The current PVC products are trading well, typically above Net Asset Value. This flies in the face of received wisdom which until recently accepted that listed entities with private assets trade at discounts to NAV.

And in many ways the premia/discount to NAV debate is a red herring to the core question of whether NAV itself is growing. It is this NAV growth that is core to SignalRank’s own model (given our focus on Series Bs, not pre-IPO stocks), not the premium/discount to NAV.

Nevertheless, what are the key factors in driving share price to trade at a premium to NAV? And how do these impact the current stable of PVCs?

Factors driving share price

Public funds of private assets typically trade at a discount to NAV. The main reasons are as follows:

• Liquidity discount: investors apply a discount to a portfolio due to the fact that the underlying assets (private companies, real estate, some crypto) cannot be instantly sold. The more illiquid the assets, the greater the discount.

• Valuation skepticism: internal valuations are set by managers, leading investors to question a NAV number that hasn’t been stress-tested in an actual sale.

• Higher fees: managers of private assets typically charge higher fees (and carry) than their public market colleagues, which has a negative impact on NAV.

• Thin demand: trading volumes in these assets tend to be somewhat low, leading to greater bid/ask spreads.

And yet. The newer models of PVCs are overcoming these factors to trade at premia to NAV, not discounts. This is primarily due to:

• Brand awareness: portfolios with assets such as OpenAI & SpaceX benefit from high retail awareness of the underlying assets.

• Asset scarcity: investors pay to access quality assets which are unavailable elsewhere, such as Anthropic.

• Thin float: PVCs operating within lock-up periods have smaller floats which will artificially impact the movement of shares, given limited access to the shares.

Some secondary components also come into play such as the listing location (with US public investors valuing growth more than in Europe), market sentiment (especially the AI frenzy today), first mover advantage (as the market does not understand PVCs yet) and portfolio concentration (with some PVCs being so concentrated as to be proxies for single assets).

But it is worth noting that these premia historically tend to compress, as new supply eventually meets demands (rights issues, competing vehicles, eventual IPOs of the underlying companies that remove the exclusivity premium).

Again, NAV growth itself should be more important than premia/discounts to NAV.

Premia/discount to NAV for existing PVCs

Listed VCs are not a new phenomenon in Europe. Government / public-funded capital remains the largest source of LP capital in Europe. It has historically been challenging for non-Tier 1 European managers to raise commercial capital, forcing some to resort to the public markets as a source of capital.

These platforms have typically underperformed. This is perhaps of no surprise, given these GPs’ inability to access generational companies, combined with thin European capital markets and a conservative European public market investor base.

Covid-mania led Molten Ventures (Figure 1) and Augmentum (Figure 2), which are two listed VCs in the UK, to briefly trade above NAV. The norm today is more like a 35%+ discount to NAV.

Figure 1. Molten Ventures share price & NAV evolution since 2016

Source: Hargreaves Lansdown

Figure 2. Augmentum’s share price & NAV evolution since 2018

Source: Hargreaves Lansdown

It is not all bad news in Europe. Flat Capital, run by Klarna CEO (Sebastian Siemiatkowski), has significant positions in OpenAI as well as Klarna itself. This has led Flat Capital to trade at a premium to NAV since Q3 2024 (Figure 3).

Figure 3. Flat Capital’s share price & NAV evolution since 2022

Source: Claude

Turning to the more recent US PVCs, DXYZ has consistently traded at a premium to NAV (Figure 4) since listing in 2024.

This spiked at 20x+ at the initial listing and jumped again with Trump’s 2024 re-election (which benefited SpaceX which was 50%+ of the portfolio at the time), before stabilizing at an average of 380% premium to NAV for 2025. This subsequently reduced further as DXYZ raised almost $250m in Q4 2025, bringing the average premium to NAV for 2026 to 40%.

Figure 4. DXYZ’s share price & NAV evolution since listing

Source: Yahoo Finance

There is less history for Robinhood Ventures and Fundrise which both listed in Q1 2026.

Robinhood Ventures (RVI), underwritten by Goldman Sachs, has seen a more stable trading pattern (Figure 5). The lack of a initial pop probably speaks to a portfolio that is underweight AI, focused more on winners from the prior cycle and consumer-facing products that will appeal to their 27m account holders. The success of the Fundrise listing did drag the RVI share price above NAV, and RVI currently trades at a 7% premium to NAV.

Figure 5. Robinhood Ventures’ share price & NAV evolution since listing

Source: Yahoo Finance

By contrast, Fundrise’s share price evolution is much more in-line with DXYZ (Figure 6). The combination of a portfolio with prized assets and a very thin float has led VCX to trade wildly in initial sessions. Current share price as at 3/31/26 is above 600% premium to NAV.

Figure 6. Fundrise’s VCX share price & NAV evolution since listing

Source: Yahoo Finance

Closing thoughts

We have previously written about why fund structure matters. In the US, the closed-end fund model allows the share price to diverge from NAV (like DXYZ), while other structures, including the interval fund structure that ARK Ventures selected, are tied exactly to NAV.

This led to a public spat between DXYZ’s CEO, Sohail Prasad, and ARK’s Cathie Wood.

In the long term, this is noise. As more and more PVCs come to market offering similar products, the premia to NAV will likely compress, unless tied directly to expected NV growth.

Indeed, what will differentiate managers is those whose products can deliver consistent NAV growth and those who cannot.

The information contained in this document has not been independently verified and no representation or warranty expressed or implied is made as to, and no reliance should be placed on, the fairness, accuracy, completeness or correctness of this information or opinions contained herein. Certain statements contained in this document may be statements of future expectations and other forward looking statements that are based on management’s current view and assumptions and involve known and unknown risks and uncertainties that could cause actual results, performance or events to differ materially from those expressed or implied in such statements. None of Signal Rank Corp or any of its affiliates, advisors or representatives shall have any liability whatsoever (in negligence or otherwise) for any loss howsoever arising from any use of this document or its content or otherwise arising in connection with this document. This document does not constitute an offer or invitation to purchase or subscribe for any shares. For further details, please refer to the Offering Documents describing Signal Rank Corp. Nothing contained herein shall be construed as tax, accounting or legal advice. Signal Rank Corp does not provide tax, accounting or legal advice. Any such statement herein was written to support the marketing or promotion of the transaction(s) or matter(s) to which the statement relates. Each taxpayer should seek advice based on the taxpayer’s particular circumstances from an independent tax advisor.

March 2026 saw $35.5 billion deployed across 695 funding rounds involving 2,321 distinct investors. Capital concentration remained elevated: the ten largest deals accounted for 52.1% of all capital, led by OpenAI’s $10 billion corporate follow-on. Excluding the top ten, the remaining 685 rounds deployed $17.0 billion — a more grounded view of core venture activity.

Series A rounds led by dollar volume with 158 deals totaling $5.0 billion, while seed rounds were the most numerous at 261 deals worth $2.6 billion. Series B activity held steady at 78 rounds and $4.3 billion. The US captured 72.5% of total capital ($25.7 billion across 277 rounds), with Europe ($4.2 billion, 185 rounds) and Asia ($3.3 billion, 166 rounds) filling out the picture.

On the investor side, Y Combinator led deal participation with 30 rounds, while Andreessen Horowitz led 8 rounds — the most of any investor in March. Software, Science & Engineering, and AI remained the dominant sector categories by capital deployed.

Market Pulse

695 rounds closed in March 2026, deploying $35.5 billion in total capital. 695 distinct companies raised, backed by 2,321 investors.

The headline number carries a significant skew: OpenAI’s $10 billion after-corporate round alone represents 28.2% of the month’s total. The top 10 deals captured 52.1% of all capital deployed — $18.5 billion across just 1.4% of all rounds.

Adjusting for that concentration, the median round across stages varied widely: pre-seed rounds landed at $1.0 million, seed at $4.1 million, Series A at $17.3 million, and Series B at $34.9 million.

6-Month Capital Trend

Over the trailing six months (October 2025 through March 2026), monthly round counts ranged from 695 to 1,085. January 2026 saw $51.3 billion deployed across 875 rounds, and February spiked to $180.6 billion (driven by exceptionally large mega-rounds). March’s $35.5 billion across 695 rounds represents a return to a more typical range.

Round counts have declined steadily from the October 2025 peak of 1,085 to March’s 695 — a 36% drop over six months. This likely reflects seasonal patterns combined with ongoing market selectivity.

Stage-by-Stage View

Pre-Seed: 81 rounds deployed $153.1 million at a median of $1.0 million. Activity remains robust at the earliest stage, though round sizes stay compressed.

Seed: The most active stage by volume with 261 rounds and $2.6 billion in total capital. The median seed round came in at $4.1 million, with an average of $9.9 million reflecting a long tail of larger seed rounds.

Series A: 158 rounds deployed $5.0 billion at a median of $17.3 million. Series A continues to be the largest priced-round category by total capital.

Series B: 78 rounds at $4.3 billion, with a median of $34.9 million. Nexthop AI ($500 million) and Cloaked ($375 million) anchored the largest B rounds.

Series C: 42 rounds deploying $3.1 billion at a $44.5 million median. Sierra Space’s $550 million round led the stage.

Series D+: 23 rounds across Series D through G, collectively deploying $5.2 billion. Shield AI’s $2.0 billion Series G was the largest late-stage deal.

Corporate/SAFE/Other: 52 rounds deployed $15.2 billion, dominated by OpenAI’s $10 billion after-corporate round and PT Chandra Asri Alkali’s $800 million SAFE.

Top 10 Deals

The ten largest rounds ranged from $500 million (Mind Robotics, Series A) to $10 billion (OpenAI, after-corporate). Seven of the top ten were US-based. Notable non-US entries include Advanced Machine Intelligence (Paris, $1.03 billion seed) and Legora (Stockholm, $550 million Series D).

Geography

United States

The US accounted for 277 rounds and $25.7 billion (72.5% of global capital). San Francisco dominated with 54 rounds and $11.7 billion — much of that driven by OpenAI. New York followed with 49 rounds and $2.5 billion, establishing itself as a clear second hub. San Diego ($2.1 billion, 6 rounds) and Palo Alto ($1.4 billion, 9 rounds) rounded out the top four.

Global

Beyond the US, Europe posted 185 rounds totaling $4.2 billion, with Paris ($1.2 billion, 16 rounds) and London ($830 million, 38 rounds) leading the continent. Asia recorded 166 rounds and $3.3 billion, with Beijing ($698 million) and Shanghai ($294 million) as the top markets. Stockholm ($687 million, 8 rounds) punched above its weight among European cities.

Who Is Winning

Sector Breakdown

Software led all sector categories at $26.4 billion across 339 rounds, followed by Science & Engineering ($25.1 billion, 286 rounds) and AI ($22.8 billion, 207 rounds). These categories overlap significantly — many AI companies also tag under Software and Data. Financial Services ($4.3 billion, 86 rounds) and Health Care ($2.7 billion, 114 rounds) were the largest non-tech verticals.

Lead Investor Activity

Andreessen Horowitz led 8 rounds in March, deploying $11.1 billion in lead capital (heavily weighted by OpenAI). NEXEA also led 8 rounds, though at the other end of the size spectrum. General Catalyst led 7 rounds worth $496 million, and Accel led 6 rounds deploying $1.1 billion.

Repeat Investors

Y Combinator appeared in 30 rounds — the most active investor in March by participation. General Catalyst (16 rounds), Andreessen Horowitz (15), and Accel (10) followed. Coatue appeared in 9 rounds, concentrating on later-stage deals. Among European firms, Balderton Capital (5 rounds) and Speedinvest (5 rounds) were the most prolific.

SignalRank Qualifiers Snapshot

17 Series B rounds in March featured qualified for SignalRank Series B pro-rata underwriting. Nexthop AI ($500 million), Sunday ($165 million) and Wonderful ($150 million) topped the list. The qualifier set spanned sectors from AI infrastructure to biotech, with the majority of companies headquartered in the US.

What This Means For

VCs

Capital concentration remains a defining feature: half of all capital flows to just 10 deals. For funds outside the mega-round bracket, the competitive landscape is actually more moderate — 685 rounds split $17.0 billion, offering plenty of deployment opportunity. Series A pricing held steady with a $17.3 million median, but the gap between median and average ($31.6 million) signals a growing bifurcation in round sizes.

Founders

Seed-stage founders saw healthy activity with 261 rounds closing in March. Pre-seed remained accessible at a $1.0 million median. For Series A-stage companies, the 158 rounds completed suggest steady but selective investor behavior. The decline in total round count from October’s 1,085 to March’s 695 indicates rising selectivity — founders should prepare for longer processes.

LPs

The trailing 6-month trend shows month-to-month volatility driven by mega-rounds (February’s $180.6 billion spike). Underlying round counts have softened, dropping 36% from October. The investor quality data shows experienced investors continuing to concentrate at Series B and C — a signal that smart money sees value in growth-stage opportunities even as early-stage volume contracts.

It isn’t.

I have long thought of venture capital as having become three distinct asset approaches: broadly seed, venture, and growth. Kyle Harrison suggests four, and I think he is directionally right.

In “Venture Capital Doesn’t Exist”, he argues that what we keep calling “venture capital” is really a conflation of four different things: seed stage investing, traditional venture, supercharged growth investing, and what used to be small-cap growth tech stocks. That feels right to me. I would add a fifth: the emergence of secondary buying being packaged for retail investors as something like public venture capital.

The changing nature of venture capital is the most important unifying theme in the recent run of excellent essays on venture. All are worth reading:

• Kyle Harrison’s “Venture Capital Doesn’t Exist” ( )

• Dan Gray at Credistick’s “Diseconomies of Scale”

• Jeff Becker’s “Why You Should Never Go Into VC” ( )

• Nicolai Rasmussen’s “The Math on Venture Capital Is Broken” ( )

• And, in a slightly different but highly relevant direction for early-stage investing, Dan Gray’s “The Reformation Will Be Funded”

While venture capital does not exist in the singular, concentration very much does. And that concentration is symbolic of the changes. It is happening at all stages, but perhaps most surprisingly at the earliest stages.

Concentration At Seed And Series A

That is what the numbers show.

The cleanest lens is the top 5 investors, measured by SignalRank’s Seed and Series A investor scores.

On that basis, both Seed and Series A are concentrating into fewer hands.

