Beyond Pay Premiums: More Than One AI Talent Market

The headlines are impossible to ignore. Cutting-edge AI labs and others are competing aggressively for a relatively small pool of elite AI researchers, with reports of compensation packages measured in tens to hundreds of millions of dollars.

Those stories are real. But they may be causing boards, executives, and compensation committees to ask the wrong question. Rather than wondering how much to pay for AI talent, they should be wondering which AI talent market their organization is actually competing in.

The prevailing narrative suggests that every company must decide how much more it is willing to pay for AI talent. Yet conversations with compensation leaders, technology executives, and boards suggest a more nuanced reality is emerging. Organizations are pursuing different AI talent strategies, each with distinct implications for compensation design, workforce planning, and organizational culture

The future of AI compensation may not be defined by a single market practice. Rather, it will likely be shaped by how organizations expect AI to create value and the talent strategies they pursue to capture that value. 

Our experience suggests that AI talent is not a single market, but rather three distinct talent markets differentiated by objectives, talent profiles, and approaches to value creation. These different markets come with distinct compensation implications for boards to consider. 

AI Talent Is Not One Market

One of the most common mistakes in discussions about AI talent and compensation is treating "AI talent" as a single category.

In reality, there are meaningful differences between organizations that are building foundational AI technology, embedding AI into products, and using AI to improve productivity across their workforce. While some organizations may exhibit characteristics of more than one category or move between them over time, most AI strategies today broadly fall into one of three groups. 

Frontier AI Creators

The first group, “Frontier AI Creators,” includes the companies that design and build the most advanced, large-scale machine learning models, such as OpenAI, Anthropic, and xAI. Their competitive advantage depends directly on breakthroughs in models, infrastructure, and research. As a result, they are competing for an extremely limited population of the most elite machine learning researchers, model architects, and AI infrastructure specialists.

For these organizations, extraordinary compensation may be rational. A single breakthrough in model capability, infrastructure, or product performance could create value measured in billions of dollars.

AI Product Builders

A second group, “AI Product Builders,” includes technology companies building AI-based products. These organizations may need engineers with AI expertise, but they are generally not trying to invent the next frontier model. Instead, they are looking for people who can apply AI capabilities to improve products, automate workflows, create new customer experiences, and establish a competitive advantage within their industry.

Importantly, many of these organizations are not looking for the same talent as Frontier AI Creators. Rather than competing for researchers building the next generation of foundation models, they are often seeking engineers, product leaders, and domain experts who can apply AI capabilities to solve business problems.

Many technology, fintech, healthcare, and software companies fall into this category. Their response has generally been more measured. Rather than creating entirely separate compensation architectures, they often target a higher-percentile pay position (e.g., 75th vs. 50th), apply selective premiums to existing guidelines, consider one-time awards, or create special incentive programs for critical talent.

AI-Enabled Operators

A third group, “AI-Enabled Operators,” is emerging as perhaps the most interesting and consequential for the broader economy. These organizations are less concerned with hiring elite AI researchers and more focused on becoming AI-enabled enterprises. Rather than competing directly with Frontier AI Creators, they are investing in AI transformation leaders, power users, and broader workforce adoption. For these organizations, the objective is not to build the next foundation model or to develop AI-enabled products. It is to use AI to improve customer outcomes, automate workflows, and increase workforce productivity.

For these companies, the key metrics are often productivity, efficiency, workforce leverage, and revenue per employee. AI becomes less a recruiting challenge and more a workforce transformation challenge.

These distinctions between Frontier AI Creators, AI Product Builders, and AI-Enabled Operators matter because they drive fundamentally different compensation decisions. They also shape how organizations allocate capital, evaluate performance, and ultimately measure the success of their AI investments. 

How Companies Are Responding

The compensation approaches emerging across the market generally reflect both an organization’s AI talent strategy and its perspective on value creation. As organizations struggle to assess the long-term value of AI talent and investments, compensation is increasingly moving away from guaranteed, time-based rewards toward structures that tie a greater portion of compensation to outcomes. While traditional salary, bonus, and equity programs remain foundational, many organizations are experimenting with mechanisms that better align rewards with retention, business performance, product success, and long-term value creation.

The four approaches we most often see to address AI-related compensation include:

1. Premium Market Positioning 

AI Product Builders are responding through selective market premiums, targeting compensation above traditional market levels. For example, several growth-stage companies reported positioning key AI engineering talent above the 75th percentile and applying premiums of 20–30% to traditional engineering benchmarks. These investments are typically concentrated in roles viewed as critical to execution. Frontier AI Creators also employ this strategy, but may be less disciplined when compared to premium-based frameworks and more willing to pay whatever the market demands. 

2. Performance-Oriented and Retention-Focused Rewards

Organizations are increasingly experimenting with project-based bonus pools, milestone-driven equity awards, retention programs, and performance-conditioned grants that tie rewards more directly to business outcomes and long-term contribution. We have seen this in practice at both Frontier AI Creators and AI Product Builders, where organizations combine time-based retention awards with milestone-based upside that can increase payouts by 2–3x. 

