Future Outlook and Policy Shifts

Today on the AI daily brief We're recapping the month of May, one of the single most consequential AI months we've had in a very, very long time AI Daily Brief is aaily podcast and video about the most important news and discussions in AI All right, friends, quuick announcements before we dive in First of all, thank you to today's sponsors, KPMG, Robots and Pencils, Zencoder and Out Systems Get an ad free version of the show, go to patreon. com slash Ai daily brief or you can subscribe on novel podcasts. If you want to learn more about sponsoring the show, send us a note at sponsors at Ai dailybrief. a Today is the first day of June. And while I don't always use the first of the month to look back and reflect on the month that was, in this case, I think it's pretty important. We are now experiencing the second big AI transitional moment of twenty twenty six Although you could argue that the first actually began in the end of twenty twenty five in the November and December period where Claud Code and Codex were on the rise, and we got the series of models, including Opus four point five and GPT five two, which of course all came together to unleash the true agent era at the beginning of twenty twenty six At this point, you've heard me talk ad nauseum about the fact that everyone went home for the holidays, started hacking around on clawed cod or with these new models, and discovered that what you could do had changed fundamentally That led into the open claw period where all of a sudden, people were getting their hands messy with harnesses in a new way and just an absolute explosion of new behavior around AI. Not only were software engineers actually using agentic coding tools in a mainstream way, not just vibe coding prototypes and things like that, but actually pushing agent created code into production But the people who had previously just been knowledge work style vibe coders using tools like Lovable and Repplet were moving to a way more advanced period, building much more extensive and complex applications in harnesses like Clawud Code and Codex, or even spinning up and building entire agents and agentic systems thanks to tools like openpClaw and later Hermes Re signaling that the times had changed Now, one of the consequences of this shiftting behavior is that the most relevant economic unit for AI companies ceased to be the seat and instead shifted to the token. And what I mean by that is that revenue for open AI and anthropic was no longer constrained to what percentage of their users they could convert into paid seats either on the consumer or on the enterprise side But instead, how much API revenue they were getting through people actually using and consuming tokens? API based usage looks very, very different from an economic standpoint than seat based usage put just a little personal example on it When I dropp the personal context portfolio builder at contextsportfolio. ai turnurns out a lot of you wanted to use it so much that it racked up about a five thousand dollars bill over the first six weeks or so of it existing Compare that five thousand dollars to the two hundred dollarars a month claw seat that I'd been paying for forever You're talking about more than two years worth of clawed max seats in spend with a single six week project Now, this is of course, where the massive explosion in revenue came from for the foundoundation model companies this year OpenAI searched to thirty billion do in ARR. and Anthropic went even farther, even faster, getting as we recently learned, all the way up to forty seven billionars in annualized run rate as of right now And To go from three billion dollars in revenue, which was where they were at the beginning of twenty twenty five to forty seven billion doars in annualized revenue a year later is just staggering And the realization of what this meant kind of where this month started This was perhaps best captured in an article in the Atlantic called So aboutbout that AI bubble And while the author themselves wasn't apologizing for getting it wrong or anything like that arle itself served as a bit of a Ma culpa for the Q four period in which the media's obsession was the idea of AI as a bubble. Now remember, in Q four, the argument had never been that AI wasn't invaluable It's that the ability for the foundoundation model labs, i. e. the token sellers to realize that value felt too many as unlikely to be able to keep up with the cost of this incredibly extensive AI infrastructure buildout in the form of all these compute deals and all the things that we heard about throughout the back half of twenty twenty five That starts to look very different when you see the type of growth numbers and frankly, the type of pure raw revenue numbers that companies like Open AI and Anthropic were putting up And again, because it was not based on seats, which have a natural and imaginable cap, and was instead based on tokens, where we were seeing these numbers despite it being the very, very beginnings of us scratching the surface of how much AI we could use. A lot of people, to their credit, readjusted their priors about the possibility of an AI bubble and really calibrated up their expectations of just how big this could all get Now this part of the story has never gone away, and one of the big themes throughout May was as summed up in a single headline from the New York Times, Howanthropic Got So Big So Fast. closed the month with a sixty five million dollars