Is it 1999 all over again? I was there for the dot-com bubble. I saw the irrational exuberance, the insane valuations for companies with no revenue, and the eventual crash. As an active angel investor in the AI space, I have a front-row seat to the valuation madness, and I’m here to tell you: it’s happening again.
But this time, it’s different. It’s not about eyeballs and pageviews. It’s about intelligence. And the big players are making a huge mistake.
I’ve built and sold two companies in Silicon Valley. My first, MovieLaLa, was a movie marketing platform we sold to Gfycat. My second, RemoteTeam, was acquired by Gusto, where I now lead a team building tools for distributed workforces. I’ve also been an angel investor in over 200 companies, including some of the biggest names in AI: Anthropic, OpenAI, Scale AI, and Hugging Face. I’m not saying this to brag. I’m saying this because I’ve seen this movie before, and I know how it ends.
The Trillion-Dollar Misunderstanding
Big Tech companies are pouring billions of dollars into building their own proprietary AI models. They’re in an arms race to create the biggest, most powerful models, and they’re telling everyone that these models are the future of everything. They’re not wrong, but they’re not entirely right either.
They’re missing the point. The real value of AI isn’t in the models themselves. It’s in the application of those models to solve real-world business problems. And that’s where the big guys are fumbling the ball.
They’re so focused on building the "perfect" model that they’re forgetting about the customer. They’re building technology for the sake of technology, not for the sake of solving a problem. It’s a classic case of the innovator’s dilemma.
I see it all the time in my work at Gusto. We’re a payroll and HR company. We have a ton of data. We could be using that data to build all sorts of fancy AI models. But we’re not. Why? Because our customers don’t care about fancy AI models. They care about running their businesses. They care about paying their employees on time. They care about complying with labor laws. They care about hiring the right people.
So instead of building a giant, monolithic AI model, we’re focused on building small, targeted AI-powered features that solve specific problems for our customers. For example, we use AI to help our customers classify their employees correctly. This is a huge pain point for small businesses, and it’s something that AI is really good at.
We’re not trying to boil the ocean. We’re just trying to make our customers’ lives a little bit easier. And it’s working.
The AI Consulting Trap
Another area where I see a lot of hype is in AI consulting. There are a ton of companies out there that are promising to help businesses "transform" themselves with AI. They’ll come in, do a bunch of analysis, and then give you a big, fat report with a bunch of recommendations. And then they’ll charge you a fortune for it.
I’m not saying that all AI consultants are bad. But I am saying that you need to be careful. A lot of these companies are just selling snake oil. They’re promising the world, but they can’t deliver.
I’ve seen it happen time and time again. A company will spend millions of dollars on an AI consulting project, and at the end of it, they’ll have nothing to show for it but a big bill and a bunch of frustrated employees.
Why does this happen? Because AI is not a magic bullet. It’s a tool. And like any tool, it’s only as good as the person using it. If you don’t have a clear understanding of the problem you’re trying to solve, then AI is not going to help you. It’s just going to make things more complicated.
So before you hire an AI consultant, make sure you’ve done your homework. Make sure you have a clear understanding of the problem you’re trying to solve. And make sure you have a realistic expectation of what AI can and can’t do.
Where the Real Opportunities Are
So if the big tech companies and the AI consultants are getting it wrong, where are the real opportunities in AI? I believe they’re in the long tail. They’re in the niche applications that are too small for the big guys to care about.
I’m talking about things like:
- AI-powered tools for specific industries. For example, a tool that helps lawyers review contracts, or a tool that helps doctors diagnose diseases.
- AI-powered tools for specific job functions. For example, a tool that helps salespeople write better emails, or a tool that helps marketers create more effective campaigns.
- AI-powered tools for specific tasks. For example, a tool that helps you schedule meetings, or a tool that helps you take notes.
These are the kinds of applications that are going to have a real impact on people’s lives. And they’re the kinds of applications that are going to create a lot of value for investors.
I’m putting my money where my mouth is. I’ve invested in a number of companies that are building these kinds of niche AI applications. And I’m confident that they’re going to be very successful.
The Bottom Line
Don’t believe the hype. The AI revolution is not going to be televised. It’s going to happen in the trenches, one application at a time.
So if you’re an entrepreneur, don’t try to build the next Google. Try to build a tool that solves a real problem for a real customer. And if you’re an investor, don’t bet on the big guys. Bet on the little guys. They’re the ones who are going to change the world.
I’ve seen it happen before, and I’m seeing it happen again. The future of AI is not in the hands of the few. It’s in the hands of the many. And that’s a future I’m excited to be a part of.
Frequently Asked Questions
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.
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.
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.