# What Anthropic’s IPO Filing Reveals — Transcript (2026-10-27)

https://aidailybrief.ai/e/2026-10-27 · Listen: https://pod.link/1680633614

---

[00:00:00] Well, it just keeps getting more exciting. The Anthropic IPO is inching ever closer, and this week we got the full S1 prospectus. Everyone has spent the last 24 hours poring over every detail of the 400-page document

looking to see if there were any surprises coming their way Overall, what we got is a picture of a business that completely redefineshow fast a company can scale

Although ultimately what people think of it isn't just going to be about how it's doing now

But also the commitments it's made for the next few years. 

We're gonna get into all that and more as we look at everything important in the Anthropic S1



The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI



All right, friends, quick announcements before we dive in. First of all, thank you to today's sponsors, KPMG, CData, Granola, and Blitzy

To get an ad free version of the show, go to patreon dot com slash aidailybrief, or you can subscribe on Apple Podcasts And to learn more about [00:01:00] sponsoring the show, send us a note at sponsors@aidailybrief.ai. One more quick note before we get into today's episode. A big thank you to everyone who has checked out my new podcast, How We Got Here

The feedback on the first episode about how tech lost America hasbeen really helpful, as have all of your follows, ratings, and reviews. as I mentioned on yesterday's episode, this launch week is where all of that listening and rating is absolutely the most impactful.

So if you haven't checked out "How We Got Here" yet, and you have any time this week to do so, I would so appreciate it

You can find a link in the show notes or go to howwegothere.com 

Welcome back to the AI After releasing more than a hundred mathematical proofs last month, OpenAI has added three new significant discoveries across mathematics, computer science, and physics. In mathematics, AI models proved a local smoothing conjecture proposed in 1991 as a general case across all dimensions.



This conjecture proposes that focusing waveforms results in a single short-lived peak

Guth, Wang, and Zhang proved the conjecture in two dimensions in 2019, while Wang and Ka proved the associated [00:02:00] Kakeya conjecture in three dimensions in 2025. 

OpenAI's result generalizes the proof to every dimension

In In computer science, OpenAI disproved the exponential time hypothesis, 



known in the field of complexity theory as ETH





this hypothesis is deeply related to the more well-known P versus NP problem dealing with computational complexity. It suggests that for an algorithm called 3SAT, adding variables necessarily increases time to compute exponentially. That would mean that for certain classes of problems, no computational shortcut exists.





this hypothesis was assumed to be true for decades, so disproving it breaks one of the core assumptions in computational science

science Finally, in physics, OpenAI is partway to an existence proof of certain interacting quantum field theories containing gauge fields and fermions in four dimensions. This work aims to produce a simplified version 

of a

unified standard model, which combines gravitational forces with electromagnetism and subatomic forces

It contains a proof of the existence of pure Yang-Mills theory, which is one of the seven Millennium Prize problems. problems. The results were each found by OpenAI's [00:03:00] internal models



And for those of us for whom that was maybe the least understandable segment on the AI Daily Brief ever, all we can do is turn to the peanut gallery to get a sense from people who are actually in these fields which of them matter most.

The short is that while each of those results is incredibly important in their field, disproving ETH 

drew the strongest response on X Mathematics professor and Fields medalist Timothy Gowers wrote, " For those who don't know

ETH is the hypothesis that 3SAT needs exponential time. Very surprising and consequential that it is false A16Z's Martin Casado commented, " Wow, ETH being false is a stunning result."

Many remembered learning about ETH in college as a rock solid assumption they could rely on, so to see it disproved is very impactful. 

Practically, this means that some cryptographic algorithms may have shortcuts that are faster than brute force guessing And that calls into question one of the key assumptions in the field

As a fun As a fun aside, I was literally about to call it a load-bearing assumption

But given how overused load-bearing is in AI right now 

I went instead for the much less impressive key assumption Weird times we're living 

in in 

in any [00:04:00] case, Anthropic researcher Levin Dalpaji noted that Anthropic had independently come to the same conclusion about ETH about two weeks ago

Mathematician and founder Consti Wöland considered the further implications, writing, " The complexity theory result is

actually huge. If it survives peer reviews, this could be one of the biggest computer science breakthroughs in decades."