At Seed, the top 5 firms by the latest Seed Rank accounted for 3.11% of all seed dollars in 2020. In 2025, they accounted for 14.35%. In 2026 year-to-date, they are at 16.43%. These numbers represent the amount raised int he rounds, not the amount each firm contributed. If two of the top five invested the amount raised will still be accurate.

At Series A, the top 5 firms accounted for 10.83% of all dollars in 2020. In 2024, they were at 14.74%. In 2026 year-to-date, they are at 15.16%.

That alone should change the conversation.

Here is the simplest picture of what is happening.

The thing that jumps out is not just that Series A starts more concentrated than Seed. It is that Seed is catching up fast. Five investors are approaching 20% of all early-stage investment dollars, out of more than 7,000 active seed investors and more than 10,000 Series A investors.

That matters because it intersects with another argument that has become harder to ignore: the underlying math of venture looks increasingly broken.

Nicolai Rasmussen makes that case in “The Math on Venture Capital Is Broken”. If the required return for venture is in the low 20s, but many funds are delivering something materially below that after years of lock-up and illiquidity, then the asset class has a structural problem.

His conclusion is sharp:

“The funds that will win the next cycle are the ones willing to concentrate, get their hands dirty, and earn their returns through involvement, not just capital deployment.”

Concentration as strategy is clearly happening.

And if the biggest five firms absorb the best startups by writing the largest checks, then, ipso facto, the other 10,000 firms will struggle to deliver strong returns of any kind.

One response is the Rasmussen response: larger ownership, fewer bets, deeper involvement, more control, more work. In other words, stop pretending that passive capital deployment is enough and move closer to a model where you can actually influence outcomes. That may be right, but it also projects the death of many realistic early-stage venture business models.

This is really about market power. A small number of firms can become better at winning access, winning allocation, and winning distribution even if the asset class itself is not becoming more attractive. Megafunds may not deliver outsized power-law returns, but they will still contain the winners. Gross multiples may be lower, but deployed at great scale. And LPs will continue to fund that strategy.

For years, the central critique of venture has been scale. In “Diseconomies of Scale”, Dan Gray says it plainly: “venture capital does not scale.” I think that is basically right. Big funds are not naturally designed to maximize venture-style outcomes.

But a business can fail to scale economically and still scale institutionally.

That is what the current market looks like.

At Seed, the top 5 firms touched just 1.01% of rounds in 2020 but captured 3.11% of dollars. In 2025, they touched 1.56% of rounds and captured 14.35% of dollars. In 2026 year-to-date, they are at 1.73% of rounds and 16.43% of dollars.

That is not a story about doing a lot more deals. It is a story about controlling a larger share of the important ones.

This is the Seed view.

The Seed top 5 are:

• Andreessen Horowitz

• SV Angel

• Sequoia Capital

• Lightspeed Venture Partners

• Menlo Ventures

That list has a logic to it. It is about brand, access, network density, and founder pull. But what is most striking is the slope. Seed used to look more diffuse. It no longer does.

Series A is a different kind of market.

It was already concentrated in 2020. The top 5 firms accounted for 10.83% of dollars then, compared with Seed’s 3.11%. It dipped, then rebuilt. By 2024 it was 14.74%. By 2026 year-to-date it is 15.16%.

So Seed is an accelerating concentration story. Series A is an entrenched and accelerating concentration story.

This is the Series A view.

The Series A top 5 are:

• Lightspeed Venture Partners

• SV Angel

• Sequoia Capital

• Andreessen Horowitz

• General Catalyst

Again, not random.

This is where the phrase “venture capital doesn’t exist” becomes genuinely useful. It does not just mean there are different stages and styles of investing. It means there are different market structures hiding under the same label.

Seed is still closer to company formation, experimentation, and access creation. Series A is much closer to a gated signaling market. One is becoming more concentrated. The other has been concentrated for a long time. But both only prosper if early-stage funds continue to discover the best founders and the best ideas. Capital concentration at these stages is a bit like a parent eating its children. The future of the entire value chain relies on great Seed and Series A investors.

That is also why the current reformation language resonates. In “Protestant Capital”, Dan Gray describes the alternative model as “arrive first, invest early, let the market catch up.” That is a very different business from running a large multistage platform. It implies conviction, asymmetry, and real differentiation. It is not just a smaller version of the incumbent model.

And that is the real strategic implication of the data.

The market is not simply bifurcating between large and small. It is squeezing the middle. At the same time, it is struggling for liquidity and opening up to secondary exits to retail investors as one possible solution. “Rome is burning” feels more apt than any obviously positive message.

The firms that look safest from the outside are consolidating market share. But if the diseconomies-of-scale argument is right, that does not automatically mean they are restoring the return profile LPs actually need. Meanwhile, the firms that might produce true outlier returns can no longer survive by being generically early-stage. They need a genuine edge. And LPs need to back them.

This is where Jeff Becker’s line in “Why You Should Never Go Into VC” hits hard: “the market is concentrating against you.”

Against whom? Against generic founders raising without elite access. Against generic GPs trying to build generic funds. Against LPs who confuse institutional safety with economic adequacy.

That is the tension at the center of venture right now.

The category is fragmenting. The capital is consolidating.

For founders, that means there is no single fundraising market anymore. There is a hierarchy. If you can access the top firms, the value of that access is rising. If you cannot, you need to be realistic about which market you are in and what kind of syndicate can actually help you.

For LPs, the implication is even sharper. If the market keeps concentrating into a handful of firms, manager selection matters more than ever. But if the same firms winning that concentration battle are doing so in a business whose economics are deteriorating with scale, then the obvious allocation path may not be the right one.

For emerging managers, the message is brutal. There is less and less room for the generic venture fund. You need either genuine structural differentiation or the ability to drive outcomes through access, ownership, and involvement. Probably all three.

This is the broader context across both markets.

So the conclusion is simple. Venture capital does not exist.

What exists are several different capital businesses, each with different economics, different forms of edge, and different market structures.

But across those businesses, one trend is now unmistakable: more of the money is being controlled by fewer firms. And retail investors are being lined up via public secondary vehicles to supply liquidity to those who need it.

That will not solve venture’s return problem. It may, in fact, be one of the clearest signs of it. And retail investors in Robinhood’s $25 a share RVI have already experienced that valuations of later stage secondary assets are potentially flaky and subject to not holding up.

My own focus is to channel capital to early-stage funds by doing pro-rata investments into their best companies. That is SignalRank’s purpose. It channels capital from investors to seed managers and their Series B-stage companies. These companies still have significant future growth and can be attractive to retail investors looking for venture returns. Happy retail investors and happy seed stage managers is a winning combination. The impact of these flows is to counter the concentration impact.

The flywheel of capital channelled into the seed ecosystem, producing greater capital by investing at Series B, can live in that virtuous cycle. I think the health of the early-stage ecosystem, and thereby the whole ecosystem, needs that flywheel.

Three rounds broke the venture capital market in February. OpenAI raised $110 billion in a corporate round. Anthropic raised $30 billion in a Series G. Waymo raised $16 billion in a Series D. Combined, those three companies absorbed $156 billion -- 88% of all venture capital deployed globally during the month. Strip them out and February was a $21 billion month with 751 other rounds. That underlying number is roughly in line with November and December 2025.

Executive summary

February saw $177.4 billion deployed across 754 rounds into 753 companies, with 2,602 active investors. The numbers are historically unprecedented, but the story is really about three companies, not the broader market.

• Total capital deployed: $177.4 billion

• Total excluding OpenAI, Anthropic, and Waymo: $21.4 billion

• Deal volume: 754 rounds across 753 companies

• Active investors: 2,602

• Average round size: $235.2 million

• Median round size: $7.0 million

The gap between average ($235M) and median ($7M) is the widest we have recorded. It tells you everything about how concentrated February was.

6-month capital trend

The trailing six months show what February looked like in context. September through December 2025 settled into a $23-46 billion range. January 2026 spiked to $50.3 billion (mostly xAI’s $20B). February then tripled January.

The question going forward: are we entering a regime where one or two mega-rounds per month define the totals, or was February an anomaly? The honest answer is that nobody knows. What is clear is that AI foundation model companies now raise at a scale that used to be reserved for sovereign debt issuances.

Investment by stage

The stage breakdown is dominated by three categories that each carried a single outlier. Corporate rounds ($110.9B) means OpenAI. Series G ($30B) means Anthropic. Series D ($19.2B) is mostly Waymo with ElevenLabs and Wayve adding $1.7B between them.

Below the outliers, the early-stage market looks normal. Series A produced 184 rounds at $5.4 billion total, with a $15.9M median. Seed had 276 rounds at $1.4 billion with a $3M median. Pre-Seed added 106 rounds. Together, Seed and Pre-Seed represented 50.7% of all deal volume -- a healthy early-stage pipeline.

Series B activity was solid: 75 rounds totaling $4.5 billion, with a $30M median. MatX ($500M for AI chip hardware) was the standout. Series C had 27 rounds at $2.1B, with Skyryse ($300M for flight automation) and other companies contributing.

Geographic distribution

US capital by state

California’s share of US venture capital reached 95.9% in February, driven almost entirely by San Francisco and Mountain View (where OpenAI, Anthropic, and Waymo are headquartered). This is the highest concentration we have seen. New York was a distant second at 1.5%, with Texas, Washington, and Massachusetts each capturing less than half a percent.

Global capital by city

San Francisco absorbed $142.5 billion across 66 rounds -- OpenAI and Anthropic alone account for $140 billion of that. Mountain View (Waymo, plus several others) added $16.7 billion. After those two cities, New York ($2.5B, 49 rounds) and London ($1.9B, 35 rounds) led the global table.

The US captured $169.2 billion total, followed by China ($2.0B), the UK ($2.0B), India ($0.8B), and Germany ($0.4B). Mexico appeared in the top ten for the first time in recent months thanks to Kavak’s $300M Series F.

Who is winning

Sector breakdown

The raw sector numbers are misleading because the top four categories (AI, Software, Data and Analytics, Science and Engineering) all appear on OpenAI, Anthropic, and Waymo’s tags. With those three removed, the more informative sectors are Health Care ($3.1B), Hardware ($2.8B standalone), Manufacturing ($2.6B), Internet Services ($2.5B), and Financial Services ($2.4B).

Health Care’s $3.1 billion spread across 110 rounds, making it the most diversified sector by deal count. Financial Services had 107 rounds -- significant breadth, though lower total capital. AI remains the dominant theme, but the non-AI economy is raising money too.

Lead investor analysis

Accel led 9 rounds in February, more than any traditional VC firm, ranging from Anthropic to early-stage deals in India. Andreessen Horowitz led 8 rounds including Waymo and ElevenLabs. Peak XV Partners (formerly Sequoia India) led 7, reflecting continued India-focused activity.

A note on the data: Exel by Merak (18 leads) and AlphaLab (14) are accelerator programs that lead small rounds at volume. Their presence at the top of the lead count reflects the mechanics of accelerator batch investing, not the same kind of capital deployment as a Sequoia or Andreessen lead.

Repeat investors

Y Combinator appeared in 19 rounds during February, the most of any investor. Accel appeared in 12, Andreessen Horowitz in 12, General Catalyst in 11, Lightspeed in 11, and Bessemer in 10. This overlap cluster represents the firms that show up everywhere in February’s deal flow.

Bessemer, Sequoia, General Catalyst, Lightspeed, Accel, and Andreessen Horowitz all appeared in the Anthropic and/or Waymo cap tables, which inflates their total capital deployed. But their presence in 8-12 rounds each, including seed and Series A deals, reflects genuine breadth.

Smart money flow

Investor quality (measured by SignalRank’s percentile scoring) concentrated at Series B and Series C. Series B rounds attracted investors with an average score of 3,578 and 68 high-score (85th+ percentile) investors. Series C averaged 5,897 and had 29 high-score investors. Series A had 152 high-score investors -- the largest absolute count -- reflecting the volume of deals that attract experienced backers.

Pre-Seed investor scores remain low (average 50), which is expected: these rounds are led by angels, micro-funds, and first-time managers who have not yet built scoring track records.

Top 10 deals of the month

1. OpenAI (USA) - Corporate Round: $110.0B -- SoftBank, Amazon, and NVIDIA co-led

2. Anthropic (USA) - Series G: $30.0B -- Coatue, D.E. Shaw, Dragoneer, Founders Fund, GIC, ICONIQ, MGX co-led

3. Waymo (USA) - Series D: $16.0B -- Alphabet, DST Global, Dragoneer, Sequoia co-led

4. Wayve (UK) - Series D: $1.2B -- Balderton, Eclipse, SoftBank Vision Fund co-led

5. Cerebras Systems (USA) - Series H: $1.0B -- AI chip company

6. MatX (USA) - Series B: $500M -- AI hardware

7. Ayar Labs (USA) - Series E: $500M -- Optical I/O chips

8. ElevenLabs (USA) - Series D: $500M -- AI voice synthesis

9. Inertia (USA) - Series A: $450M -- Energy infrastructure

10. Vestwell (USA) - Series E: $385M -- Retirement savings platform

Capital concentration analysis

Concentration was extreme across every late-stage category. Corporate rounds: OpenAI alone was 99.2% of the category. Series G: Anthropic was 100%. Series D: the top three rounds (Waymo, Wayve, ElevenLabs) captured 92.2%. Even at Series E, the top three (Ayar Labs, Vestwell, SambaNova) captured 78.6%.

The earlier stages were more diffuse. Seed’s top three rounds captured only 12% of seed capital. Series A’s top three captured 19.7%. This is the normal shape of a functioning venture market -- concentration increases with stage.

SignalRank qualifiers snapshot

February produced 33 SignalRank-qualifying Series B companies(investor score at or above the 85th percentile). The top scorers were Heron Power (energy/manufacturing, $140M) and Braintrust (AI/software, $80M), both backed by Andreessen Horowitz.

The qualifying companies span AI hardware (MatX, Positron, Code Metal), AI software (Braintrust, Goodfire, Basis, Letter AI), robotics (Bedrock Robotics, Gather AI, Overland AI), security (VulnCheck, Reco), energy (Heron Power, Andercore), fintech (Bretton AI, Ownwell, Jump, Novig), legal (Lawhive), biotech (Turbine, Altesa, BreezeBio, Alaffia Health), and defense (Forerunner). 33 qualifiers is the highest single-month count we have reported since launching this newsletter.

