s be clear: you have to pay up. Table stakes are astronomically high. We’re not talking about a 10% premium for an "AI role" anymore. A top-tier machine learning engineer can command a base salary of $350,000, and that’s before you even start talking about equity. The average compensation packages at places like OpenAI are hitting numbers that would make a Wall Street banker blush.
When I was building RemoteTeam, we were selling the dream of distributed work before it was cool. We couldn’t match the cash offers from the giants, so we got creative. We gave our first five engineers 1.5% in equity each. That was almost unheard of for a seed-stage company back then. It was a massive bet, and it’s how we landed a world-class team.
Today, that same offer would get you laughed out of the room. I’m seeing AI engineers get 2x or even 3x the equity grants of a comparable non-AI software engineer. Your old equity budget is now a fantasy. If you’re not prepared to give up a significant chunk of your company, you won’t attract the people who can actually build it.
Stop Selling a Job, Start Selling a Mission
But here’s the thing—throwing money at the problem won’t solve it entirely. The absolute best people, the top 1%, aren’t just mercenaries. They have options. They can get a mountain of cash and a pile of stock anywhere. If you want them to choose you, you need something more.
I learned this the hard way at MovieLaLa. We were building a cool social app for movie fans. It was fun. But we were trying to hire a recommendation engine expert, and we were up against Google, who was offering double the salary to work on YouTube’s algorithm. We couldn’t win on cash or, frankly, on the technical challenge. We were a consumer app, not a deep tech powerhouse.
What do the truly brilliant minds in AI want? They want to work on problems that will echo for a decade. They want to be part of a story they’ll be telling for the rest of their lives. What is your mission? Why does your company have to exist? If you can’t articulate that in a way that gives someone goosebumps, you’ve already lost.
The New AI Compensation Trinity
So, what does a winning package look like in this crazy market? It’s a simple, three-part trinity. You need all three, or it falls apart.
Top-of-Market Salary: Don’t lowball. Don’t say you’ll "make up for it in equity." Pay the cash. It shows respect and it de-risks the decision for the candidate. You have to be in the top 10% of the market, period.
Aggressive Equity: This is your biggest weapon as a startup. I advised a seed-stage generative video startup recently. They were benchmarking their equity offers against other seed-stage companies. I told them to stop. I said, "Start benchmarking against OpenAI’s packages. It means giving up a bigger chunk of your company, but it’s either that or you build a team of B-players." They made the hard choice, and they just closed a round from a top-tier VC because of the talent they secured.
A World-Changing Mission: This is the tiebreaker. When the money is comparable, the mission is what wins. Are you building another SaaS tool to optimize ad spend, or are you building something that will fundamentally change how we live and work? Be honest with yourself. The best people want to build the future, not just a feature.
There Is No Other Way
This isn’t a phase. The demand for elite AI talent will only grow, and the supply will struggle to keep up. The companies that win the next decade will be the ones that understand this new reality.
You have to be bold. You have to be generous. And you have to be building something that actually matters.
Forget the old rules. The war for talent is here. It’s expensive, it’s brutal, and it’s the only thing that matters. So, are you ready to fight?
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.