← The AI Daily Brief: Artificial Intelligence News and Analysis
The AI Daily Brief: Artificial Intelligence News and Analysis
The AI Chart Everyone Is Getting Wrong
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Today on the AI Daily Brief, the shift from token maxing to token panic happened so quickly. I'm going to explain why things are a lot different than a lot of the charts and analysis running around would make you think. Before that in the headlines, a preview of the upcoming SpaceX IPO. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. Alright friends, quick announcements before we dive in. First of all, thank you to today's Sponsors, KPMG Section, Zencoder and OutSystems. To get an ad free version of the show, go to patreon.com aidaily brief or you can subscribe on Apple Podcasts. If you want to learn more about sponsoring the show, send us a note at sponsorsidailybriefai or you can check out the new aidailybriefai sponsors. By the way, one of the things that we have now on the new AI Daily Brief site, in addition to every episode having a whole page that organizes it into easy to share chunks, is a sponsors page where you can go see all of the offers we've shared, like for example, getting a free month of Bolt Pro. You can find all of that at aidailybrief AI Sponsors and while you're there, check out the rest. Send me ideas. We're gonna be adding a lot here. For now though, let's talk about the first big AI IPO of the year. We have a bit of an exciting Friday today. After months of anticipation, SpaceX is conducting the largest IPO in history. Now this has been one of the most hyped up events in markets for a very long time. Investment banks have been battling it out for institutional sales and the retail frenzy is already off the charts. As of close of trading on Thursday, Bloomberg reports that retail investors submitted more than $100 billion in orders. Yes, that is billion with a B. Now, SpaceX was only selling 75 billion worth of stock and reportedly reduced the retail allocation from 30% to 20%. That means the retail allocation was almost 7x oversubscribed and would have been enough to fill the entire IPO by itself. The sale was priced at $135 per share, a flat price set by SpaceX earlier in the process. That pricing implies a valuation just shy of 1.8 billion, meaning the company will debut as the seventh largest company in the world, ahead of Saudi Aramco, Tesla and Metta. Some anticipate the flat pricing will increase Day one volatility as there was no price discovery mechanism in the IPO process. The and much of the commentary has already declared this a retail bloodbath waiting to happen and possibly an obvious market top for the AI bull run. A rare opinion piece for Reuters declared there's a serious risk that investors piling into the world's largest IPO will get burned, especially the retail crowd. The analysis focused on the relative lack of revenue for a company of this size. Their 2025 financials showed a $5 billion loss on $18.7 billion in revenue. In contrast, Meta delivered $200 billion in revenue last year, and even a Tesla that isn't at the top of its game managed 95 billion. Even after SpaceX signed mega data center deals with Anthropic and Google over the past month, they're well short of revenue numbers that put them up alongside those other companies. There also has been criticism around the way the company was marketed, with Goldman Sachs simultaneously conducting the IPO and providing wildly bullish research analysis. In a report last week, they forecast that SpaceX could hit 474 billion in revenue by 2030, with their AI division growing a hundredfold. To some, this was less about plausibility and more about an analyst with a clear incentive forecasting a bajillion dollars in revenue. There is also the sideshow of Elon Musk on the verge of becoming the world's first trillionaire. Based on Bloomberg's net worth calculations, Elon was worth just shy of 700 billion last month, with more than 60% of his wealth tied up in SpaceX. The IPO pricing would bring his net worth to 971 billion, so any significant pop would push him over the line. Now, many expect the IPO to be a bit of a circus, but one of the big questions is what will the implications be for the anthropic and OpenAI IPOs to come? Some are seeing this as the first chance the US market has to price an AI model company, meaning that if SpaceX does well, it could imply even greater valuations for the Frontier Labs. Then again, there's also the potential with that line of thinking that SpaceX puts in the market top theoretically, making it more difficult for OpenAI and Anthropic to get their IPOs out the door at a premium valuation. I tend to disagree with this as the right way to look at things. Two big reasons why. The first is the simple one. You can't really apply anything around Elon Musk to anyone else, love him or loathe him. He kind of operates in his own vortex. And I don't necessarily think that people are going to read this as a referendum on AI models as much as a pricing on the Elon market halo. Secondly though, I don't think that anyone is really looking at this as an AI model