Speaker · Updated
Ev Randle: latest market views
Benchmark
Summary
Everett 'Ev' Randle is a General Partner at Benchmark who was previously a Partner at Kleiner Perkins and an investor at Founders Fund, Bond Capital and Vista Equity Partners, with investments including Anthropic, Rippling, Flock Safety and SpaceX.
Chorus has 23 views from Ev Randle across 2 episodes on Uncapped with Jack Altman since October 29, 2026. Their latest calls: bullish on Anthropic, Sequoia Capital and Fireworks AI.
Where they stand
Their latest bullish or bearish call on each company.
- AnthropicBullish ·
- Sequoia CapitalBullish ·
- Fireworks AIBullish ·
What they talk about most
- Open-source AI models · 4
- AI infrastructure & data centers · 3
- AI: overall outlook · 2
- Anthropic · 2
- Venture capital & startups · 2
- Enterprise AI adoption & AI software · 2
- Frontier AI labs & model race · 1
- Sequoia Capital · 1
- US elections & politics · 1
- US–China AI & chip race · 1
- Cybersecurity · 1
- Fireworks AI · 1
The latest views
Newest first. Every quote is checked against the episode's transcript.
AnthropicBullish
Anthropic's final IPO pricing will be raised above the initial range, to at least a $1.5T valuation.
The initial range of $70 to $78 per share implies roughly $1.3T to $1.4T. Companies usually start with a range they can raise once demand builds and the offering is oversubscribed. Everett expects the range to be increased and the final price to land at least $1.5T, possibly $1.6T, with the aim of popping after listing. He notes the high end of the range would raise about $60B of cash.
“my guess is that they end up increasing the range, and the final price ends up at least 1.5, if not 1.6.”at 1:28
Uncapped with Jack Altman: Benchmark Breakfast #2 | November 12, 2026 ·
AI labs' low switching costs make their revenue volatile, so they will trade at a discount to software.
Customers in the API business can switch providers easily. OpenAI cut prices and took share from Anthropic for a few months over the summer, and that slowed Anthropic's growth. Everett argues this makes lab growth and stock prices inherently volatile. Unlike SaaS, which had steady sequential growth from sales-capacity models, labs will not show that stability, so they will likely trade at a discount to pure software unless it changes.
“it's very clear that, especially in the API business, the switching costs are not high.”at 8:39
Uncapped with Jack Altman: Benchmark Breakfast #2 | November 12, 2026 ·
Losing Jensen Huang as Nvidia CEO would be a major headwind for open source and startups.
Jensen has been the key figure arming open-source challengers to the frontier labs and has funded many US open-source startups. Nvidia is structured around him, and it would be hard to imagine it without him, so it would slow down and execute less aggressively. Everett says the spy allegations have cost him the ability to be vocally pro-China, pro-open source, and pro-chip exports, even though Ben Thompson gives rational reasons for selling chips into China to build on CUDA.
“if Jensen is removed from the CEO role, that is a huge headwind. Again, not just to China... Not just to Chinese open source. Bad for open source writ large.”at 13:47
Uncapped with Jack Altman: Benchmark Breakfast #2 | November 12, 2026 ·
American open source has become immensely critical as Chinese open source becomes harder to rely on.
American open-source models have trailed Chinese ones for many reasons, including less aggressive distillation. If Chinese open source becomes harder to access and a more tenuous relationship, the strategic need for American open source rises sharply. The episode has lit a match under this, and the strategic imperative had already been growing before these events.
“If America cares about open source at all, if Chinese open source is going to be harder and harder and be more tenuous as a relationship and all these things, American open source has just become immensely critical.”at 16:13
Uncapped with Jack Altman: Benchmark Breakfast #2 | November 12, 2026 ·
AI infrastructure & data centers
AI ecosystem revenue cannot justify the planned data center capex, so the buildout and its estimates will shrink.
Estimated 2027 data center capacity outside China has fallen from about 70 GW to 40–50 GW, a cut of roughly 40% at the low end. That cut lowers CapEx numbers and questions the return on the spending. Revenue from the AI ecosystem is not large enough or growing fast enough to justify the capex planned for the next two years. Companies with weaker balance sheets, such as neoclouds financing data centers with debt, are most exposed if capital markets tighten and the ROI is questioned.
“when you get down to it, the revenues from the AI ecosystem just aren't big enough at this point, and they're not growing fast enough at this point to justify the amount of CapEx that's being contemplated for the next two years.”at 21:41
Uncapped with Jack Altman: Benchmark Breakfast #2 | November 12, 2026 ·
A major AI capability breakthrough is fairly likely within 12 to 24 months, adding to the next leg of the trade.