OpenAI's Greg Brockman urged everyone to take a breath, commenting, 

P still probably doesn't equal NP. Now, Now, the other big part of the story was how OpenAI went about these releases. the company caught a lot of criticism after last month's giant math dump, leaving many mathematicians feeling likethey were becoming dispossessed from their field by an outside force.

More practically, the results produced a giant verification problem, with mathematicians struggling to make progress on confirming the generated proofs.

This time, OpenAI decided to partner more closely with expert scientists. The mathematics result has been released in full with complete prompts and AI outputs as advised by the existing council of mathematicians. For the computer science result, OpenAI is making the results public but is working with a group of scientists to explain it further.

for the physics result, OpenAI is keeping the results private as they're [00:05:00] incomplete. However, they are working closely with a group of physicists led by Nobel laureate David Gross to continue the work. OpenAI noted that they are seeking no academic credit and insist that the human scientists get sole recognition for the work they do to advance the result.

That wasn't the only news out of OpenAI yesterday The The company has released a new model, GPT 6.1 Tera, but a lot of folks are wondering if this mid-size model actually has a place in the lineup. During the rollout of the GPT 5.6 family, Tera felt to some like the orphaned model alongside Sol and Luna.

Sol was the high performance model for difficult problems, while Luna was the cheaper model for simpler problems. Few had a use case for a middle ground model 

that was only 

kind of 

intelligent and 

kind 

of cheap To make matters worse, by adjusting the effort settings for Sol and Luna, users could beat the performance of Terra at a cheaper cost for every point along the effort curve.





with 6.1 Terra, OpenAI is presenting the model as the balanced option They wrote, They wrote, " Terra continues to advance the cost intelligence frontier across our model families, providing a new balance of speed, capability, and [00:06:00] cost."

And primarily, OpenAI has made the model useful by making it cheaper. 6.1 tera is available at 50% the cost of 5.6 tera

Which also happens to be half the cost of Seoul

Terra Terra also seems to have extremely high proficiency in computer use. On Agent's last exam, which features a range of professional tasks, Terra actually scored slightly higher than any other OpenAI model, including Astra. It completed tasks at around half the cost of Sol and one-tenth the cost of Astra, while being three times faster than both Terra even approached Opus 5.5 score on this particular benchmark with 3x token efficiency, allowing it to complete the benchmark tasks at one-ninth the price

That wasn't the general result across all benchmarks, however. For most categories, including agentic coding, Terrace scored slightly lower than Sol at a slightly reduced cost

OpenAI said that the model performs best in high volume automated tasks like reading incoming invoices and entering their details into accounting software. In other words, relatively low value tasks that don't justify the use of Soul, but still require strong computer use skills and a little more [00:07:00] intelligence than Luna can offer

Testing Testing from artificial analysis resulted in a score of forty-seven, four points shy of GPT-6-1 Soul and two points ahead of Grok 4.7. On max settings, they found that Terra's cost per task was similar to GPT-6-1 Sol on extra high settings, so likely didn't justify using Terra. They They commented, " The low and medium effort levels are Pareto optimal for cost efficiency, while the other effort levels sit behind the frontier."

One One of the interesting results was a very solid performance on the new AA Coding Agent Index

Terra scored 48, placing it just behind GLM 5.3 and just ahead of DeepSeek V4-1 Flash So, so trying to wrap our heads around this, while the original version of Terra suffered from being not quite good enough and not quite cheap enough at the same time, this iteration does seem to improved enough to become good enough



for at least some volume of daily driver type tasks AI content creator Claire Vo commented, " No need to watch a review. Here's everything you need to know. It's fast, writes well, runs my computer all day now. Great default for codecs

And in fact, that [00:08:00] runs my computer all day now is the thing that most people assume that this model is meant for. The rise of personal agents gives Terra a potential niche for always-on agents

that didn't really exist when the model class was first released. In other words, there's now a large category of tasks where you might want something a little more powerful than Luna, but more cost-efficient and faster than Sol Peter Yang asked his audience, " Should Terra be the default for codecs now instead of Soul?"

