Vertical AI
Vertical · Healthcare
26 deals + 11 tier-1 leads
DENS
LEAD
ACCL
TKT
RNWY
ENTRY
$29M
B/A STEP
2.5x
LEADS
11 VCs
Q1'26
4 new
Top 3 → drawer
LEAD · Balderton Capital
Chapter II · Concentration
Top 10 = $528B, accounting for 81% of the total. Top 30 = 90%. The remaining 373 companies split less than 20%.
Oct 2024 – Apr 2026 total $649B funding, aggregated by company. Dark = Top 10 (528B / 81%), gray = 11–30 (the rest).
Rank
#1
#2–3
#4–10
#11–30
81%
Top 10 Companies / Total Funding
$528B / $649B · 18-month window · OpenAI alone captured 34%
OpenAI
Stargate Project
Anthropic
xAI
Aligned Data Centers
Databricks
Scale AI
AI Campus Paris JV (MGX-Bpifrance-Mistral-NVIDIA)
Cursor (Anysphere)
Ampere Computing
Anduril Industries
Safe Superintelligence
Mistral AI
Reflection AI
DigitalBridge
CoreWeave
Moonshot AI
Wayve
Anysphere
Kalshi
Skild AI
Ramp
Thinking Machines Lab
Intel
Crusoe
Physical Intelligence
Tap or hover any tile to see company details, total raised, and share of total
ighty percent of the capital went to ten companies. That's money in the bank, not pledges.
Concentration doesn't mean the door is closed. Outside the Top 30, another 120 early-stage companies raised Seed or Series A: Rillet (Vertical AI · $95M), Emergent (Agents & Applications · $93M), Aspora (Other Tech · $88M), Modus (Agents & Applications · $85M), Gimlet Labs (AI Infra · $80M). They're scattered across the long tail.
The question: within that 80%, how much is genuine consensus from multiple VCs, and how much is a single big bet creating a mirage?
II → III
From companies to sectors—separating real heat from fake heat.
Chapter III · Hot vs. Fake-Hot Tracks
30 sub-sectors absorbed 92% of the capital. Only a handful pass all three tests: high deal count, high valuations, and multiple lead investors. The rest owe their heat to one VC betting big on one company.
3 columns measure deal count / valuation density / lead ecosystem breadth. Darker color = higher rank. All 3 columns dark = true consensus; only 1-2 dark = single VC or single company inflating the average.
LowHigh
Each column normalized independently · 5-step quantize
3
Truly hot segments (all 3 signals in top 1/4)
30 candidates / 52 total segments · 2 more show "heat mirage" (active or high valuation, but only 1-2 VCs betting)
hree metrics must all rank in the top quartile for a sector to qualify as genuinely hot: deal count, median valuation, and number of distinct lead investors. Only 3 sectors pass: General LLM, Coding Agents, Cloud Compute.
Databases is a textbook fake-hot: ranks #10 in deal count, #2 in valuation, but only 3 lead investors. The entire sector's valuation is propped up by Databricks ($134B) alone. Self-driving is the same pattern—Wayve single-handedly inflates the average. Healthcare is the opposite: plenty of deals, multiple leads, but valuations haven't taken off yet.
How big is a typical round in each sector at each stage?
III → IV
From sector granularity to stage granularity: typical single-round size at each stage.
Chapter IV · Stage Funnel
52 sectors × 5 stages, median single-round raise. This isn't cohort survival—companies in the window are at different stages, so you can't read "how many make it." What you can read: at each stage, how big is a typical check.
Median single-round funding for 52 verticals at each stage (log scale). Top 10 highlighted, 42 others as background. This is not survival rate — different companies at different stages within an 18-month window cannot be read as 'how many survived.'
60x
Foundation models · Seed to D+ multiplier
Seed median $220M to D+ median $13B
Hover line / tag to see per-stage figures · Click to drill down
Click tag to drill down ·
52
verticals total
hree sectors, three rhythms.Foundation Models runs mega-rounds throughout: Seed $220M, Series A $1.3B, Series B $627M, Series C $2.0B, Series D+ $13B. Small teams can't even get a ticket.
Coding Agents follows the SaaS growth playbook: Series A $55M, Series B jumps 4× to $218M, Series C doubles again to $400M, then growth slows post-D. Early rounds price in fast; later rounds price in customers and retention.
