''' I keep hearing the same story. A founder corners me at an event, eyes wide with a mix of terror and excitement, and tells me they’re raising a massive seed round for their new AI company. They’re talking nine figures. $100 million, $200 million, sometimes more. They think this is the only way to compete, the only way to build a “world-class” AI team.
I usually let them finish, then I ask a simple question: “Why?”
Why do you think you need that much money? What are you going to do with it? The answers are always vague. “We need to hire the best AI talent.” “We need to buy a ton of GPUs.” “We need to compete with the big guys.”
I’m Sahin Boydas. I’ve been in Silicon Valley for a while now. I’ve built and sold two companies, RemoteTeam to Gusto and MovieLaLa to Gfycat. I’ve been fortunate enough to be an early investor in over 200 startups, including some names you might recognize like Anthropic, OpenAI, Scale AI, and Hugging Face. I’ve seen a lot of pitches. And I can tell you, the obsession with nine-figure seed rounds is a trap. It’s a distraction from what really matters.
In this article, I’m going to share what I’ve learned about what actually makes an AI pitch successful. And here’s the first secret: it has almost nothing to do with the amount of money you raise.
The Only Slide That Really Matters
You can have the most beautiful deck in the world. You can have market size projections that go up and to the right forever. You can have a demo that looks like magic. But for an early-stage AI company, none of that is as important as your team slide.
When I’m looking at a seed-stage investment, I’m not just betting on an idea. Ideas are cheap. I’m betting on the people. I’m betting on their ability to take an idea, run with it, hit a brick wall, pivot, and keep running. This is even more true in AI, where the technology is moving so fast that your initial idea is almost guaranteed to be wrong.
So when I get to the team slide, I’m not just looking for a list of impressive names from Google Brain or FAIR. In fact, sometimes that’s a red flag. I’m looking for something else entirely.
Secret #1: Your Team Doesn’t Need to Be Famous, It Needs to Be Hungry
The “AI talent war” is real. If you’re trying to hire the same handful of famous researchers that every other startup is chasing, you’re going to have a bad time. You’re going to spend a fortune, and you’re going to end up with a team of mercenaries who will jump ship for a better offer.
I’ve had much more success with a different approach. Instead of looking for the most experienced people, I look for the hungriest. The ones who are obsessed with the problem you’re solving. The ones who have something to prove.
At RemoteTeam, we were building a platform to help companies manage their remote employees. This was before remote work was cool. We couldn’t afford to hire expensive engineers from big tech companies. So we hired people who were passionate about remote work themselves. People who were living the problem we were trying to solve. They weren’t the most experienced, but they were the most dedicated. And that made all the difference.
So how do you find these people? You have to get creative.
- Look for side projects. The most passionate people are always building things in their spare time. Look for interesting projects on GitHub. Go to hackathons. You’ll be amazed at the talent you can find.
- Hire for slope, not y-intercept. This is a classic one, but it’s more important than ever in AI. The field is moving so fast that what someone knows today is less important than how quickly they can learn. I’d rather hire a brilliant 22-year-old who’s obsessed with LLMs than a 40-year-old with a PhD in a subfield of AI that’s no longer relevant.
- Sell the mission. You can’t compete on salary with Google or OpenAI. But you can compete on mission. If you’re working on something that truly matters, you can attract people who are motivated by more than just money. At MovieLaLa, we were a bunch of movie nerds who wanted to build a better way to discover films. We didn’t have the best perks, but we had a mission that we all believed in.
Secret #2: Your Moat Isn’t Your Model, It’s Your Data… and Your Team
Every AI startup pitch has a slide about their “competitive moat.” Usually, it’s something about their proprietary model or their unique algorithm. And usually, it’s bullshit.
Let’s be honest. For 99% of AI startups, your model is not your moat. You’re probably using a pre-trained model from OpenAI or Anthropic and fine-tuning it on your own data. And that’s fine! You don’t need to build your own foundation model to build a great AI company.
Your real moat is your data. The unique, proprietary dataset that you’ve collected and that no one else has. This is what will allow you to build a product that’s truly differentiated.
But even more important than your data is your team’s ability to do something smart with it. Your team’s unique insights into the problem you’re solving. Your team’s ability to move quickly, to iterate, to build something that people actually want to use.
When I invested in Scale AI, they didn’t have some magical, secret algorithm for data labeling. They had a team that was obsessed with the problem of data labeling. They understood the nuances of the problem better than anyone else. And they were able to build a solution that was 10x better than anything else on the market.
Secret #3: A Demo is Worth a Thousand Slides
I’ve seen so many AI pitches that are all talk and no action. They have a 50-page deck full of buzzwords and technical jargon. They talk about their “novel architecture” and their “state-of-the-art results.” But when I ask to see a demo, they get all cagey. “Oh, it’s not quite ready yet.” “We’re still working on the UI.”
This is a huge red flag. If you’re an AI company, you should be able to show me something that works. I don’t care if it’s ugly. I don’t care if it’s buggy. I just want to see that you can actually build something.
A simple demo that shows you can solve a real problem for a real user is more powerful than any slide deck. It shows that you’re not just a team of researchers who are good at writing papers. It shows that you’re a team of builders who can ship product.
One of the best pitches I ever saw was from a company that was building an AI-powered tool for lawyers. The founder didn’t have a deck. He just opened his laptop and showed me the tool. He uploaded a contract, and the tool automatically highlighted all the key clauses and potential risks. It was simple, but it was magical. I invested on the spot.
Secret #4: The Best Funding Round is the One You Don’t Have to Raise
This brings me back to my original point. The obsession with raising huge seed rounds is a trap. It’s a vanity metric. It doesn’t mean you have a better company. It just means you’ve given up more of it.
Having less money forces you to be disciplined. It forces you to focus on what really matters. You can’t afford to hire a team of 50 engineers to build a product that no one wants. You have to be scrappy. You have to be creative. You have to be relentless in your focus on the customer.
I’m not saying you should never raise money. But you should raise as little as you possibly can to get to the next milestone. And you should always be thinking about how you can get to profitability as quickly as possible.
The most successful AI companies of the next decade won’t be the ones that raise the most money. They’ll be the ones that are the most capital-efficient. The ones that can do more with less. The ones that are run by founders who are more interested in building a great business than in being on the cover of a magazine.
So the next time you’re thinking about your AI pitch, don’t obsess over the size of your seed round. Obsess over your team. Obsess over your data. Obsess over your customers. And for the love of God, build a demo.
If you can do that, you won’t need to ask for a nine-figure check. The investors will be begging to give it to you. '''
Frequently Asked Questions
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.
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.