By Chris Gale

The conversation around artificial intelligence has been dominated by a familiar image, a boardroom, a new AI initiative and a round of layoffs. Month after month brings another headline about workforce reductions at a large technology company or consultancy. The conclusion is usually presented as self-evident. AI is here, people are being replaced and companies are learning to produce more with fewer employees.

That interpretation captures part of what is happening - maybe... that’s a really big maybe - but it misses a larger shift that is unfolding. AI can automate tasks, compress workflows and reduce the labor required for a given amount of work. Yet the outcome at the company level depends on decisions that sit outside the technology itself, including who owns the business and how leadership defines growth.

Fund administration offers a useful test. The industry is built around reporting, reconciliations, investor communications, compliance, documentation and repeatable operating processes. It should be among the sectors most exposed to automation. Two firms operating in this market show why the employment story is more complicated than the headlines suggest.

Two Companies, Two Growth Engines

Company A has 88 employees today, up 47 percent over the same period. That implies a starting workforce of roughly 60 people and an increase of about 28 employees. Its median employee tenure is 2.1 years, or about 25 months.

Company B has 326 employees, up 96 percent over two years. That implies a starting workforce of roughly 166 people and an increase of about 160 employees. Its current workforce is therefore almost twice its size two years earlier. Median employee tenure is 1.8 years, or about 22 months.

Both heavily advertise their use of AI and technology prowess, so assuming they’re both true to their word, one thing can be seen straight off. AI is not killing jobs in the net.

But beyond that, the raw comparison is striking. Company B added about 5.7 times as many employees as Company A and now has 3.7 times the workforce. Its percentage growth rate was also 49 percentage points higher. Read without context, those numbers might suggest that Company B has the stronger operating model or the more successful technology strategy.

Ownership changes the interpretation.

Company B received a majority growth investment from a strategic investor in about three years ago. The transaction was intended to help the business scale its services, and management retained significant ownership. Growth capital of this kind is expected to support hiring, geographic expansion, get more sales and marketing, deeper management capacity and, where appropriate, acquisitions. Company B’s workforce expansion is therefore evidence that it is executing a capital-backed growth strategy. It is not, by itself, evidence that its AI strategy is outperforming Company A’s.

Company A remains independent and has no external equity sponsor. It has still expanded its employee base by nearly half while elevating its technology leaders and adding new leadership experienced in enterprise-scale people management and operations. Its growth is being financed and organized differently.

Different Leverage, Different Economics

Company B is combining capital leverage, human leverage and technology leverage. It has institutional capital specifically intended to accelerate growth, and its headcount has expanded accordingly. Success may be measured through rapid platform growth, broader market reach and higher enterprise value ahead of a future liquidity event.

Company A is relying more heavily on technology leverage and operational leverage. Its objectives may include profitable growth, continued independence, greater revenue per employee and the ability to compete with much larger organizations without matching them hire for hire.

Neither approach is inherently superior. They create different economics and different signals. Company B demonstrates that adopting technology does not prevent large-scale hiring when capital and market demand support expansion. Company A may offer the cleaner test of whether AI can help an independent firm narrow the scale gap with a larger, better-capitalized competitor.

The tenure figures reinforce the point on ownership. Company B’s 1.8-year median tenure is consistent with a workforce weighted toward recent hires after a growth investment. Company A’s 2.1-year median tenure, while only about four months longer, accompanies strong growth with less dramatic workforce expansion. The data does not tell us that one culture is healthier. It does show the two organizations are at different points in differently financed growth strategies.

AI Can Reduce Labor Intensity While Employment Grows

The cases point to a distinction that gets lost in the public debate. AI can reduce the number of employees required per unit of work while total employment rises.

Imagine an administrator that once needed 100 employees to support 100 funds. If automation allows 70 employees to support the same workload, labor intensity falls by 30 percent. But if the firm uses that capacity to support 200 funds, it may employ 140 people. Jobs per fund decline while total jobs increase by 40 percent.

That pattern is consistent with the available evidence here. Company B nearly doubled its workforce. Company A grew headcount by nearly half. Both operate in a business full of tasks that can be automated. Again, in an industry in which margins are under heavy pressure, neither looks like an organization using AI primarily to shrink its workforce.

I’ve heard at least one expert in this particular industry say it may be too soon to say what the impact will be. But they say we’ll see it soon, as in 2027.