What this means for

VCs

The mega-round syndicate list for Anthropic reads like a who’s-who: Coatue, D.E. Shaw, Dragoneer, Founders Fund, GIC, ICONIQ, MGX, Goldman Sachs, JP Morgan, Blackstone, BlackRock. Waymo’s cap table includes Sequoia, Andreessen, Tiger Global, Silver Lake, Kleiner Perkins. These are no longer traditional venture syndicates -- they are blended allocations combining VC, crossover, sovereign, and PE capital. The practical effect for smaller VCs: Series D+ is increasingly closed to firms without existing board positions or strategic leverage.

The opportunity for smaller funds is the 751 rounds that were not OpenAI, Anthropic, or Waymo. The early-stage market (382 seed and pre-seed rounds) is where most VCs can still compete on sourcing, conviction, and speed.

Founders

February’s data shows that the capital markets are wide open for AI companies at any stage. MatX raised a $500M Series B. Inertia raised a $450M Series A for energy. Heron Power raised $140M at Series B for manufacturing. If your company touches AI or energy infrastructure, investor appetite is high and round sizes are growing.

For everyone else: the market is functional but selective. Health Care, Financial Services, and Commerce each produced dozens of rounds. You can raise, but round sizes at seed and Series A remain concentrated around $3M and $16M respectively, and you will compete for attention in a month where AI consumed most of the headlines.

LPs

The concentration numbers demand attention. Three rounds absorbed 88% of all capital deployed in February. An LP’s vintage-year returns now depend heavily on whether their GPs had allocation in OpenAI, Anthropic, or Waymo. Funds that participated in even one of those rounds will report dramatically different gross returns than funds that did not.

The broader question is whether this concentration is sustainable. These rounds are priced as if the AI foundation model market will produce multiple trillion-dollar companies. That might be correct. But the capital at risk is large enough that a single valuation reset could ripple across LP portfolios.

Methodology and data notes

Data sourced from SignalRank with help from Crunchbase and Preqin. Analysis covers announced rounds from February 1-28, 2026. Stage classifications follow Crunchbase categories (Pre-Seed, Seed, SAFE, Series A through Series H, Corporate Round, Undisclosed). Only rounds with disclosed amounts above zero are included. Post-IPO debt and equity rounds are excluded. Geographic data uses company headquarters location. Investor scoring uses SignalRank’s percentile-based system where 85th percentile and above indicates high-signal backers.

The trailing 6-month trend covers September 2025 through February 2026. All capital figures are in USD.

Subscribe to The State of Venture at:

The State of Venture

A resource for all stakeholders to discuss the state of venture capital

By Signal Rank Corporation

The FT pointed to this chart (Figure 1) to show that retail now represents 20%+ of trading volumes in the US public markets, back to % levels not seen since the 1980s.

Figure 1. Retail share of US trading volume

Similarly, the AUM for retail assets in the private markets is expected to scale rapidly. Deloitte anticipates exponential growth ahead, with $2.4 trillion allocated by US retail investors in private assets by 2030 (Figure 2).

Figure 2. US retail allocation to private capital to 2030 (AUM, $ trillions)

US policymakers seeking to open up 401(k)s to crypto and private markets alike will certainly help in achieving these numbers. This trend does have some echoes with history. To use the modern parlance, the “democratization of credit” helped fuel a speculative frenzy in the 1920s. Will the “democratization of alternatives” have a similar impact?

Institutional actors are paying attention. The ILPA warned its members that retail vehicles have distinct governance and regulatory requirements from institutional funds, as well differences in liquidity, valuations, fees and transparency. Similarly, Moody’s argues that selling funds to retail introduces new risks to GPs including “reputation loss, heightened regulatory scrutiny and higher costs” and potentially “systemic consequences.”

Nevertheless, the key point is that retail will be competing with institutional LPs for allocation into the same underlying companies.

This is most visible today in the pre-IPO companies (especially with the launch of the new Public Venture Capital models like Robinhood & Destiny).

Subject to regulatory considerations and appropriate structuring, SignalRank intends to explore making elements of its Series B strategy available to a broader investor base.

More broadly, we anticipate retail vehicles to deploy capital at earlier stages in the capital stack than has historically been the case.

This article was written in collaboration with my OpenClaw Agent. It was not written by it. I take all responsibility for the content. Just FYI.

PitchBook published its 2025 Annual US VC Secondary Market Watch this week. The headline number: $106.3 billion traded through US venture secondaries in 2025. That figure deserves context, and scrutiny. It accompanies the Thomasz Tunguz piece from last week.

For the first time, venture secondaries are operating at a scale comparable to the two traditional exit channels. Acquisitions delivered $140.7 billion. Public listings produced $119.6 billion. Secondaries - a market that barely registered as an institutional category five years ago - hit $106.3 billion ($91.7B direct + $14.6B GP-led).

As Tunguz acknowledged, this is the third pillar of venture liquidity. It arrived faster than most people expected.

How fast it grew

Annualized direct secondary value went from $50 billion in Q4 2024 to $91.7 billion by Q4 2025. That is an 83% increase in five quarters.

Much of this acceleration was driven by valuation surges at the top end. SpaceX moved from $350 billion to $1.25 trillion in twelve months. OpenAI’s $6.6 billion tender offer alone accounted for 6.2% of annual secondary volume. When a single transaction represents more than 6% of an entire market, that market has a concentration problem.

Concentration

On Hiive, the top 20 startups accounted for 86.4% of secondary trading value in Q4 2025. The top five alone represented 55.6%. SpaceX was the most actively traded name on Augment, representing 12.5% of total platform activity.

The vast majority of the $106 billion secondary market is a handful of mega-cap names trading among sophisticated participants. The long tail - the other 13.6% - is where price discovery is weakest, information asymmetry is greatest, and the risk of adverse selection is highest.

This has direct implications for the emerging Public Venture Capital (PVC) category. The funds now listing on public exchanges - Robinhood Ventures (RVI), Powerlaw (PWRL), Fundrise (VCX), Destiny Tech100 (DXYZ) - are overwhelmingly concentrated in the same top-20 names that dominate secondary volume. They are not diversifying beyond this concentration. They are repackaging it for retail.

$22 billion in acquisitions: who is buying the plumbing

PitchBook documents an institutional buying spree, but the full picture is bigger than their report covers. Over $22 billion in acquisitions in under a decade, the largest financial institutions in the world have been assembling private market infrastructure piece by piece.

Goldman Sachs bought Industry Ventures (~$965M), a secondary fund manager. Morgan Stanley took EquityZen, a secondary marketplace, and Shareworks for equity management. Schwab bought Forge Global (~$660M), another marketplace. JPMorgan acquired Aumni, Global Shares (EUR665M), and First Republic ($10.6B) - cap table intelligence, equity administration, and banking in one sweep. Capital One is acquiring Brex ($5.15B) for startup banking and spend management. Carta has made at least eight acquisitions (Accelex, Capdesk, Sirvatus, SVB Analytics, Tactyc, Vauban, ZenEquity) to own the equity lifecycle from the inside. Piper Sandler launched private markets trading, hiring senior talent from Forge. Nasdaq Private Market partnered with G Squared for priority access to tender offers.

And then there is BlackRock. They acquired Preqin for $3.2 billion and eFront for $1.3 billion. Preqin is the dominant private markets data and analytics provider. eFront is the operating system that alternatives managers use to run their portfolios. Together they give BlackRock the information layer and the operating infrastructure beneath all private markets - the valuations, fund performance benchmarks, deal flow data, and LP intelligence that determine how private assets are priced, compared, and allocated. When the world’s largest allocator of capital owns the definitive pricing and benchmarking infrastructure for private markets, the information advantage that has historically favored private market insiders starts to erode. At least for BlackRock’s institutional clients.

One detail that has not gotten enough attention: several of these acquisitions were European companies. eFront was French. Preqin and Global Shares were British. SVB UK was absorbed in the First Republic deal. American financial giants have been quietly absorbing European-built private market infrastructure.

The common thread across all of it is intermediation: transaction fees, fund management revenues, data subscriptions, and the ability to offer wealth management and institutional clients private market exposure through institutional-grade channels.

What does this mean for PVC funds?

The competitive threat is not deal flow. It is distribution. When Goldman’s wealth advisors can offer clients access to secondary venture through Industry Ventures’ managed funds at NAV, the case for buying DXYZ at a 46% premium or Powerlaw at a 2/20 fee structure gets hard to make. The banks are not competing with PVC funds for SpaceX shares. They are competing for the same investor dollars, and offering a product that is cheaper, better governed, and available without a public market premium.

Tender offers are becoming standard

One of PitchBook’s most consequential findings: company-led tender offers are becoming the default liquidity mechanism. Rather than employees and early investors selling ad hoc on secondary platforms, leading startups are running structured, recurring liquidity programs.

This is healthy. It gives companies control over their cap tables and reduces unauthorized transactions. But it also means the best secondary access is increasingly gated. You get into a tender offer because the company invites you, not because you found a willing seller on a platform.

For PVC funds relying on secondary market access as their core strategy, this is a structural headwind. As companies formalize their liquidity programs, the window for uninvited secondary buyers narrows.

The temporary vacuum ahead

PitchBook projects a decline in secondary volume if SpaceX, OpenAI, and Anthropic go public in 2026, as all three are actively preparing to do. These three companies likely represent 30-40% of current secondary volume, so the short-term impact would be real.

But the medium-term effect is positive. More IPOs mean fresher pricing benchmarks, since public market prices reduce the bid-ask spread problem in private secondaries. They mean an expanded universe - 70 new companies saw their first secondary trade in 2025 ($492M), and IPO exits free up capital to flow toward this broader market. And better price discovery attracts more buyers and sellers, growing the market beyond its current top-20 concentration.

Structural, not cyclical

PitchBook’s core thesis - that secondaries are structural, not cyclical - appears correct. The market grew during the 2021 boom (driven by access demand) and grew again during the 2022-2024 drought (driven by liquidity need). A market that is relevant at both extremes of the cycle is not going away.

The question is not whether secondaries will persist. They will. The question is who benefits from the institutionalization of this market, and who bears the risk.

When Goldman Sachs acquires a secondary fund manager to offer its wealth clients managed venture exposure at NAV, the value proposition is straightforward. When a PVC fund packages similar secondary positions into a retail-facing vehicle trading at a premium to NAV with higher fees and weaker governance, the value proposition for the retail buyer is harder to defend.

US venture secondary dry powder reached $11.8 billion as of June 2025, up 2.8x since 2022. But that still represents only 3.9% of primary venture capital. The secondary market is growing fast, but it is still small relative to the primary market it serves. And 86% of its volume is concentrated in 20 companies.

The structural shift is real. The distribution of its benefits is not yet determined. That is the most important question in the emerging PVC category.

Who benefits?

The infrastructure builders are winning. Goldman, Morgan Stanley, Schwab, BlackRock, JPMorgan, and Carta did not acquire these platforms and data companies out of generosity. They acquired them because secondary transactions generate fees - management fees on fund products, commissions on marketplace trades, advisory fees on structured tender offers, data subscriptions on analytics platforms.

The more the secondary market grows, the more these institutions earn. Their incentives are aligned with market expansion regardless of whether the underlying assets perform.

The sophisticated sellers are also winning. Employees at SpaceX, OpenAI, and Anthropic who access company-led tender offers are getting liquidity at prices set by informed buyers in competitive processes. Early-stage VCs selling LP stakes in top-decile funds are getting fair value because the buyer pool now includes institutional secondary funds with $11.8 billion in dry powder. These are participants with information, leverage, and alternatives.

And this is, on balance, a good thing. The venture ecosystem has suffered for years from a liquidity bottleneck.

Companies are now staying private for a decade or more. Employees unable to access the value they helped create. Early investors locked into positions long past the point where portfolio management would dictate a sale. Secondaries relieve that pressure. They give founders an alternative to premature IPOs. They give employees real money instead of paper wealth.

They give early-stage VCs the ability to return capital to LPs without waiting for an exit that may be years away. More liquidity, flowing more freely, through more channels, is net positive for the health of the venture ecosystem.

Some institutional buyers on the secondary market are doing this well. They build diversified portfolios through disciplined entry-point selection while providing genuine liquidity to sellers who need it. Funds like Dave McClure’s Practical Venture Capital and OnePrime Capital are good examples: rigorous on price, systematic about portfolio construction, and performing a real economic function by giving sellers a bid when they need one. Informed buyers, willing sellers, fair prices, value creation on both sides. That is what a healthy secondary market looks like.

The problems can begin when this dynamic gets repackaged for a different audience. Are retail participants - the end buyers of PVC fund shares - winning too, or are they simply the last link in a chain that extracts value at every prior step?

Consider the path a secondary share takes before it reaches a retail investor through a PVC fund. A seller (often an insider with superior information) sells to a secondary buyer (often the PVC fund itself, paying a negotiated price), who packages it into a fund (adding management fees, performance fees, and operating expenses), which lists on a public exchange (where market dynamics can push the share price to a premium over the fund’s already-marked-up NAV). Each step adds cost. Each intermediary takes margin. The retail investor at the end of this chain is paying the cumulative markup of every participant who touched the asset before them, and is driven by brand recognition from fully priced assets.

This is not inherently predatory. Intermediation has costs, and access has value. The question is whether the total cost of that intermediation is proportionate to the access provided. When that underlying asset value is itself derived from secondary market prices that may reflect optimistic marks rather than realized exits, the effective premium to realizable value could be substantially higher.

The institutional channels opening up through Goldman’s Industry Ventures, Morgan Stanley’s EquityZen integration, Schwab’s Forge Global platform, Nasdaq Private Markets, and BlackRock’s Preqin-powered private markets intelligence offer a comparison worth examining.

These channels provide secondary venture exposure to HNW and institutional clients at or near NAV, with institutional governance, audited valuations, and transparent fee structures. They are not democratizing access - they are reserved for clients who meet wealth thresholds. But they demonstrate what fairly priced secondary access looks like. The gap between that price and the price a retail PVC investor pays is the cost of “democratization,” and investors should know exactly what that cost is before they celebrate being invited to the table.

