Sep 10, 2026 · 48m · a16z
Why Investors Are Rethinking Everything for the AI Era
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On The A16Z Show, partner David George and institutional LP Aram Verdian explore how artificial intelligence is intensifying venture power law dynamics, forcing institutional allocators to rethink portfolio construction, fund sizing, and legacy software investments.
How this conversation actually went
Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. How this is scored →
The host as informed peer 6.2 Guest teaching 3.7 Guest disagreement 1.6 The host pushing back 1.3
speaking balance: gold is the host, purple is the guest (3 minute bins)
Sharpest disagreement ▶ 21:50 Calling out vanity ARR and cohort roundtripping
Aram Verdian aggressively critiques early AI venture valuations, dismissing accelerator startups claiming five million in ARR within thirty days as annualized vanity math without underlying retention.
Hardest push from the host ▶ 17:37 Pushback on large firms waiting out seed roundsJen Kha explicitly rejects Aram's hypothesis that multistage venture firms wait out pre-seed rounds for certainty, explaining that a16z aggressively uses Speedrun to secure early orbit and preserve follow-on control.
Biggest teaching moment ▶ 11:28 The 20 out of 3000 VC firms statisticAram Verdian surprises the host by correcting her assumption that 20 percent of venture funds generate 3x net returns, showing that only 20 absolute firms out of 3,000 achieved that track record over twenty years.
The host holds their own ▶ 26:25 GPs fired for omission vs LPs for commissionJen Kha demonstrates institutional mastery by diagnosing allocator behavior, explaining that GP careers are broken by errors of omission while LPs only face career termination for errors of commission.
the scores for every segment, with the reasoning behind each
| Chapter | Topic | The host as informed peer | Guest teaching | Guest disagreement | The host pushing back | Why |
|---|---|---|---|---|---|---|
| Compounding Scale: Why Capital and Compute Redefine Venture | 5 | 2 | 1 | 0 | Jen Kha frames the discussion by referencing historical pushback against a16z's initial billion-dollar fund a decade ago. David George outlines how capital deployed directly into compute uniquely compounds competitive advantages unlike human-scaling constraints. | |
| AI Attacking the GDP: A Market Beyond Traditional Software TAM | 4 | 4 | 1 | 0 | Aram Verdian and David George detail AI's expansion into the 30 trillion dollar GDP, noting AI hit 100 billion dollars in revenue in four years versus SaaS's fifteen. The host contributes by projecting frontier labs reaching hundred-billion dollar valuations. | |
| Winner-Take-All Categories, Market Proliferation, and Venture Math | 6 | 3 | 1 | 2 | Jen Kha probes whether AI markets will follow historical winner-take-all patterns by sharing how legal counsel billable hours grew through Harvey. David George clarifies that while new categories will proliferate, category-level power laws remain strict. | |
| Consistency in Venture: The Top 20 Firms and Proper Portfolio Sizing | 6 | 6 | 2 | 2 | Aram reveals that only 20 out of 3,000 venture firms have achieved consistent 3x net returns, prompting the host to clarify whether he meant 20 percent or 20 firms. Jen contextualizes this dynamic by citing Endowment Eddie on founder preference driving large fund sizes. | |
| The Death of the Middle: Boutique Specialists vs. Full-Stack Platforms | 7 | 3 | 3 | 4 | When Aram suggests large firms intentionally avoid pre-seed rounds to wait for derisked Series A/B stages, Jen Kha pushes back by citing a16z's Speedrun accelerator. The host argues that full lifecycle coverage and early brand alignment are necessary to secure downstream access. | |
| Evaluating Real Traction: High Multiples, Vanity ARR, and Deep Usage | 5 | 5 | 2 | 1 | Aram questions inflated valuations and vanity ARR multiples in early-stage rounds like Cursor. David George explains that deep customer usage analysis matters far more than early revenue metrics, detailing Harvey's post-reasoning inflection. | |
| The LP Dilemma: Incentive Misalignment and Errors of Omission | 8 | 2 | 1 | 1 | In response to Aram's prompt about LP pushback, Jen Kha unpacks the structural divergence between allocator and GP career incentives. The host articulates that GPs get fired for errors of omission on category winners, whereas LPs face termination only for errors of commission. | |
| Allocator Strategy: Concentration, Resiliency, and Private Equity Headwinds | 4 | 5 | 1 | 0 | Aram outlines LP portfolio construction, cautioning that spreading commitments across 60 funds dilutes performance to mediocre industry averages. He points out that only 15 to 20 public SaaS companies currently command multiples over 10x revenue. | |
| Legacy SaaS Traps: Terminal Value and Private Credit Vulnerabilities | 7 | 4 | 2 | 2 | Jen highlights how scale venture M&A now rivals top private equity buyouts, referencing EA and Medline. Aram emphasizes that legacy PE software bought at 20x EBITDA faces severe terminal value risk since public markets now equate 1% growth to 3% EBITDA. | |