Vertical Healthcare takes the smallest steps, but each is backed by revenue: Series A $32M, Series B $141M, Series C $126M, Series D+ $363M. Other Agent Apps has the lowest barrier: Seed $30M, Series A $29M. Less capital, but competition is also scattered.
Now look at whose hands the money comes from.
IV → V
Who leads, how many co-invest—that determines if someone picks up the next round.
Chapter V · Lead Signal Strength
29 firms × 46 sectors, 279 lead rounds. The column header shows how many independent firms have led in that sector. Higher number = more potential lead investors for your next round.
Columns = sectors (sorted by distinct lead investors), rows = 29 active VCs (sorted by total leads). Darker cells = higher concentration in that sector.
011
cell = lead count · column header number = distinct lead VCs for that tag
0
sectors backed by only one lead VC
If that VC stops following on, there's no one to anchor the next round · Conversely: 20 sectors have 3+ independent leads = true consensus
nly 31 sectors have been led by ≥ 3 independent VCs. The most concentrated is General LLM: 22 firms, 30 total leads. The top three are Shanghai Guotou Pioneer Fund×3, SoftBank Vision Fund×3, Lightspeed Venture Partners×2.
5 sectors have only one lead investor: Vertical · Education, Creative Tools, MLOps, Other. If that fund changes direction or closes, you're starting from scratch for your next round.
The most active firm is Andreessen Horowitz, with 45 leads across 22 sectors—16% of the total sample.
All those numbers are nominal. How much is real cash vs. compute credits on paper?
V → VI
Nominal amounts aren't the same as cash in bank.
Chapter VI · The Capital Structure Trap
639/666 deals are pure cash. The other 22 are compute-for-equity or hybrids, comprising 72% of nominal value. The gap between headline numbers and actual cash can be 2–5×.
Grouped by investor type - the 4 cards above show capital structure breakdown rules; the 3 iceberg bars below show the gap between mega deal headlines and actual cash.
70%
Cash discount on largest strategic deal
Microsoft -> OpenAI Oct '25: $250B nominal -> $75B cash equivalent
Investor Type - Capital Structure Cheat Sheet
Top-tier VC / Growth Funds
Coatue, Sequoia, Thrive, a16z
Headline = Cash
Mostly cash equity, clean structure
Lock-in:
No lock-in
Sample:
2
deals
| $7.0B
nominal
Blended cash rate:
71%
Sovereign / Policy Capital
MGX, QIA, Temasek, Bpifrance, SoftBank
Mostly Cash
Mostly pure cash; some mega deals include compute quotas
Lock-in:
Geopolitical / strategic agreements (soft)
Sample:
10
deals
| $44B
nominal
Blended cash rate:
61% | Single deal minimum: 40%
Chip Manufacturer CVC
NVIDIA
GPU Quotas + Commercial Commitments
Minimal cash, primarily future GPU allocation rights
Lock-in:
Hardware supplier lock-in
Sample:
2
deals
| $100B
nominal
Blended cash rate:
100%
Hyperscaler CVC
Microsoft, Amazon, Google, Alibaba, Meta
Small deals = cash; Mega deals = 60-80% cloud credits
Mega deal headlines significantly inflated
Lock-in:
Cloud lock-in + revenue sharing
Sample:
10
deals
| $301B
nominal
Blended cash rate:
35% | Single deal minimum: 20%
Mega Deal - Headline vs Cash (% Breakdown)
Each bar = 100% headline nominal. Dark solid portion = cash equivalent; Light hatched portion = headline premium
(cloud credits / commercial commitments). Click for company details.
wo ledgers: press-release figures and actual cash are different numbers. Crunchbase records press releases; this report tracks both—headline amounts and real cash equivalents, side by side.
Microsoft × OpenAI (Oct 2025): The press release said $250B. Zero immediate cash—all five-year Azure lock-in. Cash equivalent $75B, a 30% haircut.
Amazon × Anthropic (Apr 2026): Nominal $25B ($5.0B cash + extended commitment), but Anthropic must commit $100B back to AWS. Cash equivalent $5.0B, a 20% haircut. Compare that to the Nov 2024 deal, which only discounted 12%. In two years, the cash-to-compute ratio in cloud giant contracts shifted from 9:1 to 1:4.
NVIDIA × OpenAI (Sep 2025): Letter of intent for $100B cash + $100B tied to GB200 purchases. Cash equivalent ranges from $10B to $100B—the spread itself signals structural uncertainty.