fundraising round, valuing them just under a trillion dollars. And there was also a competitive dynamic to this, with the month seeing anthropic racing out ahead of competitor openp AI when it came to business adoption, according to statistics from Ramp. And we even got what Anthropic anticipates to be not only their first profitable quarter, but the first profitable quarter for any of the big foundoundation model labs Once again, the psychological impact of achieving profitability with this type of growth rate, with this type of expenditure really reset people's expectations. And yet, as we're coming out of the month, we are once again in the midst of a massive shift in understanding You could sum this up with a recent AxiOos article, AI sticker shhock hits corporate America I believe that the period we are heading into now is one that is fundamentally defined by constraints And the second half of this month and really the meta trajectory of this month all about starting to realize what that meant and what it was going to look like. Going back a couple of months previously, Uber made headlines in April when its CTO shared that the company had burned through its entire twenty twenty six AI budget in just four months Now, on the one hand, when I saw this article, it didn't seem all that surprising to me in the sense that you have to think that those token budgets were being figured out, probablyably not even in the November or December period where the Opus four five level models had started to really be brought to bear, but before that And so of course, they weren't expecting the type of usage that we were going to see once agentic AI really came online And yet at the same time, this became the capstone story for a lot of different things going on. Every day it felt like we were getting some new token maxing story, where some company or another was creating some sort of internal leaderboard, incentivizing people to consume as many tokens as they possibly could I did a couple of episodes about this whole token maxing idea, actually providing more of a defense for it, evenven though I think that a lot of people's first instinct, which is completely reasonable, is to point out the truism of Good Heart's Lw that once you start to measure something, it ceases to be a good measure because people just start to game the measure rather than whatever it was intended to measure And what's more, in the context of token maxing You're measuring an input rather than an output when inherently and in the long run, outputs are all that's going to matter Now my argument was, of course, about the value of experimentation and in fact, the necessity of experimentation in a period where no one knows the best way to use these tools But it would be very clear very quickly that there would be consequences of this idea of token maxing that would rear their ugly heads soon Once again, it was Uber that helped shift the conversation when in an interview with Uber's COO this time, The COO shared a lot of skepticism about how much value they had actually gotten from that AI budget that they had burned through in just a few months. This got reinterpreted and reduced to headlines like the informations. Uncharacteristically oversimplified, Uber COO says AI lacks ROI and really has brought up this whole conversation once again embodied in this AI sticker shock piece from Axios. Now my contention is that we are in a secular shift from one business model paradigm of AI to another. In short, we're moving from an AI subsidy era to a token scarcity era So what do those terms mean and what are the implications Well, first of all, let's talk about the idea of an AI subsidy era The idea of the subsidy era is that for some time, especially the max level subscriptions from the labs, the one hundred dollar, two hundred dollar, three hundred dollar a month type of subscriptions While perhaps being profitable for some portion of users, werere very, very frequently very unprofitable. We don't know for sure estimates around the actual value of the tokens that you could theoretically consume on one of those two hundred dollars a month plans It'd sometimes be ten or twenty times that two hundred dollars value In other words, the most active power users of those Max plans were sometimes getting two thousand, four thousand, five thousand, even ten thousand dollars of value out of just two hundred dollars a month. And that was the AI subsidy There was a lot that was really great about that. I basically haven't for a second at any point over the last six months, pauseed to consider the financial implications of any dumb idea I wanted to try on GodeX or Claud Gode. I just start building it, I just start releasing it. We have been in a letter rip kind of place That may work for me as an independent content creator whose job it is basically to do that and then share what I learn with you all. But for companies that starts to get a little bit tricky On both sides of the equation. In provider companies, there's only so long they can subsidize that type of usage to the tune of ten or twenty X. and if they cease to subsidize it There's only so long that the companies that are now paying for it on a per usage basis can actually afford to do so And that shift in business model is the first big implication of the AI subsidy era ending Over the course of the month, we had a number of different companies announce that they were shifting from a flat seat sort of model two more usage based