One AI-enabled online gaming platform’s People & Total Rewards leader recently proposed a Research Ownership Unit (ROU) framework designed to link rewards directly to realized AI-enabled value creation—an example of how some organizations are beginning to rethink compensation around measurable outcomes rather than talent scarcity alone.

3. Selective "10x Talent" Investments

Frontier AI Creators are most likely to use this approach. Media reports surrounding these labs suggest concentrated investments in a small number of researchers viewed as critical to future model development and competitive advantage. While the scale of some recent AI-related packages has attracted significant attention, concentrated investments in individuals with frontier-level technical skills are not entirely new and have appeared periodically throughout the technology sector.

Beyond outsized compensation opportunities, some frontier AI organizations have differentiated themselves through structured pre-IPO equity liquidity programs that smaller competitors can’t match. At the same time, many organizations appear to be moving toward structures that combine retention and performance elements, balancing attraction and retention objectives with a desire to tie extraordinary rewards to measurable value creation.

4. Strategic Refusal

A growing number of organizations, including AI-Enabled Operators, are deliberately choosing not to participate fully in the AI compensation arms race. Instead, they are maintaining existing compensation philosophies while focusing on AI adoption, productivity, culture, and workforce enablement rather than competing aggressively for scarce talent. 

This approach is most common among organizations focused on AI adoption and workforce transformation rather than competing directly for frontier AI talent. Successful companies pursuing this strategy typically have a strong culture and compensation philosophy, often articulated by the CEO. Whether this approach proves sufficient will ultimately depend on the organization's AI strategy, talent needs, and ability to create a compelling employee value proposition beyond compensation alone.

Prevalence of Compensation Approach for Each AI Talent Strategy

Compensation strategy should follow business strategy when deciding whether to concentrate spend on scarce technical talent, balance talent premiums with execution incentives, or focus primarily on workforce adoption and productivity gains.

Compensation Alone Is Not Enough

One of the most consistent themes emerging from conversations with compensation leaders is that compensation is only one component of the AI talent equation.

While headlines focus on extraordinary compensation packages, many organizations cannot realistically compete with Frontier AI Creators on compensation alone. More importantly, they may not be able to replicate the broader environment that attracts and retains elite technical talent.

Several compensation leaders noted that the most sought-after AI researchers are often motivated by more than compensation. The opportunity to work on frontier models, collaborate with elite technical teams, access significant compute resources, and tackle industry-defining problems can be as important as pay itself.

Several leaders also noted that smaller and earlier-stage companies often compete using a different value proposition altogether. In many cases, this challenge is compounded by private-company equity that is difficult to value or has lost some of its recruiting power as liquidity timelines have extended. While they may not be able to match the cash compensation, liquidity, or scale of larger AI organizations, they can offer broader role scope, accelerated career progression, greater ownership, and direct influence over products and company direction. For many employees, particularly those attracted to entrepreneurial environments, the opportunity to build a function, shape strategy, or assume leadership responsibilities earlier in their careers can offset some of the compensation advantages offered by larger organizations.

For AI-native startups, this often creates a different talent proposition. Rather than competing directly with Frontier AI Creators on compensation, they compete on impact, learning opportunities, organizational visibility, and the potential upside associated with helping build a successful business from an earlier stage. While this approach will not attract every candidate, it can be highly effective for individuals motivated by growth, autonomy, and long-term value creation.

This creates an important strategic reality for organizations. Companies that are not pursuing frontier AI research may be better served by focusing on AI-enabled product builders, AI transformation leaders, and broader workforce enablement rather than attempting to compete directly for a small pool of elite researchers. In many organizations, the most important AI talent may not be the individuals building the underlying technology, but those capable of translating AI capabilities into products, processes, and quantifiable business outcomes.

At the same time, many organizations are beginning to question whether competing for elite AI talent is the right objective. For companies focused on AI adoption rather than AI creation—which likely represents the vast majority of organizations across both technology and traditional industries—the larger challenge may be enabling thousands of employees to use AI effectively.

This shift is also influencing compensation design. Rather than paying solely for scarce skills, organizations are increasingly exploring ways to link rewards to measurable outcomes, including revenue growth, productivity gains, cost savings, and other forms of AI-enabled value creation.

Ultimately, the most important question for boards may not be how much to pay AI talent. It may be determining how they intend to create value from AI in the first place, and then, which pool of AI talent they need to search from.

Looking Ahead

The future of AI compensation is unlikely to be defined by a single model.

Frontier AI Creators will continue to compete aggressively for elite researchers. AI Product Builders will invest in AI-enhanced products and services. AI-Enabled Operators will focus on helping existing employees use AI more effectively.

Recent announcements from technology companies seeking to flatten organizational structures, improve productivity, and reduce management layers highlight a broader shift. As AI capabilities continue to improve, boards will increasingly focus not only on AI investments themselves, but on whether those investments are translating into measurable business outcomes.

For boards and compensation committees, the challenge is no longer simply responding to AI pay inflation. It is determining which AI talent matters most, how that talent creates value, and how talent, compensation, and workforce strategy can work together to support innovation, productivity, and growth.

Ultimately, the organizations that succeed will likely be those that align AI investment with clear business outcomes and evaluate those outcomes as rigorously as the investments themselves.