01 The real strategic asset is the "dual-stack bridge person"
The figures below follow Metix AI database methodology (data as of roughly 2026 H1). The population = current engineering and research professionals at 15 U.S. AI-compute / chip companies (current employees pulled by authoritative company_id, then cleaned to a "hardware design + ML systems" role scope). All figures are aggregate statistics; the report displays no personal information.
People who understand both physical silicon (RTL / verification / physical design) and ML systems (CUDA / compilers / kernels) are the hard currency of this race. Of the 56,861 across the full map, only 8.4% are identifiable as dual-stack, and even NVIDIA has only 14.6% (~1/7); at legacy chip giants they're nearly extinct (Broadcom 2.2%, Intel 5.0%). Dual-stack density is the thermometer of how AI-native a company really is.
It is, among the 14 other companies, the #1 talent feeder for 9 of them — 13% of NVIDIA, 21% of AMD, 32% of Rivos and 31% of Tenstorrent come from Intel, and of those roughly 89% held technical roles at Intel. The foundation of the compute race is, to a large degree, being laid by senior engineers who flowed out of Intel.
Massive inflow, minimal outflow. As a talent source for other companies, NVIDIA generally accounts for only 5–9% (the lone exception is Groq, at 34%).40 monthsMedian tenure + a roughly 10x move in NVDA stock = the hardest golden handcuffs in the industry, locking the 44% of "4-years-plus veterans" in place.
You can source straight from it: SambaNova = Oracle / Sun (41%), Rivos = the Apple-chip crew (the ones Apple sued), Groq = ex-NVIDIA (34%), AWS's in-house chip team Annapurna = internal Amazon transfers (54%). Read the lineage and you know which company to raid for which kind of engineer.
02Stock Map: scale lives at the giants, density at the startups
Start with the pie. These 15 companies hold 56,861 current engineering and research professionals in the U.S., but the distribution is wildly uneven: the 6 chip / GPU giants account for 55,478 (97.6%), while the 8 challengers combined hold just 1,290. The starkest contrast: Intel, with the most people (21,549), sits near the bottom on AI dual-stack density (5.0%).
Split each company into three buckets — "metal-leaning / model-leaning / dual-stack" — and the picture snaps into focus: legacy chip makers are pure-hardware battalions, while AI startups tilt the scales toward the model side. Below is the full roster (median tenure / median career computed to the report date).
| Company | Camp | Eng & research | Metal % | Model % | Dual-stack % | Median tenure | Median career | PhD % |
|---|---|---|---|---|---|---|---|---|
| NVIDIA | GPU duo | 12,134 | 51.5 | 31.5 | 14.6 | 40 mo | 15.0 yr | 14.1 |
| AMD | GPU duo | 6,139 | 67.2 | 16.7 | 11.7 | 36 mo | 15.1 yr | 10.8 |
| Intel | Legacy chip | 21,549 | 49.9 | 8.7 | 5.0 | 68 mo | 16.5 yr | 20.1 |
| Qualcomm | Legacy chip | 8,196 | 48.1 | 15.0 | 8.1 | 58 mo | 15.8 yr | 12.1 |
| Broadcom | Legacy chip | 5,313 | 42.0 | 4.8 | 2.2 | 90 mo | 21.8 yr | 9.9 |
| Marvell | Legacy chip | 2,147 | 65.2 | 6.2 | 5.2 | 51 mo | 19.6 yr | 11.1 |
| SambaNova | Challenger | 179 | 61.5 | 45.3 | 26.8 | 48 mo | 14.7 yr | 6.7 |
| Etched | Challenger | 141 | 77.3 | 27.0 | 21.3 | 12 mo | 12.5 yr | 8.5 |
| Tenstorrent | Challenger | 304 | 84.5 | 26.3 | 21.1 | 16 mo | 14.9 yr | 12.5 |
| Cerebras | Challenger | 222 | 49.1 | 39.6 | 19.8 | 24 mo | 15.4 yr | 15.8 |
| d-Matrix | Challenger | 88 | 68.2 | 59.1 | 40.9 | 18 mo | 15.5 yr | 22.7 |
| Lightmatter | Challenger | 157 | 78.3 | 17.8 | 12.7 | 17 mo | 15.4 yr | 30.6 |
| Rivos | Challenger | 140 | 87.1 | 10.0 | 9.3 | 40 mo | 13.8 yr | 8.6 |
| Groq | Challenger | 59 | 47.5 | 33.9 | 20.3 | 30 mo | 16.7 yr | 6.8 |
| AWS Annapurna | Cloud in-house | 93 | 71.0 | 59.1 | 41.9 | 19 mo | 10.4 yr | 15.1 |
03Dual-Stack Scarcity: a monotonic "AI-native" gradient
Line up "dual-stack density" by company and you get an almost perfectly monotonic gradient: from Broadcom (2.2%), which only does networking / analog chips, climbing all the way to d-Matrix and AWS Annapurna (~41%), built from the ground up to make chips for large models. The closer a company sits to large models, the more "understands both silicon and models" people its résumés contain.
Share of "dual-stack bridge people" by company (hardware ∩ ML systems)
Three tiers
Legacy chip makers Broadcom / Intel / Marvell / Qualcomm ≈ 2–8%. They are pure-silicon battalions (metal-leaning 42–65%, model-leaning often single digits), with dual-stack all but extinct.
GPU duo AMD / NVIDIA ≈ 12–15%. Living at the "GPU × ML" intersection for years, they carry the thickest dual-stack layer among the giants.