The structural shift in venture secondaries is real and durable. A $106 billion market that functions at both ends of the cycle is not going away. But structural shifts are morally neutral. What matters is not the existence of the shift but the terms on which different participants access it, and whether the people bearing the most risk are the ones with the least information.

A Better Model

There is a different model. BlackRock acquired Preqin for its breadth - fund performance benchmarks, LP data, deal flow aggregation across all private market asset classes. It is a horizontal data play. But Preqin’s venture coverage is, by design, retrospective. It tells you how funds performed, not which companies will perform. It catalogs the past.

A data platform that does something Preqin cannot would have future predictive potential. It would score each private venture backed company in real time, based on the quality, concentration, and track record of their early-stage backers. That is not a database of what happened. It is a predictive filter for what is likely to happen - a quantitative signal that identifies which companies, at the moment they reach scale, have the backer profiles most correlated with outlier outcomes.

The difference between cataloging 100,000 private companies and algorithmically identifying the top decile at a specific inflection point is the difference between a library and a lens.

This matters for the secondary market because the core problem PitchBook’s data reveals - 86% concentration in 20 names, information asymmetry favoring insiders, retail investors at the end of the markup chain - is fundamentally a selection problem. Most participants in the secondary market are buying names they recognize. A systematic scoring model that evaluates every company against the same quantitative criteria offers something the secondary market currently lacks: a basis for selection that is neither brand-driven nor insider-dependent.

The infrastructure build in venture secondaries is being fought on four layers: transaction execution, data and benchmarking, distribution, and selection/access. The first three are increasingly dominated by the largest financial institutions in the world. The fourth - the ability to systematically distinguish which companies have the highest probability of outlier outcomes and access them early - remains open.

In a $106 billion market where 86% of volume chases the same 20 names, the investors who can identify the next 20 before they become consensus will capture most of the value. Everyone else is paying for the privilege of arriving last.

Keith Teare is founder and CEO of SignalRank Corporation. This analysis is based on PitchBook’s 2025 Annual US VC Secondary Market Watch (published February 20, 2026) and reflects his views on the venture secondary market. It should not be construed as investment advice.

The State of Venture has a monthly reporting agent. The agent produces this report, which is then edited by humans prior to sending.

January 2026 was defined by a single number: $20 billion. Elon Musk’s xAI raised the largest venture round in history, representing 46% of all capital deployed globally during the month. This gravitational pull reshaped every metric. Remove xAI from the data and January looks like a normal month. Include it and the record books need rewriting.

Executive Summary

January delivered $43.1 billion across 639 rounds, shattering monthly records. But the headline number misleads without context: xAI’s $20B Series E alone exceeds most quarterly totals. Excluding xAI, the month deployed $23.1 billion, a strong start to 2026 but not historic. AI and robotics dominated deal flow. Autonomous vehicle companies (Waabi, Skild AI), AI infrastructure plays (xAI, humans&, Ricursive Intelligence), and enterprise AI (Decagon, Parloa, ClickHouse) captured the largest rounds.

• Total Capital Deployed: $43.1 Billion

• Total Excluding xAI: $23.1 Billion

• Deal Volume: 639 Companies / Rounds

• Active Investors: 2,333

• Average Round Size: $67.4 Million

• Median Round Size: $8.5 Million

Investment by stage

Late Stage (E+) rounds captured an astonishing $21.3 billion due to xAI, making stage comparisons meaningless. More instructive: Series C saw $6.8 billion across just 39 companies, driven by Singapore’s DayOne ($2B) and Pittsburgh-based Skild AI ($1.4B). Series A maintained healthy activity with 157 rounds totaling $4.4 billion, suggesting the early-stage pipeline is still working.

The gap between average and median round sizes tells the concentration story. At Series C, the average round was $174M while the median was $70M. A handful of mega-rounds pulled up every stage average.

Geographic distribution

US capital by state

California captured 78.2% of US startup capital in January, the highest concentration we have recorded. Palo Alto, where xAI is headquartered, drove almost all of it. Pennsylvania emerged as the surprise second-place state at 9.7%, thanks to Skild AI’s $1.4B round in Pittsburgh. Massachusetts (5.6%) and New York (2.6%) rounded out the top four.

Global capital by city

Palo Alto dominated global funding with $204.9 billion, entirely attributable to xAI. Remove that outlier and San Francisco ($20.6B) leads, followed by Toronto ($13B from Waabi’s $750M) and Cambridge, MA ($12.6B, mostly biotech). Singapore’s $4.2B reflects DayOne’s emergence as a regional heavyweight. The Bay Area’s combined total (Palo Alto + San Francisco + Redwood City + Mountain View + South San Francisco + San Mateo + Santa Clara) exceeded $238 billion.

Key trends and observations

1. The sovereign wealth surge

xAI’s $20B round brought sovereign wealth funds into venture at a scale we haven’t seen before. Qatar Investment Authority, Abu Dhabi’s MGX, and strategic investors like NVIDIA and Tesla participated alongside traditional funds like Fidelity. The same pattern showed up at Waabi ($750M), where Abu Dhabi Investment Authority, BlackRock, and sovereign-adjacent entities like Export Development Canada joined the cap table. Sovereign wealth has arrived as a venture asset class.

2. The “mega-seed” phenomenon continues

Following December’s $475M Unconventional AI round, January produced two more mega-seeds: humans& ($480M) and Merge Labs ($252M). The humans& syndicate reads like a who’s who of Silicon Valley: SV Angel, Georges Harik, Jeff Bezos, Google Ventures, NVIDIA, Felicis, DCVC, CRV. Merge Labs, a gaming/metaverse infrastructure play, was led by OpenAI with participation from Gabe Newell (Valve) and Bain Capital. These are not seed rounds in any traditional sense. They are Series A or B rounds labeled as seeds.

3. Autonomous everything

Skild AI ($1.4B Series C, led by SoftBank) is building a “foundation model for robotics.” SignalRank is an investor in Skild’s B round. Waabi ($750M Series C) is Uber’s bet on autonomous trucking. DayOne ($2B Series C, led by Coatue) is applying autonomy to vertical-specific problems in Singapore. What connects them: investors believe general-purpose AI models will unlock autonomous systems across industries. The valuations imply conviction that these companies will capture meaningful chunks of trillion-dollar markets.

4. Enterprise AI hits hypergrowth

Decagon ($250M Series D) for AI customer service, Parloa ($350M Series D) for contact center AI, ClickHouse ($400M Series D) for real-time analytics, and OpenEvidence ($250M Series D) for clinical AI all raised growth rounds. The same investor names keep appearing (Coatue, General Catalyst, Index, Thrive), which suggests a coordinated thesis: enterprise AI is now proven enough for scale-up capital.

Top 10 deals of the month by capital raised

Capital concentration analysis

Capital concentration was extreme in January. Single deals dominated entire stages.

xAI represented 93.9% of all Late Stage capital. At Series C, DayOne and Skild AI together captured 50% of stage capital. Even at Seed, humans& and Merge Labs combined for nearly 30% of all seed investment. The portfolio construction implication is stark: missing a single mega-round can define fund performance for a vintage.

Investor analysis: the repeat players

Several investors appeared across multiple January mega-rounds:

• NVIDIA / NVentures: xAI, humans&, Waabi, Skild AI, OpenEvidence, Ricursive Intelligence

• Coatue: DayOne, Skild AI, Decagon, OpenEvidence

• Lightspeed Venture Partners: Skild AI, ClickHouse, Rain, Ricursive Intelligence, Orca Bio

• Sequoia Capital: Skild AI, OpenEvidence, Ricursive Intelligence

• Felicis: Skild AI, humans&, Ricursive Intelligence

• Google Ventures: humans&, Parabilis Medicines, OpenEvidence

NVIDIA’s venture arm has become the common denominator in AI infrastructure deals, appearing in six of the top 20 rounds. This reflects both NVIDIA’s strategic interest in AI ecosystem companies and their access advantage through customer relationships.

SignalRank Series B analysis

In the SignalRank methodology, a company “qualifies” not just by revenue or hype, but by the quality of its backers across all rounds to Series B. The core thesis: top-tier Seed and Series A investors have persistent access to the best outliers and Series B investors concentrate into the best.

January produced 11 SignalRank-qualifying Series B companies. Northwood Space achieved a perfect 100th percentile score with its $100M round for spacecraft ground infrastructure.

SignalRank invested in Northwood Space and Cambium.

Summary of trends

1. The “xAI effect”

One company’s $20B round distorted every January metric. This is the new reality of venture: a single company can represent nearly half of global deployment in a given month. For data analysis, medians and xAI-adjusted figures are now more instructive than raw totals.

2. Sovereign wealth goes mainstream

QIA, ADIA, MGX, GIC, and various sovereign-adjacent entities appeared across the largest January rounds. These are not one-off investments. They represent a structural shift. Sovereign wealth funds are now direct participants in venture at scale, competing with rather than through traditional VCs.

3. The robotics moment

Skild AI ($1.4B), Waabi ($750M), humans& ($480M), and DayOne ($2B) all bet on some form of autonomous systems. January 2026 may mark the inflection point where robotics and autonomy moved from speculative to fundable at growth-stage scale. The foundation model thesis that transformed software is now being applied to the physical world.

Data sourced from SignalRank with help from Crunchbase and Preqin. Analysis covers announced rounds from January 1-29, 2026.

Subscribe to The State of Venture:

The State of Venture

A resource for all stakeholders to discuss the state of venture capital

By Signal Rank Corporation

The narrative was written by a highly trained agent with access to SignalRank’s data. This agent will produce a report each month and can also do quarterly and annual reports.

December 2025 closed the year with a striking pattern: massive capital concentrated at the extremes. A $475M seed round and multiple $300M+ Series B rounds dominated headlines, while Series C capital was captured by two Chinese mega-deals. The “hollow middle” persists - companies without AI or defense theses face an increasingly selective capital environment.

Executive Summary

December delivered $18.1 billion across 733 rounds, marking a strong close to 2025. The defining characteristic was extreme capital concentration: the top 10 deals accounted for over $4.8 billion (27% of total capital). AI infrastructure, defense technology, and Chinese EV/mobility dominated the largest rounds.

• Total Capital Deployed: $18.1 Billion

• Deal Volume: 733 Companies / Rounds

• Active Investors: 2,273

• Average Round Size: $24.7 Million

• Median Round Size: $5.0 Million

Investment by Stage

Series B dominated total capital allocation with $5.45 billion across 90 companies, while Pre-Seed and Seed together accounted for 432 companies - nearly 60% of all deal activity. The gap between average and median round sizes reveals the power law at work: a few mega-rounds pull up stage averages dramatically.

The data shows Late Stage (E+) rounds maintaining the highest average size at $172.7M, driven by growth rounds in geothermal energy (Fervo), cybersecurity (Cyera), and fintech (Airwallex). Series A averaged a healthy $24.1M, suggesting strong conviction capital remains available for post-seed companies with demonstrated traction.

Geographic Distribution

US Capital by State

California dominated US startup funding in December with 47% of total capital, followed by New York (17.2%) and Texas (15.5%). Texas’s strong showing was driven by a single outlier - Houston-based Unconventional AI’s $475M seed round. Delaware’s 6.7% share reflects the state’s role as corporate domicile for VC-backed companies.

Global Capital by City

San Francisco and New York led global funding with $16.1B and $12.9B respectively, but December’s geographic story was one of dispersion. Freiburg im Breisgau, Germany emerged as an unexpected hotspot with $10.2B in funding, driven by Black Forest Labs’ continued AI image generation dominance. Chinese cities collectively captured over $20B when aggregating Beijing, Jiading, Haidian, Changning, Chongqing, Hangzhou, Shanghai, and Wuxi Shi - underscoring China’s continued domestic venture activity despite geopolitical headwinds.

Key Trends and Observations

1. The “Mega-Seed” Phenomenon

Unconventional AI’s $475M seed round - led by Andreessen Horowitz and Lightspeed - represents the largest seed investment ever recorded. With participation from Jeff Bezos, Sequoia, DCVC, Lux Capital, and Databricks, this round signals that for AI infrastructure plays, the distinction between seed and growth rounds has collapsed entirely. The investor syndicate reads like an all-star team betting on compute infrastructure.

2. Defense Tech Ascendant

Castelion ($350M Series B), Radiant ($300M Series D for nuclear microreactors), and HawkEye 360 ($150M Series E for space-based RF intelligence) demonstrate continued institutional appetite for dual-use and defense-focused technology. The “Accelerationist Alliance” of a16z, Founders Fund, and Lux Capital appears repeatedly across these deals.

3. China’s Parallel Universe

Chinese startups raised significant capital in December despite US-China tensions. DeepBlue Auto ($873M Series C for autonomous driving), Moonshot AI ($500M Series C for AI), SIMIC Holdings($367M Series B for semiconductors), and Aipuqiang ($143M Series B for robotics) reveal a robust domestic funding ecosystem. These rounds are largely funded by state-backed or domestic Chinese investors.

4. AI Infrastructure Everything

From Black Forest Labs (image generation) to Chai Discovery(drug discovery) to Lovable (code generation) to Fal (inference infrastructure), AI-native companies commanded premium valuations and attracted the most competitive investor syndicates. The common thread: infrastructure enabling others to build with AI.

Top 10 Deals of the Month

1. DeepBlue Auto (China) - Series C: $873.5M - Autonomous driving

2. Saviynt (USA) - Series B: $700M - Identity governance

3. Moonshot AI (China) - Series C: $500M - AI foundation models

4. Unconventional AI (USA) - Seed: $475M - AI infrastructure

5. Fervo Energy (USA) - Series E: $462M - Geothermal energy

6. Cyera (USA) - Series F: $400M - Data security

7. SIMIC Holdings (China) - Series B: $366.8M - Semiconductors

8. Castelion (USA) - Series B: $350M - Defense technology

9. Lovable (USA) - Series B: $330M - AI code generation

10. Airwallex (Singapore) - Series G: $330M - Cross-border payments

Capital Concentration Analysis

The power law was pronounced in December. Single outlier deals captured significant percentages of total stage capital.

At the Seed stage, Unconventional AI alone represented 23.3% of all seed capital deployed in December. At Series C, two Chinese deals - DeepBlue Auto and Moonshot AI - combined for 44.5% of total Series C capital. This concentration underscores that for LPs, being in the right funds with access to these outliers is increasingly the only path to top-quartile returns.