billing One of the first of those was GitHub C pilot who actually made the announcement at the very end of April In their announcement post, they wrote Cope pilot is not the same product as it was a year ago. It has evolved from an in editor assistant into an agentic platform capable of running long, multi step coding sessions, using the latest models in iterating across entire repositories Aentic usage is becoming the default and it brings significantly higher compute and inference demands Today, a quick chat question and a multi hour autonomous coding session can cost the user the same amount The GitHub has absorbed much the escalating inference cost behind that usage, but the current premium request model is no longer sustainable A a few weeks later at Google IO, we got something similar, where yes, nominally the headline was that they had reduced the cost of their premier plans. Gemini Ultra Plan dropped to two hundred do, and they also introduced a new one hundred dollar one plan But they also introduced on top of that usage limits and usage base billing on top of those limits. Meaning that really for a lot of power users, this was going to represent a big cost increase Same with Anthropic who specifically focused on billing around third party tools, meaning that while the subsidy persists if you are using an anthropic specific harness like clawed code, as soon as you move to a different type of harness, or a different environment that's not owned byanthropic, you're shifting to per token billing with huge financial consequences that created frankly a bit of an uproar throughout the month So the shift in business model was one response to the AI subsidy era ending and the token shortage era beginning, but it's not the only one 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 one point four million real workplace AI interactions and found something surprising. The highest impact users aren't better prompt engineers. 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Build your agentic future In addition to that business model response, we've also seen a big uptick in the recognition that when it comes to really adopting the full capabilities of Aentic AIW Companies are just gonna need a lot of help Already, even coming into this year, there was what we call a big capabilities overhang In other words, a space between what the AI models could do and what most companies were actually getting out of them. But you sprinkle a little bit of agentic capability on top of that, especially as it moves out of the realm of just coding and into the realm of every type of knowledge work And that capability overhang has just completely exploded So much so that this month, both Open AI and Anthropic announced initiatives to more directly support enterprise level transformation The forms that they took are a little bit different OpenAI announced their deployment company, which is a majority owned but separate venture to put forward deployed engineers inside big clients. while on the anthropic front They partnered with Blackstone, Heman and Freeman and Goldman Sachs to also launch a separate as yet unnamed entnerprise AI consulting or services firm The guts of which are our friends at Fractional, so congrats to them But in that one, Anthropic has a smaller stake than OpAI does in their deployment company. In either case, they both represent the same instinct, which is that in this new period of agentic change and token shortage, more support for enterprise deployment is going to be needed Yet at the same time, while these consulting and services lines may be a necessary budget line item, the core reality of the period that we're going into is one where companies have to be much more diligent about managing AI costs And this is not going to be an easy thing to do A lot of the companies that we had heard about doing some sort of token maxing experiment, are now scrapping their AI leaderboards, with Amazon being the most recently announced example. And it's not just because of concerns around gaming those leaderboards or anything like that Also because of those shifts that we just saw in the business model, where it's just too expensive to token max now fundamental and anchor characteristic of the world that we are moving into is one where there is a structural shortage of AI tokens There simply is not enough compute to produce all of the AI that people would want to consume, meaning that the cost of AI is going to be high with all sorts of different potentially problematic implications A cool thing, however, is that we're already starting to see market based responses to that Curser announced the next generation of their composer model composer two point five And not only is it performing well, it's doing so at a much lower cost than Opus fourty seven and GPT five five. So part of response to the token shortage is market based innovation to bring the cost of tokens down without sacrificing performance Google seem to recognize that this might be part of their play as well, giving lip service on the main stage at Google IO this month to the idea of Gemini three point Fash being a way for enterprises to cut costs. However, in practice, that's not really how it's playing out As artificial analysis points out, Gemini three point five flash costs about five times as much as Gemini three flash both based on higher token prices as well as higher token usage which isn't to say that Google might not have some better shots on goal when it comes