According to a recent LinkedIn post from Carta, compensation dynamics for AI and machine learning engineers at early-stage startups have shifted notably over the past two years. The post cites internal data indicating that median equity grants for AI/ML engineers at startups with under $10 million in funding have risen 59%, while cash salaries increased only about 10% over the same period.
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The LinkedIn post suggests this divergence reflects smaller companies’ limited ability to compete on cash with larger, well-capitalized incumbents. Instead, these startups appear to be emphasizing upside potential through larger equity stakes, even as overall equity pools reportedly remained flat and headcounts declined.
Carta’s post also notes that equity packages had been cut nearly in half from 2022 to 2023, characterizing the current trend as partly a reversion toward more typical levels rather than a structural break. For investors, this may indicate that early-stage companies are rebalancing compensation in response to tighter funding conditions while still needing to attract scarce AI talent.
The commentary argues that smaller startups can leverage equity as a competitive tool in hiring, particularly in AI roles where demand is intense. For private-market investors, this approach could have mixed implications, potentially diluting cap tables but also aligning key technical hires more closely with long-term company performance.
The post further warns that relying on compensation benchmarks older than a year may leave employers at a disadvantage, given the rapid pace of change in AI-related labor markets. This underscores a broader message that investors may need to monitor evolving pay structures and talent strategies as indicators of how portfolio companies are adapting to AI-driven competition.
Carta’s reference to additional analysis by Ashley Neville and Kevin Dowd on how AI is reshaping compensation for VC-backed startups points to continued scrutiny of these trends. For investors, such data-driven insights can inform assessments of hiring competitiveness, equity dilution risks, and the sustainability of early-stage companies’ approaches to attracting AI specialists.