company. I think that the 11th hour shift to SpaceX as a Neo cloud totally changes the narrative equation. First of all, it adds a whole bunch of billions to their revenue stream that look a lot more durable and interesting. And second, it makes their whole push to get data centers in space look a lot more aligned and frankly plausible. So yes, I do think that for some the IPO will be AI related, but it's not going to be about models, it's going to be about the infrastructure build out with a very heavy dose of Elon on top of now as I record it is still early so markets are yet to open in New York. I will of course provide full coverage of whatever is important about the craziness to ensue on Monday's show. I think though, if you're trying to look for a single take that avoids the hyperbole from either side, Economist Peter Atwater kind of nailed it when he said SpaceX has created an idiot moment for investors. Buy it and it goes down, you were an idiot. Don't buy it and it goes up, you were an idiot. Now speaking of very high net worth dudes, Jeff Bezos AI startup Prometheus has closed their latest round of funding, valuing the company at a measly $41 billion. The round raised $12 billion with participation from JP Morgan, Goldman Sachs, BlackRock and Bezos himself. Prometheus aims to build what they're calling an artificial general engineer, an AI system that can design and manufacture anything, including complex equipment such as jet engines. The company has already staffed up, hiring one hundred and fifty people across offices in San Francisco, London and Zurich. In an interview, Bezos said the goal was to, quote, empower engineers to make an invention easier and faster so smaller teams can do much bigger things on much shorter time cycles. Asked about fears of an AI jobs apocalypse, Bezos dismissed the premise. He believes that AI will instead produce a labor shortage because, quote, even though you're shrinking the number of people needed by 10x, AI will create 10x more opportunities. Bezos added. There's going to be two earner income households where one earner drops out of the labor pool because there's going to be so much productivity. Alongside their plans to produce AI that can accelerate the entire pipeline for physical manufacturing, Prometheus is also looking at starting a fund for industrial buyouts. Now there is no new news on this front, but in March, the Wall Street Journal reported on talks to raise $100 billion. The fund would essentially take the private equity roll up model and apply it to the manufacturing sector using Prometheus's proprietary technology to improve productivity. All in all, Bezos dismissed the growing pessimism around AI, claiming that view is the opposite of reality. All societal wealth is driven by invention, he said. Six thousand years ago, somebody invented the plow and we all got wealthier. Then much later, somebody invented the steam engine and we all got wealthier. What Prometheus seeks to do is to offer a set of tools that dramatically accelerates that invention loop. Now, for most people, what's interesting is the physical aspect of this. As Chubby points out, the problem is that the physical economy can't be scraped. There's no Internet of manufacturing data to train on. Which is exactly why the reported hundred billion dollar vehicle to buy up legacy industrial companies is interesting. You don't find that data, you acquire the factories that generate it. Or as Dr. Singularity put it, this is how the acceleration escapes the screen and enters atoms. Next up, 1 brutal 1 meta has completed an operational split with Manus in compliance with orders from Chinese officials. Bloomberg reports that Meta has firewalled operations between the two companies. Mana's staff are no longer able to access Meta's data systems, and Meta staff can no longer use Manus tools for internal work. Now, by way of recap, Manus was one of Meta's flagship acquisitions as they reset their AI strategy. Coming into the year, they paid $2 billion for the company just as 2025 turned into 2026. In March, however, the Chinese government opened an investigation into the deal and later barred Manus founders from leaving the country. Manus had attempted to circumvent Chinese tech export controls by first relocating operations to Singapore before courting the acquisition in April, Beijing ordered the deal to be unwound. Despite the workaround, this now leaves Manus in a difficult situation, to say the least. Sources said the company is attempting to raise a billion dollars to fund a buyback, but it's unclear if there are any takers. And while the product has seen updates since the separation was ordered, there is a heck of a lot less attention on Manus since the rise of open source harnesses like OpenClaw and Hermes, and just in general, the agentic push of the core harnesses like Claude Code and Codex. In China, the unwinding of the Manus deal has cast an absolutely chilling effect. Manus strategy of decamping to Singapore before seeking foreign capital was a very common approach, which some even called the red chip corporate structure. With Beijing cracking down on Manus, the