your mind? If you can't remember, that's not because you're always right. It's because the magnetic field around you has gotten so strong that every compass in the building is pointing at you instead of at reality.

This is a true story about two companies that made very different choices about how to handle complexity. I know it’s true because I worked at both.

Each was trying to grow, serve customers better, and scale. But they responded to complexity in opposite ways. One met it with clarity and constraint. The other met it with technical intricacy.

Both believed they were making smart choices. Only one built something that lasted.

A provocative answer is that [the process of building software products using AI] is fundamentally different and none of what applies to a traditional product is useful. At the other extreme, a conservative answer is that it isn’t different at all – it is simply old wine in a new bottle. From my experience leading teams in the creation of several AI-driven products, the truth lies somewhere in the middle. The core principles are the same, but the specifics are different in significant ways.

More companies are turning to fractional product leaders—experienced VPs and CPOs who embed with the team part-time—to help them build focus, momentum, and clarity without needing to make a full-time hire. And it’s not just startups trying to save on headcount. It’s Series B and C companies that need to scale fast. It’s founder-led orgs looking to shift decision-making to the team. It’s growth-stage companies realizing that having someone ‘own the roadmap’ isn’t the same as having strong product leadership.

The arrival of AI as a creative partner and co-author means we’re entering an era where the cost of building a product approaches zero. That doesn’t make building irrelevant. It makes it foundational: a necessary but in and of itself insufficient step. The winners will be the ones who pair that speed with thoughtfulness. Who use the time saved by automation not to crank out more features, but to get closer to the user, to test better hypotheses, to sweat the details that AI can’t see.

​When product discovery is neglected, the costs quickly add up. In one case from personal experience, the cost was 10 person-years of effort spent on a failed launch that didn’t resolve the underlying customer problem. Despite the growth of dedicated product organizations, many product managers still find themselves constrained by waterfall delivery structures designed with little autonomy that hinders their long-term product success. Product Managers frequently lack the ability to direct their roadmaps based on product effectiveness and customer value. The root issue often lies in organizational culture, where delivery milestones overshadow customer value and experimentation.

AI is turning all of us into passable designers, average developers, and decent writers. But not experts. And if we’re not careful, we’ll start mistaking volume for value, and output for outcomes. “I can, therefore I will” is a thought process that destroys companies.

When organizations flatten hierarchies and outsource key decision-making to AI, they create PMs who look like they know what they’re doing but lack the deep, nuanced understanding required for long-term success. AI-driven tools promise to exacerbate this further by making execution easier, reinforcing a cycle where PMs remain stuck in the mechanics of the role rather than engaging in deeper strategic thinking.

The key difference is that teaching scales, while doing does not. If your approach to leadership is just an extension of your approach to product management, if you are still in the details and still making sure every major decision is right, you are not actually leading. You are just a really senior PM.

If AI begins to assume all roles in the product development process — acting as product manager, designer, and engineer — we’re not just witnessing an evolution in product development; we’re staring at an extinction event for traditional workflows. With AI serving as both strategist and executor, organizational communication would become highly structured, frictionless, and nearly instantaneous. But what does this really mean?

The impulse to move fast is powerful. Tech culture celebrates speed, iteration, and rapid execution. But there’s a difference between moving fast and moving with purpose. The best products aren’t the ones that accumulate the most features. They’re the ones that stay focused, intuitive, and built with intention. So the next time you’re faced with a new request, ask yourself: Are we moving fast and breaking things? Or are we taking the time to ensure we’re building the right thing?

A company sets a goal to “onboard 100 customers by the end of Q2.” On paper, it’s a textbook example of a SMART goal: specific, measurable, and time-bound. But as the quarter progresses, cracks emerge. Teams scramble to hit the target at all costs, sacrificing quality, innovation, and sustainability. Customers are onboarded, but many have a poor experience, disengage quickly, or fail to adopt the platform effectively.

What went wrong? The problem isn’t the ambition, it’s the framing. Rigid, number-driven goals often backfire, creating tunnel vision, short-term thinking, and even unethical behavior. The solution is to pair goals with meaningful constraints: parameters that balance quantity with quality and short-term wins with long-term success.

In product management, prioritization isn't just a best practice, it’s a necessity. With limited engineering resources, tight timelines, and fierce competition, deciding what to build next is a high-stakes game. Every hour spent on one feature is an hour not spent on another, and the opportunity cost of building the wrong thing can be crippling. It’s not just about delivering features; it’s about making the bold, strategic moves that will shape your product’s future and determine your company’s success. Prioritization helps you navigate these constraints by focusing on the features that deliver the most value, ensuring that every decision drives impact, aligns with business goals, and keeps you ahead of the competition.

Start by anchoring your roadmap in a clear product vision. Define the long-term impact your product aims to achieve. Without this north star, the roadmap can become just another list that lacks cohesion. The vision acts as a filter for evaluating any feature or initiative's relevance.

Your product vision should articulate the journey you're inviting users to undertake, where they, the heroes of your story, overcome challenges with your product's help. This story anchors everything else.

The problem with unchecked and unintentional complexity is that it gradually narrows the 'solution space' — the range of future improvements, adjustments, or innovations that can be realistically pursued. Picture the solution space as a playground where designers and developers have room to explore new ideas and tackle emerging challenges. As complexity accumulates without careful management, walls begin to form in this playground, limiting the freedom to experiment and improve. Changes become riskier, and small tweaks can lead to a cascade of unintended consequences. The more convoluted the system, the fewer opportunities there are to introduce meaningful, impactful updates without further complicating the product or alienating users.

In my path to product management, I've been shaped by both luck and a diverse background in computer science, economics, and data analysis. Flexibility has been key as I’ve navigated various industries, from e-Commerce to FinTech to InsurTech. In highly regulated sectors, the challenge lies in balancing innovation with compliance, anticipating regulatory changes, and managing risks. I’ve learned that well-rounded skills and strong collaboration are essential to managing complexity and adapting effectively, especially when working within strict regulations while still driving meaningful product development.

Are LLMs coming for your job as a Product Manager? Not so fast. While tools like ChatGPT are powerful, they still fall short of replacing the creative and logical reasoning that PMs bring to the table. In this post I explore four critical blockers that will keep PMs indispensable in the evolving tech landscape.