to market based solutions for token scarcity The Gemma series of models, which are their smallest and cheapest models, are actually seeing really fast adoption with the adoption of those models outpacing similar Chinese models, and a sign that people and companies are adapting to this new economic reality Now one of the things that I would expect is some pretty serious price warrying going on, with China running its tried and true playbook of artificially keeping prices low to create a competitive advantage, which it seems like might be happening around Deep Sek. who has just made a recent temporary seventy five percent price cut on their V four model permanent To be clear, that's not because DeepSeak has figured out some way to serve those tokens at seventy five percent of the cost. it's because in a world of token shortage, a lot of companies around the world are going to be forced to look away from the state of the art open AI and anthropic models to more affordable alternatives, and Deepseek wants to be right there scooping up that business Another thing that's happening in the context of this token shortage is that everything regarding AI infrastructure is as Swix, Sean Wang put it going vertical Inference provider Base ten is raising a billion dollars at an eleven billion dollars valuation, more than doubling its valuation from just one quarter ago Open Router, which can help developers automatically toggle between models that have different trade offs in terms of cost, efficiency, performance, etcera, raaised one hundred thirteen million dollars series B becoming an AI unicorn. And even more than that We're seeing some big realignments in the broader infrastructure world as well The most notable of these undoubtedly and something that happened this May that I think will have fairly dramatic implications for the industry as a whole Elon shifting into a very different type of role visa vis the AI industry Upntill now, Elon's headliner role in the AI space has been as one cheerleader of Grock, which candidly has never at any point really caught up to any of the leading models, and two as main antagonist of Sam Altman and Open AI Elon's lawsuit against OpAI this month was thrown out based on statute of limitations considerations, but that wasn't the big thing that changed. The big thing that changed is that whether it was because of economic opportunity or an assessment of the reality of where Grock sat relative to the models, or simply wanting to stick it to Sam Altman, Elon decided to team up with Anthropic The first announcement was that SpaceXAI, which is the new XAI inside of SpaceX, basically SpaceX's AI division, would allow Anthropic to use Colossus one to provide additional capacity to Claud Now Anthropic has been severely compute constrained throughout the year, causing major headaches for users. so this was very welcome news to clawed users and started to show this realignment happening However, then just a couple weeks later we found out that not only would Anthropic be using Colossus one which was SpaceX slash XAI's first big data center that they spun up at the end of twenty twenty four, But at least on a temporary basis, Anthropic would also be using Colossus too In the span of just a couple of weeks, SpaceX became a neoc clloud with absolutely massive implications for the upcoming IPO I could talk about this basically endlessly, but I think Elon moving into a place where he is focused on a thing that he does better than just about anyone, which is building big, ungodly physical infrastructure using that as his way to leverage and influence the AI race by virtue of being a self appointed czar of compute, And by providing a clear line pathway between SpaceX as Neo Cloud provider right now and future orbital datac center provider, just makes the SpaceX IPO make so much more sense and context First of all, it allows investors to get excited about a different part of the AI stack, an increasingly important infrastructure part of the AI stack as opposed to just investing in an also ran and gck And by the way, for those of you who love Grock, I'm not trying to yuck your yum. There are lots of reasons that it still has value to many people. and I don't think Elon has given up entirely on it I just think that the trendline is pretty clear at this point And it's very clear that Wall Street is right now extremely excited about the infrastructure side and effectively the entire AI supply chain. This month saw AI memory stocks absolutely surge with companies like SK Heinix and Micron becoming trillion dollar companies. And now even Meta is talking about the possibility that they could also become a cloud business as well For the first time in a very long time, Meta's AI narrative wasn't completely freaking out investors, because if they can go sell back the one hundred thirty billion dollars or whatever worth of compute that they're investing in at a premium, it significantly derisks that big CapEx spend I think the theme of the compute buildout as a response to the token shortage era is going to do nothing but grow in June, especially surrounding the SpaceX IPO Just to put one more fine point on this, when about a month and a half ago Elon started talking about orbital data centers, it was getting a lot of sci fi blank stares Now the narrative has shifted so much that Jeff Bezos is talking about orbital data centers not as a whether we can or if we will, but instead saying that two to three years feels to him to be a