Everett says many people in the AI investing circle believe there is a solid chance of a major unlock in intelligence over 12 to 24 months, though he doesn't know its form. The bull case depends on that unlock, and it would be clear when it arrives. He admits that with the stocks down, this feels like hopium, but thinks the rapid pace of model improvement makes a breakthrough quite possible.
“there's a pretty solid chance that over the next twelve to twenty four months, we don't know what it is. But there will be some massive unlock in intelligence.”at 28:58
Uncapped with Jack Altman: Benchmark Breakfast #2 | November 12, 2026 ·
A Democratic House, Senate and presidency in 2028 could undo Silicon Valley's gains in Washington.
The current administration has had the most Silicon Valley representatives in the White House and government, but DC polarization is at a very high level, making the relationship knife-edge rather than collaborative. Everett worries that if Democrats control everything, Congress could haul tech figures in front of committees over every government deal, including those by defense tech startups, which would undo progress made so far.
“you almost wonder, well, if the House, Senate, and presidency are blue... what does that mean for all the progress Silicon Valley has made in DC?”at 33:53
Uncapped with Jack Altman: Benchmark Breakfast #2 | November 12, 2026 ·
AI infrastructure & data centers
Data centers can be symbiotic with host communities when done right, as Meta's Louisiana project shows.
Everett argues that the backlash against data centers in places like Texas is overstated and that host communities can benefit. He cites Meta's Louisiana Hyperion project, which he says roughly doubled the county's local tax receipts and raised teacher bonuses by about 400%, often to the level of their salaries. He says local concerns about air and water pollution can be addressed. He also says the shift to a role reversal in which cities now bid for data center developers, as they once bid for Amazon HQ2, is an okay thing because it lets communities extract value from the build-out. He concludes the relationship can be very symbiotic if it is done right.
“that project alone doubled the local tax receipts for that county and gave teachers bonuses that were equal to their salary in many cases... So when it's done right, I think these can actually be extremely symbiotic.”at 37:04
Uncapped with Jack Altman: Benchmark Breakfast #2 | November 12, 2026 ·
US public hostility to AI and data centers is a long-run structural disadvantage against China in AI.
Everett argues that leading in AI has become a major geopolitical imperative, and that the US is at a structural disadvantage against China over the long run. He points out that China already has more solar and power generation than the US and much better public perception of AI. He says China benefits from the US public's hostility to AI and data centers, and from American political distraction, including in the Middle East. He concludes that this is a worrying dynamic for US competitiveness.
“a massive, massive imperative from a geopolitical standpoint to have the best AI models. ... China already has more solar and power generation than we do. They already have much better public perception.”at 40:11
Uncapped with Jack Altman: Benchmark Breakfast #2 | November 12, 2026 ·
Outside frontier labs, putting in huge venture capital has not yet proven high money-on-money returns.
Everett argues that the frontier AI labs were a one-time subsidy that let the mega funds deploy huge amounts of capital and earn great returns. He says the labs were uniquely able to absorb tens of billions of dollars and still generate strong returns. Everywhere else, he says, the ability to put in a lot of capital and produce high money-on-money returns is unproven. He suggests the many firms now raising huge funds will have to grapple with this question, and that the labs' success may not be repeatable.
“the Frontier Labs were sort of a one time subsidy on this phenomenon of immense ability to suck in capital and produce great returns. And everywhere else that you can put in a lot of capital is completely unproven in terms of its ability to actually produce high money on money returns.”at 43:22
Uncapped with Jack Altman: Benchmark Breakfast #2 | November 12, 2026 ·
Private markets follow public markets with a three-to-six-month lag, in a traffic-wave pattern.
Everett describes a consistent pattern in which private markets trail public markets by about three to six months and move less sharply, since they are more fragmented and have no stock chart. When public investors are hit, crossover investors who also do pre-IPO deals become less willing to pay for pre-IPO rounds, so those valuations fall. That then pressures the earlier rounds, since founders and investors can no longer count on markups. The shock works its way back toward Series A, so investors fear a logjam and become cautious.
“there's a pretty consistent pattern in private markets, which is that they follow public markets with some delay, call it three to six months.”at 49:05
Uncapped with Jack Altman: Benchmark Breakfast #2 | November 12, 2026 ·
Sequoia CapitalBullish
Sequoia's restructuring gives it a consistent identity, with fund size, strategy and people aligned.