Developer Rowan Hymans seem to have summed up the early consensus commenting, " If you're producing code, probably not. For computer use, probably yes."



Of all the model releases we've had recently, 

this is the one that most clearly reaffirms the idea that new model releases are no longer about one-to-one replacements in most cases, but instead about asking where they fit in



there are types of tasks, especially long-running computer use types of tasks, where this is now good enough and is gonna give you more

efficient and cost-effective options



that might that might not be exciting, but it could be very useful

useful I would certainly not say that the verdict is in on this one yet, as you can find opinions all across the spectrum. So even more than normal, I would [00:09:00] recommend you actually put this one through your own personal use cases or benchmarks to see if and where it can be valuable for you

you Lastly Lastly today, a story that's generating a lot of chatter. Cohere has discovered some concerning behavior while testing a popular Chinese open model. During a recent cyber capabilities test,"writes the company, 



the research team caught an Alibaba Qwen 3.8 model doing something that should never happen.

They They continued, " Asked to write a defensive detection rule, Qwen 3.8 Flash next ignored instructions and inventoried the machine it was running on, and then tried to upload a probe file to an Alibaba cloud storage bucket in Beijing." now Cohere said that their sandbox blocked the action But perhaps most concerningly, the model's reasoning trace said that no tools would be called and gave no indication of the actions being taken Many security teams use reasoning traces as a core part of their observability model, so this action could have easily evaded detection Cohere used the incident to challenge the idea that hosting a model locally removes all risk of data exfiltration.



They wrote, " One [00:10:00] comforting idea has shaped how companies and government contractors adopt open-weight AI models that anyone can download and run. If we run the weights on our own servers, the thinking goes, our data can't leave the building and our systems are secure."



now now that AI models are taking actions rather than just answering questions, however, Cohere argues that the threat profile has shifted. They wrote, " Once a model can type into a command line, whatever was baked into its weights during training gets a keyboard inside your network."



some some security professionals didn't agree with that conclusion. 



in fact, going so far as to call this report total BS

They pointed out that the model could have been testing the detection rule it had written by pinging the Beijing servers, likely a default setting embedded in the weights. And furthermore, they pointed out that Cohere had only been able to observe this behavior less than one out of a hundred thousand times running the evaluation



suggesting 

that that 

means it likely isn't a malicious action



Then again, Cohere pointed out if you're running ten million agent turns a week, hundreds of these tool calls could occur, and just one would be enough to identify your network and the agent you're running Now Now, alongside the skepticism, there were many calls for Cohere to release the full prompts [00:11:00] and reasoning traces for further investigation

After they did, AI developer Mia analyzed them and wrote, " TLDR, real anomaly, overstated implications. The claims about intent have nothing behind them. Unexpected and potentially unsafe tool behavior, yes, but not a deliberate intent, a planted backdoor, or an Alibaba-directed exfiltration mechanism."



still whatever the intent may be



the idea that a Chinese model could call home without being asked is obviously a massive problem for enterprise users

Others have reported the behavior while doing unrelated web research So to some, there's a possibility that this is a low occurrence surveillance technique

Open models are also being considered by governments for sovereignty reasons, so this behavior could be a huge problem in those settings as well

Some Some noted that Cohere is competing with Chinese models for these government contracts, so has a vested interest in spreading security doubts. still AI news aggregator Andrew Curran commented, " This is the type of thing that potentially gets new laws passed."

Reinforcing that point, State Department Under Secretary Sarah B. Rogers retweeted Curran [00:12:00] adding, " Secure trusted tech stack is a vital policy priority with good reason."