For contrast: Microsoft × Anthropic (Nov 2025) was pure cash $5.0B, press release = cash equivalent. That's a normal round.
VI → VII
Putting the same dollar into AI-native vs. AI-augmented is each investor's answer to "what is AI."
Chapter VII · AI-Native vs AI-Augmented
AI-native: remove AI, the product doesn't exist. AI-augmented: AI is a feature module. Line up 35 investors by this ratio, and each bar is a fund's answer to "what is real AI."
35 active investors sorted by AI-native share descending. Dark = AI-native deals, light = AI-augmented deals. Bar length = total deal count. Dashed baseline = dataset deal-weighted average 76%.
AI-nativeAI-augmented
Red dashed line on each bar · dataset average split
must_have · recommended
61pp
Max AI-native share gap (percentage points)
Shanghai Guotou Pioneer Fund 100% vs Balderton Capital 39% · dataset avg 76%
Founder targeting· same portfolio · active set with n ≥ 10 deals · click row for lead drill-down
AI-native projects · Top 5 investorsby AI-native % desc, then by total deals
AI-augmented projects · Top 5 investorsby augmented deal count desc, lower native % first when tied
Hover a row · see native/augmented split · click to expand lead companies
eal-weighted average: 76% AI-native. A majority bet on native.
But zoom in to individual funds and the split is sharper than labels suggest. Greylock Partners, often perceived as old SaaS money, has 88% AI-native (24/27)—more aggressive than any other Sand Hill firm. Andreessen Horowitz talks "building the future" but runs only 69% (58/83). Managing 83 bets naturally requires diversification.
Most focused: Menlo Ventures at 96%—only native platforms. Most indifferent to the AI-native label: Founders Fund (58%), SoftBank Vision Fund (44%), and Balderton Capital (39%). Founders Fund's logic: Anduril, Hadrian—AI is a component, not the product. SoftBank and Balderton buy revenue growth, not technical definitions.
Beyond the mainstream bets, are there overlooked mega-sectors?
VII → VIII
Beyond foundation models and agents, three sectors rank highest by single-round size.
Chapter VIII · Counterintuitive Hotspots
Foundation models and agents dominate the headlines. But rank by single-round size, three sectors lead: Defense AI, Robotics & Embodied Intelligence, Vertical Legal. Combined: 45 companies, 65 deals, $23.3B. Each has at least one company valued above $10 billion.
Chart VIII · Counter-Intuitive Verticals
Click company card for details · Oct 2024 to Apr 2026
$21.9B
Total raised across three verticals in 18 months
45 companies · Defense AI / Robotics & Embodied / Vertical Legal · Each with an independent company valued above $10B
Vertical · I
Anduril Series G $2.5B / Helsing Series D EUR600M (~$692M)
18mo deals
18
raised
$9.8B
companies
15
Silicon Valley avoided defense projects for over three decades. That stance is shifting fast. Anduril closed a $2.5B Series G at $30.5B in June 2025, then raised another $4.0B H+ round at $60B in March 2026. Helsing, Europe's answer, completed a EUR600M Series D at $13.8B. In drones and aerospace, Saronic and Quantum Systems both crossed the unicorn threshold. This vertical achieved two rare conditions within 18 months: mega-rounds and multiple independent companies passing the billion-dollar mark.
Spotlight companies · click for funding history
Vertical · II
Figure $1B Series C / Wayve $1.2B Series D / Agibot Series B+
18mo deals
32
raised
$10B
companies
24
Humanoid robotics was once dismissed as hype. Actual capital deployment tells a different story. Figure AI raised a $1B Series C at $39B in September 2025. Wayve followed with a $1.2B Series D at $8.6B in early 2026. China's Agibot hit $2.1B between its Series B and B+ rounds. Skild, Apptronik, FieldAI, and Agility all crossed the unicorn line. Humanoid robots, embodied foundation models, and autonomous driving now form a connected vertical, each segment anchored by at least one company valued above $2B. Outside foundation models, this is one of the few hard-tech sectors still absorbing billion-dollar bets.