Investor Analysis: The Overlapping Elite

December’s mega-rounds revealed a familiar pattern: the same elite investors appearing across multiple deals. Andreessen Horowitzled or participated in four of the five largest non-Chinese deals. Lightspeed Venture Partners co-led both the largest seed (Unconventional AI) and a top Series B (Castelion). Sequoia Capitalmaintained its presence across stages.

This “club deal” dynamic means access to elite rounds is increasingly gated by relationships. Funds outside this network face structural disadvantage in accessing the deals that drive returns.

SignalRank Series B Analysis

In the SignalRank methodology, a company “qualifies” not just by revenue or hype, but by the quality of its early backers. The core thesis is that top-tier Seed and Series A investors (the “Signal”) have persistent access to the best outliers.

December produced 13 SignalRank-qualifying Series B companies. Castelion achieved a perfect 100th percentile score, with its pedigree extending from Andreessen Horowitz at seed through its $350M Series B. Black Forest Labs (99.9th percentile) demonstrated the value of stacking elite investors: a16z, General Catalyst, NVIDIA, and Salesforce Ventures all participated.

The qualifying companies span AI infrastructure (Black Forest Labs, Lovable, Chai Discovery), cybersecurity (Adaptive Security, Vega), defense (Castelion), and enterprise software (Serval, PermitFlow). This diversity suggests signal quality transcends sector focus.

Summary of Trends

1. The “Infrastructure Thesis” Dominance

Whether AI compute (Unconventional AI), energy infrastructure (Fervo, Radiant), or software infrastructure (Fal, Lovable), the largest December rounds went to companies building fundamental capability layers. This pattern suggests investors are betting on pickaxe-sellers rather than gold miners.

2. The Club Deal Effect Intensifies

a16z, Lightspeed, Sequoia, General Catalyst, and a handful of other elite funds appear repeatedly across December’s mega-rounds. For emerging managers and outsider funds, access to these deals has become the binding constraint on returns.

3. Geographic Bifurcation

The US and China are developing parallel venture ecosystems with minimal capital flow between them. Chinese mega-rounds are funded domestically; US AI and defense rounds are funded by the usual suspects. Singapore (Airwallex), Israel (Syremis, Vega), and Germany (Black Forest Labs) emerge as the primary alternative destinations for US-aligned capital.

Data sourced from SignalRank with help from Crunchbase and Preqin. Analysis covers announced rounds from December 1-31, 2025.

Subscribe to The State of Venture at

The State of Venture

A resource for all stakeholders to discuss the state of venture capital

By Signal Rank Corporation

This post instead looks “downstream” founder talent, specifically the alumni of power law companies who go on to found notable startups. Certain companies, like Fairchild & PayPal, act as founder schools, spawning “mafia” of early employees who seek to recreate their prior company’s talent density, culture & philosophy in new startups.

A company can enter the pantheon of greats when former employees go on to have greater success still with new ventures. Brex is now bragging how they want to attract “quitters”, with 100 alumni already becoming founders. This feels like Brex doth protest too much; the “mafia” epithet is bestowed upon a company by the ecosystem, not self-designated.

Many investors specifically seek out founders who have experience as employees at hyper growth companies. We will see that this founder profile is adroit at raising capital, especially right now for anyone who has worked at a premier AI lab. The billion dollar seed round era is upon us.

The challenge of course is establishing which employees were indeed critical to the success of their alma mater. And, even then, a great employee does not necessarily a great founder make.

This post will leverage SignalRank’s data & algorithms to review startups founded by alumni of OpenAI, Google DeepMind & Meta AI alumni.

The OG tech mafia

Silicon Valley startup genealogy provides important connective tissue across generations in the ecosystem.

Fairchild, created by the “traitorous eight” in 1957, is probably the original Silicon Valley tech mafia. Its former employees became future founders of many so-called “Fairchildren” including little known companies like AMD, Intel & SanDisk. The chart below from here shows the corporate genealogy from Fairchild onwards.

More recently, the most successful group of former founders & employees is the PayPal mafia.

Cantos’ Grant Gregory created this chart here to show how many of the most successful companies in the 2010s had a PayPal fingerprint.

Which AI companies today are becoming the mafia of tomorrow?

Large established AI labs, specifically OpenAI, Meta AI, DeepMind & GoogleBrain, have started to generate founders at a decent clip. An illustrative list of notable AI start-ups from these labs is at the end of this post (Figure 3).

DeepMind & OpenAI have been the most prolific in ex employees spinning up new venture-backed AI companies (Figure 1).

The unicorn hit-rate for OpenAI spin-outs is particularly noteworthy, probably demonstrating the depth of risk capital in Silicon Valley vs London (where DeepMind is primarily based). Indeed, the aggregate capital raised in the last rounds of just four OpenAI alumni companies totals over $20bn: Anthropic ($13bn), xAI ($5bn), Thinking Machines ($2bn) and SSI ($2bn).

It should be of no surprise that the most active Series A investor (by far) for these spin-outs is a16z, followed by NVIDIA.

Figure 1. Comparing alumni companies from select AI labs

Of all the alumni companies from these labs, SignalRank is currently tracking three candidates which have raised a quality Series A but not yet raised a Series B. We would be keen to support the pro rata of existing investors in any upcoming high quality Series B of these companies.

Figure 2. Potential AI mafia candidates for SignalRank’s Series B product

Concluding thoughts

It is probably too early to talk of AI “mafia” but we are already starting to see employees from senior leadership positions at the best labs spin out on their own (despite Meta’s best efforts to attract AI talent by lavishing them with professional sports’ level of pay packets). PitchBook recently conducted research to show that OpenAI has already lost 25% of its key research talent in the last two years alone.

Capital is actively seeking these founders, meaning this trend is likely to accelerate.

Figure 3. Illustrative list of notable companies founded by AI lab alumni (adapted from here)

Over the last two decades, Silicon Valley has developed new models of credentialing to channel opportunity to exceptional founders. Programs outside of traditional educational institutions, such as Y Combinator, the Thiel Fellowship, Entrepreneurs First and Nat Friedman’s AI Grant, credential entrepreneurial potential. In doing so, they have become powerful filters for talent.

Y Combinator, founded in 2005, has backed 112 unicorns to date, including Reddit, AirBnb, Coinbase, Dropbox & Doordash. Other programs started later and are having early success in channeling resources to potential power law companies (Figure 1).

Figure 1. Unicorns per program (on Crunchbase data)

In this post, we use SignalRank’s model to examine the success of these decentralized / unaffiliated networks in identifying the next generation of successful entrepreneurs.

This post will focus on’ “upstream” early talent; a subsequent post will consider “downstream” talent for people who have worked at iconic companies that created entrepreneurial so-called ‘mafia. and go on to become entrepreneurs themselves.

Some context

The transfer of power from centralized institutions to decentralized networks is one of the core themes of our age. Trust in centralized institutions across all walks of life has never been lower, from media, politics, education and beyond.

The internet is the handmaiden to this change. We should expect higher variance outcomes in almost every sphere of human activity.

Matt Clifford, founder of Entrepreneurs First, offered an instructive framework for understanding this shift:

Variance-amplifying institutions (VAIs) are triumphing over variance-dampening institutions (VDIs). These institutions differ in what behaviours they promote and what characteristics they select for. VDIs select for norm adherence, predictability and consistency. They create negative feedback loops; bureaucracy is the canonical example. VAIs select for extremeness, surprise and virality. They create positive feedback loops; the internet is the canonical example.

One way to think about the triumph of modernity is as the gradual reduction in variance across most aspects of our lives. From the Peace of Westphalia to the welfare state, we’ve gradually built institutions that make life more predictable and narrow the range of outcomes to which we’re exposed (with some catastrophic failures along the way, of course). That era is coming to an end.

The internet changes this framing, as the ultimate variance-amplifying institution. It is permissionless, uncapped and promotes network effects. On the internet, anyone can do anything and, in theory, can reach any scale.

In this variance-amplified world, the relative signaling power of traditional credentials is lower. In turn, the relative value of entrepreneurship which can enable power law outcomes is perceived to be higher.

Universities confer predictable competence. A GPA score is legible across a cohort but less useful in terms of gauging entrepreneurship.

Kyla Scanlon just wrote a fascinating article here about the decline of university towns, arguing “America is facing its next Rust Belt moment - but it’s not our steel mills shutting down, it’s our education mills.” College enrollment is forecast to decline another 15% between 2025 and 2029 (where Scanlon argues this is in part demographics, in part lower trust in the value of college education).

The new economy prizes qualities of originality and boldness, neither of which are not captured by centralized credentials.

The new gatekeepers

Into this vacuum have stepped a series of new networks that validate potential:

• Y Combinator (YC) - institutionalized the founder network with quarterly “graduations”

• Thiel Fellowship – Peter Thiel’s team seeks out pre-college iconoclasts who can produce outsized outcomes

• Entrepreneurs First – a riff on Teach First (UK equivalent of Teach For America) that has formalized “founder matching” and pre-company incubation

• AI Grant – Nat Friedman’s micro grants to AI talent. It is unclear whether this will continue now he has joined Meta.

Each of these models is about early identification of exceptional people, providing them with small amounts of catalytic capital and community. They are the new on-ramp for elite entrepreneurship.

So how are they doing?

We analyzed companies that have gone through each program and subsequently raised seed rounds.

Figure 2 shows the number of seed rounds raised by program participants per year since 2005. YC is very active, with its graduates seeing 200+ seed rounds per annum even in the post-ZIRP era.

Figure 2. Seed rounds per year from companies backed by each program, 2005 onwards

We showed at the top the number of unicorns created per program (Figure 1). YC is leading the charge on absolute terms with 112 unicorns per Crunchbase data. The AI Grant program has backed Perplexity & Anysphere (Cursor), while Thiel fellows include the founders at Figma, Mercor, Loom, & Clay. EF’s sole unicorn is Tractable.

Given their success in identifying unicorns, it is perhaps not surprising that a number of funds have emerged to invest specifically in the best companies from the cohorts of each respective program, including Rebel Fund (YC focus), AVCF (YC), Pioneer (YC), Cathexis (YC), 1517 (Thiel fellows) and Browder Capital (Thiel fellows).

YC’s high diversification strategy generates high number of unicorns but it also impacts its unicorn efficiency, defined as unicorns divided by number of seed rounds. On this measure, the Thiel fellowship has a higher concentration of unicorn generation at almost 7% (including 2025 seed rounds which have not yet matured). As a benchmark, ~2% of all seed rounds globally go on to become unicorns.

Figure 3. Unicorn efficiency per program

When we narrow the focus to Series Bs which would have qualified for SignalRank’s product, we see a similar pattern. 301 YC companies qualified for our product, with 48 going on to become unicorns, implying a 16% hit rate. That’s half of our backtest market average, suggesting that we should apply a harsher threshold to YC companies.

Figure 4. SignalRank Index historic qualifiers per program (2012-25)

Given the 16% hit rate stat, our portfolio to date is probably overweight YC exposure. We have backed 6 YC companies, and only one Thiel company and one AI Grant company.

Figure 5. Portfolio companies in the SignalRank Index

Therefore, what?

Each of these programs is perceived as acting as a credentialing layer in the new startup ecosystem. We show here the signaling power of these programs, especially the Thiel fellows & AI Grant recipients.

However, it is worth noting that the number of companies “credentialized” by these programs is very, very small. Not many founders raise venture capital at all, and even fewer go through these programs. These programs are most useful for first-time founders, for whom brand recognition and access to rocket fuel VC dollars is relevant. In short, these programs are powerful but narrow measures of credentialing entrepreneurs.

You might even go further to argue that these networks are transitional forms of credentials. The next evolution may not look like a fellowship or accelerator at all, with AI systems instead relying on algorithmic public signals to continuously credential and connect talent in real time. In any event, the evolution of talent credentials is accelerating away from centralized institutions.

The numbers flying around AI are truly eye watering. McKinsey estimates $7 trillion will be invested in AI infrastructure before 2030. OpenAI on its own is projected to require more energy capacity than all of India in 8 years.

A contender for most outlandish claim is surely Sequoia’s Shaun Maguire for arguing that code generation could be a $10 trillion (with a t) market (or about 3x the entire GDP of the UK). 🤔

QED’s Frank Rotman has argued that the VC ecosystem has become velocitized, numb to increased pace such that normal speed feels slow. As one of our seed partners recently said on a call, VCs have “lost sight of what a $10bn valuation actually means.”

Within this ocean of gigantic numbers, The Information published the chart below earlier in the month to demonstrate OpenAI’s staggering revenue growth to $200bn revenue by 2030.

This was accompanied by an equally jaw dropping report of a projected cumulative cash burn of $115bn through 2029. We now know that NVIDIA is seeking to invest up to $100bn to cover the vast majority of this capital requirement (at least that’s what the headlines say).

Needless to say $115bn is a lot of capital to invest into a loss making entity. For context, this one company would consume 1.5x the $75bn raised each year by US VCs.

And yet. Each technology cycle requires capital commensurate with the size of the addressable market. Each cycle has a poster child of what appears to be profligate behavior at the time and looks to be visionary in retrospect: Amazon burned ~$2bn in the Web 1.0 cycle; Uber burned ~$35bn in the Web 2.0 cycle. OpenAI is now seeking general-purpose intelligence embedded across every industry.

If you believe AI has a total addressable market an order of magnitude larger than prior cycles, a triple-digit billion burn is not a sign of excess but is exactly in-line with expectations.

Not that there is much of a role for VC here. The AI infrastructure layer (including the power / energy layer) and the foundational model layer is a game of kings (literally in some cases), backed more by sovereigns and strategics than Sand Hill Road firms. Is raising 2/20 SPVs to YOLO into Anthropic’s latest round really what VCs should be doing?

These high capex layer needs to be in place in each cycle ahead of VC’s power law vintages. As we’ve written before, being early can be tantamount to being wrong.

Investing in AI is a consensus bet now. Jerry Neumann has gone further to argue that AI is not going to deliver stellar returns for investors (more akin to investing in shipping containers than software, with value accruing to incumbents and consumers rather than investors).

