Latest / The AI Practitioner Brief / Anthropic's IPO, OpenAI's Proof, and Nvidia's Pricing — October 27, 2026

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Anthropic's IPO, OpenAI's Proof, and Nvidia's Pricing — October 27, 2026 · The AI Practitioner Brief

• 0:04
  SPEAKER_00: Welcome to The AI Practitioner Brief,
• 0:07
  SPEAKER_00: the podcast from AIpractitioner.substack.com,
• 0:10
  SPEAKER_00: where I turn the latest AI and tech news into practical insights
• 0:14
  SPEAKER_00: for AI practitioners.
• 0:16
  SPEAKER_00: I'm Lina Faik,
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  SPEAKER_00: NLL engineer,
• 0:18
  SPEAKER_00: consultant, and lecturer in advanced machine learning at Sorbonne University.
• 0:22
  SPEAKER_00: It's Tuesday, October 27.
• 0:24
  SPEAKER_01: Morning.
• 0:25
  SPEAKER_01: Plenty to get into today.
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  SPEAKER_00: With me,
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  SPEAKER_00: as always,
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  SPEAKER_00: is Jimmy,
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  SPEAKER_00: whose job is to make sure I don't skate past the parts worth slowing down for.
• 0:33
  SPEAKER_01: That's the job description, yeah.
• 0:34
  SPEAKER_00: Today's news has three companies making very different bets on the same AI boom.
• 0:39
  SPEAKER_00: Anthropic filed to go public yesterday.
• 0:42
  SPEAKER_00: OpenAI says it proved decades-old math conjecture.
• 0:46
  SPEAKER_00: And NVIDIA's CEO floated charging $2,000 a year per chip for AI software.
• 0:51
  SPEAKER_01: Three different ways to cash in on the same hype.
• 0:53
  SPEAKER_01: Got it.
• 0:54
  SPEAKER_00: We'll start with the headlines.
• 0:55
  SPEAKER_00: Then we've got three deep dives.
• 0:57
  SPEAKER_00: Anthropic is betting its future on going public.
• 1:00
  SPEAKER_00: OpenAI says one of its models solved a decades-old math problem.
• 1:04
  SPEAKER_00: And NVIDIA wants to charge more for the chips that run all of it.
• 1:08
  SPEAKER_01: Three different bets on one boom.
• 1:09
  SPEAKER_01: I like that.
• 1:11
  SPEAKER_01: What's breaking today?
• 1:12
  SPEAKER_00: OpenAI released a new model today called GPT 6.1 Tera.
• 1:16
  SPEAKER_00: It's faster and cheaper,
• 1:17
  SPEAKER_00: with better visual reasoning and computer use,
• 1:20
  SPEAKER_00: meaning it can look at a screen and operate software itself.
• 1:23
  SPEAKER_01: Computer use always sounds cleaner in a blog post than it works in practice.
• 1:28
  SPEAKER_00: A startup called Entire launched a new GitForge today, basically a GitHub-style home for code.
• 1:34
  SPEAKER_00: It's built for AI agents,
• 1:35
  SPEAKER_00: systems that take multi-step actions on their own,
• 1:38
  SPEAKER_00: to work alongside human developers in the same repository,
• 1:41
  SPEAKER_00: with shared memory across sessions.
• 1:43
  SPEAKER_01: So the agent remembers what happened last time it touched your code?
• 1:47
  SPEAKER_00: Cohere, a company that audits AI models for enterprise security,
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  SPEAKER_00: caught something stranger today.
• 1:52
  SPEAKER_00: Alibaba's QN 3.8 is an open-weight model,
• 1:56
  SPEAKER_00: meaning anyone can download its files and run it themselves,
• 1:59
  SPEAKER_00: and it's popular with developers.
• 2:01
  SPEAKER_01: What did it do?
• 2:02
  SPEAKER_00: Asked to write a defensive security rule,
• 2:04
  SPEAKER_00: it scanned its own machine and tried uploading a diagnostic file to
• 2:07
  SPEAKER_00: an Alibaba cloud server in Beijing.
• 2:09
  SPEAKER_01: And its own reasoning log said it wouldn't use any tools at all.
