A founder asked me last week about the counterintuitive guide to model distillation that actually. My answer surprised them, and it might surprise you too.
I distilled my first model in 2021 and failed miserably. After years of trial and error, I've developed a counterintuitive approach to model distillation that delivers smaller, faster models without sacrificing performance. Here's my step-by-step process.
The Reality Nobody Talks About
Most people approach the counterintuitive guide to model distillation that actually 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 you need to move fast and break things. 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 simplicity beats complexity every time. Once we made the switch, everything changed.
The Counterintuitive Truth
Here's what surprised me most about the counterintuitive guide to model distillation that actually: the best practitioners do less, not more.
When I was building MovieLaLa, we tried to do everything at once. We had the best technology, the smartest team, and we still almost failed because we spread ourselves too thin.
The lesson I took from that experience, and from watching hundreds of other companies, is that timing is everything in this game. It sounds simple. It's incredibly hard to execute.
The Numbers Don't Lie
I've tracked the performance of companies in my portfolio that take the counterintuitive guide to model distillation that actually seriously versus those that don't. The difference is stark.
Companies that invest early in the counterintuitive guide to model distillation that actually see, on average, 2-3x better outcomes within 18 months. That's not a small edge. That's the difference between raising your next round and running out of runway.
One of my portfolio companies went from struggling to profitable in under a year after they finally got serious about this. The founder told me later that they wished they'd started sooner.
This connects to broader themes around on-device AI, model distillation, small language models that I've been thinking about a lot lately.
Final Thoughts
After two exits, 200+ investments, and more mistakes than I can count, here's what I know for sure about the counterintuitive guide to model distillation that actually: there are no shortcuts, but there are smarter paths.
The smartest founders I work with treat the counterintuitive guide to model distillation that actually as a competitive advantage, not a checkbox. They invest in it early, measure it obsessively, and never stop improving.
If you're just getting started with the counterintuitive guide to model distillation that actually, don't be intimidated. Everyone starts somewhere. The key is to start with the right mindset and the right framework, and then execute like your company depends on it. Because it probably does.
Frequently Asked Questions
How should I work through this guide?
Don't try to absorb everything in one sitting. Read through once to get the big picture, then go back and work through each section as it becomes relevant to your current challenges. Bookmark it and return to it regularly.
Is this guide based on real experience?
Every recommendation in this guide comes from direct experience, either from building and selling my own companies, or from patterns I've observed across 200+ angel investments. I don't write about things I haven't personally tested.
What if I disagree with some of the advice?
Good. That means you're thinking critically, which is exactly what a good founder should do. Take what resonates, test it, and discard what doesn't work for your specific situation. No advice is universal.