We don’t share this view. As Marc Andreessen has argued previously, there was not a Tech Bubble in 1995-2000, but a Telecom Bubble. Similarly, the huge capex investments into AI (maybe the “AI Infrastructure Bubble”), including OpenAI’s $115bn burn, will create all kinds of interesting unexpected opportunities for entrepreneurs and investors alike.

Capital has undoubtedly been misallocated in this AI Infrastructure Bubble, but just as internet adoption surged during and beyond the Telecom Bubble, AI adoption will continue apace. As such, VC’s power law vintages for AI are likely still ahead of us.

Figure 1. Active public funds have lagged their respective benchmarks

Source: Apollo

The challenge is to agree on what an “index” constitutes given the inability to fund every round in the market. At SignalRank, our answer to this question is to argue that 30 investments a year is a statistically representative sample of the top 5% of Series Bs selected by our model.

While the majority of ETFs and mutual funds are now in passive vehicles, it remains early days in the creation of passive private structures. As such, the Great Man / Woman theory remains dominant. Equidam’s Dan Gray published a piece today on how VC LPs back “confidence over competence”.

This is the private market equivalent of what John McQuown, father of passive investing, explained in relation to public markets:

“the vacuousness of the traditional methods of portfolio management, which, were little more than ‘ . . . a variation of the Great Man theory. A Great Man picks stocks that go up. You keep him until his picks don’t work any more and you search for another Great Man. The whole thing is a chance-driven process. It’s not systematic and there is lots we still don’t know about it and that needs study.’”

Correlation Ventures previously argued that the value of a venture capital firm is little more than the value of the individual GPs. When a partner moves from one venture firm to either a worse or better performing one, the performance of that partners’ investments remain largely the same. In fact, persistence in performance by partners is six times more important at explaining fund returns than firm attributes.

Yet we still subscribe to the argument that the core product that VCs are selling is their (collective) decision-making engines, not the individual decision-making of a sole GP. Indeed, Correlation’s own analysis showed that individual partner signal only explained 12% of all returns (with 86% of returns explained by other factors).

Indeed, our core investment data model at SignalRank is based at the firm level, not the individual. This is for various reasons:

1. The data is better at the firm level than the individual level, and we want to compare apples to apples. Not all firms attribute deals to individuals.

2. No one individual scores high enough to drive investment decision on our model

3. Investment decisions are usually taken by committees, not individuals (even if sourced / led by individuals)

Having said that, some individuals do deliver stellar performance. One name that consistently comes up in conversations as a high Series B performer is a16z’s Martin Casado.

In this post, we consider Martin Casado’s performance as an illustration of what exceptional GP performance looks like – and why the Great GP theory remains seductive.

A caveat is that this analysis is based on publicly available Crunchbase data which we know does not show the full picture (especially as we are investors in two Martin Casado Series Bs which have not yet been announced). But it is directionally illustrative.

Martin Casado’s performance

Martin Casado is a GP at a16z, focused on AI, data & analytics. He is a seed to Series D investor, primarily focused on Series A & Series Bs.

On our internal GP ranking system, he has consistently ranked as a top 10 GP over the last five years (and ranked #1 last year).

Since 2015, our data shows he has invested in 25 Series Bs, of which an astonishing 40% have gone on to become unicorns (Figure 2). 64% of his Series Bs went on to raise a Series C (compared to 33% for the market average).

Figure 2. Unicorn hit rate for Series B investments, 2015-25

Source: Crunchbase

This unicorn hit rate is not because he is paying higher prices at Series B to create unicorns at Series B. The average Series B he participates in is $34m (with a median of $30m).

This Series B performance is really derivative of his Series A investing, given that 68% of his Series B investments were follow-ons from his Series A investments.

In fact, his Series A investing is perhaps more remarkable still in that 25% of his Series As have gone on to become unicorns (compared to 3% for the market average).

Before we get too far into hagiographic territory, it is worth pointing out that Casado on his own represents 10% of all a16z Series Bs and 14% of all a16z Series B unicorns. Andreessen Horowitz is called Andreessen Horowitz for a reason; the firm’s two founders are active investors and have led the firm since inception. LPs are buying the Andreessen Horowitz funds, not the Martin Casado funds.

Instead, Casado’s results should perhaps be seen within the context of the a16z machine, where he is empowered by a16z’s brand, platform, capital and decision-making engine.

Casado could of course spin out as a solo GP. But, as in the music industry, there is risk that not every solo career eclipses the former success of the band.

Some concluding thoughts

The argument for passive structures in the public markets has been won, with the majority of ETFs and mutual funds now in passive models (Figure 3). The equivalent percentage for private markets is probably <1% today.

Figure 3. % of ETFs and mutual funds in passive vehicles

Source: Apollo

Martin Casado’s track record shows why the Great Man/Woman theory endures in the private markets: exceptional individual talent can produce extraordinary results. But we would argue that his success is somewhat inseparable from the structure of a16z.

And a16z may well be in the 10% of active funds that outperform the industry benchmarks over 10 years (in part because the engine includes great GPs like Casado). But for most VC allocators who cannot investing directly in a16z, we expect the Great GP theory to give way to more systematic approaches (just as has happened in public markets).

This is unexpectedly leading to an improvement in SignalRank’s access to high quality Series Bs.

Contrary to concerns of crowding out, the venture majors’ larger round sizes and increased syndicate size actually creates more entry points for our model.

The venture majors

Sapphire’s Beezer Clarkson recently wrote this post about the state of seed & Series A, questioning whether “venture is broken”.

She looked at trends within mega-funds at the early stage, showing that the most active seed investors in the Bay Area & NY are now the mega-funds (Figure 1.)

Figure 1. The most active seed investors in the Bay Area & NY since 2022

The top four funds on the table above, specifically a16z, Sequoia Capital, General Catalyst and Lightspeed, are arguably now in a class of their own.

Each started as an early stage VC (as opposed to say other scaled venture investors, such as Insight, SoftBank or Thrive) and has made recent structural changes to enable accelerated asset accumulation with significant success:

• a16z has been an RIA since 2019; closed $7.2bn suite of funds in 2024

• Sequoia has moved to an evergreen structure whose AUM has hit $20bn

• General Catalyst is rumored to be considering an IPO; closed $8bn new funds in 2024

• Lightspeed recently registered an RIA; closed $7.1bn in 2022 and rumored to be raising $7bn in additional capital

This analysis by Abraham Thomas about the creation of these “venture majors” resonates; quoted in full here:

• “Market-making is simultaneously more lucrative, less risky, and more scaleable than price-taking. As a result, the handful of firms that succeed in capturing market-maker network effects quickly achieve escape velocity. This has led to a new class of player in the industry: the venture majors.

• These firms are vertically integrated — they play at every stage, from pre-seed to Series Z. They are horizontally expansive — they play in every sector, from software to robotics to biotech to defence to crypto. And increasingly, they play across geographies, and up and down the capital structure stack. No tech financing event is out of scope for them.

• At the same time, they’re not really hunting alpha. Outperformance is not the point; rather, because of their increasing economies of scale, they just want to be part of every (material) deal. The result is, essentially, beta on the private tech market — it may not be a true ‘index’, but it offers highly-correlated directional exposure to the market as a whole, which is almost the same thing.

• LPs love this. Most institutional investors want sector exposure first, and in-sector outperformance second. The venture majors provide precisely this.”

This analysis speaks most clearly about the four funds above, which we will collectively call the venture majors in the rest of this post.

Analyzing the venture majors

Clarkson’s analysis above focused on 90 mega-funds. If we just look at the four venture majors, are there any other recent trends we can observe? What is the impact on SignalRank (if any)?

1. All investment rounds

The number of announced investment rounds by the venture majors each year has not increased commensurate with their increase in AUM. Each of the venture majors is still only making ~100 announced rounds per year.

The biggest changes have been greater seed activity and larger round sizes at every stage.

Figure 2. All announced rounds by the venture majors (pre-seed → Series F), 2012-2025

Source: Crunchbase

2. Increased seed activity

The number of seed investments has increased markedly over the last 15+ years for the venture majors. At current pacing, GC is on track to make 50 announced seed rounds in 2025 (interestingly in an opposite direction to the other three whose seed activity has decreased in 2025 relative to prior years).

Figure 3. # of seed investments per year by venture major, 2012-2025 annualized

Source: Crunchbase

3. Larger seed rounds

A significant change has been the size of seed rounds. The maximum seed round participated in by any of the venture majors in 2010 was $2m. The lowest maximum of any seed round by a venture major was $25m in 2025 (see Figure 4).

Figure 4. Maximum seed round size by venture major, 2022 onwards

Source: Crunchbase

4. Admirable seed conversion to Series A

12% of all global seed investments since 2022 (from January 2022 to today) have raised a Series A. This increases to 21% for the top 100 seed investors on SignalRank’s model.

The venture majors’ seed conversion is higher still, roughly in-line with the top 20 seed investors on our model (Figure 5). With significant capital behind each of them, this is perhaps not so surprising.

Figure 5. Venture majors’ seed conversion for 2022+ seed investments to Series A (compared to top 20 seed investors and top seed investor on SignalRank’s model)

Source: Crunchbase; SignalRank

5. Stable Series B dynamics

The number of Series Bs that the venture majors have invested in increased but not in-line with asset accumulation. And the unicorn hit-rate has not improved over time.

SignalRank’s target number of investments is also superimposed here to demonstrate how making 30 investments per year provides SignalRank with significantly higher diversification than any one venture major.

Figure 6. # of Series B investments by venture majors, 2012 to 2025 annualized

Source: Crunchbase

Again, SignalRank’s backtest unicorn hit rate is superimposed here to show the consistency of a high diversification strategy. Data to 2021 to provide Series Bs with 3+ years to achieve unicorn status.

Figure 7. Series B unicorn hit rate for venture majors, 2012 to 2021

6. Expansion in Series B round sizes

The biggest change at Series B has been the round size expansion. Average Series B round sizes by the venture majors has increased by ~7x since 2012 (Figure 8). This compares to ~5x for all Series Bs over the same timeframe ($12m in 2012 to $54m in 2025).

Figure 8. Average Series B round size for venture majors (2012 vs 2025, $m)

7. Why this matters to SignalRank

This increase in round size has led to a doubling in the average number of investors in a venture major Series B round (Figure 9).

Figure 9. Average # of investors in venture major Series B rounds (2012 vs 2025)

This round participant count is important for SignalRank. It demonstrates that the number of potential access points for our pro rata strategy has increased over time.

The received wisdom that a “Tier 1 lead” will squeeze out other investors in quality Series Bs does not bear out when analyzing the data.

Concluding thoughts

The stratification of the venture ecosystem continues apace. The four venture majors represent a differentiated type of diversified asset manager relative to even other “mega-funds.”

For SignalRank, the implications are clear. Larger Series B rounds and more syndicate participants mean our pro rata model is strengthened, not weakened.

Platform shifts broaden the spectrum of possible outcomes: opportunity for high upside, but also amplified risk.

There is consensus that AI is a big deal. But no-one knows exactly how far & how fast the technology will develop. This creates substantial financial (& career) risk in being a traditional VC these days, making just 1-2 investments per year.

It is precisely at these moments why a highly diversified model such as SignalRank’s should appeal to allocators. We are aiming to make 30 investments per year at scale, with our structure also offering high vintage diversification. One investment today provides an investor with access to both our historic & future vintages.

This post considers the difficulty in predicting the future, how this applies to VC and then suggests that SignalRank’s systematic & diversified approach can offset some of these challenges.

The challenge with predicting the future

The future is of course unknowable, reflecting more about present concerns than anything else. The only thing we can be certain of is that most predictions will be wrong.

This is the topic of this book, which was the inspiration for this post, which looks at the history of predictions about the future.

It can be fun to look back at prior predictions, although this does give the false impression of wisdom just by living later. There is a risk of unfairly inflicting on prior generations “the enormous condescension of posterity,” to borrow a phrase from the historian EP Thompson.

There are lots of good examples of the futility of predicting the future. Figure 1 is perhaps the most compelling chart to make the point, demonstrating effective Fed Funds rate versus market expectations.

Figure 1. Effective Fed Funds rate versus market expectations (from Man Group)

Another great memo on predicting the future comes from Donald Rumsfeld of all people. He wrote this one page memo, just six months before 9/11: “All of which is to say that I’m not sure what 2010 will look like, but I’m sure that it will be very little like we expect, so we should plan accordingly.”

This is also quite a good list of the most inaccurate technology predictions in the last 150 years.

VCs trying to predict the future

Venture capital is a high variance asset class with high returns and high loss ratios. Even where there is platform stability (as during the mobile & cloud era from 2006-22), it is challenging to identify & back the right opportunities.

In fact, the perverse thing about VC is that the best funds have higher loss ratios. Here’s some data from StepStone and Primary:

Zeroes are expected. Daybreak’s Rex Woodbury talks about how “zeroes that could have been fund-returners should be celebrated as good swings: this is a business of home-runs, not second-base hits.”

We also ran some analysis to consider how firms have performed in the post ZIRP era. We see that only 30% of seed investments since 2022 by the top 20 seed managers (per our model) have raised a subsequent priced round (or 12% for all seed managers).

If we interrogate this further by looking at the cohort by cohort data, the picture is not much better. Only 35% of 2022 seed investments by the top 20 investors have raised a subsequent priced round.

In short, VC is a really hard business.

Even when VCs are already investors in a startup, it is challenging to predict outcomes. This can lead to significant misallocation of precious reserve capital into portfolio companies that will not return the fund. Some humility from 20VC’s Harry Stebbings here:

How a systematic approach can help

Individual bias reduction

This paper evidences that VCs are making “predictably bad” investments by over weighting data on founder traits. This is but one example of cognitive bias that can lead to significant misallocation of capital.

A systematic approach should reduce some of these individual biases. By requiring a company to have been backed by at least three world class investors across three separate rounds, our model reduces reliance on any one individual. Our approach also effectively eliminates any biases internally within our team, by giving the algorithm primacy in decision-making.

A caveat is that any new system will of course bring a new set of potential biases, including data bias, temporal anchoring bias, access bias, survivor bias and others. Nevertheless, in aggregate, our backtest suggests that this system should consistently deliver top quartile performance across vintages.

Diversification

Diversification is the only free lunch in finance.

