TLDR; Building AI costs real money GPUs aren't free, and top AI talent demands high salaries. You need cash plus equity, not one or the other.
I see it all the time. Founders with brilliant AI ideas but limited cash, trying to convince top developers to work for equity alone. Here's why that usually doesn't work.
The Money Problem You Can't Ignore
Let's talk about what AI development actually costs. When you're building AI, you're not just writing code. You're training models, and training costs money real money.
GPU costs are brutal:
- Training a decent language model can run $10,000 to $100,000+
- Fine-tuning existing models still costs thousands
- Even inference (using the model) adds up with enough users
- These aren't one time costs; they're ongoing
Top talent costs more:
- Good ML engineers are getting $200K+ salaries at big tech companies
- Data scientists with real experience aren't cheap either
- DevOps people who can handle AI infrastructure? Even more expensive
When someone says "I'll work for equity," ask yourself why they're willing to turn down that kind of money. Usually, it's because they can't get those jobs in the first place.
The Data Reality Check
Here's another thing founders forget: you need someone who really knows data. Not just "I can write SQL queries" data person, but someone who understands how to prepare, clean, and structure data for AI training.
Good data people are like gold right now. Every company needs them, there aren't enough to go around, and they know their worth. They're not going to work for a piece of paper when Google, Meta, or OpenAI will pay them actual money plus stock.
What good data people do:
- Figure out what data actually matters
- Clean it without breaking it
- Structure it so models can actually learn from it
- Set up the pipelines to keep it flowing
You're not going to find someone who can do all that working for free. The market just doesn't work that way.
Why Pure Equity Fails
I get the appeal. You're cash strapped, you've got big dreams, and equity feels like a win win. But here's what actually happens:
The equity-only pitch attracts:
- Junior developers trying to build experience
- People who couldn't land jobs at established companies
- Well meaning folks who overestimate their skills
- Side project warriors who'll disappear when their main job gets busy
Meanwhile, the people you actually need:
- Are getting offers from multiple companies
- Have families and bills to pay
- Know they can get both salary AND equity at established startups
- Won't risk their career on an unproven idea
The Mixed Approach That Works
Smart founders figure out there's a middle ground. Not enough cash to pay full market rates, but enough to show you're serious.
What the split looks like:
- 60-70% of market salary in cash (enough to live on)
- Meaningful equity (not 0.1%, something that actually matters)
- Clear milestones and expectations
- Honest conversation about the risks
Why this works better:
- You're not asking someone to risk everything
- The cash shows you've got some traction or funding
- Good people will take a pay cut for the right opportunity
- You're attracting professionals, not desperate people
What This Means for Your Startup
If you're building an AI company with no cash and just equity dreams, you're playing the wrong game. You need to figure out how to get some money friends and family, angel investors, whatever it takes.
Because here's the truth: the best AI people aren't sitting around waiting for equity offers. They're getting multiple offers from companies that can pay them what they're worth. Your job isn't to convince them to work for free. Your job is to get enough resources to compete for their attention.
