Robo-advisors are designed to be logical and unemotional, but they’re also designed by humans. I’m exploring the subtle psychological biases that are built into these platforms and how they can influence your investment decisions without you even realizing it. This is a must-read for any robo-advisor user.
The Reality Nobody Talks About
A lot of the advice on robo-advisors rests on assumptions that made sense five years ago. The world has moved on.
The first thing to understand is that most founders overthink this and underspend on execution. 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 you need to move fast and break things. Once they make the switch, everything changes.
Why Most Approaches Fail
Many common approaches to robo-advisors are flawed at the root.
The root cause is usually one of three things:
Copying what big companies do without understanding why they do it. What works for Google doesn't work for a 10-person startup.
Over-engineering the solution when a simple approach would work better. I've seen teams spend six months building something that could have been done in two weeks.
Ignoring the human element. Technology is the easy part. Getting people to actually use it is where the real challenge lives.
What I've Learned From 68 Companies
A few patterns in robo-advisors come up again and again.
The founders who handle robo-advisors well understand that you should focus on one thing and do it exceptionally well.
The AI Angle
Any discussion of robo-advisors in 2026 has to include 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.
Some teams use AI to get far more out of robo-advisors. Others spend heavily on AI that solves the wrong problem. The difference comes down to understanding what you're actually trying to achieve.
This connects to broader themes around robo-advisors, AI risk management, AI trading that I've been thinking about a lot lately.
Final Thoughts
There are no shortcuts in robo-advisors, but there are smarter paths.
The strongest teams treat robo-advisors 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 robo-advisors, 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'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 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.
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.