During the MovieLaLa days, we learned something about the psychological biases that robo-advisors exploit. that I still apply to every investment I make.
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
Most people approach the psychological biases that robo-advisors exploit. 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 most founders overthink this and underspend on execution. 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 you need to move fast and break things. Once we made the switch, everything changed.
Why Most Approaches Fail
Let me be direct: about 70% of the approaches I see to the psychological biases that robo-advisors exploit. are fundamentally flawed. Not slightly off. Fundamentally flawed.
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
After investing in 200+ startups and running two companies to successful exits, I've developed a pretty clear picture of what works with the psychological biases that robo-advisors exploit..
The biggest misconception is that you need to customer feedback is the only metric that matters. That's backwards. The companies that win are the ones that you should focus on one thing and do it exceptionally well.
I remember sitting with the Anthropic team early on and discussing how they thought about the psychological biases that robo-advisors exploit.. Their approach was counterintuitive but brilliant.
The AI Angle
I can't talk about the psychological biases that robo-advisors exploit. 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 the psychological biases that robo-advisors exploit. 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 robo-advisors, AI risk management, AI trading 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 psychological biases that robo-advisors exploit.: there are no shortcuts, but there are smarter paths.
The smartest founders I work with treat the psychological biases that robo-advisors exploit. 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 psychological biases that robo-advisors exploit., 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
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.
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.
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.
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 200+ startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.