SignalRank is a classic index-like strategy in that we are sector, geographically, manager & vintage agnostic. With a target 30 investments per year, this should lead to high diversification which should lead to more consistent returns.

Our Monte Carlo simulations show how our expected 5 yr MOIC per annual cohort increase to above 4.0x with 80% confidence once 30+ investments are made per year.

We have argued before here that vintage diversification is also particularly underrated during times of rapid change.

More recently, Equidam’s Dan Gray (as well as Carta’s Peter Walker) have been arguing here about how VCs should be aiming for higher diversification.

A final thought - some predictions of our own

Despite all this, we couldn’t resist making a few predictions of our own, publishing our view of what the venture capital ecosystem will look like in 2030.

Let’s see how wrong we are in 2030...

After two years of retrenchment, Series B activity in FinTech is rebounding. Crunchbase reports $22bn invested in FinTech in H1 2025 (across all rounds), representing 5% growth relative to H1 2024.

This momentum has been supported by recent successful FinTech IPOs, including Circle & Chime. Wealthfront, Gemini and Navan are also slated to IPO later this year.

The growth-at-all-costs mentality that characterized the previous FinTech investment cycle has been replaced by a more measured approach. The focus is on B2B payments, institutional-grade B2B infrastructure & (of course) AI-native FinTech.

As a share of all Series Bs, FinTech has climbed back to 16%, continuing its long-term upward trajectory (Figure 1).

Figure 1. Number of FinTech Series Bs as a % of all Series Bs

Source: Crunchbase

SignalRank’s algorithms seek to identify the top 5% of all Series Bs. FinTechs now represent almost one third of all qualifying Series Bs on SignalRank’s approach. In fact, as Figure 2 shows, more FinTechs have qualified for our product this year than AI companies.

Figure 2. FinTech & AI companies as % of all Series B SignalRank qualifiers

Source: Crunchbase & SignalRank

There have been 23 FinTech qualifiers in 2025 YTD (Figure 3). SignalRank is an investor in four of these 23 (highlighted in green).

Figure 3. All FinTech qualifiers with 2025 Series Bs

Of SignalRank’s 36 portfolio companies, 7 are FinTech companies (Figure 4). Or 19% (which is slightly underweight relative to the whole qualifying set).

Figure 4. FinTechs within SignalRank’s portfolio

So what?

AI continues to absorb much of the VC world’s attention & capital. But other non-AI sectors have adapted to the new post ZIRP environment and are beginning to attract capital again with much more operational discipline & more capital efficient models.

The systematic nature of SignalRank’s approach enables our investment product to capture these shifts, giving our investors exposure to the FinTech revival.

The product offered by VCs has not changed. Floodgate’s Mike Maples observed that a VC’s product at its core is its decision-making engine. There is ordinarily no such thing as proprietary deal flow and many investors add minimal value. The uniqueness of a partnership comes down to the strength, culture, and dynamism of a firm’s decision-making process. LPs are buying a firm’s process; the results should take care of themselves.

When viewed through this prism, VC cannot scale. It remains an apprenticeship-based artisanal craft, not a commodifiable & industrialized data-driven product. In fact, adding additional humans to an investment committee is likely to make for worse decisions, not better.

VCs have liked to integrate sourcing, capital & value-add services to this core decision-making process. It’s the whole product, packaged as one for LPs. But the history of technology, at least as told by Clay Christensen, is that modularity beats integration every time: “the transition from proprietary architecture to open modular architecture just happens over and over again.”

What if you applied this logic to venture capital? What if you outsourced decision-making? What if you separated decision-making from sourcing, capital & value add services? What if technology was applied to the venture capital industry itself?

Jim Barksdale, the former CEO of Netscape, is credited with saying, “there's only two ways I know of to make money in business: bundling and unbundling”. In this post, we are going to use the framework of unbundling/rebundling to consider how venture capital could be rebuilt for scale.

The unbundlers

There is an infamous conversation between Marc Andreessen & Jim Barksdale with Harvard Business School here to consider how distribution technologies enable bundling & rebundling. They discuss how the internet frees distribution from the constraints of “time and place.”

The proliferation of digitally-distributed data is finally started to lead to the unbundling of venture capital, freeing it from similar constraints of time and place.

We are going to consider three different models of unbundling VC: solo GPs, guilds & AI models.

Solo GPs

LPs have historically backed partnerships of multiple GPs, where diversity of opinion, complementary skillsets and access to differentiated networks should improve outcomes. Not to mention the ability to diligence more opportunities by adding additional bodies.

Yet the relative importance of an individual’s brand within a partnership has been increasing for a while. Correlation Ventures argued in 2017 that the value of a firm is no more than the sum of its partners. Founders are keen to work with specific individuals (who are ideally ex operators, not Excel jockeys), instead of specific firms. This has happened simultaneously to the professionalization of angel investing. And of course the growth of social media. Contrary’s Kyle Harrison has an admirable series on the current unbundling of venture capital where he focuses on the rise of solo GPs.

Footwork’s Nikhil Basu Trivedi first pointed out the rise of solo capitalists in 2020. The collapse of the industrial unicorn complex since 2022 has acted as a catalyst to this trend. High profile GPs in their 30s and 40s are increasingly electing to spin out on their own rather than spend the next ten years delivering 1.0x DPI for the 2020/21 funds.

Pitchbook wrote a recent article on the growing phenomenon of solos GPs and listed notable recent funds (including Elad Gil who has raised a $1bn solo fund and is rumored to be in market for a new $1.5bn fund):

Figure 6. Notable recent solo GP funds

Solo GPs are undoubtedly unbundling themselves from the traditional VC product. An individual investor cannot of course scale. Yet the development of a network of solo GPs could enable a more diverse and broader set of investment products which in aggregate could scale better into niche areas than the traditional product allows for. Individuals can take their interests where they want.

Guilds

Syndicates enable individual investors to make stand-alone investment decisions on individual opportunities, thereby unbundling capital and decision-making from the core VC model.

Investment syndicates are nothing new. The Band of Angels, created in the 1990s by a group of ex HP & Sun engineers, is credited with being the first high technology specific angel investment group in the United States. Digitally-enabled models like AngelList syndicates (including former employer angel groups, such as AirAngels from AirBnb) are the inheritors of this model.

There is a current growth in digitally-enabled communities focused on specific verticals, similar to guilds of yore. These are groups based on the loose bond of former employee affinity but based on vertical expertise. This allows these communities to diligence tools they would use in their profession and to be more hands-on with products. Examples include FOG Ventures (based around the Operators Guild, largely focused on CFOs) or PeopleTech Partners (based around Chief People Officers).

The challenge with these groups is that they typically represent smaller pools of capital. They also tend to be reliant on a few core nodes in the network who do most of the heavy lifting (sourcing, memos, managing deals, etc).

AI models

We have discussed before how data-driven VC is becoming more normalized. It is possible for a machine / AI agent to screen/scrape/source opportunities at a scale that is not possible for an individual human. There are multiple services that will unbundle the top of the funnel for VC sourcing. See Andre Retterath’s site for examples of such services.

SignalRank itself leverages industry-level data to offer an unbundled product in three respects:

1. The most disruptive (and controversial) element is the unbundling of investment decisions. Our quantitative approach outsources decision-making to the best investors in the world – we only invest into companies where the best decision-making engines in the world have supported a company across three separate rounds. We exclusively follow rounds. There is a curious tension that we can scale our own product (our decision-making engine) by leveraging the excellence of less scalable third party decision-making engines.

2. We unbundle power law companies from the entire fund. We do not need to become an LP to access the break out companies. To reference an example in the Andreessen/Barksdale piece, you don’t have to buy the whole CD to get one track. We are offering a data-driven Spotify playlist.

3. We are intellectually honest with our partners. We solely bring capital as passive investors. Vanguard does not proclaim to offer value add to the S&P 500 companies. Neither do we. Nevertheless, in the same way that the S&P 500 has become a well established brand of quality, so we hope that in time being described as a “SignalRank Index” company will become a badge of honor.

The rebundlers

Rebundling often happens when new distribution technologies or paradigms enable a superior integrated offering.

A harder question to answer is what a rebundled tech-enabled VC would look like. Here are we are going to focus on product-led rebundling & network-led rebundling.

Product-led

Back in the day, A16Z redesigned the game with their platform model. They package modular value-add services delivered by their army of functional experts into comprehensive offerings. But it is challenging to argue that the core of this approach is powered by a new distribution technology. And the product (decision-making engine) remains largely unaffected by tech.

It is likely that AI agents will be at the center of any new form of rebundled VC. We know of various groups who are building AI agents that have been trained on all the available VC literature, so a founder can have a conversation with a Paul Graham / Vinod Khosla / Mary Meeker chatbot about how to approach whatever problem they are seeking to solve.

An alternative vector of growth is to build a venture capital firm like a product-led technology start-up. Kyle Harrison cites Paradigm as a good example. “What would a venture fund look like with a CEO, CTO, a Chief Product Officer? A Head of Sales?" (Figure 2)

Figure 2 Paradigm’s org structure

Network enabled rebundling

Mario Gabriele, who runs The Generalist, recently wrote a fun piece about “Mullet Capitalists.” These are groups which have built tech-enabled communities where one form of monetization is via venture capital. He references groups which offer “recruiting firm in the front, venture in the back.” Bain Capital was the pre-digital OG of this strategy as a spin out from the Bain consultancy. The new mullet capitalists are digitally enabled.

Acquired, Not Boring, 20VC are all media groups that command online communities of tech founders, operators and investors. Similarly, Superconnector is an outsourced SDR network which can supercharge portfolio support. These networks generate their own organic (and unbundled) sourcing. Capital is then stapled on.

You can imagine how this rebundled approach could be applied to VC value-add writ large. Could there be an rebundled value-add VC for each column in Kyle Harrison’s diagram below (Figure 1)?

Figure 1. Kyle Harrison’s framework for VC value-add

Concluding thoughts

It can be tempting to overstate the case either for stasis and for rapid innovation within the venture ecosystem. The truth is somewhere inbetween.

The reality is that the megafunds are scaling AUM at quite a clip without huge changes to the core business model. They are absorbing ever higher proportions of capital. And delivering returns at scale (see Figma).

Yet GC claims to have outgrown venture capital. They are branching into buyouts and private credit. And these funds are still small by comparison to PE.

For further growth to continue (and for challengers to make a dent in this ecosystem), we expect to see some of the models outlined above achieving escape velocity.

As Sequoia’s Doug Leone said, the only constant in VC is change.

Figure 1. Mark Suster implored investors to invest in lines

This sound advice is being largely ignored. Venture capital is a proper global asset class now. There is greater competition for individual opportunities which requires compressed investment processes. Compound’s Michael Dempsey wrote a lament that investors today are investing in dots, not lines.

A highly connected ecosystem (echo chamber?) encourages this memeification of venture. The market mapping. The creation of socially-approved “spaces”. I would also argue that the punctuation of the calendar around Y Combinator’s now quarterly Demo Day accentuates the idea of investing around themes and exacerbates investor FOMO.

There is a constant ebbing and flowing of “hot” themes which rarely translate into value. Daybreak’s Rex Woodbury recently published this comparison between the theme du jour and the most highly valued company created per year (Figure 2).

Figure 2. Rex Woodbury’s themes vs value

Woodbury is similarly critical of mimetic thinking, especially the ‘____ for X’ model (a la ‘Uber for X’). He cites 31 companies on the YC website that describe themselves as the “Cursor for X”; “every good VC wants to be in the company that comes before the x."

An investor’s job is to find singular founders who can build category-generating companies. The good news is that this is exactly what the best investors are doing, evaluating founders on a standalone basis (not a relative basis within socially approved themes). Founders Fund’s Trae Stephens wrote eloquently here about how VC hype happens “after the winning company in that space has already been created, discovered, funded, and become popular.”

Our data supports Stephens’ post by showing how defense became a "space" only after Anduril raised its Series C and once socially viable (via Google trends; Figure 3).

Figure 3. Aggregate $ raised by defense companies & searches for Anduril (both rescaled), compared to Anduril round timing

AI sees a similar phenomenon once OpenAI released ChatGPT (7 yrs after founding). VC dollars are highly correlated with social proof (via Google trends; Figure 4).

Figure 4. $ invested in AI & Google trend data on AI (both rebased), compared to key OpenAI milestones

This is also not to argue that the best investors operate in a vacuum. Clearly they do not. Indeed, an investor’s antenna is constantly seeking to identify the next legendary platform shift on which founders can build companies (Figure 5).

Figure 5. Platform shifts from Benedict Evans’ latest presentation

Yet, contrary to popular opinion, the best investors are decent at resisting the hype. They are building networks & relationships (creating lines, not dots) in unusual places. In the words of Equidam’s Dan Gray, they are “confident weirdos.”

When we look at the highest ranked Series Bs (via our methodology), we see a similar difference to the “hot” themes in Rex’s list (Figure 6). Maybe the best investors know what they're doing?

Figure 6. #1 ranked Series B per SignalRank’s methodology

Floodgate’s Mike Maples put it succinctly by arguing that “discipline and patience are a form of arbitrage. Play your game by seeking inefficient markets while everyone else is regressing to the mean.” Or in Yale’s David Swensen’s words, investors need to have “uncomfortably idiosyncratic portfolios” to generate returns for LPs.

In this post, we first consider how the public markets share a similar dispersion of returns as seen in the private market. We see how quality assets command premia to the rest of the market. We then consider how a similar dynamic occurs in the private market, and how scarcity of access can even drive valuations beyond Net Asset Value.

It goes without saying that listed entities without quality assets trade at a discount to NAV.

Power laws in public markets

The P/E premium of the Magnificent 7 (Meta, Amazon, Apple, Microsoft, Alphabet, Tesla & NVIDIA) over the remaining S&P 493 has been consistently above 40% since 2018, and at times as high as 100% (Figure 1).

Figure 1. Goldman Sachs analysis on Magnificent 7 vs S&P 493 (May 2025)

This is not surprising given that Morgan Stanley calculates in Figure 2 that the top 10 stocks by market cap delivered 69% of economic profit in 2023 (with economic profit defined as return on invested capital minus the weighted average cost of capital times invested capital).

Figure 2. Economic profit of Top 10 companies by market cap vs rest of US market

Fundamentals ultimately drive valuations; value concentration has led to market cap concentration. The top 10 stocks now account for more than 25% of the market cap weight of the US stock market (Figure 3).

