How big tech AI talent poaching affects the AI talent wars

What's happening right now

The AI talent market has shifted in measurable, immediate ways. Compensation is up, recruiting timelines have stretched and big tech uses more aggressive methods to pull talent away from enterprises.

Here's a quick roundup of the market at the moment.

1. Hyperscalers ramp up AI labs

Meta, Google, Microsoft, Amazon, Anthropic and OpenAI are all expanding their internal AI research operations and competing for the same narrow pool of specialists. The most aggressively recruited positions are foundation model engineers, distributed systems experts and AI safety researchers, according to Artur Balabanskyy, chief technology officer and head of engineering at TapForce.

2. Acquisitions absorb startup talent

Rather than recruiting individual candidates, big tech companies now increasingly buy entire startups to absorb their engineering teams. Acqui-hires are a growing tactic, said Lacey Kaelani, a recruiting specialist at Metaintro who tracks AI hiring trends. She cited examples like Openclaw, which was recently acquired by OpenAI.

3. Talent pools and best practices are shifting

The best candidates are not submitting applications. Instead, top companies go directly to targets using LinkedIn, professional networks and tech communities, Kaelani said. For example, Meta and Microsoft have reached out directly to senior machine learning (ML) engineers and applied researchers that Balabanskyy's organization has worked with, he said.

Balabanskyy also tracked a concrete effect on recruiting timelines: Roles that once took two to three months to fill now stretch to four to six months or more. Eric Vaughan, CEO of IgniteTech, said he experienced this firsthand when another company recruited his chief AI officer on LinkedIn after being mentioned in a national business publication, and the company offered a substantial signing bonus.

Additionally, AI and ML salaries have increased 35-45% over the past two years, Kaelani said. Senior roles now reach $300,000 to $650,000 per year.

The talent pool is also no longer local or regional. Big tech offers flexible remote arrangements as a key recruitment lever, Kaelani said. This means organizations everywhere now directly compete with companies headquartered thousands of miles away.

The IT skills shortage is no longer a problem confined to the tech sector alone.

Financial services firms are in the mix, as well, said Michael Morris, global head of platform and talent at Randstad Digital. Specifically, the targeted employees are legacy-skilled workers in mainframe or Java development and those who have invested in AI training to build capability from within.

Overall, the IT skills shortage is no longer a problem confined to the tech sector alone.

Why big tech is poaching AI talent

The forces driving big tech AI talent poaching come down to simple facts: The timeline is short, the talent pool is small and the stakes are high.

1. The timeline is short

The generative AI race has made speed the only priority. Big tech companies are not willing to wait for organic talent development while competitors move fast.

"Speed and knowledge," Kaelani said. "Competitors working on AI are moving much more quickly. Big companies can't do that and therefore have to pay a premium to snatch up the talent before a competitor does."

2. The talent pool is small

Experienced AI talent is in short supply. Frontier expertise in large language model training and AI safety is globally scarce, according to Morris, which forces companies to pay premiums rather than build. It's a dynamic that IT leadership has seen before.

"It's consistent with what we've seen historically when cloud, mobile and DevOps capabilities were in high demand," Morris said. "It's simply a case of skilled talent being in the driver's seat."

Big tech isn't competing for people focused on deployment because they're still thinking about building models rather than using them at scale.

3. The stakes are high

The legal environment is tightening. As companies pay more for AI talent, they also work harder to keep it from walking out the door. Organizations now enforce confidentiality obligations and trade secret protections more aggressively, especially when departing employees worked on high-impact projects, said Kelsey Szamet, a partner at Kingsley Szamet Employment Lawyers who advises on workforce mobility.

Also, not all AI roles are equally at risk. Big tech competes for model builders, not deployment specialists. While the AI practitioners most valuable for internal enterprise transformation are not the same ones being poached, they are still hard to find.

"Big tech isn't competing for people focused on deployment because they're still thinking about building models rather than using them at scale," Vaughan said.

How AI talent poaching affects the market

The AI talent wars are spreading across the tech labor market, with measurable effects already evident. They include the following:

• Salaries escalate. Even organizations that are not directly targeted see compensation benchmarks rise. Vaughan said he tracked salary premiums of 30-40% for conventionally titled AI roles.
• Mid-market and enterprise companies lose talent first. Hyperscalers are best positioned to win compensation battles. Enterprises and mid-market firms, with tighter budgets, are frequently the first to see AI practitioners recruited away.
• Consulting and contractor costs rise. Contractor and consulting rates have climbed significantly alongside salaries, compounding the cost pressure for organizations that turn to outside specialists when internal talent walks away, Balabanskyy said.
• Time-to-hire lengthens. The time it takes to fill roles now runs two to three times longer than it did 18 months ago for conventionally titled AI roles, Vaughan said. Longer recruiting cycles mean critical projects sit without key contributors for extended periods.
• Internal AI projects stall. Many projects have been delayed or narrowed in scope because ML engineering or model deployment talent was unavailable, Balabanskyy said. That is the most direct consequence of IT leaders trying to keep AI roadmap commitments.

Key takeaways for CIOs

The AI talent wars show no structural signs of easing. A few priorities stand out.

Budget for wage pressure now. AI role compensation won't revert to pre-2023 norms. CIOs must build current market rates into budget planning and account for upskilling existing staff as an alternative to external hiring.

What CIOs can do now:

• Audit salaries of current AI roles against market rates.
• Fund upskilling programs as a budget line item.

Plan for longer recruiting cycles and higher retention risk. The talent gap may widen before it narrows. Research autonomy and access to strong computing resources matter as much as pay, Balabanskyy said.

What CIOs can do now:

• Start retention conversations before key employees get poached.
• Extend recruiting timelines in project planning to reflect the new reality.

Watch for the expertise paradox. The fast pace of AI creates an expertise issue that affects both recruiting and retention.

"An expert from a year ago isn't necessarily one today," Vaughan said.

What CIOs can do now:

• Hire for adaptability and continuous learning, not just credentials.
• Reassess AI role requirements regularly as the tooling landscape shifts.

Reduce dependency on individual AI contributors. When a single AI leader becomes the face of a program, they become a target.

What CIOs can do now:

• Distribute AI knowledge across teams.

Don't overlook manager capacity. Organizations often miss this retention factor. When managers lack the bandwidth to support reskilled employees, attrition rises, said Stephanie Newland, director of workforce development at TEKsystems Global Services.

What CIOs can do now:

• Invest in manager coaching alongside technical development programs.

The real shortage isn't AI experts. It's leaders who understand that AI changes what your entire organization needs to look like.

Treat AI workforce risk as a permanent strategic variable. The organizations making the most progress treat AI talent as a long-term workforce strategy, not a short-term hiring problem.

"Stop fighting for the same talent everyone else wants," Vaughan said. "The real shortage isn't AI experts. It's leaders who understand that AI changes what your entire organization needs to look like."

What CIOs can do now:

• Reframe AI talent strategy. Build an AI-capable workforce instead of chasing a shrinking pool of credentialed experts.

Sean Michael Kerner is an IT consultant, technology enthusiast and tinkerer. He has pulled Token Ring, configured NetWare and been known to compile his own Linux kernel. He consults with industry and media organizations on technology issues.