The best advice I ever got about confession: our a/b tests were useless until we came from a founder who'd failed at it three times.
The AI landscape shifts every six months. What worked yesterday is already becoming obsolete. Based on my research and conversations with industry leaders, here are the critical trends in a/b testing ai that will separate the winners from the losers in the next 18-24 months. Ignore them at your peril.
The Reality Nobody Talks About
Most people approach confession: our a/b tests were useless until we 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 your team matters more than your technology. 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.
The Framework That Actually Works
I'm going to share the exact framework I use when evaluating confession: our a/b tests were useless until we. It's not complicated, but it requires discipline.
Step 1: you need to move fast and break things This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: simplicity beats complexity every time 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 confession: our a/b tests were useless until we are the ones that treat it as an ongoing process, not a one-time project.
The AI Angle
I can't talk about confession: our a/b tests were useless until we in 2026 without mentioning AI. As someone who's invested in Anthropic, OpenAI, Scale AI, and Hugging Face, I have a front-row seat to how AI is transforming this space.
The short version: AI makes good practitioners better and bad practitioners worse. It's an amplifier, not a replacement.
I've seen companies use AI to 10x their confession: our a/b tests were useless until we capabilities. I've also seen companies waste millions on AI solutions that solved the wrong problem. The difference comes down to understanding what you're actually trying to achieve.
This connects to broader themes around a/b testing ai, data science, product growth 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 confession: our a/b tests were useless until we: 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 confession: our a/b tests were useless until we 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 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.
Do all experts agree with this view?
No, and that's fine. The best ideas in business are often contrarian. I share my perspective based on my experience and data, but I encourage you to seek out opposing viewpoints and form your own conclusions.