Never a dull moment, But for now, that is gonna do it for the headlines. Next up, the main episode A new study from KPMG and the University of Texas at Austin found that when people work with AI, similar skills don't guarantee similar outcomes. Researchers studied more than five hundred early career professionals and found that the best performers consistently amplified the value of AI by guiding, evaluating, and refining its outputs.

These top performers, called AI amplifiers, weren't defined by what they knew alone, but by how they worked with AI. Learn more about what separates AI amplifiers from everyone else at kpmg.com/us/aiamplifiers. 

This episode is brought to you by Cdata. Companies rolling out AI keep hitting the same wall. Token spend is climbing, security is holding agents back, and no one knows how AI [00:13:00] is truly being used. The root cause, AI is cut off from the systems and context your business runs on

That's why CData built Connect AI Gateway. One control point between your AI and your systems

Built on the connectivity layer powering Palantir, Google, Salesforce, and hundreds more, each agent gets exactly the data and tools it needs

It governs down to the record and lets you finally see your org's complete AI usage in one place. Accurate answers, a fraction of the token cost, and a gateway that learns your business with every interaction. See how at cdata.ai. 

You're in back-to-back meetings all day. You're trying to stay present, but you're also worried you'll forget the decision, the action item, the important next step. This is a scenario that we absolutely live here at the AI Daily Brief. 

Given how fast things move and change and how quickly we're evolving everything that we do

That's where Granola comes in. Granola is an AI-powered notepad for You jot down rough notes like you always do, and in the background, Granola transcribes and turns them into clear, useful notes when the meeting ends. No bots joining your [00:14:00] calls, no distractions, just a clean notepad that helps you focus. During or after the call, you can chat with your notes, ask Granola to pull out action items, help you negotiate, write a follow-up email, or even coach you using recipes, which are pre-made prompts

Once you try it on a first meeting, it is very hard to go without



head to granola.ai/aidaily

And use code AIDaily To get 100% off for three months for new users

That's granola.ai/aidaily and code aidaily to get 100% off for new users for three months 

Blitzy deeply understands your code base before it writes code. Here's the first place that pays off: security in the age of AI

Vulnerabilities don't live in isolation. They live buried inside millions of lines of interconnected code, where patching one thing quietly breaks three others. That's why surface-level scans fail Blitzy starts from its knowledge graph of your entire application, identifies and surfaces CVEs across the full estate, proactively recommends patches, and can execute the PR Each fix is grounded in how your systems connect and validate so nothing new breaks.

And the knowledge graph dynamically [00:15:00] updates, keeping you ahead of an ever-accelerating threat landscape

One Blitzy customer resolved 21 active CVEs across six core microservices in four days. Zero compile errors, every validation scan clean, months of planned work fixed in less than a week

Security remediation grounded in real architectural context at the speed of compute. Harden your code base at blitzy.com. That's B-L-I-T-Z-Y.com



Welcome back to the AI Daily Brief

After months of waiting and a steady trickle of leaks to the press Anthropic has made their S1 public as they make final preparations to go public. We now have all the details, all the numbers, all the risk disclosures you could possibly want. Today we are going to talk through that Look at where the biggest areas of consensus and disagreement are in the interpretation



And see what it all says for the likely prospects of the company heading into the next couple of weeks

Every's Every's Dan Shipper jokingly wrote, " Big news. The Anthropic S one doesn't say or it might kill slash [00:16:00] unemploy everyone in the first paragraph. A win for humanity."

Which Which is not to say that it doesn't have some big and bold statements. For anyone who listened to my How We Got Here episode 



that discussed the Facebook and Google IPO letters.

Anthropic added to that canon, " We believe that AI is one of the most consequential technologies in human history. If managed well, it could rapidly accelerate scientific innovation greatly increase economic growth rates, bring cures for major diseases within reach, and solve problems that have resisted generations of human effort.