Spotlight companies · click for funding history
Vertical · III
Harvey: D / E / F / Strategic / G in 18 months, $3B to $11B
18mo deals
13
raised
$1.9B
companies
6
Legal AI is the least flashy vertical but the most aggressively funded. Harvey stacked five rounds in 18 months: Series D ($300M at $3B), E ($300M at $5B), F ($150M at $8B), a strategic round ($200M, March 2026, $8B), and G (April 2026, $11B). That averages out to one round every two to three months. Nordic-based Legora followed to $1.8B. London's Lawhive and Wordsmith are scaling. In legal, a single sentence can replace hundreds of thousands of dollars in billable attorney hours. The marginal ROI for AI displacement is exceptionally high, and enterprise willingness to pay is strong. This vertical does not require superintelligence — existing models plus workflow integration already deliver results.
Spotlight companies · click for funding history
DeepViews dataset ·
63
unique funding rounds ·
45
companies · aggregated across 7 verticals; deal count deduplicated by (company, date, round); amounts reflect publicly disclosed round totals.
hat these three have in common: model quality doesn't matter much. Defense AI sells "don't break in combat." Embodied AI's bottleneck is motors and gearboxes, not algorithms. Legal AI wraps existing APIs with compliance guardrails, billing for work that used to cost $600/hour. Pricing in these businesses tracks contract value, not model capability.
Helsing is in Germany, Wayve in the UK, Agibot in China, Legora in Sweden. Not one of them is in the Bay Area. The AI capital map is more complex than "Silicon Valley plus China."
VIII → IX
Same round, different regions—valuations can differ by 3×.
Chapter IX · Geographic Arbitrage
666 investments split into 14 capital-flow routes. Foreign capital into the US: 88 deals (13%); China's internal circulation: 74 deals, with only 2 flowing out. Europe's Series A post-money median is one-third of Silicon Valley's—the most visible entry-price gap in the dataset.
Note on scope: this chapter aggregates all 666 investments (including 14 historical-context deals from 2024-02..2024-09). A strict 18-month window (2024-10..2026-04) yields 652, with CN→CN ≈ 68 / US→US ≈ 383 / US→EU ≈ 55 / EU→EU ≈ 49—same shape, same arbitrage thesis.
Left = capital origin (investor HQ); Right = deal target (company HQ). Ribbon width proportional to deal count; color by origin. Click a ribbon to see top 5 companies in that corridor.
Foreign to US88 13%
CN to CN74 11%
Total flow666 deals
72%
Share of all deals flowing to US companies
Foreign to US: 88 deals + US to US: 389 deals. Contrast: CN to CN 74 deals = closed loop; no non-CN-origin capital flows to CN observed in this dataset
Default capital corridor
88deals foreign to US
SG / ME / JP pools almost exclusively bet on the US — SG
88%, ME
95%, JP
83%. EU has
21 deals flowing to US, but prefers local (EU to EU
50 deals,
70%). Foreign to US totals
88 deals, or
13% of the dataset
; largest cross-border corridor is
SG to US.
Click to highlight SG to US ribbon
CN closed loop
74CN to CN deals
CN to CN: 74 deals; CN outflow only
2 deals (CN to US: 1 / CN to EU: 1). No non-CN-origin to CN flows observed in this dataset — CN target is almost entirely covered by domestic capital.
Click to highlight CN to CN ribbon
Series A entry valuation gap
3.0xUS / EU median
US $1.5B (n=23) vs EU
$500M (n=5). EU sample is very small; this is the median within this dataset only, not representative of the broader market.
See Series A median valuation table below
Tap or hover any ribbon to see origin to target deal count. Click to enter drill-down.
Series A post-money median valuation by target region
Arbitrage check: entry price at the same stage across geographies
USUS-headquartered companies
$1.5B
n=
23
CNChina-headquartered companies
$500M
n=
3
EUEU-headquartered companies
$500M
n=
5
Interpretation: The US Series A median valuation is roughly 3x that of EU / CN -- same stage, same time window, but significantly lower entry valuations in Europe and China (note: EU n=5, CN n=3, limited samples, not representative of the broader market).
S→US: 389 deals, the largest corridor. SG→US 29, ME→US 21, JP→US 15, EU→US 21. Global capital flows one-way into Silicon Valley.
Series A post-money median: US $1.5B (n=23), Europe $500M (only 33% of US, n=5), China $500M (n=3, very small sample). Same stage, same window—Europe and China price significantly below the US. This is the dataset's clearest entry-price gap. Capital sources: US→EU 56, plus EU local 50. China is a closed loop—no foreign capital inflow recorded in the dataset.
IX → X
Stack the signals from the first nine chapters, and eight sectors emerge.