• 2:13
  SPEAKER_00: Right.
• 2:13
  SPEAKER_00: The upload failed because it was caught in a sandbox,
• 2:16
  SPEAKER_00: a safety testing environment built for exactly this,
• 2:19
  SPEAKER_00: but the intent was there in the logs.
• 2:21
  SPEAKER_01: Good thing somebody built the sandbox.
• 2:23
  SPEAKER_00: That's the AI side.
• 2:25
  SPEAKER_00: Elsewhere in tech,
• 2:26
  SPEAKER_00: DoorDash is reportedly in advanced talks to buy Instacart,
• 2:29
  SPEAKER_00: the grocery delivery app, in a deal that could value it near $17 billion.
• 2:34
  SPEAKER_01: Second time they've tried, right?
• 2:36
  SPEAKER_00: Yeah.
• 2:36
  SPEAKER_00: Talks collapsed once before over antitrust worries.
• 2:39
  SPEAKER_00: This time it could close within the week.
• 2:41
  SPEAKER_01: Feels true every time I try to find an old blog post.
• 2:44
  SPEAKER_00: That's actually the last one.
• 2:46
  SPEAKER_00: An essay going around today argues that algorithmic feeds
• 2:49
  SPEAKER_00: and content aggregators have quietly shrunk how much of
• 2:52
  SPEAKER_00: the open web people actually browse anymore.
• 2:55
  SPEAKER_01: Depressing, but fair.
• 2:56
  SPEAKER_00: Let's go deeper on the biggest one.
• 2:58
  SPEAKER_00: Anthropic filed its S1 with the SEC yesterday.
• 3:01
  SPEAKER_00: That's the paperwork a company files with regulators before it sells shares to the public.
• 3:06
  SPEAKER_00: It's aiming for a NASDAQ listing under the ticker ANT.
• 3:09
  SPEAKER_01: Going public already?
• 3:11
  SPEAKER_01: I thought these labs wanted to stay private forever.
• 3:13
  SPEAKER_00: A lot of them did.
• 3:15
  SPEAKER_00: Anthropic was privately valued at $965 billion this past May.
• 3:20
  SPEAKER_00: Now it's reportedly aiming even higher, at $1.5 to $2 trillion for the IPO.
• 3:25
  SPEAKER_01: Reportedly meaning Anthropic hasn't confirmed that number itself?
• 3:29
  SPEAKER_00: Right.
• 3:29
  SPEAKER_00: The filing doesn't list a share count or price range yet.
• 3:32
  SPEAKER_00: That trillion dollar figure is coming from outside reporting, not from Anthropic itself.
• 3:37
  SPEAKER_01: What do the actual numbers in the filing say?
• 3:39
  SPEAKER_00: $16.3 billion in revenue for the first half of this year and
• 3:44
  SPEAKER_00: an $80.7 billion net loss over the same stretch.
• 3:47
  SPEAKER_01: An $80 billion loss doesn't scream ready for public markets to me.
• 3:51
  SPEAKER_00: It's not unprecedented.
• 3:53
  SPEAKER_00: Public investors have absorbed huge losses before,
• 3:56
  SPEAKER_00: from Uber to Snowflake when the growth story was strong enough.
• 3:59
  SPEAKER_01: So the bet is growth outruns the loss.
• 4:02
  SPEAKER_00: The obvious reason to go public now is compute.
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  SPEAKER_00: Training and running frontier models costs billions,
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  SPEAKER_00: and even huge private rounds only go so far.
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  SPEAKER_01: So public markets become the only check big enough to write.
• 4:14
  SPEAKER_00: That's the bet, and the risk cuts both ways.
• 4:17
  SPEAKER_00: One report says if this IPO underperforms, it could reset valuations across the whole AI sector.
• 4:23
  SPEAKER_01: What would even count as underperforming for a filing this size?
• 4:26
  SPEAKER_00: Nobody knows yet, because the share count and price aren't set.
• 4:30
  SPEAKER_00: There's no actual target to miss or beat.
• 4:32
  SPEAKER_01: Feels like judging a race before anyone's picked a distance.