Anthropic aims to realize these benefits while navigating and preventing the risks that come with a technological revolution this fast and this powerful. There are four things that we would like any investor in Anthropic to understand: the incredible potential of AI, the risks we must address, Anthropic's unique approach to building a company and operating in the world, and the ways in which our behavior may differ from that of a purely profit-maximizing company.

Anthropic certainly gives themselves space to do all that. The document weighs in at a meaty four hundred pages, with over ninety pages just dedicated to the risks that could face the company

For those For those [00:17:00] unfamiliar with an S1, also known as the prospectus, this is a document that is designed to give potential investors full disclosureabout a new stock



For those of you who are watching this episode, the visualization that you see on screen will also be available on the website companion at aidailybrief.ai. But with that said, let's start with the numbers. Anthropic earned sixteen point three billion in revenue across the first half of the year. That's up from four point six billion for all of 2025 Anthropic reportedly told investors that they earned $16.8 billion in Q3, so that incredible growth seems to be continuing. That said, we didn't get any confirmation of Q3 numbers in the S1, with that line left blank

Anthropic claimed a $974 million operating profit inQ2, which was their first profitable quarter. That's under GAAP accounting standards, so we can take that as the real deal

Gross operating margins were calculated at eighty-five percent for the first half of the year, up from forty percent across twenty twenty-five, meaning margin is increasing

However, research sales and administrative costs are rising alongside, leading to an operating margin of negative 4.3%. The S1 also confirmed that [00:18:00] scary $80 billion net loss for the first half of this year. However, as I mentioned last week, 72 billion of that

was an accounting change related to convertible debt. debt.

essentially, This is just a paper loss caused by stock price appreciation, not a cash loss

One number that wasn't previously reported was Anthropic's compute spend. They've spent a total of 19.2 billion on compute since 2023. 8.9 billion in compute spending came in the first half of this year, and Anthropic is spending around 70% of their compute budget on internal work rather than serving it to customers Anthropic said that 90% of their code is now written by Claude, and internal Claude use, valued at API prices, 

surpassed payroll as an expense in April



the the numbers that are getting a lot of attention is that Anthropic has made hundreds of billions of dollars in future compute spend commitments over the coming decade Somewhere between 417 and 431 billion of that is non-cancellable, 

including major commitments to Google, Amazon, Microsoft, and Broadcom



The total signed commitments are above 600 billion, And estimates could lead out $800 billion in commitments

Now, Now, most people are [00:19:00] reacting to those big headline numbers. But remember that these commitments are spread out over more than a decade, with some contracts running twenty years. If you think about it then as closer to twenty to forty billion dollars a year, the numbers are still high, but a lot more palatable, especially given how much money Anthropic is making

An An interesting curveball is that Anthropic has a ton of cash in the bank, a hundred and thirty-two billion to be exact in cash and cash equivalents the The company has raised a total of 141 billion throughout their history, so almost all of it is still sitting in the bank earning interest



that interest income is pretty significant at 1.1 billion for the first half of the year, representing around 6.5% of total revenue



Semi Analysis went viral with a post that called this likely the most efficient scaling of a business in the history of capitalism

The The S1 also provided a ton of details on what Anthropic's customer base looks like. 

revenue

revenue quality has been one of the big questions hanging over the filing. and for many, Anthropic has adequately addressed those concerns.

The company reported that 

as of September, they have over 11,900 customers with [00:20:00] $100,000 or more in annual spend

More than 1,900 customers are spending more than a million dollars, and Anthropic has around 250 customers spending $10 million plus annually

Net Net revenue retention was at nine hundred percent as of September, meaning that the average customer has nine X'd their spend since this time last year

A more A more useful way to understand the shift in the business is that as of last December, Anthropic had around fifteen hundred enterprise customers spending over a hundred thousand dollars annually, and that is the count that has now jumped to just under twelve thousand

Still, Anthropic noted that less than zero point one percent of a Fortune five hundred customer's expense goes towards Anthropic services. the implication being that they've only just started to eat into corporate IT budgets, and there's a whole lot more budget to consume

Box is Box is Aaron Levy wrote, " While that number likely will eventually asymptote somewhere, there's no reason it can't be 10 to 50X higher than this, reaching a mere 1% to 5% of spend, which would still be a fraction of what most startups are spending. Thus, you can see how big a divide there is between the frontier users of AI and the rest of the world still."