Chapter X · Recommended Sectors
Five dimensions scored: deal density lead diversity recent acceleration entry round size valuation runway. Thresholds: ≥4 deals, ≥3 lead investors, ≤50 deals (excludes saturated mega-tracks). Take the top 8.
5 signals, weighted composite: deal density, lead diversity, recent acceleration, entry ticket, valuation runway. Candidate filter: 18-mo deals >= 4, unique leads >= 3, deals <= 50 (excludes saturated mega-tracks).
DENSdeal densityLEADlead diversityACCLQ1'26 accelerationTKTentry ticketRNWYB/A valuation step-up
#1
Highest composite score: Vertical · Healthcare
5-signal weighted score 0.86 · 26 deals · 11 unique leads · 4.0x acceleration
Vertical AI
26 deals + 11 tier-1 leads
DENS
LEAD
ACCL
TKT
RNWY
ENTRY
$29M
B/A STEP
2.5x
LEADS
11 VCs
Q1'26
4 new
Top 3 → drawer
LEAD · Balderton Capital
Agents & Applications
25.6x B-round step-up + 24 deals
DENS
LEAD
ACCL
TKT
RNWY
ENTRY
$55M
B/A STEP
25.6x
LEADS
9 VCs
Q1'26
3/1
Top 3 → drawer
LEAD · Thrive Capital
Agents & Applications
13 Q1 closes + 6 tier-1 leads
DENS
LEAD
ACCL
TKT
RNWY
ENTRY
$28M
B/A STEP
—
LEADS
6 VCs
Q1'26
13 new
Top 3 → drawer
LEAD · Andreessen Horowitz
Agents & Applications
18 deals + 7 tier-1 leads
DENS
LEAD
ACCL
TKT
RNWY
ENTRY
$20M
B/A STEP
—
LEADS
7 VCs
Q1'26
6/2
Top 3 → drawer
LEAD · Sequoia Capital
Vertical AI
7 tier-1 leads + 6.8x B-round step-up
DENS
LEAD
ACCL
TKT
RNWY
ENTRY
$25M
B/A STEP
6.8x
LEADS
7 VCs
Q1'26
3/1
Top 3 → drawer
LEAD · Index Ventures
Other Tech
20 deals + 3.5x 18-mo acceleration
DENS
LEAD
ACCL
TKT
RNWY
ENTRY
$12M
B/A STEP
—
LEADS
4 VCs
Q1'26
7/2
Top 3 → drawer
LEAD · Sequoia Capital
AI Infra
5 Q1 closes + 3.9x B-round step-up
DENS
LEAD
ACCL
TKT
RNWY
ENTRY
$65M
B/A STEP
3.9x
LEADS
6 VCs
Q1'26
5 new
Top 3 → drawer
LEAD · Andreessen Horowitz
Robotics & Embodied AI
6.0x 18-mo acceleration + 6 tier-1 leads
DENS
LEAD
ACCL
TKT
RNWY
ENTRY
$140M
B/A STEP
3.7x
LEADS
6 VCs
Q1'26
6/1
Top 3 → drawer
LEAD · SoftBank Vision Fund (SVF)
alculated, not curated. #1 Vertical · Healthcare: score 0.86, 26 deals, 11 lead investors, recent acceleration 4.0×. The algorithm is public—change the weights and re-rank.
Common thread: no foundation models, no self-driving. Those are either saturated or dominated by one or two giants. Verticals dominate (2 vertical sectors + 3 agent apps)—all concrete workflows that general-purpose models can't directly replace. Entry barriers are moderate: median round size $29M, median lead count 7.
One last variable: who are the founders who got funded?
X → XI
Who are the founders getting funded.
Chapter XI · Founder Profiles
759 founders, 403 companies. Four panels: prior-company distribution, serial vs. first-time by region, PhD share by sector, top 10 schools.
759 founders enriched with prior company, education, PhD status, serial founder status, and nationality. Four panels: prior company clusters, serial vs first-time, academic intensity, top schools. Click any row in Panel A to drill down.
Enriched759PhD28%Serial26%
147
total founders from ex-academia
19% of 759 enriched founders · overall 28% PhD / 26% serial
Three rules from the 759 dataset· structural observations from the data
01Top clusters are academia + IPO/acquired startup alumni
66%of named pedigrees
ex-academia 147 · ex-startup (acqd) 134 · ex-startup (IPO) 118 —
top 3 named pedigrees account for
66%; frontier labs (ex-OpenAI / ex-Anthropic / ex-DeepMind) rank lower.