little ambitious as a timeline for them Anyway, watching what happens around the SpaceX IPO will be hugely instructive, I think, in how the market is digesting this token shortage But before we wrap up, there are a couple other things that happened this month that I did want to point out as well It was relatively quiet in terms of new model releases. We did just at the end of the month get clawed Opus for eight, but part of what was interesting about that to me was how much the emphasis has shifted from models alone to the harnesses they sit in creator and entrepreneur Ry Brown wrot Unless it's a major breakthrough in model capability, I'm much more excited for super apppp updates like Codex and Claw desesktop. There's so much to be unlocked by making those surfaces better That was his response to the release of Claud Opus four eight, basically saying, I'm not interested if it doesn't come with a Claudd code update Greg Gisenberg went farther saying, I didn't cover Claud Opus four point eight on my pod because I don't think it's meaningfully better than GPT five five as of may twenty ninth. We're entering the era where model releases start to feel like iPhone releases. Remember when every new iPhone was a genuine leap? Now it's a slightly better camera and you can't really tell the difference. That's where models are heading four, six to four, seven to four, eight, each one is a little different. Nobody can agree if it's better or worse. The benchmarks say one thing, the vibes says another The thing that actually matters right now is what's happening around the models code shipped dynamic workflows this same week and that genuinely changes what one person can build And indeed, that was definitely the story of this month We talked a little bit about this new dynamic workflows approach as part of the Ous four eight coverage. and of course, May was also the month that slash goal became a real primitive, jumping from Codex where it started to something that's in Clawd code as well If you want to learn more about sllash Gal, go check out the episode we did for week's Long Read Sunday, which is a primer on how to use slash Gal, especially for knowledge work. On the narrative side, May might go down is the month where Sam Altman and Dario Amadeay finally figured out that they probably shouldn't tell everyone that the thing that they were building and which was making them unfathomably rich was going to take everyone's jobs and livelihoods and totally change the world in ways that didn't all seem that particularly good Now, I would say that the Dario reversal is much more nascent than the Sam reversal, with Sam actually putting in some time to articulate why he was changing his opinion, arguing that evidence had suggested that he had just overestimated how the transformation was going to happen, much to his delight I think that this has opened up some new narrative space, which allows for a lot more nuanced to conversation around AI on a policy perspective, for which I'm very thankful Now speaking of the policy side, you do see a lot of jockeying, particularly on the Democratic side of the aisle right now for how they're going to interact with AI And what's interesting is that it's very clear that there's not one fully embraced approach yet You've got the Bernie and AOC wing who are calling for datacenter moratoriums, but you now have Elizabeth Warren coming in basically saying let's not stop AI with dataenter moratoriums. let's get our cut She wrote an op ed last week in time called Why We need to Tax AI, and I think that the conversation about novel taxation structures like token taxes is going to become a more prominent theme in the months to come And certainly when it comes to politics in a backwards looking way, the story of May was all about the White House getting fully involved in model releases surrounding the release or non release of Anthropics mythos And what's interesting about this bringing it back to the token shortage metae of the moment is that not only is the White House thinking about cybersecurity issues, they are also positioning the US government relative to the token shortage, with a story coming out at the very end of April and the very beginning of May, that part of the reason that they were opposing Anthropics's plan to expand access to Mythos was that they knew that there was a token shortage and they didn't want other people using up the tokens that they might want to use So what comes next? Certainly a huge theme this month is going to be the SpaceX IPO. It's also likely that we'll get another open AI model pretty soon Anthropic has explicitly said that some version of Mythos will be here in the coming weeks. so I would expect that in June as well oververall, I think that a lot of the immediate term next period is going to be all about How to recalibrate for an era of token shortage business model changes to different approaches in the enterprise, to new policies. We are in for a big shift and one where I think that there are going to be serious advantage opportunities that can accrue, especially to enterprises who figure out how to manage this more quickly and more efficiently than others We will discuss exactly how I think companies can come out this token shortage in episodes to come But for now, that's going to do it for today's AI Daily brief. I'm back from traveling tomorrow and we will be back with our normal formats For now, however, appreciate you listening or watching, and until next time Pace.