Chinese tech industry has received the message that those times are over. The Financial Times reports that numerous prominent startups are looking to unwind their foreign corporate structures to reincorporate in China. Step Fund has completed the process in anticipation of a Hong Kong ipo, while Kimi creator Moonshot as well as Kling are considering doing the same. Eugene Wang, an attorney at Shanghai based Wintel Co. Said whether to dismantle the red chip structure is no longer in question. The key is how to complete the restructuring as cheaply and efficiently as possible. Unsurprisingly, the AI industry is a particular focus. Reports claim that Chinese officials are now seizing passports from key researchers and executives at private firms, which was previously beyond the pale. Both capital and talent are facing a major crackdown as Beijing seeks to secure the strategically important industry. Now over in chip land, backlogs at TSMC are driving Google to consider Samsung for parts of their next generation chips. The information reports that Google is evaluating Samsung's 2 nanometer process for some components of their 10th generation TPUs, codenamed Icefish. Until now, Google has exclusively used TSMC for the full manufacturing process. However, the Taiwanese chip maker has a years long wait list and expects they won't have the capacity to meet demand for quite some time. Customers then are beginning to look elsewhere. Earlier this week it was reported that Google had placed orders with intel for their 2028 production run. Google will still be using TSMC to produce the actual processors, but intel will provide advanced packaging services to mate the processor to networking circuits. Sources said that Google could turn to Samsung for the memory input output die which which marries the processor to memory chips. Basically what's emerging is a complex supply chain where TSMC just produces the processor which requires the most advanced fabs. Other companies including Samsung and Intel, are increasingly producing less sensitive components now. Similar to the intel news from earlier in the week. This doesn't appear to be a case of people being dissatisfied with TSMC's quality. It is simply the case that long wait times are forcing chip makers to look elsewhere to keep up with demand. Meanwhile, private equity companies continue to pile into data center investments as KKR and Nvidia announced a $10 billion construction company. The new company is called Helix Digital Infrastructure and will feature private equity giant KKR and Kuwait sovereign wealth as capital partners. Nvidia will participate in the venture through the deployment of their chips and related infrastructure and power company Vistra is attached to provide energy. Helix said they have 10 billion in committed capital and have disclosed that they will be a wholly owned subsidiary of KKR. Adam Solipsky, the former CEO of AWS, is attached to lead the new venture. In a LinkedIn post he commented, data centers, power and connectivity have all too often been built on separate tracks. That fragmentation has become an industry wide bottleneck. This is slowing down the benefits of AI worldwide. Now this is one of several similar deals in recent months, with Broadcom announcing a similar tie up with Apollo and Blackstone earlier this week. At the same time, real estate firm JLL reports that almost half of data center projects around the country are being delayed. So is it the case that increasingly bringing chip makers, utilities and capital providers together in a single vehicle will reveal itself to be the best way to ensure that all the components come together for a successful project? With Helix, we have another chance to see finally today, Goldman Sachs believes everyone is underestimating the AI infrastructure boom by a fairly wide margin. Now you wouldn't think most people would call the current Forecast for AI CapEx spend conservative, but that's exactly what Goldman strategists led by Ryan Hammond have done this week. The median Wall street analyst believes the AI industry will deploy $920 billion to build AI data centers next year, rising from around $800 billion for this year, according to Hammond's team. Those are rookie numbers in a research note they wrote Consensus 2027 hyperscaler CAPEX estimates are too conservative. Their team now expects $1.1 trillion in AI spending for 2027 as a baseline scenario and 1.4 trillion in a bullish scenario. Now their key assumption is that AI demand is still in the opening innings. They expect to see Token consumption increase 24x through 2030, driven by the widespread deployment of agents, analysts wrote. Higher input costs also put upward pressure on the nominal dollars of capex required to support a given amount of token consumption. In other words, excessive demand will keep the pressure on supply chains driving build out costs even higher. Now you might be thinking that's a pretty bold claim in a week where many on Wall street are focused on a corporate push to rein and token budgets. But Goldman believes that's just noise, with the signal being the expanding order books for the