One of the biggest challenges in financial AI is building models that are not only accurate but also explainable. I’m diving into the world of explainable AI (XAI) and why it’s so critical for building trust and transparency in financial applications. This is a must-read for any AI developer in finance.
What I've Learned From 66 Companies
A few patterns in explainable AI come up again and again.
The founders who handle explainable AI well understand that the market doesn't care about your roadmap.
The Reality Nobody Talks About
A lot of the advice on explainable AI rests on assumptions that made sense five years ago. The world has moved on.
The first thing to understand is that customer feedback is the only metric that matters. 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 the data tells a different story than your gut. Once they make the switch, everything changes.
Real Talk: What Actually Matters
Here is what actually matters in explainable AI.
First, execution speed beats perfection. Every time. Companies rarely fail because they moved too fast on explainable AI. 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 explainable AI in a vacuum. Get out of the building. Talk to real people.
This connects to broader themes around AI risk management, algorithmic trading, robo-advisors that I've been thinking about a lot lately.
Final Thoughts
There are no shortcuts in explainable AI, but there are smarter paths.
The strongest teams treat explainable AI 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 explainable AI, 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
Q: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.
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:What's the most common pushback you get on this?
People often push back by citing exceptions or edge cases. And they're usually right that exceptions exist. But building a strategy around exceptions rather than patterns is a losing game for most founders.
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.