Carta has the latest data out on what we’ve all seen across our portfolio companies, even in “non-native AI” start-up: founders are having to pay a lot more for AI engineers. A lot more. Especially in equity.
This makes sense when you see OpenAI, Anthropic, Databricks pay top of market — and much more. But many founders think those names are outliers, that they will “hold the line” for fairness or other reasons in their AI engineers for their B2B startups.
Good luck with that.
It’s Not Just You. Everyone Is Paying a Lot More for AI Engineers. Especially Equity.
Here’s what jumped out from Carta’s latest compensation data comparing January 2024 to recent months:
Senior Director level AI engineers are seeing equity increases of 40%. Not 4%. Forty percent. In less than two years.
While equity has skyrocketed across all levels, salary increases have been much more modest:
- Mid-Level (L2): Salary up 9.4%, equity up 13%
- Senior: Salary up 3.3%, equity up 15%
- Manager: Salary up 5.5%, equity up 20%
- Senior Manager: Salary up 12.5%, equity up 27%
- Director: Salary up 12.2%, equity up 30%
- Senior Director: Salary up 12.2%, equity up 40%
Why Equity Is the New Battlefield
This isn’t random. There are three macro forces at play here that every SaaS founder needs to understand:
1. The “AI or Die” Mentality Every startup thinks they need AI engineers yesterday. Whether you’re building the next ChatGPT competitor or just trying to add “AI-powered” to your marketing copy, the talent pool feels impossibly small. And when everyone’s fishing in the same pond, prices go up.
2. The Big Tech Poaching Problem Google, Meta, OpenAI, and Anthropic aren’t just competing with you for revenue—they’re competing for your people. And they have deeper pockets. The only way smaller companies can compete is by offering disproportionate upside through equity.
3. The Perception of Massive AI Upside Right or wrong, AI engineers believe they’re building the future. They want to be compensated like it. A 15-20% equity bump feels reasonable when you think you’re working on technology that could 10x the company.
What This Means for Your Startup
If you’re raising your next round, factor this into your hiring plan. That $500K you budgeted for two senior AI engineers? Make it $650K, and twice the equity you’d planned. And yes, your dilution calculations just got messier.
If you’re not funded yet, this creates a catch-22. You need AI talent to build compelling AI products, but you can’t afford AI talent without funding. Consider these alternatives:
- Partner with AI consulting firms for specific projects
- Hire strong generalist engineers and upskill them
- Focus on AI integration rather than AI innovation initially
If you’re lightly funded or just raised a bunch of SAFEs, this might be your competitive advantage. You can afford these premium packages on whatever terms you want.
Equity Inflation Is Here. It Is What It Is.
These grants aren’t just 40%+ larger or more … they dilute everyone. AI start-ups have less headcount on average than their pre-AI peers, but far more dilution.
Not every AI startup will become the next OpenAI. In fact, most won’t. But right now, AI engineers are pricing themselves as if they all will. This creates unrealistic expectations that could lead to retention issues down the line when the equity doesn’t materialize as expected.
Three Strategies That Actually Work
1. The “AI-Adjacent” Hire Instead of competing for “AI Engineers,” hire excellent software engineers with strong math backgrounds and train them internally. A Stanford CS grad who’s built distributed systems can learn PyTorch. An OpenAI researcher probably can’t learn how to ship reliable software.
2. The Consultant-to-FTE Pipeline Start with project-based work through top-tier AI consultants. If they deliver, convert them to full-time with a premium package. This de-risks your investment and gives you time to assess cultural fit.
3. The Equity Clawback Structure Build performance milestones into your equity grants. If someone’s getting a 30% equity bump because “AI is the future,” make sure that equity vests based on AI-related business outcomes, not just time served. Folks are often OK with this, as well as longer vesting periods (5 years) — if everyone getting the outsized grants gets the same terms.
The Bottom Line
The AI talent market isn’t going to normalize anytime soon. Every month you wait to hire critical AI talent, the market price goes up. But throwing money at the problem isn’t a strategy.
Be deliberate about which roles actually need AI expertise versus which roles just need strong engineering fundamentals. Pay market rate for true AI innovation roles, but don’t let the “AI premium” creep into every engineering hire.
Most importantly, remember that the best AI products aren’t built by AI engineers alone—they’re built by great product teams that happen to include AI expertise. Don’t let the talent market distract you from building a balanced team that can actually ship.
