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As the war for AI talent continues, it’s become pricier than ever.
At least that’s the case for a small group of companies seeking to capitalize on the revolution that proponents of the technology are promising.
Meta, which is going all-in on AI investments with the creation of a “superintelligence” lab, is reportedly offering multi-million dollar pay packages to poach top AI talent from competitors like OpenAI. At least one offer reached $1.5 billion, but the prospective hire turned it down, according to the Wall Street Journal.
Some of the companies Meta is looking to poach AI talent from are outside the tech firms it has traditionally competed with for workers. To field his superintelligence team, CEO Mark Zuckerberg is reaching out to employees not only from OpenAI, but also Anthropic and Thinking Machines Lab, which OpenAI alumni founded and only launched in the last five years.
While not all HR leaders can afford to hire AI talent for the price Meta is paying, they may well be taking a similar approach in their hiring strategy, forming separate AI “peer groups” to understand where top talent works, and what they earn, according to compensation experts.
How companies use peer groups to recruit workers, set salaries. Establishing peer groups is a longstanding practice in executive compensation. When determining executive pay packages, compensation committees will look at market data from a select basket of companies they typically compete against (at Meta, for example, this group included Alphabet, Microsoft, and Netflix in 2023, according to a recent proxy filing).
When determining peer groups, employers typically “look at [their] competitive labor market for this pocket of employees that [they’re] thinking about,” Jonathan Jebson, compensation insights director with the compensation software firm Compa, told HR Brew. They ask themselves, “who are we hiring from? Who is hiring our people?” Companies also take into account factors like size and industry when selecting a peer group.
While this practice is common when setting pay for executives, it’s also used by companies seeking “more granular benchmarking data” for a broader set of employees, Jebson explained.
Such data is particularly valuable for companies hiring for AI-related roles as the field is fairly nascent, said Tom McMullen, senior client partner with Korn Ferry. “It’s not a mature space,” he said. “The technology is new…organizations are figuring out how to use it.” While employees may be using AI in their jobs, their roles may not be entirely dedicated to it, he said.
“When you don’t have firmed up roles…the published compensation surveys aren’t going to survey those jobs because the jobs aren’t firm,” McMullen explained, though he added that HR professionals may be able to glean how companies are paying for AI roles through job postings, given the increasing prevalence of pay transparency in many states.
To help determine what pay might look like for roles requiring AI skills, compensation teams are identifying peer groups that look different from the set of organizations they would typically compete against for most roles, said Compa CEO Charlie Franklin.
Compa sees many companies “explicitly targeting” Magnificent 7 firms for their AI peer groups, he said, referencing a term to describe a group of tech companies with large market capitalizations that includes Microsoft, Meta, and Nvidia. Organizations are looking not only to these firms, but also “some of the largest, most famous, private AI companies” to pull AI talent from, he added.
Show them the money. Once HR teams identify an AI peer group, they have to figure out if their company can afford talent from the firms they’re targeting, sources told HR Brew.
The fact that salaries are still sky-high for certain AI skills means engineering and HR teams are having to strike a balance in discussions about talent, Jebson said. While an engineering lead might be hoping to hire a candidate that hails from OpenAI, Anthropic, or a big tech company, the compensation team must then ask, “Can we effectively afford this?” and consider how hiring an elite machine learning engineer may affect internal pay equity.
Compensation teams lean on peer groups to guide these conversations, Jebson said. If the market for pay looks too high for a very narrow peer group, they may point to a different set of companies where talent is more affordable.
Paying top-of-market salaries for AI talent may be out of the question for many smaller startups, but Kaitlyn Knopp, founder and CEO of the compensation management software firm Pequity, encourages them to consider other factors that may drive talent to their company. A generous equity offer, work-life balance, and the opportunity to “work really hard at cool, new problems” are all things that could hold sway over a promising candidate, she said.