hyperscalers. Now, as you will see very soon, I am firmly in the Goldman camp on this one, and there is in fact one specific new narrative on Wall street that I would very much like to take on. Now, One of the most important AI questions right now isn't who's using AI, it's who's using it. Well, KPMG and the University of Texas at Austin just analyzed 1.4 million real workplace AI interactions and found something surprising the highest impact Users aren't better prompt engineers. They treat AI like a reasoning partner. They frame problems, guide thinking, iterate, and push for better answers. And the good news? These behaviors are teachable at scale. If you're trying to move from AI access to real capability, KPMG's research on sophisticated AI collaboration is worth your time. Learn more@kpmg.com US sophisticated that's kpmg.com US sophisticated Here's a harsh truth. Your company is probably spending thousands or millions of dollars on AI tools that are being massively underutilized. Half of companies have AI tools, but only 12% use them for business value. Most employees are still using AI. To summarize Meeting Notes if you're the one responsible for AI adoption at your company, you need section Section is a platform that helps you manage AI transformation across your entire organization. It coaches employees on real use cases, tracks who's using AI for business impact, and shows you exactly where AI is and isn't creating value. The result? You go from rolling out tools to driving measurable AI value. Your employees move from meeting summaries to solving actual business problems, and you can prove the roi. Stop guessing. If your AI investment is working, check out section@sectionai.com that's S-E-C-T-I-O-NAI.com so coding agents are basically solved at this point. They're incredible at writing code. But here's the thing nobody talks about. Coding is maybe a quarter of an engineer's actual day. The rest is standups, stakeholder updates, meeting prep, chasing context across six different tools. And it's not just engineers. Sales spends more time assembling proposals than selling Finance is manually chasing subscription requests. Marketing finds out what shipped two weeks after it merged, ZenCoder just launched ZenFlow work. It takes their orchestration engine, the same one already powering coding agents, and connects it to your daily tools. Jira, Gmail, Google Docs Linear Calendar Notion. It runs goal driven workflows that actually finish your standup brief is written before you sit down. Review cycle coming up, it pulls six months of tickets and writes the prep doc. Now you might be thinking, didn't openclaw try to do this? It did, but it has come with a whole host of security and functional issues, which can take a huge amount of time to resolve. Zencoder took a different approach. SOC 2 type 2 certified curated integrations, tighter security perimeter, enterprise grade from day one, model agnostic and works from Slack or Telegram. Try it at zenflow Free this episode of the AI Daily Brief is brought to you by Outsystems, a leading agentic systems platform built for the enterprise. Organizations all over the world are building, orchestrating and governing agentic systems on the Outsystems platform, and with good reason. Outsystems open and unified platform allows teams to architect, deliver and scale governed agentic systems with agility. Teams of any size and technical depth can use Outsystems to build, deploy and manage AI apps and agents quickly and cost effectively without compromising reliability and security. With Outsystems, you can rapidly launch ideas from concept to completion. It's the leading agentic systems platform that is unified, agile and enterprise proven, allowing you to accelerate growth, reduce operational friction and deliver real enterprise impact with AI outsystems, build your agentic future. Welcome back to the AI Daily Brief. Oh friends, it's my favorite time of year. It's that time when some new set of numbers, or in this case, chart, inflame everyone on Wall street to go into an absolute frenzy with their AI counter narratives, proving to themselves finally, that this time they're right and the bubble is about to burst. Yes, the speed at which the investors have gone from token maxing to token panic is head spinning. And yet the chart in question, this chart, the Silicon Data LLM Token Expenditure Index as shared by Citadel Securities Shockingly, and I just mean shockingly, doesn't say what everyone on social media is saying it says in this episode, I'm going to explain why this chart, which shows a big scary downward line on something called the Token Expenditure Index, has nothing to do with token demand, nothing to do with token volume, and nothing to do with actual token expenditure. Which is not to say that there is an interesting signal there. It's just not the signal that Wall street is trying to look for. And why this matters to you, even if you are not an investor, is that the story that it is telling is part of the shift from the token subsidy era to the token scarcity era that we've been tracking and does have some interesting implications for how we