The Question AI Can't Answer for You

There has never been a moment in history when the tools available to an aspiring entrepreneur were this powerful. With the right AI stack, a single motivated person can compress months of development work into weeks, produce content and code and analysis that would have required an entire team a decade ago, and launch something that looks and functions like a real business before they've spent their first dollar on payroll. The barriers to entry have genuinely fallen.

What hasn't fallen — and what no tool can lower — is the barrier to building something that actually works. Something that solves a problem people will pay to have solved, at a price point that sustains the business, at a scale that makes the effort worthwhile. That question is as hard as it has ever been. And the danger of the current moment is that the ease of building makes it easy to confuse momentum with traction.

I've been thinking about this for a long time. Not as an observer — as someone who has lived it.

The Lesson I Learned the Hard Way. Twice. Then Again.

In the 1990s, I was part of a team building one of the early e-prescribing platforms in healthcare. The idea was right. The problem was real. Today, e-prescribing is universal — it has fundamentally changed how medications are prescribed and managed. We were early to a space that would eventually become standard practice across American medicine. We still didn't make it.

A few years later, I served as CEO of a for-profit subsidiary structured around a model that would look familiar to anyone who knows CPESN — a national network of independent pharmacies that has grown into a significant force in value-based care. The model existed and worked. We were simply operating a version of it roughly twelve years before the market, the infrastructure, and the incentive structures were aligned enough to support it.

In 2012, I was involved in another healthcare technology venture, this one focused on medication therapy management: helping patients manage complex medication regimens through pharmacist-led clinical services. Again, the direction was sound. MTM is a recognized and reimbursed discipline today. Again, we didn't get a return on the time and money we put in.

That is a specific and important distinction. It means the problem wasn't that I was wrong about the future. It means I was insufficiently disciplined about the present: about who was actually willing to pay, how much, what the competitive dynamics looked like, and whether the assumptions underlying the model would hold under pressure. I was building before I had validated the foundation.

Those experiences are expensive teachers. They are also, I've come to believe, the most common pattern in entrepreneurial failure — not bad ideas, but unvalidated ones. And AI, for all its power, does nothing to change that pattern. In some ways, it accelerates it.

Why the AI Moment Makes This More Urgent, Not Less

Here is the paradox at the center of the current enthusiasm about AI and entrepreneurship: the easier it becomes to build, the more important it becomes to think clearly before you build.

When building required significant capital, a team, and months of development time, the friction of the process itself forced a kind of discipline. You had to convince investors. You had to persuade co-founders to walk away from stable income. You had to explain yourself, repeatedly, to people with skin in the game. That friction was often frustrating. It was also, quietly, a form of stress-testing.

When a solo operator with AI tools can have a functional product in the market in a matter of weeks, that friction largely disappears. What replaces it? For most people: nothing. They move faster, build more confidently, and discover too late that velocity without direction is just a more efficient way to end up somewhere you didn't want to go.

This matters more for leaders and experienced professionals than it does for early-career risk-takers. A 28-year-old who takes a swing at a startup and misses loses a year or two. A senior leader who bets accumulated career capital, savings, and reputation on a venture that hasn't been rigorously validated is playing a different game entirely. The stakes are real. The opportunity cost is real. The consequences of a miss are real.

That's not a reason to stay on the sideline. Many of the most impactful ventures are built by experienced leaders who bring domain expertise, professional networks, and hard-won judgment that no 22-year-old can replicate. But it is a reason to think before you leap — specifically, to ask the questions that most aspiring entrepreneurs either don't know to ask or don't want to ask because the answers might be uncomfortable.

The Questions That Actually Matter

Most business planning starts in the wrong place. It starts with the idea — with enthusiasm for what could be built — and works outward from there. The deck gets built. The pitch gets refined. The financial model gets constructed. And the fundamental questions about whether the business can actually work get treated as things to figure out later.

The questions that should anchor any serious evaluation are simpler and harder than a pitch deck:

Who specifically will pay for this, and what evidence do you have that they will pay at the price and volume your model requires? Not who could benefit. Who will actually write a check — and how do you know?

What does the competitive landscape actually look like, including the competition that doesn't look like competition yet? In my e-prescribing venture, one of the most significant competitive forces wasn't another startup — it was the inertia of a healthcare system deeply resistant to changing its prescription workflow. That's not a competitor you see on a market map, but it will defeat you just as surely.

What are the critical assumptions your model depends on, and what would it take to prove or disprove them quickly and cheaply? Rita McGrath's discovery-driven planning methodology asks a deceptively simple question: what would have to be true for this to work? Then it makes you test those assumptions before you've committed the resources that assume they're already true. I didn't have that discipline in my early ventures. It cost me.

What is the minimum version of this idea that could generate real market signal? Not a polished product. Not a beautiful brand. A real signal: someone who had a real problem, engaged with your real solution, and found it genuinely valuable.

The Role of a Thought Partner

I work with leaders who are seriously considering entrepreneurship — not people who are dabbling, but people who have built real careers and are asking whether the next chapter involves building something of their own. These are consequential conversations, and they deserve more than encouragement or caution as default responses.

What they deserve is honest, rigorous thinking: about the idea itself, about the market, about the timing, about the personal fit between the leader and what entrepreneurship actually demands of them. About whether the life they're imagining actually follows from the business they're proposing to build. And about what they'd need to learn, prove, or validate before it makes sense to make a serious commitment.

That's not a conversation AI can have with you. It requires someone who has been in the arena — who has made the investments, absorbed the losses, and built the pattern recognition to know the difference between an idea that's directionally right and a business that will actually work. It requires someone who will ask the uncomfortable questions and sit with the honest answers alongside you, rather than help you build a more persuasive case for a decision you've already made.

If that's the conversation you need, I'm here for it.