Speaker 1: AI Daily Briefing. I'm ed sharp, thanks for joining me today.
Speaker 1: The forty x valuation question. Can Cognition justify its price?
Speaker 1: Cognition Ai is in early talks to raise at a
Speaker 1: forty billion dollar valuation. That's a fifty percent jump from
Speaker 1: the twenty six billion dollar raise it closed just ninety
Speaker 1: days ago, and the revenue backing it is approaching one
Speaker 1: billion dollars annualized. The question isn't whether those numbers are real,
Speaker 1: it's whether they can last. Cognition builds devon, the autonomous
Speaker 1: coding agent that attracted genuine commercial interest when most AI
Speaker 1: tools were still fighting for developer attention. The company reportedly
Speaker 1: doubled its sales in a single quarter. That's the kind
Speaker 1: of growth that makes investors move fast and ask questions later.
Speaker 1: Here's the key implication. At forty billion dollars against roughly
Speaker 1: one billion in annualized revenue, you're looking at a forty
Speaker 1: times revenue multiple. That's not a technology valuation. That's a
Speaker 1: bet that quarterly doubling continues indefinitely. In practice, every company
Speaker 1: that's ever growing this fast has eventually slowed. The question
Speaker 1: for Cognition is when, not if, the talks are early
Speaker 1: terms could shift, the deal could fall apart, but even
Speaker 1: the fact that these conversations are happening tells us something
Speaker 1: about where AI coding sits right now. The broader sector
Speaker 1: is moving just as fast. Before AI, the venture rounds
Speaker 1: in enterprise software typically came eighteen to twenty four months apart.
Speaker 1: Founders needed time to show the growth investor FOMO an
Speaker 1: annualized run rate math are doing the work that sustained
Speaker 1: performance used to do that. Compression is not purely irrational.
Speaker 1: Measurable outputs, working code, shipped features closed bugs give enterprise
Speaker 1: buyers something they can price quickly. That's different from AI
Speaker 1: jewels in categories where the value is fuzzier. The unit
Speaker 1: economics in coding automation are closer to the surface. The
Speaker 1: signal here is that when you can show a CFO
Speaker 1: exactly what the soft is doing, the sale cycle shortens,
Speaker 1: and when the sale cycle shortens, the growth numbers accelerate,
Speaker 1: and when those numbers accelerate, the fundraising logic follows. It's
Speaker 1: a tighter feedback loop than most other AI verticals have managed.
Speaker 1: Here's the thing. On the acquisition side. SpaceX is expected
Speaker 1: to close its deal to acquire Cursor this week. Cursor
Speaker 1: is a code editor that reached two billion dollars an
Speaker 1: annualized revenue, which is a number that would have seemed
Speaker 1: implausible for a developer tool a few years ago. The
Speaker 1: important distinction is what this acquisition signals. SpaceX is not
Speaker 1: a software company by identity, but elil Musk has consistently
Speaker 1: moved into adjacent infrastructure when it suits a larger strategy.
Speaker 1: A two billion dollar revenue coding tool isn't just a product,
Speaker 1: it's a distribution channel into every development team using it.
Speaker 1: That's a different kind of asset. Anthropic is now being
Speaker 1: discussed at a two trillion dollar IPO evaluation, with a
Speaker 1: potential debut as early as October of next year that
Speaker 1: would exceed space x his own record EDPO at one
Speaker 1: point seven seven trillion, and Thropic hasn't officially set a date,
Speaker 1: and the two trillion figure is investor expectation rather than
Speaker 1: confirmed pricing. That distinction matters. Investor expectations at this stage
Speaker 1: in a private company's life tend to move with sentiment
Speaker 1: as much as fundamentals. Anthropics revenue trajectory is strong, but
Speaker 1: two trillion requires that trajectory to continue without meaningful deceleration
Speaker 1: through a public listing process that takes months. Consider this,
Speaker 1: Deep Seek made a different kind of move this week.
Speaker 1: The Chinese Ai Lab raised V four dash pro output
Speaker 1: token prices from eighty seven cents per million to three
Speaker 1: dollars in ninety six cents at peak, a jump of
Speaker 1: roughly three hundred and fifty five percent. They also introduced
Speaker 1: peak and off peak billing tiers and expanded thinking effort
Speaker 1: levels to three tiers. Earlier this year, Deep Seek's aggressive
Speaker 1: pricing was read as the start of a sustained AI
Speaker 1: price war. This reversal complicates that narrative competitors including Noonshot,
Speaker 1: Ali Baba and byte Dance have closed the capability gap.
Speaker 1: Since Deep seeks are one moment went viral with less differentiation,
Speaker 1: the pressure to hold margin increases. Price hikes at this
Speaker 1: scale suggest Deep seek is betting its users won't leave easily.
Speaker 1: The regulatory picture is worth tracking. A columnist, Money Baschizard,
Speaker 1: has made a direct critique of how AI policy is
Speaker 1: being built right now. The argument is that current regulation
Speaker 1: rests on worst case predictions rather than observed effects. The
Speaker 1: mass economic displacement that policy frameworks are designed to prevent
Speaker 1: hasn't materialized in the data. Labour statistics and productivity numbers
Speaker 1: have not moved in the ways that AI pessimists projected.
Speaker 1: The tension here isn't between being for or against regulation.
Speaker 1: It's about what counts is sufficient evidence to act. Predictive
Speaker 1: caution has a cost if the predictions are wrong. Empirical
Speaker 1: patience has a cost if the predictions turn out to
Speaker 1: be early. That's a genuinely unresolved question, and its shaping
Speaker 1: how governments approach the next wave of AI policy decisions.
Speaker 1: Pulling back the thread connecting most of this is timing
Speaker 1: and proof. Cognitions forty times revenue multiple is sustainable only
Speaker 1: of doubling continues. Anthropics two trillion target depends on sustained
Speaker 1: acceleration through a month's long IPO process. Deep seeks price
Speaker 1: hike tests whether its users have real switching costs or
Speaker 1: were just there for the price, and the regulation debate
Speaker 1: is fundamentally asking how long you wait for evidence before
Speaker 1: you act. Each of those is an unresolved proof point.
Speaker 1: Watch for Cognitions round to close or store watch Anthropics
Speaker 1: official IPO filing when it comes Watch, Deep seeks user
Speaker 1: attention numbers, and Watch whether any major jurisdiction moves toward
Speaker 1: evidence based AI policy frameworks in the months ahead. Those
Speaker 1: are the signals that matter. Thanks for listening. This podcast
Speaker 1: was built using AI technology, a yes we production