all built. All right, so let's talk about where this chart started to come online. It is obviously not coming into a vacuum. If you've been listening closely over the last couple of weeks, you've seen the professional investor class really start to take notice of headlines like this one about Walmart capping usage of their internal AI tool because there was too much demand, or even more of Uber setting spending caps after it blew through its token budget in the first four months of the year. This is the natural follow up to that. And in this context, Citadel just published a research note called Tokenomics. The primary chart that it shares is the one that I just mentioned before, the Silicon Data LLM Token Expenditure Index with that big scary downward line. This of course led to the perhaps expected onslaught of social media commentators implying that this was somehow some very scary and big deal failed crypto founder Moshe writes, Citadel is one of the most significant hedge funds and they just dropped tokenomics and it's not what you would have expected. That scary sentiment, of course, went viral, getting over a half million views. There were also endless AI slot posts like this one from Thierry from Arvi, who wrote Citadel securities just put institutional weight behind what the AI bulls won't say out loud when one of the most sophisticated trading firms on earth starts writing about AI in the language of cost curves and rationing instead of limitless demand, the conversation has quietly changed. The hype was about what AI could do. The reckoning is about what it costs. Now, by the way, if you're asking me why I say that's an AI slot post, I would point you over to another Twitter post, this time from Nicholas Mugali, talking about a related topic, OpenAI's plan to cut token prices that, oh, weirdly ends with the same line. The hype was what AI could do, the reckoning is what it costs. And then of course we have Zero Hedge, who have been nearly quivering with excitement over a new doom narrative to peddle token prices down six days in a row. Longest streak since January. Make that seven days. Token price index slide back to mid January levels, fading much of the agent frenzy of the past three months. Of course, they made their point explicit with a blog post called Tokenomics Equals Panic. And unfortunately, it's not just the zero hedges. Real Vision's Andrea Stenor Larsen writes, this is the chart that everyone should be watching. If the token pricing rolls over, everything from the memory trade to the broader hardware and data center trade is over for this cycle. In my humble opinion, the whole setup depends on this. Now, as you might be able to tell by now, I am absolutely allergic to this sort of pattern of discourse when I see the whole setup depends on this or someone loudly proclaiming something that is so obviously counter to the experience that everyone is having, suggesting that somehow the agentic frenzy of the past three months has now returned to some pre agentic state. I immediately start to ask what's actually going on here? What is the chart actually trying to say? Did Citadel securities, who have been held up as evidence of all of this, actually even make any of the arguments that people are crediting to them? So let's talk about this chart first. As I think it's clear from those posts, the implication that people are trying to suggest is some combination of demand for tokens going down, volume of tokens going down, or and this I guess is reasonable given that it's called the Token Expenditure Index, the total expenditure on tokens going down. But that, it turns out, is not what this chart is actually trying to measure. And I can prove that to you by going to Silicon Data themselves who took to Twitter to clarify. They write our LLM Token Expenditure Index should really have been named the Token Expenditure Price Index because it's an expenditure or Usage Weighted Average Token Price Index. It tells you how much currently the entire market AI is paying for a million LLM tokens, irrespective of models. The naming might have led to some misinterpretations as some seem to have interpreted the index as either the total volume of tokens used or the average price of tokens. In reality, the index captures something much more subtle than either interpretation. It tells us the marginal willingness to pay for LLM models now. Much credit to them for trying to clarify, even though the more dramatic assumptions would get their name out there more. Although I even disagree with what they say their chart is saying, as I'll come to in a moment. But the specific and important note is that what this chart is measuring has nothing to do with demand, nor total volume, nor total expenditure. What this measures is the average amount the market is paying right now for a million tokens. So what this chart is actually saying and this line decrease is that in mid June the average price that the market was paying in practice for a million tokens had gone down from the peak at the beginning of June and was back at the level that it was paying around the beginning of May. Let me say that again, the