AIs hottest story: the talent shuffle

If your LinkedIn feed feels like a steady stream of Im joining a new AI startup postsyoure not imagining it. Some of the worlds top AI researchers and engineers have been moving on from Big Tech powerhouses, including Googles DeepMind, to launch startups or join faster-moving rivals. For founders, leaders, and curious readers, this shift isnt just gossipits a roadmap for where AI is heading next.

This article takes the Sifted report on DeepMind departures as a starting point and adds broader context: whats driving the exits, where the talent is going, and how startups and enterprises can respond. Well also balance the narrative with what Google DeepMind still does uniquely well.

What changed at Google DeepMind?

In 2023, Google merged its two elite AI groupsGoogle Brain and DeepMindinto a single unit called Google DeepMind to accelerate breakthroughs and products like Gemini. The company framed the move as a way to “significantly accelerate” progress by unifying research and product teams (Google).

The upside: bigger bets, massive compute, and fast paths from research to real-world products. The trade-off: more product focus, tighter priorities, and inevitably more structure. For some researchers, that can mean less freedom to explore speculative ideas or publish openlya long-running tension in industry labs.

Despite the headlines, Google DeepMind remains a world-leading lab. It produced AlphaFold, which mapped protein structures at scale and transformed biology (Google DeepMind), and continues to ship ambitious models like Gemini 1.5 with long-context reasoning across text, audio, and video (Google).

So why are top people leaving?

No single reason explains every move, but several powerful forces keep coming up in interviews, public statements, and market data.

1) Startup upside and ownership

Equity in fast-growing AI startups is a huge draw. OpenAIs share sale in early 2024 reportedly valued the company at around $80 billion, turning employee equity into life-changing wealth (Bloomberg). Many startups now offer meaningful ownership, which can outcompete Big Tech compensation for risk-tolerant talent.

2) Speed, autonomy, and clear missions

Smaller teams can move faster and give senior researchers more say in direction. Founders describe the appeal of building product directly from research, without layers of approvals. A wider industry shift from “publish-first” research toward “product-first” AI is pulling veterans into the arena  and many want their names on the next breakout app.

3) Compute access beyond Big Tech

Only labs with serious compute can train frontier models. That used to mean Big Techbut cloud alliances have changed the equation. Microsofts expanded partnership with OpenAI (Microsoft) and Amazons commitment to invest up to $4B in Anthropic (Amazon) give startups the compute and tooling once reserved for in-house labs.

4) Culture and publication norms

As research turns into competitive product IP, companies tend to publish less. For scientists who value open research or fundamental work, that shift can feel like a loss. The State of AI Report 2024 documents a broader trend: more top researchers moving from Big Tech labs into startups focused on applied, product-led development.

5) Brand risk and product pressure

High-profile product missteps can create internal pressure. In early 2024, Google paused Geminis image generation after inaccurate historical depictions drew strong criticism (The Verge). Shipping at Googles scale brings scrutiny that some researchers would rather avoid, especially if theyre motivated by rapid iteration over brand safety.

Where are they going?

• New labs and startups: Ex-Googlers and ex-DeepMinders have co-founded or joined companies like Anthropic, Cohere, and xAI. xAIs team page lists multiple researchers with DeepMind and Google pedigrees (xAI).
• OpenAI and Anthropic: Frontier-model labs offer compute, fast cycles, and high compensation through equity and tender programs, creating strong pull factors.
• Microsofts new AI unit: In March 2024, Microsoft hired Mustafa Suleyman (DeepMind cofounder and later Inflection AI CEO) and Kare9n Simonyan to lead a new consumer AI groupa high-profile example of talent mobility across the ecosystem (Microsoft), (CNBC).

Importantly, this isnt a one-way street. Google DeepMind continues to hire aggressively and retains many leaders, while also incubating adjacent bets like Isomorphic Labs (a DeepMind spin-out focused on drug discovery).

Is this a DeepMind problem or an industry shift?

The safest answer: its mostly an industry-wide shift. The State of AI Report 2024 highlights a broad migration of senior talent from Big Tech labs to startups seeking speed, ownership, and product impact. At the same time, Big Tech labs still drive foundational research and operate at unmatched scalewhich attracts a different kind of builder.