02PhD is not a uniform label — sector gap is stark
47pphigh-low sector gap
Foundation Models 58% PhD (n=
102
) vs
Agents & Applications 10% (n=
116
) — deep-tech sectors skew academic; application sectors less so.
03Serial founder share: EU lags US/CN
6pphigh-low region gap
CN 29% (
12
/
42
) ·
US 28% (
152
/
551
) ·
EU 22% (
37
/
165
) ·
CN
/
US
nearly tied
; CN n=42 is small
.
Panel A
Click any row to expand the founder list for that cluster
Frontier lab / FAANGOperator / startup / financeAcademiaOther
Panel B
Left = serial founder share; longer bars indicate higher serial ratio
28%
22%
29%
Serial foundersFirst-time founders
Panel C
Overall average 28% (dashed line)
Red dashed line = overall average
28
%. Dark blue bars indicate above average, light blue below.
Panel D
Stanford University
MIT
UC Berkeley
Harvard University
CMU
Cambridge
École Polytechnique
UPenn
Georgia Tech
Tsinghua University
verall: 28% PhDs, 26% serial entrepreneurs. Foundation-model founders are majority PhDs; AI infrastructure about one-third; verticals see PhD shares drop notably. The two longest bars in Panel A: academia (publish then start) and acquired startups (cash out then re-enter).
PhD share by region: US 25%, Europe 33%, China 55%. Chinese founders have the highest academic credentials—Tsinghua/Peking PhDs plus GPU/robotics hard-tech combos are most common. US big-tech alumni and serial founders bring the average down.
Anthropic: 6/7 from OpenAI; Jared Kaplan from academia. Cursor: 4/4 studied or just graduated from MIT—no big-tech stints. Mistral's bench is DeepMind London + FAIR Paris. Moore Threads: all three founders from AMD China. Biren: one AMD, one SenseTime.
All data from public datasets: 666 investments, 403 companies, 36 investors, 759 founders. Source data, methods, and limitations are fully disclosed in Chapter XII.
XI → XII
Data sources, methods, limitations.
Chapter XII · Methodology
Data window, coverage, limitations, source ledger, raw downloads.
The observation window runs from October 2024 to April 2026, an 18-month span. The starting point: the week OpenAI closed its $6.6Bconvertible note (valuation $157B) on 2024-10-02— the first inflection point in frontier-model valuations within the current AI capital cycle. The endpoint: late April 2026, capturing the closing of Anthropic Series G, xAI Series E, Mistral Series C, and Wayve Series D mega-rounds. All timestamps use the announced date (public press release), not closing date or wire date.
Handling out-of-window context anchors: To preserve full funding history for anchor companies, the dataset retains 14 deals from 2024-02..2024-09 as context (e.g., SSI 2024-09 Series A, Wayve 2024-05 Series C, Cohere 2024-07 Series D), allowing Ch9/Ch11 to trace founder paths and anchor-valuation trajectories. A strict 18-month filter (announced date ≥ 2024-10-01) yields investments = 652, unique companies ≈ 395. This report aggregates by the full 666 (structural conclusions hold under strict-window filtering); for strict-window analysis, reference _meta.total_investments_strict_window (= 652).
27 anchor investors (must_have + recommended lists), plus 47 known external entities = 83 investor nodes.9 Shanghai SOE vehicles were unbundled into separate entities, so the 27 anchors expand to 36 at the node level.
Source-URL coverage has two layers: every investment record has at least one public source (100%, 666/666); company profiles (founded year, HQ, one-liner, founders, ai_layer, etc.—15+ fields) hit 95% coverage. The uncovered 5% are mostly stealth companies or early-stage projects with only founder LinkedIn as the single source.
Four known boundaries that readers should note when citing this dataset.
Limit ·
Mega-round double-counting
OpenAI's total_committed in the dataset shows
$446.65B; Anthropic shows $97.85B. These two figures
should not be summed directly: in mega-rounds, individual investor tickets are not disclosed, so everyone's nominal_commitment is filled with the round total, causing cross-investor sums to double-count. Correct approach: aggregate at the round level using round_total_usd.
