Anthropic の IPO 目論見書(S-1)が明かしたもの

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 redefines how 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 going to 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, Seedata, Granola, and Blitzy. To get an ad-free version of the show, go to patreon.com slash AI Daily Brief, or you and subscribe on Apple Podcasts. And to learn more about sponsoring the show,

send us a note at sponsors at 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 has been 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. After releasing more than 100 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 Call proved the associated Kakeya conjecture in three dimensions in 2025.

OpenAI's results generalizes the proof to every dimension. 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. 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. The results were each found by OpenAI's 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 Casato 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 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 any case, Anthropic researcher Levin Doppogy noted that Anthropic had independently come to the same conclusion about ETH about two weeks ago.

Mathematician and founder Konsti Woland 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, 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 like they 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 are 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 insists 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 company has released a new model, GPT-6.1 Terra, but a lot of folks are wondering if this mid-sized model actually has a place in the lineup. During the rollout of the GPT-5.6 family, Terra 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, Terra continues to advance the cost intelligence frontier across our model families, providing a new balance of speed, capability, and cost. And primarily, OpenAI has made the model useful by making it cheaper. 6.1 Terra is available at 50% the cost of 5.6 Terra,

which also happens to be half the cost of Sol. 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's 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, Terra 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 Sol but still require strong computer-use skills and a little more intelligence than Luna can offer. Testing from artificial analysis resulted in a score of 47, four points shy of GPT Sol and two points ahead of Grof 4 On max settings they found that Terra cost per task was similar to GPT Sol on extra high settings so likely didn justify using Terra They commented

the low and medium effort levels are Pareto optimal for cost efficiency, while the other effort levels sit behind the frontier. 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-53 and just ahead of DeepSeat V4-1 Flash. 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 codex. And in fact, that 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 Codex now instead of Sol? Developer Rowan Hyman seems 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 going to give you more efficient and cost-effective options. That might not be exciting, but it could be very 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 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. Lastly today, a story that's generating a lot of chatter, Cohear 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 QEN 3.8 model doing

something that should never happen. They continued, asked to write a defensive detection rule, Quen 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 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 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 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 100,000 times running the evaluation, suggesting that that means it likely isn't a malicious action. Then again, Cohere pointed out, If you're running 10 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, alongside the skepticism, there were many calls for Cohere to release the full prompts 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 noted that Cohear 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 Undersecretary Sarah B. Rogers retweeted Curran, adding, secure, trusted tech stack is a vital policy priority with good reason. Never a dull moment, but for now that is going to do it for the headlines. Next up, the main episode. A new study from KPMG in the University of Texas at Austin found that when people work with AI, similar skills don't guarantee similar outcomes. Researchers studied more than 500 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 slash us slash AI amplifiers. 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 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 the 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 meetings. 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 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 slash ai daily and use code AI daily to get 100 off for 3 months for new users That granola slash AI Daily and Code AI Daily 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 services CVEs across the full estate, proactively recommends patches, and can execute the PR. Each fix is grounded in how your systems connect and validates or nothing new breaks. And the knowledge graph dynamically 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 scanned 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 codebase at Blitzy.com. That's B-L-I-T-Z-Y dot 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 Dan Shipper jokingly wrote, Big news! The Anthropic S1 doesn't say or it might kill slash unemploy everyone in the first paragraph. A win for humanity! 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 needy 400 pages, with over 90 pages just dedicated to the risks that could face the company. For those unfamiliar with an S1, also known as the prospectus, This is a document that is designed to give potential investors full disclosure about 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 $16.3 billion in revenue across the first half of the year. That's up from $4.6 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 in Q2, 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 85% for the first half of the year, up from 40% across 2025, 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 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. 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 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-cancelable, 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, most people are reacting to those big headline numbers, but remember that these commitments are spread out over more than a decade, with some contracts running 20 years. If you think about it then as closer to $20 to $40 billion a year, the numbers are still high, but a lot more palatable, especially given how much money Anthropic is making. An interesting curveball is that Anthropic has a ton of cash in the bank, $132 billion to be exact in cash and cash equivalents. 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. And 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 S1 also provided a ton of details on what Anthropik's customer base looks like. 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 $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 revenue retention was at 900% as of September, meaning that the average customer has 9x'd their spend since this time last year. A more useful way to understand the shift in the business is that as of last December, Anthropic had around 1,500 enterprise customers spending over $100,000 annually. And that is the count that has now jumped to just under $12,000. Still, Anthropic noted that less than 0.1% of a Fortune 500 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. Fox's 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 diversity broaden out. At the end of Q1 this year, around 36% of their revenue came from their top five customers. That number is now down to 21%. The S1 also disclosed the exact partner share through distributors like Google and AWS. 48% of Anthropic's revenue flows through these partners, who take a fairly decent cut, which wasn't fully disclosed before. 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 Anthropik belief that they basically uninsurable They wrote 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, much more tangibly, Anthropic warned that revenue is slowing, albeit from a blistering pace.

Anthropic grew revenues by around 1,000% since last year, but said that they do not expect that to continue. Quarter-on-quarter growth for Q3 was around 45%, so still incredibly strong, but Anthropic said they have basically no idea how fast they'll grow in the 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. The 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, Anthropics founders asked investors to give them the benefit of the doubt on their sky-high infrastructure commitments. In a letter attached to the S-1, they wrote that their compute spending, quote, may seem irresponsibly large at first glance, but ultimately described it as bold but sane. 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 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 BBB, 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 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 up, Tony Zyrinka, an investment-grade portfolio manager at Impact 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, the rating will come down to how the rating agencies view the commitment schedule, short operating history, and circular counterparty relationships. One option is that Anthropic can continue to rely on partners for funding rather than running to the bond market. Currently, Anthropic has $6.2 billion in debt owed to Amazon in the form of convertible notes, and they have the option to borrow another $77 billion from Amazon, Broadcom, and Google in the form of additional promissory notes. They also have access to a $15 billion 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 $35 billion vehicle with Broadcom in June and have just signed a new vehicle for $63 billion again with Broadcom. Both deals will finance TPU purchases from Broadcom and were structured with Apollo and Blackstone. 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 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 $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 $22.5 trillion by 2031, saying that they believed 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 in the clod. 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 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 S1 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. 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 interesting conversation was around the relationship between cheaper tokens and more revenue. Gavin Baker wrote, Revenue per 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 Tungus 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. Javon's paradox in the real world. 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 have 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 going to do it for today's AI Daily Brief. appreciate you listening or watching as always and until next time peace