SpaceX's Financials Leaked: Is it Worth $2TN | Meta Debuts Muse Spark: Are They Back in the AI Race?

SpaceX's Financials Leaked: Is it Worth $2TN | Meta Debuts Muse Spark: Are They Back in the AI Race?

The Twenty Minute VCApr 16, 20261h 30m

Jason Lemkin (guest), Harry Stebbings (host), Rory O’Driscoll (guest), Rory O’Driscoll (guest), Jason Lemkin (guest), Harry Stebbings (host)

Anthropic Mythos and AI-enabled hackingCybersecurity arms race and market reactionsNVIDIA vs hyperscaler custom silicon (Amazon Trainium)Anthropic moving into app-building/vibe coding toolsPublic SaaS “60% solution” and monetization failureMeta Muse Spark and closed-vs-open-source shiftOpenAI ads ramp and enterprise go-to-marketCIO “token maxing” and budget centralizationSpaceX leaked financials and $2T valuation logicThoma Bravo retreat from growth equityIPO timing: SpaceX vs Anthropic vs OpenAI

In this episode of The Twenty Minute VC, featuring Jason Lemkin and Harry Stebbings, SpaceX's Financials Leaked: Is it Worth $2TN | Meta Debuts Muse Spark: Are They Back in the AI Race? explores aI security scares, SaaS doom loops, and SpaceX valuation math collide Anthropic’s withheld ‘Mythos’ cybersecurity model is framed as a genuine step-change because agentic speed and autonomy dramatically increases vulnerability discovery and attacker capacity.

AI security scares, SaaS doom loops, and SpaceX valuation math collide

Anthropic’s withheld ‘Mythos’ cybersecurity model is framed as a genuine step-change because agentic speed and autonomy dramatically increases vulnerability discovery and attacker capacity.

The hosts argue many incumbent SaaS firms are shipping ‘60% solutions’ in AI agents—useful but not monetizable—driving a valuation regime shift from growth multiples to value-style cash-flow multiples.

Meta’s Muse Spark is viewed as a credible ‘back in the game’ moment after Llama 4 disappointment, with Meta’s strategic rationale being existential control over foundational AI rather than token sales.

OpenAI’s push into advertising is treated as inevitable and achievable at huge scale, but still insufficient alone to justify expectations without a major enterprise revenue engine.

SpaceX’s leaked numbers intensify debate about a potential $2T IPO, with the bull case relying on ‘Elon discount rate = 0’ assumptions about time-to-market and probability of success for future adjacent businesses.

Key Takeaways

Agentic cybersecurity tools change the threat model from ‘possible’ to ‘inevitable.’

They argue Mythos-like systems aren’t just marginally better at finding bugs; they can scan vast codebases autonomously and quickly, turning vulnerability discovery into a high-volume ‘machine gun’ dynamic that forces defenders to assume any missed flaw will be found.

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Cybersecurity vendors should benefit, not suffer, from stronger AI hacking capability.

Rory contends the market selloff in cyber stocks was backwards: if attackers get more powerful, enterprises will spend more on defensive tooling, frameworks, and operational security processes to match the arms race.

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Doom messaging can build culture—but it can also erode credibility and inspiration.

Jason says Dario Amodei’s repeated catastrophic warnings feel like ‘boy who cried wolf,’ while Rory argues the grandiosity is often sincerely held and functionally useful as a rallying cry (analogous to ‘going to Mars’) even if predictions are overstated.

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Incumbent SaaS is trapped if its AI agents aren’t good enough to charge for.

Jason’s core test is monetization: a ‘60%’ agent must be bundled/free, so it can’t drive re-acceleration; without re-acceleration, public software gets repriced into a lower multiple bucket regardless of installed-base ‘moats.’

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Moats retain customers, but they don’t create AI-era excitement or growth.

He argues long contracts and switching costs produce ‘prisoners,’ not new demand, and that the AI era rewards standout capability rather than checkbox features protected by legacy lock-in.

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Enterprise AI buying is shifting from rogue spend to centralized token budgets.

The ‘token maxing’ theme suggests CIOs will cap and allocate AI usage like any other scarce resource, which could advantage vendors with strong enterprise packaging, sales motions, procurement readiness, and reliable capacity.

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SpaceX’s $2T case rests on aggressive assumptions about future businesses.

Rory frames the valuation as plausible only if you assign near-100% probability and minimal time discounting to initiatives like direct-to-cellular and space-based data centers—summarized as ‘Elon discount rate = 0’ and ‘failure probability = 0.’

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Notable Quotes

Mythos just kicked off on its own, agentically… It’s the difference between a rifle and a machine gun.

Rory O’Driscoll

I am just so burnt out of the Boy Who Cries Wolf.

Jason Lemkin

If your agents are only 60% as good, you’re in a slow death spiral.

Jason Lemkin

The Elon discount rate is zero, and the Elon probability of failure rate is zero to get to $2 trillion.

Rory O’Driscoll

Consumers don’t wanna buy cognition… When I go to work, I wanna buy intelligence.

Rory O’Driscoll

Questions Answered in This Episode

What concrete technical behavior made Mythos feel like a ‘machine gun’ versus older models—automation, tool use, persistence, or scale of codebase coverage?

Anthropic’s withheld ‘Mythos’ cybersecurity model is framed as a genuine step-change because agentic speed and autonomy dramatically increases vulnerability discovery and attacker capacity.