Everett argues that organizational choices matter only insofar as they align with the firm's identity and fund size, and that there is no absolute right or wrong structure. Sequoia had previously seemed unclear about whether it was a global behemoth, a scarce boutique, or a slightly fancier version of other firms. The recent changes, he says, show a clear identity: a tight-knit SWAT team investing globally, defined by scarcity, selection, and a very high bar. He concludes that for the first time in years Sequoia's fund size, strategy, and people are aligned, and that misalignment is usually when things go badly.
“And so I think for the first time in the last few years, in my mind, Sequoia has a very consistent identity strategy, fund size, everything. Like, everything's nice and aligned.”at 58:36
Uncapped with Jack Altman: Benchmark Breakfast #2 | November 12, 2026 ·
AnthropicNeutral
Anthropic's IPO may not pop on day one despite strong numbers, given growth deceleration and consumption revenue.
The S-1 is strong on its face, with immense growth and operating leverage. But Ev says it is not a slam dunk at the open because of two open debates. First, Anthropic grew 145% quarter over quarter in Q2 and about 45% in Q3, a sharp deceleration. Second, roughly 80% of revenue is consumption-based, unlike SaaS peers such as CrowdStrike or Datadog, which have steadier growth and multiyear contracts. He calls these concerns somewhat fair even though the business works.
“I don't necessarily think that means that it's going to be a slam dunk out of the gates in terms of how public investors in the public markets value it and if there's a ton of share price appreciation right out of the IPO.”at 5:57
Uncapped with Jack Altman: Benchmark Breakfast #1 | Jack and Ev ·
AI infrastructure & data centers
The AI supply-chain bottleneck trade keeps producing new derivative stocks that can multibag each year.
After ChatGPT, investors bought Nvidia, then the derivatives: Super Micro, Dell, Vertiv, Arista, Vistra, Oklo, Bloom Energy, GE Vernova, memory and storage names, and now Intel as agents use CPUs again. Each year brought new derivatives that produced multibagger returns. Ev's evidence is largely a list of past returns, and he expects the pattern to continue.
“Or like, there's like three new derivatives every year that you could have gotten, like, a multibagger return on in the public markets.”at 14:59
Uncapped with Jack Altman: Benchmark Breakfast #1 | Jack and Ev ·
Fireworks AIBullish
Fireworks, an inference cloud in Benchmark's portfolio, is growing very fast as AI coding derivatives scale.
Ev places Fireworks among the inference clouds that have grown with the code-gen wave. He says Benchmark is a large investor and that the company is growing like crazy. The argument is qualitative, with no revenue or customer figures.
“The inference clouds, obviously, we're large investors in Fireworks, is growing like crazy.”at 15:54
Uncapped with Jack Altman: Benchmark Breakfast #1 | Jack and Ev ·
Enterprise AI adoption & AI software
AI code generation is the most impactful product-market-fit moment in tech history and could keep doubling yearly.
The breakout moment came when Opus 4.5 in November 2025 supercharged Claude Code. That created a wave of derivative companies (Supabase, Clickhouse, Vercel, Modal, E2B, Exa, Chainguard, Temporal) growing from 10 to 100 to 300. As code volume rises, derivative products must grow with it. Ev sees agent-run software factories spreading to the rest of the economy, so durable code-gen demand could keep doubling each year even if the 10x years end. He calls it on the order of the iPhone moment.
“It's probably the most impactful product market fit moment in the history of technology.”at 17:36
Uncapped with Jack Altman: Benchmark Breakfast #1 | Jack and Ev ·
Enterprise AI adoption & AI software
AI companies buying tokens from labs is acceptable; they will become the large companies, as internet natives did.
Ev concedes that if only AI companies buy AI tokens, that is more leverage in the system, and that buyers in banks or health systems would be healthier. But his view is that AI-native companies are at the frontier and will become massive by out-competing firms that do not build software factories. He compares this to Stripe, which got big by serving DoorDash, Shopify and Instacart before old-economy firms adopted it. He says he has no problem with circularity.
“I don't really have any problem with AI companies buying AI tokens, like the circularity, because I'm like, look, these are companies.”at 27:06
Uncapped with Jack Altman: Benchmark Breakfast #1 | Jack and Ev ·
Open source stays about 5-10% of frontier-lab AI revenue, a roughly $100B pool if growth holds.