Limit ·
Cash + cloud credit dual ledger
Only a few cloud-giant → frontier-model deals have strict dual-ledger accounting: Microsoft → OpenAI, Amazon → Anthropic, Google → Anthropic, Alibaba → Meitu. NVIDIA investments are logged as cash_equity(though most carry GPU commitments, they're not credit-for-equity structures); Alibaba's "soft lock-ins" in Zhipu / Moonshot are not split out.
Limit ·
Edge-case HQs
The following edge cases are retained and assigned to the relevant region: Cohere (Canada), Cybereason (IL→US, relocated to Boston), Manus / Butterfly Effect (SG, but US market focus), Genesis AI (US/Paris dual), RIVR (CH, ETH spinoff). The following are strictly out of scope and excluded: Pomelo (AR), Sakana AI (JP), Mujin (JP), TSMC (TW), Halter (NZ), Binance (KY).
Limit ·
Confidence distribution
Each investment is tagged high / medium / low confidence. Final distribution: high 512 (77%) / medium 111(16%) / low 43 (6%). Medium / low cluster around mega-round investor-level tickets being undisclosed (Anthropic Series F lead attribution, xAI Series E partial participants), and China deals with only secondary-source coverage (some StepFun rounds, Alibaba participations in certain deals via secondary reports).
The table below lists source links for every investment record in the dataset. By default it shows the top 50 by announced date descending; click "Load 50 more" to paginate, or "Show all" to expand all 666 rows. For offline auditing, "Download dataset.csv" provides the full flat file (three tables joined into a single file).
Figure XII · A100% of 666 investments cite a public source URL; the rest verified via cross-reference
100%
of investments have a public URL source
665 / 666 investments · the remaining 1 verified through cross-referencing public reports
| Date | Investor | Company | Round | Source |
|---|---|---|---|---|
| Apr 2026 | NVIDIA / NVentures | VAST Data | Series F | cnbc.com ↗ |
| Apr 2026 | Tencent Holdings (Tencent Investment / Tencent Industrial Investment Fund) | DeepSeek | Strategic | bloomberg.com ↗ |
| Apr 2026 | Amazon (incl. AWS Strategic Investment & Alexa Fund) | Anthropic | Strategic | aboutamazon.com ↗ |
| Apr 2026 | Y Combinator | Mintlify | Series B | mintlify.com ↗ |
| Apr 2026 | Google / Alphabet (incl. GV + CapitalG + Google strategic) | Wealth.com | Series B | wealth.com ↗ |
| Apr 2026 | Sequoia Capital | Factory | Series C | techcrunch.com ↗ |
| Apr 2026 | Andreessen Horowitz | Harvey | Series G | harvey.ai ↗ |
| Apr 2026 | Andreessen Horowitz | Hilbert | Series A | axios.com ↗ |
| Apr 2026 | Google / Alphabet (incl. GV + CapitalG + Google strategic) | Recursive Superintelligence | Pre-Seed | techfundingnews.com ↗ |
| Apr 2026 | Lightspeed Venture Partners | Signal Labs | Seed | lsvp.com ↗ |
| Apr 2026 | Sequoia Capital | Auctor | Series A | globenewswire.com ↗ |
| Apr 2026 | Google / Alphabet (incl. GV + CapitalG + Google strategic) | nEye Systems | Series C | convergedigest.com ↗ |
| Apr 2026 | Sequoia Capital | Ineffable Intelligence | Seed | aibusinessreview.org ↗ |
| Apr 2026 | Alibaba (incl. Alibaba Strategic Investment & Alibaba Cloud Capital) | ShengShu Technology | Series B | cnbc.com ↗ |
| Apr 2026 | Google / Alphabet (incl. GV + CapitalG + Google strategic) | Anthropic | Strategic | techcrunch.com ↗ |
| Apr 2026 | Lightspeed Venture Partners | Modus | Series A | businesswire.com ↗ |
| Apr 2026 | Google / Alphabet (incl. GV + CapitalG + Google strategic) | Stipple Bio | Series A | stipple.bio ↗ |
| Mar 2026 | Andreessen Horowitz | Treeline | Series A | prnewswire.com ↗ |
| Mar 2026 | Coatue Management | OpenAI | Strategic | openai.com ↗ |
| Mar 2026 | MGX | OpenAI | Strategic | openai.com ↗ |
| Mar 2026 | Microsoft / M12 | OpenAI | Strategic | openai.com ↗ |
| Mar 2026 | Sequoia Capital | OpenAI | Strategic | openai.com ↗ |
| Mar 2026 | SoftBank Vision Fund (SVF) | OpenAI | Strategic | group.softbank ↗ |
| Mar 2026 | Temasek Holdings | OpenAI | Strategic | openai.com ↗ |