Anthropic has also seen their revenue [00:21:00] diversity broaden out. At the end of Q1 this year, around thirty-six percent of their revenue came from their top five customers. That number is now down to twenty-one percent

The S1 also disclosed the exact partner share through distributors like Google and AWS. 48% of Anthropic's revenue flows through thesepartners who take a fairly decent cut, which wasn't fully disclosed before

Now, when it Now, when it comes to the risk disclosure section, the catastrophic risks were all in there, as well as disclosures touching on climate change and the data center backlash

Notably, these risks manifested in Anthropic's belief that they're basically uninsurable. They wrote, " "No single No single insurer, and potentially the insurance industry in the aggregate, may have sufficient capacity or expertise to underwrite insurance commensurate with such exposure."

Still, Still, much more tangibly, Anthropic warned that revenue is slowing, albeit from a blistering pace pace Anthropic grew revenues by around 1000% since last year, but said that they do not expect that to continue Quarter on quarter growth for Q3 wasaround 45%, so still incredibly strong.

But Anthropic said they have basically no idea how fast they'll grow in the [00:22:00] future

Mundane balance sheet management is one of the major risks that can't be overlooked. Anthropic noted that there's a risk that they fail to secure enough compute or that the cost of compute could rise significantly. They also flagged that compute partners like SpaceX, Google, and AWS may not always be fully aligned, their terminology, with Anthropic's long-term interests.

Contracts are in place, but Anthropic is warning investors that they may not be honored as AI competition heats up. Anthropic said that this was the reason they were starting to pursue construction of their own data centers to reduce reliance on partners that could turn into ruthless competitors

Overall, Overall, Anthropic's founders asked investors to give them the benefit of the doubt on their sky-high infrastructure commitments In a letter attached to the S1, they wrote that their compute spending, quote, " May seem irresponsibly large at first glance," but ultimately described it as bold but sane



the compute the compute conundrum leads naturally to a warning about debt financing. Anthropic warned that they may need to seek financing sooner than they anticipated to meet infrastructure obligations That 132 billion will probably see them through until the end of the year, and what's likely to be the largest IPO fundraise in [00:23:00] history will help

But it seems unlikely that they'll be able to fund their infrastructure spend entirely from income In that light, Anthropic notes that Anthropic said little about their pathway to an investment-grade credit rating. After going public earlier this year, SpaceX managed to secure an investment-grade rating by the narrowest of margins, achieving triple B, which is the last ranking above junk.



SpaceX wasn't profitable at the time, but achieved investment grade through a combination of monopoly power in satellite internet and space launch, as well as a gigantic equity valuation In other words, in part, the ratings agencies viewed SpaceX's lack of profitability as a choice they could turn off whenever they wanted to

It's It's unclear how many of those factors hold true for Anthropic. It's probably a stretch to say they could turn a profit by flicking a switch, given how much revenue is contingent on training multiple new models every year

Summing it Summing it up, Tony Zrinka, an investment-grade portfolio manager at Impax Asset Management

said, quote, " The state of Anthropic is that they have very strong liquidity and extraordinary future obligations. The balance sheet supports an investment grade rating at today's leverage. However, However, the rating will come down to how the rating [00:24:00] agencies view the commitment schedule, short operating history, and circular counterparty relationships."

One One option is that Anthropic can continue to rely on partners for funding ratherthan running to the bond market. Currently, Anthropic has six point two billion in debt owed to Amazon in the form of convertible notes And they have the option to borrow another seventy-seven billion from Amazon, Broadcom, and Google in the form of additional promissory notes.