| Enterprise AI Diffusion and the Limits of Superficial AI Adoption | 8 | 3 | 2 | 1 | Jen notes CalPERS shifting asset allocation dramatically into venture and growth, and compares bolt-on AI in PE firms to putting Sears on a website. David George supports this thesis with internal data showing top quartile AI adopters spend 7,000 dollars monthly per employee compared to a 12-dollar median. | |
| Navigating the Liquidity Crunch: Long Holding Periods vs. Compounding Outliers | 8 | 3 | 2 | 3 | When Aram cites extended liquidity timelines as a primary LP objection, Jen Kha counters with an empirical case study from a16z's Fund 1. The host explains that when offering liquidity on their 16-year-old Stripe position, every LP chose to let it compound to avoid taxable distributions. | |
| The Next $100 Trillion Company and Infrastructure Bottlenecks | 6 | 4 | 1 | 0 | Jen asks the guests to project the next 100 trillion dollar market cap firm, leading David George to identify untapped frontiers in robotics, autonomy, and healthcare. Aram argues the decisive constraint on growth is speed to power and grid transmission rather than chip compute. |
Statements from this episode (40)
George: Tech Investing Power Law Is Most Extreme in Decades
“Right now, clearly the power law is more extreme than it has been in the last, you know, 10 to 20 years of technology investing, probably going back to, you know, the emergence of the network effect driven consumer companies.”
George: Capital Directly Compounds Advantage for AI Labs
“But right now, especially with the labs for the first time, you know, in my career, you can take capital and throw it at a company and it compounds their advantage.”
Verdian: AI reached $100B revenue in 4 years vs 15 for SaaS
“We've reached a hundred billion in revenue in AI. It took SaaS 15 years to get to the same point. AI did that in four years.”
Verdian: No prior tech wave addressed $30T in GDP simultaneously
“There hasn't been a technology paradigm that hits on 30 trillion in GDP at the same time.”
George predicts companies staying private longer will not reverse
“Our asset class is five to six trillion dollars of value, and the dynamics around companies staying private longer, They're not going to reverse.”
George: Top decile venture outcomes have increased from $10B to $40B
“The top decile outcomes, I think, used to be ten billion and now they're like forty billion.”
Kha predicts top venture outcomes will reach $100B with OpenAI and Anthropic
“Soon to be probably a hundred billion by the time Anthropic and then OpenAI came, come out.”
George predicts AI wave will create over $25T in new market cap
“The last cycle created 25 trillion in market cap, new market cap, and a bunch of that went to the incumbents. But a lot of it went to startups and the new startups, and, you know, each one of these subsequent waves gets bigger than the prior one, and so, you k…”
Verdian: AI TAM can be 10x larger than traditional SaaS
“So the TAM of AI can be 10 X plus bigger than traditional SaaS or healthcare IT.”
George: US economy spends roughly 40x more on labor than software
“If you just look at like how much of, you know, dollars are spent in the U S economy on labor versus software, it's like something like 40 times more.”
George: a16z's best-performing early-stage venture funds have a 60% loss rate
“And so, you know, if you look at our best performing venture funds, Over time, I think the loss rate is 60% or so.”
George: Growth-stage venture loss rates should be 10% to 20%
“Now at the growth stage, like the loss rate will be lower, but it's probably going to be in the 10 to 20% range, and that's appropriate because with that, you will get investments that we make that, you know, 10 X or more in returns.”
Verdian: Only 20 of 3,000 US VC firms consistently returned 3x net
“Like, we've looked at the data of 3000 venture capital firms in the U.S. Only 20 have achieved consistent three X net returns over the last two decades.”
Verdian: Late-stage funds require 5% to 10% position sizing in top company
“Venture-like returns are possible in late stage, but your best company should be five, 10% plus of your fund. That way you can actually return the fund on a single company. Fund returning math in late stage didn't exist before. It now does.”
Verdian: Average 10-year VC return trails public markets at 1-2x net
“If you look at Cambridge data, the average venture return over the last 10 years is one to two x net. You will do better in private equity. You'll definitely do better in the public markets. You don't need to lock up your money for 10 years.”
Kha says mid-stage venture requires early and late-stage backing to succeed
“Like, it's just virtually impossible to have this sort of mid-stage business effectively without having the early stage and then also the late stage to come behind it as well.”
Verdian: Nearly 2,000 sub-$150M seed funds currently operate in the US
“Pre-seed seed, so think sub-one-fifty funds. There's thousand, thousand, close to 2000 today in the U.S. Alone.”
Verdian: Cursor recently raised at a $400M valuation on $3M ARR
“Tell me if I'm wrong, three million ARR, four hundred million dollar round, or somewhere maybe around there.”