average cost of a million tokens that buyers were paying in mid June had gone down from a peak of what they were paying for a million tokens at the beginning of June and was around what they were paying for a million tokens at the beginning of May. Now as we'll discuss, there is some interesting signal there, but what it's not is signal again on anything related to total demand for tokens, total volume of tokens consumed or total expenditure on tokens, it's just about the average price paid for a million tokens. Now here's where I disagree with their assessment. They say it tells us the marginal willingness to pay for LLM models. The idea being that if you see the average price paid for a million tokens go down, it means that some portion of the market is necessarily shifting their buying behavior from the most expensive basket of tokens to two lower cost options. And I think that that's partially true, or at least that could be one interpretation of what this data is saying. It could also be saying, however, that the costs for tokens on offer from the frontier have gone down. Now we know that that's not the case in this period, but as we'll discuss, that might be something that happens coming up soon. And more importantly, certainly for any listeners of the AI Daily Brief, it will come as no surprise that companies appear to be looking for lower cost token options. As I have loudly said on this show and elsewhere, I think every AI company is now in the token efficiency business. The equation is really simple. The shift from assisted to agentic use cases radically increases the amount of AI that companies use. Because of the real constraints of the physical world, there are only so many tokens to go around. As demand starts to outpace supply, the prices that people pay get high. And companies which never had to think about token efficiency or mixed basket models of some types of tokens for one use case and other types of tokens for other use cases now all of a sudden do. That process is exactly what we've been tracking closely for the last couple of weeks. And so in many ways this chart just reflects exactly what we've been talking about. The reason however, that I think it's even a little bit less impactful than they think, even when interpreted correctly. And why I don't believe that at least in full, they are right to say that. It tells us. The marginal willingness to pay for LLM models has to do with the sources of their data or specifically the data they don't have. The silicon data. LLM token expenditure index does not measure anything about the average prices that people are paying directly to the major labs themselves. They have no insight, in other words, into the direct customer to OpenAI relationship or the direct customer to anthropic relationship. Now obviously on a percentage basis, the vast, vast majority of token expenditure is going to be direct to those companies. So what source of data could they possibly have? The silicon data index draws only from third party token routers. Wait you might be sitting there saying, you mean the token routers that people explicitly go to use to get access to cheaper tokens? Yes, the token routers whose entire purpose in the market is is to route different use cases more efficiently to lower cost and better models for their particular need, bringing costs down. So this chart, which is neither about total demand, nor about total volume, nor about total expenditure, but just the weighted average price of a million tokens, is based on data from companies whose entire purpose in the market is to provide lower cost alternatives. My argument then is that this is going to greatly exaggerate actual shifts in behavior away from high cost frontier models and towards lower cost alternatives. Which again, is not to say that there isn't signal. I think that one could view this as a really good leading indicator of where advanced AI users are oriented. In fact, I would argue that we're likely to see some follow on behavior over the course of the next six to 12 months that looks fairly similar even from the companies that have their direct relationships with OpenAI and Anthropic. But what this certainly doesn't reflect is the average experience of buyers in the market right now. It just simply doesn't. And to be clear, the points that Citadel is even making in this are much less bombastic than those who are screenshotting it all over X. In fact, I would bet that if you go read the note, you probably agree with a lot of it, and a lot of it will remind you of what we've been talking about here. The simplest version of the point that they're trying to make is that not all AI demand looks the same anymore, and increasingly will be separated into different categories. They put it as a bifurcation in frontier versus everyday AI usage, which sounds not dissimilar to the discussion here a couple of days ago about whether consumer and normal ChatGPT usage should even be considered as the same thing as workai. At no point does Citadel argue that the implications are some cratering of demand for the most expensive tokens, they write. We do not think this implies