AI-accelerator challengers ≈ 20–42%. Built for large models, they pull the model-side talent share to two or three times that of the giants.
This is a "floor," but the gradient is real
Dual-stack is identified by keywords from self-reported titles + skills, so it tends to undercount (many people never fill in all their skills). But even under a stricter "physical silicon ∩ model" scope (dropping CUDA / compilers from the metal side), the gradient still holds monotonically: Broadcom 1.7% → NVIDIA 8.4% → d-Matrix 34%.
In other words: the absolute numbers go higher, the relative ranking doesn't move. On the question of who is scarcer, the conclusion is robust.
04The Intel Factory, and every company's "lineage"
Where do the people come from? The answer is surprisingly consistent: Intel. It is the #1 talent feeder for 9 of the other 14 companies — and the more hardcore the silicon startup, the higher Intel's share. AI may be the war Intel lost, but many of the soldiers fighting it were sent out by Intel.
Share of "ex-Intel" employees by company (of that company's eng & research talent)
Beyond Intel, the remaining 5 "non-Intel-lineage" companies each have their own origin — and every one can be verified in the public record. Read the lineage and you know where to raid for which kind of engineer:
| Company | Top lineage | Share | Public verification |
|---|---|---|---|
| Groq | Ex-NVIDIA | 34% | The founder came from Google's first-gen TPU, but the engineering bench is NVIDIA-bred — "founder lineage ≠ team lineage." |
| SambaNova | Oracle + Sun | 24% + 17% | Co-founder Rodrigo Liang comes from the SPARC-processor lineage at Oracle / Sun, and the whole team carries database-hardware DNA. |
| Rivos | Apple | 20% | Sued by Apple in 2022 for hiring its chip team and stealing SoC secrets, settled in early 2024 — the data precisely confirms that cohort of Apple-silicon people. |
| Etched | Apple + Intel | 16% + 13% | Founded in 2022 by Harvard dropouts to build a Transformer-specific ASIC (Sohu), filling out its hardware bench by hiring Apple / Intel silicon veterans. |
| AWS Annapurna | Internal Amazon | 54% | The Trainium / Inferentia team is driven mainly by internal transfers — a cloud provider's in-house chips are "grown from within," not hired from the market. |
05NVIDIA's Golden Handcuffs: lots in, little out
Intel's people scatter outward; NVIDIA barely leaks anyone — as a talent source for other companies it generally accounts for only 5–9% (the lone exception being Groq). The reason is written in the tenure: the median current tenure of NVIDIA's U.S. core is 40 months, and 44% have already passed 4 years. Layer on NVDA's roughly 10x run across 2023–2025 — and these are the hardest golden handcuffs in the industry today.
Current-tenure distribution of NVIDIA's eng & research talent (n≈11,894)
The mobility window is the 31% under 24 months
Roughly 3,700 people have been on board under two years, their initial RSUs far from fully vested — they forfeit the least paper gains by jumping, making them the most realistic mobility window in the NVIDIA camp. The further you go past 48 months, the tighter the handcuffs: that 44% (~5,200 people) hold deeply in-the-money vested stock and are nearly impossible to pry loose.
Startups play it exactly the other way
Etched (median tenure 12 mo), Tenstorrent (16 mo) and Lightmatter (17 mo) are all very "young" — they use pre-IPO equity to run the reverse play: pry people out before they fully vest at a giant, betting pre-IPO options against the giant's already-realized certainty.
06Hunter Profile & Field Playbook
Translate the structure above into executable sourcing moves. Remember three things first: this is a war fought with veterans, you source by metal / model tags split into lanes, and timing decides whether you can pry someone loose.
All veterans, no new grads
The full-map median career is 13–22 years; even the youngest, Etched, sits at 12.5 years, and Broadcom is as high as 21.8. The compute war is fought with senior engineers — don't bring a "new-grad hiring" budget and pitch to this market.
Source by metal / model tags
Want pure silicon design (RTL / physical design / verification) → go to Rivos (metal-leaning 87%), Tenstorrent (85%), Lightmatter (78%), Etched (77%).
Want Compilers / ML systems → go to AWS Annapurna, d-Matrix (model-leaning 59%), SambaNova (45%), Cerebras (40%).
PhD density shapes the pitch
Want a research foundation (photonics / analog / compilers) → Lightmatter (PhD 30.6%), d-Matrix (22.7%), Intel (20.1%).
Want delivery-focused engineers where a PhD isn't necessary → SambaNova (6.7%), Groq (6.8%), Etched (8.5%).
Questions this report answers
- How many U.S. AI-compute / chip professionals are in this x-ray?
- We x-rayed 15 U.S. AI-compute / chip companies and 56,861 current engineering and research professionals, reading their résumés as data.
- What population definition produced the 56,861 count?
- The figures below follow Metix AI database methodology (data as of roughly 2026 H1). The population = current engineering and research professionals at 15 U.S. AI-compute / chip companies (current employees pulled by authoritative company_id, then cleaned to a "hardware design + ML systems" role scope).
- How should this report be cited?
- Metix AI Talent Intelligence, 2026-06-16. AI Compute & Chip Talent Map 2026 | Metix AI. https://metix.ai/reports/mapping/ai-infra-chip-talent-2026
Want the full list of a specific slice of these 56,861 people?
This report is an aggregate x-ray; to get down to named individuals, Metix AI can filter a contactable candidate list by "dual-stack / metal-leaning / model-leaning + company + tenure window," and can generate the same talent x-ray for any target company.
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