Ev uses the top three or four inference clouds (about $6B run-rate by year-end) plus OpenAI and Anthropic (about $170B run-rate if their disclosed growth extrapolates) as an imperfect proxy. That gives open-source inference roughly 3% of revenue today, which could about double with the hyperscalers included, so he calls it 5-10% of open versus closed revenue. He says it is not a projection, but if frontier labs keep their growth for three to four years and reach a combined $1T, open source at 10% would be about $100B. He expects consolidation into roughly three inference champions (Fireworks, Baseten, Modal) splitting that pool. He also argues fine-tuned small open models can reach Sonnet-level performance at very low cost on specific tasks.
“So we can say 5% to 10% in terms of revenue for open source versus closed.”at 38:11
Uncapped with Jack Altman: Benchmark Breakfast #1 | Jack and Ev ·
AI-enabled attacks now reach personal devices and critical systems; hardware can't be patched easily.
Ev says companies with strong engineering teams are finding company-ending vulnerabilities day after day when probed with AI. Cyberattacks used to mean a leaked password. Now AI makes them creepy and intrusive. He cites a researcher who used Claude to disable a webcam's recording light and access the microphone, and a French supermarket whose refrigeration controls were manipulated. Systems that were thought secure, such as decades-old open-source code, firmware and hardware, are now exposed. Software can be patched, but hardware largely can't, so smart devices may need to be disconnected. He says hardening will take a long time, possibly the biggest code modernization project in history, which is why the threat feels real now.
“you can patch the software, but can't really patch the hardware.”at 43:32
Uncapped with Jack Altman: Benchmark Breakfast #1 | Jack and Ev ·
Uncensored open-weight 'obliterated' models will become key enterprise tools for defending against AI hacking.
Ev describes obliterated models as open-weight models with safety guards removed. He says adversaries, including groups in China, are already using them for hacks, and that Hugging Face reportedly needed them to run an incident response that frontier labs' safety guards would have blocked. Because the adversaries' tools have no safety controls, he argues enterprises need the same kind of model to red-team their own infrastructure, which makes it an incredible defense tool. He expects to hear much more about this.
“It's also an incredible defense tool for enterprises.”at 45:15
Uncapped with Jack Altman: Benchmark Breakfast #1 | Jack and Ev ·
AI improves fastest where cheap verifiable feedback exists, so physical-world gains lag digital ones.
Ev argues AI improves fastest when it can generate large amounts of trustworthy feedback cheaply. Code is the ideal case because unit tests verify it and experiments scale in the digital world. Cursor and Claude Code improved quickly from user feedback, and every such interaction feeds training data. Biology and robotics have real bottlenecks: physical experiments are slow, and a single experiment may answer only a small part of the question. He sees an inverse relationship: the things that most improve the physical world are the ones AI improves most slowly.
“AI just tends to improve fastest when it can generate large amounts of trustworthy feedback cheaply.”at 50:18
Uncapped with Jack Altman: Benchmark Breakfast #1 | Jack and Ev ·
Robotics & autonomous vehicles
Robotics faces slow, costly data feedback loops, so physical AI needs Manhattan-Project-scale effort.
Ev says robots are expensive to deploy and maintain, and there is no large customer base that can press a button to generate data. Unlike ChatGPT, which reached 100 million users in months through a website, robotics feedback is slow. Labs got the internet as a one-time data subsidy for LLMs, while physical domains lack that and need a wide diversity of data. He cites Brendan Foody at Mercor for this framing. He concludes physical AI won't happen overnight and will need investment on the scale of the Manhattan Project.
“robots are expensive. They're expensive to deploy, expensive to maintain. There isn't a big customer base that can press a button.”at 51:10
Uncapped with Jack Altman: Benchmark Breakfast #1 | Jack and Ev ·
AI-driven bio progress will likely have a much larger impact than other AI gains.
Ev uses his father's cancer as an example. Recent immunotherapy drugs gave him a ten-plus-year prognosis and put him in remission, where a diagnosis twelve years earlier would have been fatal. He argues we are only at the start of perhaps one hundred to one thousand times that much progress across every disease area. Once AI helps solve biology, with the investment that will require, the impact should be much larger than from the other AI applications so far. The argument is partly personal but grounded in the survival-rate shift and the scale of the opportunity.
“And we are just at the start of probably one hundred to one thousand times that amount of things happening, it's like every single disease area, every single cancer.”at 54:24
Uncapped with Jack Altman: Benchmark Breakfast #1 | Jack and Ev ·
Shows
- Uncapped with Jack Altman · 2 episodes
Ask your AI what Ev Randle thinks
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