that the frontier of inference intensive AI will be abandoned, only that it is likely to be concentrated among a narrower set of firms with the balance sheets to absorb the compute costs, the research depth to deploy it effectively, and most important, the operating domain to scale the rewards from solving genuinely hard problems. Another way to put that is that they're arguing that most of the most expensive AI is increasingly going to flow to the firms that can use it best. But in a world of token scarcity where there's Already not enough of the best AI to go around. Doesn't that just sound like the market efficiently allocating the most expensive AI to the people who can use it the most effectively? That's not an AI bubble popping, that's an AI market rationalizing. By the way, despite the construction of that statement, that was absolutely not written by an LLM. It just came out of my mouth. But maybe I'm spending a little bit too much time with the LLMs if now I'm talking like that. Now there's another really important part of this discussion. As people have been talking about all of these caps being set, one of the things that gets lost in the discourse is that those are the very most advanced firms who have already consumed sufficient AI to get to the level of agentic usage where they would have to start putting caps. The vast majority of companies aren't even close to that. And here's one example. Finance company Ramp tracks how their customers spend money on AI. They've been tracking it for quite some time now. And they note that as the share of businesses using AI approaches 100%, the focus at their economics lab has started to shift to tracking the intensity of adoption. They are now tracking things like spend per employee. And that spend per employee is a really important number right Now. The top 1% of firms, those who are fully AI pilled, are spending about $7,500 per employee on AI. That's a lot, right? Those are the type of numbers where you're gonna see firms start to really ask what the ROI of that is or start to consider caps and more efficient options. But again, that's just the top 1%. When you move down to the top 10%, that number comes all the way down to $610 per month, which you will note is less than half of Uber's $1,500 monthly per employee cap. And the median firm in their index. Right firmly in the middle of AI usage across all of Ramp's customers, who, by the way, are going to be more tech savvy than the average business. AI spend sits at 1138, not $1,138, $11.38. If we are looking at the market implications of a shift in the basket of token consumption towards lower cost options, we have to contrast that against total growth in token demand and total growth in token volume. In other words, actual token expenditure when the median company is still only spending 11 bucks a person on AI, the sheer amount of growth in total AI that will be consumed. It is very hard for me to imagine a scenario, at least in anything in the short or medium term, where the growth in the total amount of AI consumed does not massively, and I mean massively, outweigh any shift in the balance away from the most expensive tokens to less expensive tokens. Put differently, if every firm followed Uber's example and set the cap at $1500 per month, the total increase in the Market size for AI as firms go from $11.38 per employee per month to $1500 per month is going to dwarf any lost revenue on the other side because companies start to get more efficient. And what about these reports that OpenAI is considering drastic price cuts as a preemptive strike against anthropic who they think might also cut costs in a vicious price war for customers? Will that tank revenue for the whole industry? Maybe. But then you have to ask about token margins. Analyst Max Weinbach wrote. If OpenAI does drop token pricing, this is likely because they've heard from customers they can't adopt AI at volume at the current pricing margin is high now for served tokens, they could cut prices by like 60% and still be profitable, in my opinion. Now Max isn't coming out of nowhere. While no one knows for sure the margins except the labs themselves, Weinbach has done a lot of work to get to the unit economics of API tokens, and his estimates are pretty similar to a lot of the other estimates that I've seen, which tend to guess something like 70% margins on API pricing for the most inference intensive tokens. So summing up the argument is not that this token expenditure price index isn't a useful signal. It's telling a similar story to the one that we've been exploring here and that I think will shape the next period of especially enterprise AI. But at the end of the day, all of these shifts look a lot more to me like markets doing what markets are meant to do and figuring out how to allocate scarce resources at the right price to different types of customers. Look, if you take nothing else away from this, if you ever see someone end a tweet with an ellipsis, run in the other direction. For now, that's going to do it for today's AI Daily Brief. Appreciate you listening or watching. As always. And until next time, peace.