Most of what you've read about the dual-track process: preparing for an ai ipo is wrong. I know because I believed it too, and it cost me.
Smart founders prepare for both an IPO and an M&A simultaneously. I'll explain the 'dual-track' process, how it maximizes your leverage, and the key steps to prepare your AI company for both potential outcomes.
The Framework That Actually Works
I'm going to share the exact framework I use when evaluating the dual-track process: preparing for an ai ipo. It's not complicated, but it requires discipline.
Step 1: the data tells a different story than your gut This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: your team matters more than your technology Once you have the foundation right, this becomes much easier. I've watched founders struggle with this for months when the answer was staring them in the face.
Step 3: Iterate relentlessly Nothing works perfectly the first time. The companies in my portfolio that nail the dual-track process: preparing for an ai ipo are the ones that treat it as an ongoing process, not a one-time project.
The Reality Nobody Talks About
Most people approach the dual-track process: preparing for an ai ipo with assumptions that made sense five years ago. The world has moved on. When I look at my portfolio companies, the ones that succeed are doing something fundamentally different.
The first thing to understand is that the best solutions are often the simplest ones. I've seen this play out across dozens of companies. The pattern is unmistakable.
At RemoteTeam, we learned this the hard way. We spent months going down the wrong path before realizing that the market doesn't care about your roadmap. Once we made the switch, everything changed.
Real Talk: What Actually Matters
I'm going to cut through the noise and tell you what actually matters when it comes to the dual-track process: preparing for an ai ipo.
First, execution speed beats perfection. Every time. I've never seen a company fail because they moved too fast on the dual-track process: preparing for an ai ipo. I've seen plenty fail because they moved too slow.
Second, measure everything. If you can't measure it, you can't improve it. Set up tracking from day one, even if it's basic.
Third, talk to your users. This sounds obvious but you'd be amazed how many founders build their the dual-track process: preparing for an ai ipo strategy in a vacuum. Get out of the building. Talk to real people.
This connects to broader themes around AI exit strategies, AI startup pivots, AI talent wars, AI market sizing, AI due diligence that I've been thinking about a lot lately.
The Bottom Line
Look, the dual-track process: preparing for an ai ipo isn't rocket science. But it does require intentionality, consistency, and a willingness to learn from mistakes.
If you take one thing from this article, let it be this: start now, start small, and iterate. The founders who win at the dual-track process: preparing for an ai ipo aren't the ones with the best strategy on paper. They're the ones who execute, learn, and adapt faster than everyone else.
I've been doing this for over a decade. The patterns are clear. The companies that take the dual-track process: preparing for an ai ipo seriously outperform the ones that don't. Every single time.
If you're working on something interesting in this space, I'd love to hear about it. Drop me a line.
Frequently Asked Questions
What experience informs this perspective?
This perspective comes from over a decade of building companies in Silicon Valley, two successful exits (RemoteTeam to Gusto, MovieLaLa to Gfycat), and investing in 200+ startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.
How has this view evolved over time?
My thinking on most topics has changed significantly over the years. Early in my career, I held many conventional views that experience proved wrong. I try to update my beliefs when the evidence changes.
What's the most common pushback you get on this?
People often push back by citing exceptions or edge cases. And they're usually right that exceptions exist. But building a strategy around exceptions rather than patterns is a losing game for most founders.
How can I apply this thinking to my own situation?
Start by identifying the core principle behind the opinion, not the specific example. Then ask yourself: does this principle apply to my context? If yes, test it in a small, low-risk way before going all in.