| Mar 2026 | Thrive Capital | OpenAI | Strategic | openai.com ↗ |
| Mar 2026 | Bpifrance | Mistral AI | Strategic | cnbc.com ↗ |
| Mar 2026 | Coatue Management | Sycamore Labs | Seed | techcrunch.com ↗ |
| Mar 2026 | Y Combinator | Starcloud | Series A | techcrunch.com ↗ |
| Mar 2026 | Andreessen Horowitz | Glimpse | Series A | techcrunch.com ↗ |
| Mar 2026 | GIC (Government of Singapore Investment Corporation) | Harvey | Strategic | harvey.ai ↗ |
| Mar 2026 | Index Ventures | Granola | Series C | granola.ai ↗ |
| Mar 2026 | Sequoia Capital | Harvey | Strategic | harvey.ai ↗ |
| Mar 2026 | Balderton Capital | Dash0 | Series B | balderton.com ↗ |
| Mar 2026 | Menlo Ventures | Gimlet Labs | Series A | globenewswire.com ↗ |
| Mar 2026 | Andreessen Horowitz | Deeptune | Series A | fortune.com ↗ |
| Mar 2026 | Balderton Capital | Reson8 | Pre-Seed | balderton.com ↗ |
| Mar 2026 | Index Ventures | Parallel (healthcare) | Series A | indexventures.com ↗ |
| Mar 2026 | 5Y Capital | Excalipoint Therapeutics | Seed-Extension | businesswire.com ↗ |
| Mar 2026 | Sequoia Capital | XBOW | Series C | bloomberg.com ↗ |
| Mar 2026 | Andreessen Horowitz | Cape | Series C | revli.com ↗ |
| Mar 2026 | Andreessen Horowitz | Arc Boat Company | Series C | revli.com ↗ |
| Mar 2026 | Andreessen Horowitz | Atlys | Series C | revli.com ↗ |
| Mar 2026 | Andreessen Horowitz | Eclypsium | Strategic | revli.com ↗ |
| Mar 2026 | Andreessen Horowitz | Mega | Series A | revli.com ↗ |
| Mar 2026 | Y Combinator | Replit | Series G | trendingtopics.eu ↗ |
| Mar 2026 | Menlo Ventures | Axiom | Series A | siliconangle.com ↗ |
| Mar 2026 | Y Combinator | Gumloop | Series B | tamradar.com ↗ |
| Mar 2026 | Andreessen Horowitz | Mind Robotics | Series A | techfundingnews.com ↗ |
| Mar 2026 | Coatue Management | Replit | Series F | blog.replit.com ↗ |
| Mar 2026 | Google / Alphabet (incl. GV + CapitalG + Google strategic) | Translucent | Series A | businesswire.com ↗ |
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Raw data is provided in two formats:
dataset.csv
Flat CSV with three tables joined (investment × investor × company, one row per investment).
investments.json / investors.json / companies.json
Original three-table JSON files.
The dataset is released under CC BY 4.0 — commercial use and derivative work permitted with attribution.
Personal capacity. This report is published by the author in an independent personal capacity. All views, judgments, and phrasings are the author's personal opinions and do not represent the position of the author's current or former employers, nor of any investor, portfolio company, limited partner (LP), or any other third party.
Data sources. All data cited in this report is sourced from public channels — company announcements, press releases, regulatory filings, mainstream financial media, and publicly available third-party databases. No private, internal, or NDA-protected data is used. Source URLs for every investment record can be audited line-by-line in the Source URL Ledger in §04.
No conflicts of interest. The author has no employment, consulting, advisory, board, equity, debt, or any other economic relationship with any investor, fund (GP / LP), or portfolio company referenced in this report. The contents are not sponsored, reviewed, or influenced by any external party.
Not investment advice. This report is for informational purposes only and does not constitute investment advice, securities recommendations, or any offer or solicitation. Readers should make independent judgments and assume responsibility for their own investment decisions; the author is not liable for any consequences arising from the use of this report. Reasonable efforts have been made to verify the data, but no express or implied warranty is made as to its completeness, accuracy, or timeliness.
Updated 2026-04-24XII / XII · end of report
此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。