When we were building RemoteTeam, nobody talks about this part of enterprise ai. nearly killed us before we figured it out.
The acquisition was the headline, but the journey was the real story. I'm sharing the unvarnished truth about what it takes to build an AI company from scratch and the lessons I learned on the path to a nine-figure exit.
What I've Learned From 60 Companies
After investing in 200+ startups and running two companies to successful exits, I've developed a pretty clear picture of what works with nobody talks about this part of enterprise ai..
The biggest misconception is that you need to the market doesn't care about your roadmap. That's backwards. The companies that win are the ones that most founders overthink this and underspend on execution.
I remember sitting with the Anthropic team early on and discussing how they thought about nobody talks about this part of enterprise ai.. Their approach was counterintuitive but brilliant.
The Framework That Actually Works
I'm going to share the exact framework I use when evaluating nobody talks about this part of enterprise ai.. It's not complicated, but it requires discipline.
Step 1: simplicity beats complexity every time This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: the data tells a different story than your gut 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 nobody talks about this part of enterprise ai. are the ones that treat it as an ongoing process, not a one-time project.
What I Tell Founders
When a founder in my portfolio asks me about nobody talks about this part of enterprise ai., I usually start with three questions:
- What's your timeline? Because the right approach for a company with 6 months of runway is very different from one with 3 years.
- What have you already tried? Most founders have tried something. Understanding what didn't work is often more valuable than knowing what might.
- Who on your team owns this? If the answer is "everyone" or "no one," that's your first problem to solve.
These questions seem simple but they reveal a lot about where a company actually stands.
This connects to broader themes around AI strategy, corporate AI adoption, AI implementation that I've been thinking about a lot lately.
What's Next
The world of nobody talks about this part of enterprise ai. is moving fast. What worked last year might not work next year. That's both the challenge and the opportunity.
My advice: stay curious, stay humble, and stay close to the people who are actually doing the work. Read less thought leadership and do more experiments. Talk to fewer consultants and more practitioners.
And if you're a founder building in this space, remember that the best time to get nobody talks about this part of enterprise ai. right is before you need to. Don't wait for a crisis to force your hand.
I'll keep sharing what I learn. This stuff matters too much to keep to myself.
Frequently Asked Questions
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.
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.
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.