Get the full analysis with uListen AI

If cyber stocks ‘should’ rise in an AI hacking arms race, which categories win most: code scanning, identity, runtime protection, incident response, or managed services?

The hosts argue many incumbent SaaS firms are shipping ‘60% solutions’ in AI agents—useful but not monetizable—driving a valuation regime shift from growth multiples to value-style cash-flow multiples.

Get the full analysis with uListen AI

Jason’s ‘60% solution must be free’ claim is strong—what product/packaging examples would qualify as a true ‘chargeable agent’ in ServiceNow, Salesforce, or HubSpot?

Meta’s Muse Spark is viewed as a credible ‘back in the game’ moment after Llama 4 disappointment, with Meta’s strategic rationale being existential control over foundational AI rather than token sales.

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How much of SaaS’ valuation reset is about AI feature parity versus procurement behavior (token budgets) and gross margin compression from inference costs?

OpenAI’s push into advertising is treated as inevitable and achievable at huge scale, but still insufficient alone to justify expectations without a major enterprise revenue engine.

Get the full analysis with uListen AI

Meta’s Muse Spark is ‘back in the game’—what would demonstrate real catch-up: benchmark leadership, internal ad targeting lift, or developer adoption?

SpaceX’s leaked numbers intensify debate about a potential $2T IPO, with the bull case relying on ‘Elon discount rate = 0’ assumptions about time-to-market and probability of success for future adjacent businesses.

Get the full analysis with uListen AI

Transcript Preview

Jason Lemkin

I don't buy Dario anymore. He may well be the second greatest founder of all time behind Elon, but I am just so burnt out of the Boy Who Cries Wolf

Harry Stebbings

Starting off on the agenda, Anthropic unveils Mythos, but withholds it from public release because it's too good at hacking. Number two, public software stocks tumble to new lows with Citi saying there really is no floor. Optimistic. And then finally, Meta debuts Muse Spark. It's Alex Wang's first model from Meta's Super Intelligence Labs. Does it save Meta in the race to catch up?

Jason Lemkin

So I'm pretty bullish actually on OpenAI and the enterprise.

Rory O’Driscoll

I think it's a two-way fight. Anthropic has the advantage of clarity and focus. OpenAI has the advantage of the consumer business.

Jason Lemkin

If your agents are only 60% as good, you're in a slow death spiral. It appears to be the most expensive IPO at scale of all time.

Rory O’Driscoll

The Elon discount rate is zero, and the Elon probability of failure rate is zero to get to $2 trillion.

Jason Lemkin

I can't open the Strait of Hormuz myself. I can't do this. Like enough already. Let me just use my tokens. Ready to go?

Harry Stebbings

[rock music] Guys, I am so excited for this show. Uh, as we always, uh, have, we're gonna start with Anthropic. What else could we start with but Anthropic unveiling Mythos with the preview withheld from public release because it is too good at hacking. Discovered thousands of zero day vulnerabilities. Admittedly, some were quite old. Um, how did we think about this? Did it deserve the reaction that it got?

Rory O’Driscoll

Which reaction are you talking about, Howie?

Harry Stebbings

I would say widespread fear that, that was then shown in a loss of market cap of a lot of public companies in the US.

Rory O’Driscoll

Let's leave to one side the was it a marketing stunt or did they have not have compute? Let's focus on what Mythos does in terms of cybersecurity and what your correct response to that would be. And, you know, if you read a lot of the stuff, it, it finds a whole ton of vulnerabilities, including some that have been literally there for years, right? So that's kind of the, oh my God, that's scary. And then you see... A-a-and that's why they withheld it and shared it with a bunch of security vendors, right? And then you see a bunch of kind of counterarguments that basically some version of this, which is using older models and using them well, you can actually get to the same outcome, right? You can find the same security vulnerabilities, right? And that's the counterargu- And there was a whole bunch of Twitters that said, "Eh, this is not a big deal." And, you know, I'm processing through from the outside and, and, and my conclusion is those people who said it's not a big deal are wrong, and Anthropic is right. And I was thinking about the metaphor here today, right? Because what they were saying is... And it's actually very interesting about the whole agentic revolution. It's kind of a microcosm that allows us to talk about a lot of things. It's like, basically, they are right, which is... Sorry, the, the, the naysayers are right, which is that you can take an older model, you can point it at some of these issues, you can kind of query, you can direct it a couple of times. And someone actually did the exercise of here's how I found the same bugs. I had to steer the model a little bit, and you got it, right? Um, but the comment is, Mythos just kicked off on its own, agentically, goes and looks at all the code and finds them on its own, right? And the metaphor I was trying to look at here is very simple. It's like, it's the difference between a rifle and a machine gun. In one sense, both of them can kill someone, right? But one shoots one bullet and then stop and reload, and the other just spews guns, bullets out. And in the First World War, you know, we all tragically learned that machine guns are... It might be the same thing, but quantity makes a huge difference. And I think that's what's really going on here. The speed at which this can process reason across large code bases means that they're just gonna find more bullets. They're gonna shoot more bullets. So it's not... So the kind of the, the Twitter cynical, it's not that different isn't true because it's the, it's the capabilities to do so much so quickly with such human direction that makes it definitely a quantum step difference in terms of real capability. My big aha was it's, it's, it's not overblown in the sense it can find stuff.

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