Artificial intelligence-powered startups don’t rely as much on human power to get rolling – and they may be setting the example for the next wave of startups, as well as larger, more established companies.
This doesn’t relate to AI replacing jobs, but rather, an increase in the number of enterprise launches accomplished in a leaner-and-meaner fashion than ever before.
AI-native firms run 25% smaller than traditional startups, with 15% fewer entry-level workers, and 15% fewer managers, according to a recent study out of Harvard Business School and INSEAD. The researchers analyzed close to 50,000 Y Combinator and PitchBook-listed venture-backed startups and found hierarchies at AI-native companies were also flatter — all while maintaining the same value as their non-AI-centric counterparts.
Companies covered in the study were launched between 2020 and 2024. Among the Y Combinator launches, AI first-deal counts in 2024 are nearly eight times the 2020 average, according to the researchers, Hyunjin Kim of INSEAD and Rembrand Koning of Harvard Business School. They employed Y Combinator’s AI self-tagging protocols to determine a firm’s AI profile.
While the proportion of AI-driven firms is on the rise, the amount of activity they are generating may possibly lead to more labor demand overall, they speculate. The share of engineers in AI firms is 13% greater than in non-AI startups.
AI’s impact on both internal work as well as products were tracked. Embedding AI into products, beyond layering on AI tools into existing workflows, “is a primary way startups are scaling knowledge work without large teams of knowledge workers,” Kim and Koning said.
These AI firms emphasized engineers within their teams, and downplayed sales, finance, operations, and administration, they also found. “They skew senior, with the share of entry-level workers just under 15% lower and the share of senior workers about 20% higher.” These “small teams of skilled founders building ventures that historically might have required much larger hierarchical organizations.”
Importantly, these smaller AI firms “raise similar amounts of funding and hit valuations on par with non-AI-native firms,” the research finds. “As a consequence of being smaller, AI-tagged startups raise roughly 20% more capital per employee and carry higher valuations per employee.”
At this time, these smaller firms tend to be concentrated in Silicon Valley, employing workforces “that are more male, more likely to hold advanced degrees, and drawn from more prestigious employers and institutions.”
The researchers also explored the ways these startups actually employ AI. At least 43% use AI to fully automate tasks workers used to do, while another 24% build AI tools designed to augment existing workers at the firms to which they sell. Another segment, 15%, build AI infrastructure, providing a capability that another developer integrates into their own AI product.
There are implications beyond the startup realm, as what happens in Silicon Valley tends to spread across the business landscape. “AI may not simply make existing organizations more efficient – it may change what organizations look like and do,” said Kim and Koning. “If firms can increasingly import capabilities from foundation models rather than build them through people, they may no longer be constrained by human attention and processing. The managerial problem shifts from accumulating internal capacity to building and integrating external capabilities.”
There are additional implications for those seeking entry-level jobs versus joining more entrepreneurial startup teams. “A growing share of employers are never going to post jobs,” according to Eva Chan, career expert at Resume Genius. “Most workers rely on early roles as stepping stones to build experience — yet those stepping stones are being engineered out of the companies everyone is chasing,” she said.
