After 200+ angel investments, I've seen the same what i learned after 5 years of optimizing llm inference mistake destroy companies over and over.
After working in inference optimization for five years, I'm sharing the unexpected lessons I've picked up. This is about practical results and the trade-offs most people don’t talk about.
What I've Learned From 79 Companies
After investing in 200+ startups and running two companies to successful exits, I've developed a pretty clear picture of what works with what i learned after 5 years of optimizing llm inference.
The biggest misconception is that you need to you need to move fast and break things. 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 what i learned after 5 years of optimizing llm inference. Their approach was counterintuitive but brilliant.
The Reality Nobody Talks About
Most people approach what i learned after 5 years of optimizing llm inference 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 simplicity beats complexity every time. 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 data tells a different story than your gut. Once we made the switch, everything changed.
Lessons From the Trenches
I want to share a few specific lessons I've picked up over the years. These aren't theoretical. They come from real companies, real failures, and real successes.
Lesson 1: The best time to start thinking about what i learned after 5 years of optimizing llm inference was yesterday. The second best time is now. Don't wait until you have the perfect plan.
Lesson 2: Hire for attitude, train for skill. The best what i learned after 5 years of optimizing llm inference practitioners I've met weren't the most technically gifted. They were the most curious and persistent.
Lesson 3: Your competitors are probably getting this wrong too. That's your opportunity. While everyone else is following the same playbook, you can zig when they zag.
This connects to broader themes around token economics, model distillation, inference optimization that I've been thinking about a lot lately.
Wrapping Up
I've shared a lot here, and I know it can feel overwhelming. But here's the thing about what i learned after 5 years of optimizing llm inference: you don't need to get everything right on day one. You just need to get started and keep improving.
The founders in my portfolio who excel at what i learned after 5 years of optimizing llm inference share one trait: they're relentlessly practical. They don't chase perfection. They chase progress.
That's the mindset I'd encourage you to adopt. Start where you are. Use what you have. Do what you can. And keep pushing forward.
As always, I'm rooting for you.
Frequently Asked Questions
How long did it take to see results?
Most meaningful business results take 3-6 months to materialize. Anyone promising overnight success is selling something. The companies in my portfolio that grew fastest were the ones that stayed patient and consistent.
What would you do differently looking back?
I'd move faster on the things that were working and cut the things that weren't sooner. Most founders, myself included, hold onto failing strategies too long because of sunk cost. Speed of learning is everything.
What was the biggest challenge in this case?
Almost always, the biggest challenge is people and alignment, not technology or strategy. Getting the right team focused on the right problem is harder than any technical challenge I've encountered.