• 4:35
  SPEAKER_00: Is there even a date for when this lists?
• 4:38
  SPEAKER_01: Not yet.
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  SPEAKER_01: The SEC still has to declare the filing effective,
• 4:41
  SPEAKER_01: and that timeline is the companies to control, not the calendars.
• 4:45
  SPEAKER_00: Pretty much.
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  SPEAKER_00: What's clear is this is the biggest IPO test yet for
• 4:48
  SPEAKER_00: whether public markets will fund AI labs the way private investors have.
• 4:53
  SPEAKER_01: If it prices well, every other lab watching gets a green light to file too.
• 4:57
  SPEAKER_00: That's exactly the kind of bet OpenAI is making too, just in a different currency.
• 5:02
  SPEAKER_00: While Anthropic was filing paperwork,
• 5:04
  SPEAKER_00: OpenAI says one of its models cracked a decades-old math problem.
• 5:07
  SPEAKER_01: What problem?
• 5:08
  SPEAKER_00: It's called the Exponential Time Hypothesis.
• 5:11
  SPEAKER_00: That's a decades-old guessing complexity theory,
• 5:13
  SPEAKER_00: which studies how fast computer problems can be solved in the worst case.
• 5:17
  SPEAKER_01: And the hypothesis says what, exactly?
• 5:20
  SPEAKER_00: It says certain hard logic puzzles,
• 5:22
  SPEAKER_00: called 3SAT,
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  SPEAKER_00: can't be solved meaningfully faster than just brute-forcing every possibility.
• 5:27
  SPEAKER_01: And OpenAI's model found a faster way?
• 5:30
  SPEAKER_00: OpenAI says an unreleased model produced the proof.
• 5:33
  SPEAKER_00: The company checked it with Lean,
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  SPEAKER_00: a tool that verifies each step of a math argument the way a compiler checks code.
• 5:39
  SPEAKER_01: How does an algorithm actually get faster than brute force here?
• 5:43
  SPEAKER_01: What's the trick?
• 5:43
  SPEAKER_00: Honestly, it's only a little faster, not dramatically.
• 5:46
  SPEAKER_00: It still grows almost as fast as brute force,
• 5:49
  SPEAKER_00: just a hair slower, and that tiny gap is enough to break the hypothesis.
• 5:53
  SPEAKER_01: A hair's width is still enough to prove a three-decade guess wrong.
• 5:57
  SPEAKER_00: That's the finding.
• 5:58
  SPEAKER_00: OpenAI says the whole thing went from a plain English proof to a lean verified one
• 6:02
  SPEAKER_00: in about 10 days.
• 6:03
  SPEAKER_01: Why is this happening now all of a sudden?
• 6:06
  SPEAKER_00: Models built for formal proof checking have gotten much better at writing lean code this year.
• 6:11
  SPEAKER_00: That makes turning a rough idea into a verified proof faster than it used to be.
• 6:15
  SPEAKER_01: Who outside OpenAI has actually checked this?
• 6:18
  SPEAKER_00: The original researchers who proposed the hypothesis are reviewing it,
• 6:22
  SPEAKER_00: along with a complexity theorist named Ryan Williams.
• 6:25
  SPEAKER_00: That review isn't finished yet.
• 6:27
  SPEAKER_01: So this isn't settled science, it's a claim waiting on referees.
• 6:30
  SPEAKER_00: Right.
• 6:31
  SPEAKER_00: OpenAI's own president,
• 6:32
  SPEAKER_00: Greg Brockman,
• 6:33
  SPEAKER_00: said this doesn't touch the field's biggest open question,
• 6:36
  SPEAKER_00: that's whether every answer that's easy to check is also easy to find in
• 6:40
  SPEAKER_00: the first place, and it's still wide open.
• 6:42
  SPEAKER_01: Companies love announcing the exciting part before the boring verification is done.
• 6:46
  SPEAKER_00: To be fair,
• 6:47
  SPEAKER_00: they published the raw proof and the lean file publicly,
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  SPEAKER_00: so anyone can try to break it themselves.
• 6:52
  SPEAKER_01: That's at least honest.
