I chased complex AI ethics frameworks for half a decade, getting it all wrong. I'm sharing my painful journey from buzzword-chasing to building responsible AI that ships. This is the stuff nobody tells you about the gap between theory and reality.
The Counterintuitive Truth
In AI ethics, the best practitioners often do less, not more.
The lesson: most founders overthink this and underspend on execution. It sounds simple. It's incredibly hard to execute.
The Framework That Actually Works
Here is a simple framework for evaluating AI ethics. It's not complicated, but it requires discipline.
Step 1: the best solutions are often the simplest ones
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 that get AI ethics right treat it as an ongoing process, not a one-time project.
The Reality Nobody Talks About
A lot of the advice on AI ethics rests on assumptions that made sense five years ago. The world has moved on.
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.
Many teams spend months on the wrong path before realizing that customer feedback is the only metric that matters. Once they make the switch, everything changes.
Real Talk: What Actually Matters
Here is what actually matters in AI ethics.
First, execution speed beats perfection. Every time. Companies rarely fail because they moved too fast on AI ethics. Many fail because they moved too slowly.
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. It sounds obvious, but many founders make decisions about AI ethics in a vacuum. Get out of the building. Talk to real people.
This connects to broader themes around AI regulation 2026, EU AI Act, deepfakes, ai-ethics|AI bias, AI governance 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 you don't need to get AI ethics right on day one. You just need to get started and keep improving.
Founders who do well at AI ethics tend to 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
Q: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.
Q: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 234 startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.
Q: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.