In short: the market is segmenting. Startups optimize for velocity and novelty; incumbents optimize for reliability, distribution, and safety at scale. Talented people are choosing the environment that best matches their personal goals.

What it means for founders and leaders

Hiring playbook in the AI talent war

• Lead with mission and ownership: Be explicit about what unique problem youre solving and the equity upside tied to it. Senior talent wants impact and alignment.
• Offer compute as a benefit: If you cant afford your own cluster, partner for credits or grants and make clear how candidates will access GPUs to do their best work.
• Design for speed without chaos: Keep teams small, remove layers, and set crisp research-to-product pathways. Promise rapid iterationand deliver on it.
• Respect publishing: Create policies that allow reasonable publication and open-source contributions without compromising IP. This helps retain scientifically minded hires.
• Pay market-competitive compensation: Use data from recent raises and secondary markets to calibrate equity offers; AI talent tracks outcomes in companies like OpenAI and Anthropic (Bloomberg), (Amazon).

Build, buy, or partner?

• Build in-house: Invest early in a core research-engineering team if AI is central to your moat. Signal long-term commitment with a strong technical leader.
• Buy or invest: Acquire small teams for speed to market, or invest strategically to secure access and influence without full integration.
• Partner smartly: Leverage model providers and cloud alliances for compute and tooling. Many winning AI products layer proprietary data and UX on top of solid foundation models.

What it means for Google DeepMind

For Google DeepMind, the challenge is to keep its dual identity: a place for groundbreaking science and a machine that ships safe, reliable products to billions of users.

• Double down on unique strengths: Deep scientific problems at the nexus of AI and science (e.g., biology, materials) where DeepMind has a track record (Google DeepMind).
• Make publishing part of the pitch: Clear commitments on research openness can differentiate against startups that keep everything proprietary.
• Empower intrapreneurs: Create internal startup pods with ownership, rapid cycles, and protected compute to mimic the autonomy people seek outside.
• Keep product trust high: Learning quickly from issues like the Gemini image pause and communicating transparently builds credibility with both users and researchers (The Verge).

Bottom line

The movement of talent out of Google DeepMind is real, but its best read as part of a larger pattern: AI is maturing from lab research into a product-driven industry. That creates space for startups to move quickly and for incumbents to industrialize AI at scale. If youre building or investing in AI, align your strategy to the kind of people you want to attractand the kind of work they want to do.

FAQs

Is Google DeepMind losing its edge?

No. It continues to publish significant research and ship models like Gemini 1.5. But the center of gravity is broader now, with startups pushing fast on product.

Where do ex-DeepMind researchers typically go?

Many join or found startups (e.g., xAI, Cohere) or move to labs like OpenAI and Anthropic. Some also join Big Tech units with fresh mandates, like Microsofts consumer AI group.

Whats the biggest pull factor for leaving?

A mix of ownership (equity), autonomy, and the chance to ship quickly. Access to compute via cloud alliances also levels the playing field for non-Big Tech labs.

Should startups try to hire only top 1% researchers?

Not necessarily. Balanced teams that blend strong engineering, product, data, and MLOps often ship faster than research-only teams.

How can enterprises compete for AI talent?

Offer meaningful problems, clear paths from research to impact, and room to publish. Pair competitive pay with autonomy and reliable access to compute.

Sources

1. Google: Introducing Google DeepMind (April 2023)
2. Google DeepMind: AlphaFold highlighted research
3. Google: Gemini 1.5 announcement (Feb 2024)
4. State of AI Report 2024
5. The Verge: Google pauses Gemini image generation (Feb 2024)
6. Microsoft: New AI leadership hire  Mustafa Suleyman and Kare9n Simonyan (Mar 2024)
7. CNBC: Microsoft hires Inflection AI cofounder (Mar 2024)
8. Bloomberg: OpenAI valued at about $80B in tender offer (Feb 2024)
9. Amazon: Up to $4B investment in Anthropic (Sep 2023)
10. xAI: Team page