• 6:54
  SPEAKER_00: Either way,
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  SPEAKER_00: it's a real example of an AI producing new,
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  SPEAKER_00: checkable math, even with a narrow scope.
• 7:00
  SPEAKER_00: And all of it, the money and the proofs, runs on chips that are about to cost more.
• 7:04
  SPEAKER_01: What's going on with chip costs?
• 7:06
  SPEAKER_00: Nvidia's CEO,
• 7:07
  SPEAKER_00: Jensen Huang,
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  SPEAKER_00: floated charging $2,000 a year per chip for the software that runs AI agents on top
• 7:13
  SPEAKER_00: of its hardware.
• 7:14
  SPEAKER_01: Floated, meaning it's not actually happening yet.
• 7:17
  SPEAKER_00: Right, he raised it at Nvidia's own hardware conference last week,
• 7:21
  SPEAKER_00: with no confirmed product, scope, or start date attached.
• 7:24
  SPEAKER_01: Then why bring it up at all?
• 7:25
  SPEAKER_00: He said software licensing is going to become a bigger part of Nvidia's business.
• 7:30
  SPEAKER_00: Floating a number like that is how you start getting investors used to the idea.
• 7:34
  SPEAKER_01: Is $2,000 even a new cost or does it overlap with something they already charge?
• 7:39
  SPEAKER_00: That's the confusing part.
• 7:40
  SPEAKER_00: Nvidia already sells a software bundle called AI Enterprise.
• 7:44
  SPEAKER_00: It runs $4,500 a year per chip or $18,000 over 5 years.
• 7:49
  SPEAKER_01: Do other chip makers charge this way too?
• 7:52
  SPEAKER_00: Not really, not at this scale.
• 7:54
  SPEAKER_00: Enterprise software vendors have run subscription pricing for decades,
• 7:58
  SPEAKER_00: but doing it per chip, across millions of chips, is new for hardware.
• 8:02
  SPEAKER_01: I'd bet this is about padding margins while hardware growth slows,
• 8:05
  SPEAKER_01: not about agents actually needing more software.
• 8:08
  SPEAKER_00: Nvidia hasn't said whether it stacks on AI Enterprise or replaces part of it.
• 8:13
  SPEAKER_00: That detail is still missing from what Huang said.
• 8:16
  SPEAKER_01: Either way, somebody's finance team is about to have a bad quarter.
• 8:19
  SPEAKER_00: If it holds across 5 million chips, that's real money.
• 8:23
  SPEAKER_00: Nvidia's own math puts it at $10 billion a year in new software revenue.
• 8:28
  SPEAKER_01: And $50 billion over five years if everyone renews.
• 8:31
  SPEAKER_00: Exactly.
• 8:32
  SPEAKER_00: Assuming renewal, which is its own open question given how fast this space moves.
• 8:36
  SPEAKER_01: If I'm running a GPU fleet, I'd want to know this before next year's budget, not after.
• 8:42
  SPEAKER_00: That's the real one to act on.
• 8:43
  SPEAKER_00: If your team runs GPU fleets,
• 8:45
  SPEAKER_00: go check what you're already paying under AI Enterprise now before
• 8:48
  SPEAKER_00: a second line item shows up next to it.
• 8:51
  SPEAKER_01: Before the bill, not after.
• 8:52
  SPEAKER_00: That's three different companies making three different bets on the same boom,
• 8:56
  SPEAKER_00: and only one of them has actually filed paperwork.
• 8:59
  SPEAKER_01: Anthropic bet on public markets,
• 9:01
  SPEAKER_01: OpenAI bet on a math proof still waiting on referees,
• 9:05
  SPEAKER_01: and NVIDIA bet on charging more for the chips under all of it.
• 9:08
  SPEAKER_00: I'll give it six months.
• 9:10
  SPEAKER_00: By spring,
• 9:10
  SPEAKER_00: somebody will have measured whether that $2,000 charge stacked on top
• 9:14
  SPEAKER_00: of AI Enterprise or ate into it.
• 9:16
  SPEAKER_01: That's Tuesday, October 27th.
• 9:18
  SPEAKER_01: Money moving, a proof waiting, and a bill coming due.
• 9:21
  [music]