Yet, as the chart below demonstrates, this concentration is not historically unique. Going even further back in history, the top 10 stocks in 1900 represented 38% of the market per this analysis.

Figure 3. Stock market concentration in the US, 1950-2023

This is all another way of saying that the dispersion of returns (aka the power law) is as skewed in the public markets as in the private markets.

By buying the S&P 500, you are wittingly or unwittingly buying into a set of power law distributed assets.

Figure 4 from Goldman Sachs shows how the returns are a reflection of powerful fundamental growth (in the form of EPS), and not irrational exuberance.

Figure 4. 12 month trailing earnings per share, Magnificent 7 versus S&P 493

Again, a longitudinal study by Bessimbinder found that just 86 stocks accounted for half of all wealth creation (above Treasury bills) in the U.S. stock market going back to 1926. All of the wealth creation in that time came from just 4% of stocks (1,092 companies). 58% of stocks failed to beat T-bill returns over their lives.

Bessimbinder’s study also helps explain the success of passive index strategies, as active strategies, which tend to be less diversified, mostly underperform. If indices outperform active managers in power law distributed public markets, surely the same logic should apply to private markets?

Valuing private assets in the public markets

This brings us to the central question of how investors might value private assets in a public markets wrapper.

The combination of quality and scarcity can lead funds to trade at a premium to NAV.

Quality

As with high quality public assets, we would anticipate that high quality private assets with compelling growth stories command premium valuations.

Investors are paying for the growth rates of private companies with the potential to deliver power law outcomes. SaaStr’s Jason Lemkin recently shared this chart on the declining growth of public SaaS companies. Compare this to the AI apps growing from zero to $100m “ARR” in <2 years.

Part of the premium also derives from the fact that high quality VC-backed assets tend to be category defining businesses, meaning they are valued (on a stand alone basis), not priced (on a relative basis). SpaceX, for example, is sui generis. 🚀

And these highly valued assets drive returns. StepStone just showed a positive correlation specifically in secondaries between premium valuations and returns.

Scarcity

The sense of scarcity of access to these assets drives valuation higher still.

If we look at the top 10 private companies by reported valuation, all would be within the top 300 companies in the S&P 500 by market cap (Figure 5, excludes Chinese companies). Altimeter calls this category “quasi-public” companies.

Based on today’s market caps, SpaceX & OpenAI would be within the top 30 companies (above Coca-Cola, GE, Cisco, Salesforce, Chevron, Disney, McDonalds, Morgan Stanley, American Express, AT&T, Goldman Sachs, etc).

Figure 5. Ranking of top 10 private companies in the S&P 500 by market cap

The distinction in investors’ minds between public and private is becoming more blurred. Coatue just published the list in Figure 6 of future “Coatue Fantastic 40” companies by market cap that combines both public and private companies.

Figure 6. The Coatue Fantastic 40 based on projected 2030 market cap

Yet there are very few places that a public investor can access these private companies, which is surprising given the size of these private assets. Hence the premium paid by investors to access these quality assets.

Examples of listed VC vehicles with high quality & scarce assets

We point to two listed vehicles with private market strategies which trade at a premium to NAV, namely Destiny XYZ (~7x NAV) & Flat Capital (~2x NAV). The commonality is exposure to high profile quality US assets (as well as both being founded by CEOs of notable FinTechs).

DXYZ, set up by the founder of Forge, is aiming to build a portfolio of 100 pre-IPO companies. Of the 23 companies in their portfolio today, the SpaceX SPV represents 52% of NAV. The market clamor for SpaceX is such that DXYZ has a market cap of ~$500m, or roughly a 600%+ premium to the $70m NAV.

Flat Capital, set up by the founder of Klarna, has a portfolio of private companies including Klarna (22% of NAV) and OpenAI (7%). Flat, which trades on Nasdaq First North Growth Market in Europe, has a market cap of ~$130m which is a 88% premium to the $68m NAV.

Both entities have the added benefit to investors of immediate 100% liquidity (subject to market conditions); their structures ensure that investors can simply sell their shares without requiring any unwinding of underlying illiquid positions. DXYZ in particular has healthy trading volumes. Greater liquidity itself should support valuation.

SignalRank, which is not currently listed, believes that our Series B investments (all in preferred stock direct on cap table) today are in the brand names of tomorrow; we expect some of our portfolio to be better known by the time any future listing of SignalRank were to take place. We also anticipate higher diversification than existing private market indices, with a target portfolio of 150 companies after five years.

Structure drives valuation

A caveat here is that premia based on scarcity of access are likely to be somewhat ephemeral, as more listed vehicles come to market (and as secondary markets mature). Our argument is more that focusing on quality assets should lead to a valuation which very closely reflects NAV (or a premium to NAV).

Indeed, fund structure & valuation are becoming increasingly pertinent questions in venture. Evergreen structures are gaining ground (see Figure 7). BlackRock’s Larry Fink is looking to democratize (and ultimately tokenize) alternatives. General Catalyst is considering going public.

Figure 7. Pitchbook’s forecast of wealth-focused evergreen fund AUM, 2024-29

In terms of valuation & structure, there is a core distinction between asset owners (which tend to be valued relative to NAV) and asset managers (which tend to be valued relative to Fee Related Earnings).

SignalRank is currently valued at NAV.

We would ultimately like to be valued on a DCF.

The unpredictability of cashflow from private investments has historically impacted the valuation of listed VCs. Our long-term intention at SignalRank is to be systematic on entry and systematic on exit, with secondaries targeted at fixed periods of time for each vintage. Predictability should support more traditional (DCF) valuation methodologies.

In sum, the challenges surrounding liquidity in venture means there is greater focus on structural innovation. Our argument here is simply that a focus on quality assets will drive premium valuations.

Some final thoughts

I’d like to report a chart crime committed by JP Morgan (Figure 8.)

Figure 8. Public and private manager dispersion (based on 2015-25 returns from this report)

This chart does support SignalRank’s argument for indexing venture capital. It demonstrates the dispersion in returns observed across private asset classes. A diversified index (as long as it can access the top quartile) should deliver more consistent returns.

Yet the comparison to global large cap equities is misleading because the majority of public market equities are already in passive & diversified structures, so appear to exhibit a tighter band of dispersion.

We saw already how the public markets have a similar dispersion to the private markets. The above chart is not an apples to apples comparison.

An alternative chart would keep the current public market data, but replace the private market data with new private market indices which should also deliver consistent returns across more diversified portfolios…

Coatue’s combined public & private market strategy gives them a compelling viewpoint to contextualize trends within the VC market. The core themes are the size of the AI opportunity and speed of AI adoption.

They also created an index of their own, the Coatue Fantastic 40, which ranks the expected market caps in 2030 of the fastest growing tech companies (Figure 1, excluding companies with expected multiple contraction, so Google/Apple are notable absences).

The inclusion of private companies & crypto here is a big nod to the blurring of lines between public / private markets (and potentially a subtler nod to the recent launch of their public/private interval fund).

Figure 1. The Coatue Fantastic 40 - ranked by projected 2030 market cap (slide 101)

This forward-looking list led to a vibrant internal discussion at SignalRank about our own view of what the private markets will look like in five years time. This post covers our top 10 predictions.

At the end of this post, we also thought it would be fun to ask our favorite LLMs their own view of what the VC ecosystem will look like in 2030.

SignalRank’s ten predictions for the 2030 VC ecosystem

1. The first trillion dollar private company

Based on market cap, SpaceX & OpenAI would be within the top 30 companies in the S&P 500 if listed today (see Figure 2, which excludes Chinese companies). These two are each larger by market cap than each of Coca-Cola, GE, Cisco, Salesforce, Chevron, Disney, McDonalds, American Express, AT&T, Goldman Sachs, etc.

Figure 2. Ranking of top 10 private companies in the S&P 500 by market cap

These companies should have gone public years ago. But the IPO has lost some of its allure for generational companies, in the face of deeper private capital markets, more liquid secondary markets and increased regulatory scrutiny for public companies.

It is not too much of a stretch to envisage one of these companies increasing in value by just 3x to become the first $1tn private market company.

2. The arrival of the uno-corn, the one person billion dollar company

The leveraging of AI tools is enabling companies to achieve scale with much smaller developer teams. In March 2025, Y Combinator reported that 25% of startups in its Winter 2025 batch had codebases that were 95% AI-generated.

This new development model has enabled revenue per employee to rocket (Figure 3) and the time to reach $100m ARR to contract (Figure 4).

Figure 3. Revenue per employee for top AI startups

Figure 4. Years from $1m to $100m ARR

These trends led to Sam Altman discussing in this interview when to expect a radical aspirational idea to become reality: the arrival of the first one person $1bn company. We wrote some more thoughts on this idea here.

3. A Tier 1 VC will go public

As multi-stage GPs scale asset accumulation, they increasingly resemble corporations and not artisanal partnerships. The logical conclusion is for these venture asset managers to go public, enabling superior access to capital, liquidity and smoother generational handover.

General Catalyst is said to be exploring its options (while a16z is rumored to have been flirting with the idea). This echoes a similar development in private equity in the last 20 years.

4. Passive indices will achieve 1% penetration of new US VC raised

Our belief is that the private markets will increasingly resemble the public markets, with the development of passive index funds alongside existing active managers.

Passive fund AUM overtook active AUM in the public markets for the first time in 2024 (Figure 5). The development of passive indices for private markets is in its infancy.

Figure 5. Active vs passive AUM ($tn)

The NVCA estimates that 538 US VC funds raised $77bn in 2024. SignalRank on its own aims to raise $150m per year. It should be reasonable to expect to see ~$1bn raised for passive strategies per year.

5. 50% of 2021 zombiecorns will still not have raised an up round (but will not have died either)

In Redpoint’s annual market update, they demonstrated how 72% of 2021 unicorns had not raised an up round in the last three years (Figure 6). It feels reasonable to assume that this number remains above 50% for the next five years.

There is a clear distinction in the VC datasets between companies created pre or post LLM. The pre LLM paradigm for company building no longer attracts VC capital. Yet many of these 2021 unicorns raised so much capital (and have cut costs for the new market reality) such that the reports on their expected death have been greatly exaggerated. But no IPO, no M&A, and no PE interest. Many are stuck.

Figure 6. Redpoint’s review of 2021 unicorns

6. There will be an AI correction

It’s called a hype cycle for a reason.

There is consensus (always dangerous) that AI is the next big technology platform. The big question is around timing. We wrote previously here about how early it is in the AI era.

Folks are starting to compare 2025 AI valuations to 2021 dynamics. It feels like we are some way off some of the 2021 behavior. No-one is spending $69m on an NFT just yet.

But there is still an abundance of optimism when it comes to AI’s potential. See Figure 7, a characteristically understated slide from a recent SoftBank presentation (unclear whether or not this is to scale).

Figure 7. SoftBank’s Evolution of Humanity

We are in the midst of the largest capital allocation to any single technology in human history. The vast majority of this capital will be incorrectly allocated. A correction must ensue at some point (even if the overall long-term trend is correct).

7. Retail overtakes endowments in terms of new allocations to VC

Endowments have historically represented 15-20% of VC capital. Their acute exposure to new political and economic realities (including the denominator effect, limited DPI & new taxation proposals) is leading to a rethink in investment strategy & allocation.

A counterbalancing development is the emergence of new alternatives products offered to retail investors by the world’s largest financial platforms. BlackRock’s Larry Fink’s annual letter envisions the democratization / indexation of alternative investments. He recommends moving from a 60/40 stocks/bonds portfolio to 50/30/20 stocks/bonds/private assets.

Bain expects private wealth & SWFs to account for approximately 60% of growth in alternative AUM over the next decade (Figure 8).

Figure 8. Global alternatives AUM

8. Geopolitical & economic instability will drive regulatory reform to accelerate VC allocation

In the 1970s, the perceived threat to the US from Japanese investors & semi-conductors led to the relaxation of the “prudent man rule” under the ERISA. This in turn allowed pension funds to allocate assets into VC & PE for the first time, enabling the US to reclaim influence in the innovation economy.

We anticipate that heightened geopolitical & economic instability today is likely to lead to similar developments in the US & beyond. The lack of economic growth in the UK already led the government to push UK pension funds to allocate up to 5% of assets into private markets by 2030 (~$60bn capital).

External threats (real or perceived) could act as an additional catalyst for governments seeking to drive capital into the most innovative parts of the economy.

9. An AI fund (with the AI as the decision-maker) will be on the Midas list

This is probably too punchy given that Midas list is so backward looking (with the 2025 list including investments made 10+ years ago). A more realistic projection is that a data-driven fund has completed an investment by 2030 which will propel the fund to a future Midas list.

We wrote here about the rise of data-driven investing in VC which mirrors a similar development in the public market over the last fifty years. An AI is now making angel investments (supposedly) all by itself. It is not a stretch to expect AI to expand into leading VC rounds itself (despite what Marc Andreessen says on this matter).

SignalRank is a quant AI strategy actively deploying today but we do not lead rounds.

10. Top 10 funds will consistently absorb 50%+ of all VC dollars

The barbelling of VC will continue. Pitchbook illuminated how nine funds took $35bn of US VC capital last year, half of the total raised. Andreessen Horowitz raised 11% of all US VC capital last year.

The Q4 2024 NVCA Pitchbook Venture Monitor makes for sober reading for emerging managers with only ~20% of VC capital now going to emerging managers. While ever more challenging for emerging managers to raise capital in general, we expect the solo GP trend (primarily spin outs) to continue to thrive.

We also anticipate this barbelling to occur at the company level. The number of companies funded each year will continue to decrease from 35-45k at its ZIRP peak to roughly half that number. Yet the average check size will increase at each stage. The winners will continue to absorb ever more capital.

What do the LLMs predict for VC in the next 5 years?

It is interesting to compare our human projections above with those generated by the LLMs. Here are the links to our prompts & responses on ChatGPT, Perplexity, Claude & Manus.

Most of their predictions feel bland and perfunctory (citing the proliferation of AI, growth of non US ecosystems, rise of ESG, etc). In short, AI slop.

Having said that, these are all predictions. We can return to this post in five years time to compare our predictions. By then, the stochastic parrots may indeed have become thinking machines.