They also have access to a fifteen billion dollar revolving credit facility from Morgan Stanley, which is a break glass in case of emergency form of borrowing



but mostly Anthropic is currently funding their commitments 

through third party vehicles with an investment grade partner. they established a thirty-five billion dollar vehicle with Broadcom in June and have just signed a new vehicle for sixty-three billion, again with Broadcom.

Both deals will finance TPU purchases from Broadcom and were structured with Apollo and Blackstone

Now, Now, after SpaceX set the total addressable market of the AI industry at 28 trillion earlier in the year, many were looking for a similar assessment from Anthropic



we we didn't quite get that as Anthropic shied away from giving a clear TAM. However, they did estimate the value of global knowledge work at [00:25:00] $40 trillion in 2025



They pointed to an estimate from IDC 

that suggested that investment in AI could yield a cumulative global economic impact of twenty-two point five trillion by twenty thirty-one, saying that they believe that impact could be even greater

Anthropic also highlighted that they have very few peers, naming only OpenAI and Google as direct competition. And in a slight jab to OpenAI, Anthropic founders pledged to keep their products free of advertising. In a letter included in the S1, the founders wrote, " Once advertising becomes part of a company's revenue targets, the pressure to optimize for engagement tends to grow.

We have chosen not to introduce that pressure into Claude."

Now, Now, obviously it's easier for Anthropic to reject that temptation given that they don't have a large customer base of free users like OpenAI, but it still highlights the extent to which they are betting it all on enterprise adoption

Overall, the S1 seems to have calmed a few nerves on Wall Street, with many reflecting on the historic nature of the IPO. Gil Luria, the head of technology research 

at DA Davidson said, " This will really be investors' first significant opportunity to invest directly in the company that is driving the AI [00:26:00] economy.

It's of monumental importance."

Ravi Viswanathan of NewView Capital commented, " Never have IPOs represented so much to the venture capital industry writ large



even the Financial Times was fairly balanced on the S one after publishing some highly critical pieces in the lead up to the public reveal

Opinion 

writer Lex advised investors to skip reading the actual document, however, commenting, " In practice, the only real decision for investors is whether to buy or not. And chances are, most people will have already made up their minds before this week."

As with SpaceX in June and the future IPO of OpenAI, they either believe the company is a big winner in a brave new world or they don't



among... So So where was the discussion among our social media chattering class?



a lot of folks were very surprised and bullish on how much cash Anthropic had on hand and how efficiently they'd scaled so far

The big concern, of course, and really the dividing line between different people, 

was how much of the bill was signed and yet to come



another another interesting conversation was around the relationship between cheaper tokens and more revenue

Gavin Baker wrote, " Revenue per [00:27:00] GPU can increase as token prices decline if per GPU token throughput increases enough. So both anthropic revenue and margins can go up as token prices go down if GPU prices go up less than the product of token price decline times per GPU throughput increase."



Thomas Tunguz put it in specific terms. When Opus 4.5 launched in November 2025 with more capability and a lower cost per task than Opus 4.1, revenue growth accelerated from 48% quarter over quarter to 132% the following quarter. Jevons paradox in the real world



There There was a lot of discussion

around founder control

Borrowing some ideas from others like Mark Zuckerberg, there is a new founders Class F of stock, which means that even though the founders control 

a

very small portion 

of the

overall equity, their voting shares give them 50.1% control over key decisions 

like

who picks the board

There is a lot more in here, but I would say that overall

I think Anthropic comes away Looking, relatively speaking, better than they did before the full S1 was released

That's exactly I think what they would've [00:28:00] wanted, giving them momentum heading into the IPO Still to echo that sentiment that at this point investors have pretty much made up their mind I still think that almost everything comes down to how you assess the possibility for future growth versus the very clear and real existing future commitments Hold aside all the details, that is the one math equation that matters and I'm not sure that the S1 changed anything about that



for now we are getting ever closer to this monumental moment in history for the AI industry But that is gonna do it 

for today's AI Daily Brief. Appreciate you listening or watching. As always, and until next time, peace 

​