Multimillion-dollar offers, nine-figure packages, and retention walls—AI experts are the new strategy, not just the staff.

I have witnessed many tech cycles, but the current competition for AI brains is unlike anything I have ever seen. This is an industrial policy with stock grants, not a hiring trend. Big Tech’s message is clear-cut: invest in the top engineers and researchers, or risk missing out on the next ten years. Furthermore, the numbers being floated are line items in leaked memos and internal comp sheets, not flashy headlines.

According to reports, Microsoft is circulating a “most-wanted” list of Meta researchers and engineers, giving them special treatment as “critical AI talent,” and authorizing multimillion-dollar compensation packages that specifically exceed the company’s typical pay caps. “Stretch” now means up to $408,000 base, $1.9M on-hire stock, $1.5M in annual stock, and cash bonuses up to 90%—and that’s before any one-time sweeteners, according to internal pay guidance.

Meta isn’t exactly defending itself. It has been aggressively luring top talent from Apple and OpenAI in recent weeks, not with fancy bonuses but with nine-figure math. Ruoming Pang, the head of Apple’s AI foundation models, was enticed by Meta with a package worth over $200 million over a number of years, according to Bloomberg and others. Sam Altman has openly stated that Meta offered $100 million in “signing” bonuses to OpenAI researchers. Whether you believe those figures or not, they are reliable enough to compel counterattacks throughout the valley.

OpenAI’s response? About 1,000 technical staff members will receive a unique one-time award on the day before the GPT-5 launch. According to The Verge, esteemed researchers receive top-tier compensation of mid-single-digit millions, while engineers typically receive hundreds of thousands, paid quarterly over a two-year period in cash, stock, or both. It’s a retention wall, not a holiday bonus.

How important AI specialists are now more than ever (and why salaries appear “crazy” but reasonable)

On the surface, this appears to be vanity bidding. It’s straightforward economics from the inside out:

1. Lack of resources at the frontier. Few people are capable of advancing alignment research into shippable safety protocols, scaling inference effectively, or advancing SOTA pretraining. Each one can unlock tens of billions in market capitalization and billions in product value, so if it reduces the risk of leadership, it is reasonable to pay eight or nine figures over a number of years. (The actions of Microsoft and Meta make that clear.)
2. Determine leverage. The marginal value of a researcher who increases training efficiency by a few percent (or extracts more capability from the same FLOPs) is far less than their compensation when your organization manages compute budgets in the tens or hundreds of millions.
3. Loops that involve data and distribution. The best individuals do more than just fine-tune models; they also select pretraining mixes, create evaluations, and use distribution tools like WhatsApp, Instagram, Office, and Windows. This tightens flywheels that are difficult for competitors to swiftly imitate.
4. Time-to-impact. The introduction of GPT-5 and the preemptive bonuses offered by OpenAI demonstrate that capability jumps continue to influence investor expectations, platform strategy, and adoption on quarterly timeframes. You’ll fall behind by a year if you miss a cycle.

What the future looks like for AI experts

In appearance, superstars will resemble founders more than workers. Titles will become more generic (“chief scientist,” “staff fellow”), and packages will combine salaries, grant monsters, and calculate budgets that are treated like profit and loss. The top researchers will work with comp to negotiate team charters, which include who they hire, what they train, and what evaluation gates they accept.

The frontier splits in two.

• Datasets, TPU/GPU clusters, and inference distribution are found in a small number of labs and tech giants, which will attract a thin layer of foundation-model and systems researchers (the ones everyone is bidding for).
• A much greater number of domain scientists and applied engineers will create vertical models and specialized, high-margin agents; they will have less headline competition but vast potential if they can find product-market fit.

Career-accelerating factors include safety and policy. Every C-suite meeting will feature you if you can create red-team protocols, decipher mechanistic interpretability artifacts, or solidify evals that regulators can rely on. Anticipate “agentic-systems safety lead” and “model governance architect” to rise to the top of the job hierarchy.

There will be more prestige that is portable. While a winning paper is important, the new resume will be running a production training that hits a capabilities milestone (or docks a safety risk). That rewards those who can ship within limitations, which is precisely why Microsoft and Meta are pursuing practitioners rather than merely theorists.

The part that is uncomfortable: culture and focus

My personal opinion is that while the money is understandable, the concentration risk is not. When a select few companies are able to secure frontier talent with multi-year packages worth $200 million to $1 billion, they also control agenda-setting, including what is studied, which risks are given priority, and whose goods are considered “normal.” The fact that these hiring waves coincide with layoffs and restructuring aimed at “funding the AI pivot” is no coincidence. The end effect is a barbell labor market, with high-end offers at the top and pressure on productivity everywhere else.

Culturally, mission still matters. Even some leaders pushing back on Zuckerberg-scale packages say they’d rather win with purpose than price. That’s not just PR—it’s how you keep the next wave from burning out or cashing out. (And yes, a lot of people will still choose the bigger number.)

What this means if you’re not Microsoft, Meta, or OpenAI

• Instead of attempting to outbid, out-define. Provide unambiguous problem ownership, direct access to decision-makers, and the option to publish quantified outcomes.
• Incorporate computation into comp. Superpowers in recruiting include a guaranteed training budget or shared access to a partner’s cluster.
• Instead of building against the frontier, build around it. With data rights, evals, UX, and go-to-market, set yourself apart from the competition using the best base models.
• Make early investments in safety talent. They are necessary for both shipping and selling.

The bottom line

Experts in AI are the uncommon input that shifts the paradigm in terms of cost, capability, and culture. This explains why Microsoft is attempting to break free from Meta by pushing past its own competitive barriers, why Meta is offering nine figures to restart its superintelligence push, and why OpenAI is actually paying to stop the bleeding. If you believe that this will end quickly, it won’t. It ends when people are no longer the bottleneck—when elite intuition is less important due to the use of tools, data, and computation. We haven’t arrived yet. Right now, the plan is to hire the right people.

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Sources for key facts used above:

• Targeting and compensation for Microsoft’s “critical AI talent” can reach $408K base, $1.9M on-hire stock, $1.5M annual stock, and 90% cash bonus. (The Indian Express)
• Microsoft’s plan to steal intellectual property and target Meta divisions (Reality Labs, GenAI Infra, Meta AI Research) was reported by Business Insider. (Business Insider)
• Nine-figure offers, including more than $200 million, were made by Meta to hire Apple’s Ruoming Pang. (Bloomberg, AppleInsider)
• According to Sam Altman, Meta offered OpenAI employees $100 million in “signing” bonuses. (Reuters)
• A special one-time bonus of ≈1,000 was announced by OpenAI the day before GPT-5. (The Verge)

Disclaimer:
This article reflects the author’s personal opinions based on publicly reported information from reputable outlets. Compensation figures, hiring activity, and timelines are subject to change and may differ from final company filings. Logos or brand references are used for identification and commentary (fair use) and do not imply endorsement or affiliation. All images in this post are AI-generated and may contain artistic approximations. Nothing here is investment, legal, or career advice—please do your own research.