George: Harvey's early software usage was mediocre despite strong commercial buzz
“They did a really good job commercially early days. Like, because they were smart. They were res, there was like a research plus lawyer combo. You know, they got some momentum and some, you know, high profile law firms to sign up early days. But, you know, the…”
George says post-reasoning AI models drove an adoption takeoff for Harvey
“Now, fast forward, post reasoning models, like that totally flipped. And you could see, you know, absolute takeoff of adoption, right? And so a bunch of different things happen at the same time. Lawyers got way more value out of the product. You could see it i…”
Kha: GPs are fired for missing winners while LPs fear bad investments
“A GP can get fired for missing out, you know, the next, you know, Facebook, the next Uber, right? Like, that is error of omission, and like, that's fireable. But LPs on the flip side only get fired if you invest into a mentor. So in some respects, like, the in…”
Verdian: LPs keep their jobs missing frontier models if tracking benchmarks
“If you miss the frontier models, back to your first question as an LP, but you kind of were along the benchmark, maybe slightly below the benchmark, you're keeping your job.”
Verdian says allocating across 50 to 70 VC funds guarantees average returns
“Because if you have an asset class where 20 firms out of 3000 do well, you should concentrate in those 15, 20 firms pretty consistently. So, when I see a portfolio with 50, 60, 70, Venture capital firms. It's very hard for me to imagine that the overall portfo…”
Verdian: Only 15 to 20 public SaaS companies trade above 10x revenue
“In the SaaS public markets, there are only like 15 to 20 companies max trading above 10 times revenues, which is quite an insane number because it used to be dozens and dozens a few years ago.”
Verdian: Most public SaaS companies over 10x revenue show AI-driven acceleration
“Every one of those companies, for the most part, is showing acceleration of growth from AI.”
Verdian: Private equity is refusing to buy slow-growing seat-based software companies
“Even private equity today, when they're doing a new investment, they're looking for something that's AI native. They're not looking to buy a workflow software company growing 10% that's seed based. Like, that's just not happening.”
Kha: Venture capital exits now far exceed private equity exits
“Exits in venture now way exceed private equity.”
Verdian: One percent of public revenue growth equals three percent of EBITDA
“Our data shows one percentage of growth in the public markets is equivalent to three percentages of EBITDA.”
George: AI diffusion beyond coding will be slow because tasks lack verifiability
“Coding hit, but that's kind of a head fake, right? Like coding, coding is perfectly documented, right? So it has like perfect data. It's verifiable and it's simulatable, right? And so like most tasks in business do not share those three attributes. And so, you…”
George: Median US firm spends $12/employee/month on AI, top 1% spends $7,000
“The median company in the US is spending 12 dollars per employee on AI. Per month. The top one percent of the data set that we've seen is spending 7000 dollars per employee on AI per month.”
Verdian: 2021-2022 software LBOs valued at 25-32x EBITDA are now worth half
“So you look at 21, 22, about two to three hundred billion in LBO software transactions happened with over two hundred billion in that taken out. The average valuation for the software deals were 25 to 32 times EBITDA. Those companies today are worth probably h…”
Verdian: Private equity cannot fix software companies by deploying AI operating partners
“You can't just throw an operating partner at the company and say, let's put AI on it. It just doesn't work. You need to completely, and if you have, by the way, if you do have a founder mentality at the management team, like, it is possible, but the board has …”
Verdian: The average unicorn company now remains private for over ten years
“So, it takes the average unicorn is private for 10 plus years, typically, and then you got all these follow-on rounds that are happening pretty quickly, one after the other, you see maybe the same logo in five, six different firms, and the question is, how do …”
Verdian predicts Anthropic is about to go public five years after funding
“Anthropic was really first funded in twenty-twenty-one. It's about to go public five years later.”
Kha: Every a16z Fund I LP kept holding Stripe after 16 years
“Actually, very famously a year and a half ago or so, we went to our fund one LPs. At that point in time, the fund one was 16 years old, and we had this position in Stripe that we invested at the seed stage. And we asked all of our LPs, like, hey, do you want l…”
George predicts consumer AI's final form will be proactive agents, not chatbots
“The end use case for consumers is not going to be a chatbot interface. Like that's the skeuomorphic version. We're going to have a native version. It's going to be proactive. It's going to do work on our behalf.”
George predicts robotics will surpass language AI categories within ten years
“We are nowhere on robotics, but I think robotics is going to be bigger than the language stuff. And I think it's going to happen in the next 10 years.”
Verdian says AI's primary bottleneck is supply-side infrastructure, not end-user demand
“The bottleneck in AI today is not demand. It's on the supply side. So you got energy, the grid data center, then you got chips, then you got frontier models and apps.”