Meanwhile, technology may be allowing each firm to absorb more clients, assets, transactions and complexity. The productivity gain creates a choice. Leadership can harvest it as cost reduction, reinvest it in growth or combine the two. Ownership and incentives shape that choice.

The Missing Metric

Headcount alone cannot establish which model is producing greater AI-driven operating leverage. The most useful next metric would be revenue per employee, measured consistently over time. Assets under administration per employee, clients or funds per employee, operating margin and service-quality measures would provide additional evidence.

And one veteran leader in the field also points out you need to consider the asset class you focus on. For example, venture clients tend to be much smaller than buyout and that'll change the impact of some of the measurements.

That being said, if Company B’s revenue is rising in line with its 96 percent headcount growth, it may be scaling largely through capital and hiring. If revenue is increasing materially faster than headcount, technology may be generating meaningful operating leverage as well.

The same test applies to Company A. If revenue, clients or assets administered are growing substantially faster than its 47 percent increase in employees, the firm would be demonstrating that technology and operational discipline can substitute for some portion of the capital and workforce scale available to a sponsored competitor.

Until those measures are available, the responsible conclusion is narrow but important. These cases show job growth alongside automation and AI. They do not prove that AI alone caused the hiring, and they do not prove that more hiring equals a better AI strategy. But anecdotally, it sure seems like it.

And a wiser head than mine has asked another question. What growth yields these two outcomes?  For the companies decreasing their staff because of automation and AI, is their growth less than the growth rates of companies A and B? Is it industry-specific, or is there a hard line regardless of industry? We’re going to find out.

But let’s come back to the difference between the two and where you should place your bets for 2027.

Ownership Shaping the Outcome

The deeper divide is not between companies that use AI and companies that do not. It appears to be at this stage between different philosophies of what productivity gains are for.

An investor-backed company may use AI to scale a platform rapidly, add capacity, improve margins, and increase enterprise value. An independent owner may use the same technology to preserve autonomy, strengthen service, improve resilience and compete above the company’s weight class. Both may hire, though Company B may be an exception to the generalization that private capital-backed companies in particular tend to fire.

It may be all about the growth rate, and then that determines where you fall regarding hiring or firing, or whether you’re pushing to non-U.S. low-cost jurisdictions.

But in 2017, Ellul, Pagano, and Schivardi reported in the Review of Financial Studies that family-owned firms provide greater employment and wage stability compared to businesses owned by outside backers, effectively acting as insurers of their workforce. They hold onto workers through downturns more than non-family firms do.

And then in 2019, the Bagel and Troege paper from Annales framed the question as family firms versus private capital and concluded that family firms are more sustainable, have higher corporate social responsibility for those interested in that, and take a longer-term perspective on profitability. The contrast with private capital's shorter hold period and return-driven model is the core argument.

A Rutgers/NBER employee stock ownership plan (ESOP) study found employee-owned firms laid off staff at only one-fourth the rate of non-ESOP companies during the pandemic, and during the Great Recession, 12.1 percent of private sector employees reported being laid off while 2.6 percent of employees in ESOP companies were laid off.

And putting aside private capital’s reputation, the difference between the two cases we’re looking at lies in why they hire, how quickly they hire and what return the organization expects from each added person.

That is why the loudest layoff headlines today provide an incomplete guide to AI’s employment effects and appear misleading. AI is eliminating tasks, changing roles, and lowering labor requirements in specific workflows. In growth markets, those gains can support expansion rather than contraction. And when I say growth markets, maybe the overall market is not growing, but insurgent firms like Company A may be looking to grow within what is otherwise a mature market, pulling away market share from incumbents.

Jobs may move toward the firms that use the technology most effectively, even as fewer employees are needed for each client, fund or dollar of assets administered. Growth happens.

Company B shows that a business at least talking about AI and technology enablement, can hire at scale when institutional capital is available to fund expansion. Company A may demonstrate something more structurally significant: that an independent business can grow nearly 50 percent, strengthen its technology and operating leadership, and challenge larger competitors without surrendering ownership and be highly capital efficient.

And for my money, Company A is where a category definer, or market definer, takes shape.

The technology does not dictate whether productivity becomes layoffs, hiring or some combination of the two. Owners, investors, and leaders make that decision. AI changes the economics and potential. Ownership determines what the organization does with it and how it deploys them.