Same, but different: how pay for AI jobs is different from past trends  

As companies race to develop AI models and superintelligence capabilities, the demand for talent is outstripping supply and, unsurprisingly, driving up compensation. This surge is reminiscent of past tech booms, but on a scale and intensity never seen before. Notable massive investments in AI infrastructure and talent acquisition strategies from singular employers provide a compelling case study of how the industry is evolving. Drawing on Mercer’s latest research of high-tech total rewards leaders and industry-leading published data, this article explores how organizations are adapting their compensation strategies to attract and retain top AI talent.

Various major technology inflection points in the Internet era, the mobile revolution, and the rise of cloud computing have brought fierce competition for specialized talent. The Internet boom in the late 1990s saw demand for engineers specializing in web development skyrocket. Companies competed fiercely, offering premium salaries and generous stock options to build their online presence. There were clear pay differentials between Web Development Engineers, who were in high demand, and Network Engineers, whose skills were also essential but less hyped. Rapid scaling and equity incentives were key tools companies used to secure talent, setting a precedent for future tech waves.

At the turn of the millennium, after the Y2K threat had passed, the next revolution shifted the focus to app developers and engineers skilled in mobile platforms. Not surprisingly, salaries for these roles surged as companies fought for dominance in the mobile device market. Strategic acquisitions and aggressive talent poaching became common tactics to build competitive mobile teams. This time, the pay differential between App Engineers and Infrastructure Engineers widened, reflecting the premium placed on user-facing innovation.

Starting in the early 2000s, the cloud computing era brought a new emphasis on scalable infrastructure skills. Cloud engineers and architects commanded skyrocketing pay as companies invested heavily to build resilient, scalable platforms. Data scientists also gained prominence, though cloud architects often commanded higher compensation due to their critical role in infrastructure design. This period reinforced the importance of coupling talent acquisition with infrastructure investment to maintain a competitive advantage.

Each of these inflection points involved strategic mergers and acquisitions (M&A), significant infrastructure investments, and, yes, higher pay. From what we have experienced so far, the current AI boom mirrors these patterns but on an even larger scale.

The potential for breakthroughs in superintelligence fundamentally drives the race for AI talent. Companies are investing billions in infrastructure and computational resources. This funding frenzy is fueling record seed rounds and M&A activity, particularly among companies developing foundation models. With AI infrastructure spend in 2025 estimated at $375 billion USD globally¹, up from $33.5 billion in cloud infrastructure spend in 2015², the sky-high talent spend seems reasonable in context.

The fight for top talent extends beyond pay to include unique opportunities to work on foundational AI models, access to cutting-edge hardware, and autonomy in research. Top talent with specialized skills in AI research and massive computer infrastructure is commanding headline-grabbing pay packages. While it’s not unusual for supply and demand to affect market pricing, these conditions are extremely challenging for employers to navigate while also balancing broader labor issues such as internal equity, pay transparency, and cost control.

While tech giants battle it out, what can mid-size or non-tech companies do? Oftentimes, it is to hire a proven AI leader — someone with a track record of success — who can build out the rest of the team. These leaders command the highest pay premiums, reflecting both their expertise and the critical role they play in shaping an organization’s AI strategy.

At lower levels, the picture is more nuanced. The responsibilities of software engineers and AI specialists often overlap, making it harder to draw clear lines and harder to justify large pay differentials. As a result, the biggest compensation gaps are found at the top, where experience and leadership are at a premium.

The compensation numbers are considerable. The most recent quarterly research from Mercer | Comptryx shows that at P4 and higher levels, AI jobs are commanding up to a 30% premium over software engineering roles at equivalent levels. This pronounced differential at senior and leadership levels highlights the fierce competition for specialized AI talent and underscores the strategic importance organizations place on these roles.

While there is variation by location and sector, this divergence continues through higher professional levels. Further, the divergence tends to increase with compensation levels. That is, a slight premium in base, a moderate one in total cash, and a considerable one when looking at total direct compensation.

“Blitzhiring” and “back door acquisitions” (involving startups being acquired, primarily for their teams) are now common strategies among industry giants. Retention and incentive strategies in these new transaction types are also evolving. Mercer’s M&A 2025 Retention Insights Report finds that high-tech companies offer higher retention bonuses and equity in their transactions, averaging 100% higher than non-tech peers, thanks to their robust equity plans.

Despite aggressive investment in AI talent, most leaders remain uncertain about how to measure its long-term economic impact. According to a survey of global tech industry total rewards leaders, 91% of respondents indicated they are unsure how to effectively quantify or link the economic value of AI talent being hired. This uncertainty is compressing the period for long-term incentives, often to just two to three years, while overall incentive amounts remain high. Organizations are balancing generous, accelerated rewards with the need for flexibility in a rapidly changing market.

While the above strategies focus on competing with pay in this challenging talent market, the research also reveals that pay is only part of the solution. 

Companies are investing in next-generation infrastructure and compute capacity to attract and keep top talent. For one tech giant, innovative data centers are a cornerstone of its recruitment strategy, showing that in AI, infrastructure is as critical as compensation.

The AI talent wars show no signs of slowing down. So long as demand outstrips supply, salaries will continue to climb, especially for those with the skills and vision to lead AI initiatives. But companies are learning that pay alone isn’t enough. The most successful organizations are investing in both people and infrastructure, creating environments where top talent can thrive and innovate.

If prior tech talent cycles are a good indicator, pay for in-demand AI jobs may moderate over time, and the winners of the AI talent war will be those who struck the right balance between building a pipeline through M&A and development, investing in infrastructure, and offering accelerated compensation packages for high-impact jobs.

Our research and discussions point to several clear recommendations:

• Invest in both talent and infrastructure simultaneously. Working on the latest, biggest, unique, or most powerful technologies is as much of a differentiator as pay.
• Be prepared to offer significant pay premiums for proven AI leaders and senior professional talent, especially when building new teams.
• Recognize that pay differentials at lower levels may be less pronounced due to role blending.
• Use strategic M&A and partnerships to accelerate talent acquisition and capability building.
• Adapt incentive structures to reflect the fast pace of AI development, compressing timeframes while maintaining competitive total rewards.
• Use high-profile recruiters and strategic partnerships with academia to secure the best talent.

The current AI talent market is a high-stakes, high-investment arena reminiscent of past tech booms but distinguished by the impact, scale, and complexity of AI — both in foundational model development and in the associated compute resources required. Companies that master the art of combining competitive compensation, strategic acquisitions, and cutting-edge infrastructure or partnerships will lead the next wave of innovation.

_{¹ UBS Editorial Team. “CIO expects global AI spending to hit USD 375 bn this year.” UBS Global Wealth Management Insights, August 14 2025.}

_{² Wheatley, Mike. “IDC: Cloud infrastructure to account for 1/3 of all IT spend in 2015.” SiliconANGLE, July 8, 2015.}