# The AI Token Shortage Begins — Transcript (2026-06-01)

https://aidailybrief.ai/e/2026-06-01 · Listen: https://pod.link/1680633614

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[00:00:00] Today on the AI Daily Brief, the we're recapping the month of May, one of the single most consequential AI months we've had in a very, very long time 

The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI [00:00:15] All right, friends, quick announcements before we dive in.

First of all, thank you to today's sponsors

KPMG, [00:00:30] Robots and Pencils, Zencoder, and Outsystems

To get an ad-free version of the show, go to patreon.com slash aidailybrief, or you can subscribe on Apple Podcasts. If you wanna learn more about sponsoring the show, send us a note at sponsors at aidailybrief.ai. Today is [00:00:45] 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

now, we are now experiencing the second big AI [00:01:00] transitional of 2026

Although you could argue that the first actually began in the end of 2025, in the November and December period where, Claude Code and Codex were on the rise, and we got the series of models including Opus 4.5 GPT 5.2 Which of [00:01:15] course all came together to unleash the true agent era at the beginning of 2026.

At this point, you've heard me talk ad nauseam about the fact thatthat everyone went home for the holidays, started hacking around on Claude



Code or with these new models, and [00:01:30] discovered that what you could do had changed fundamentally.

into that led into the open Claude period, where all of a sudden people were getting their hands messy withharnesses in a new way 

And 

And just an absolute explosion of new behavior around AI



not [00:01:45] 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. previ- but the people who had previously just been knowledge work style vibe [00:02:00] coders using tools like Lovable and Replit

We're moving to a way more advanced period, building much more extensive and complex applications in harnesses like Claude Code and Codex, or even spinning up and building entire agents and agentic systems [00:02:15] thanks to tools like OpenClaude and later Hermes



really signaling that the times they had changed. Now, one of the consequences of this shift in behavior is that the most relevant economic unit for AI companies ceased to be the seat [00:02:30] and instead shifted to the token. 

And 

what I mean by that is that revenue for OpenAI 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 [00:02:45] 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

To put just a little personal example on it

When I dropped the [00:03:00] personal context portfolio builder at contextportfolio.ai, it turns out a lot of you wanted to use it So much that it racked up about a $5,000 bill over thefirst six weeks or so of it existing

Compare 

that $5,000 to the $200 a month Claude [00:03:15] seat that I had been paying for forever

You're talking about more than two years worth of Claude Max seats in spend with a single six-week project Now, this is of course where the massive explosion in revenue came from [00:03:30] for the foundation model companies this year. OpenAI surged to 30 billion in ARR, and Anthropic went even farther, even faster, getting, as we recently learned, all the way up to 47 billion in annualized run rate as of right now

To go [00:03:45] from $3 billion in revenue, which was where they were at the beginning of 2025, to 47 billion 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 [00:04:00] best captured in an article in The Atlantic called So About That AI Bubble

author, and while the author themselves wasn't apologizing for getting it wrong or anything like that, 

the 

article itself served as a bit of a mea culpa for the [00:04:15] Q4 period in which the media's obsession was the idea of AI as a bubble

Now remember, in Q4, the argument had never been that AI wasn't valuable. It's that the ability for the foundation model labs, i.e., the token sellers, to [00:04:30] realize that value felt to many as unlikely to be able to keep up with the cost of this incredibly extensive AI infrastructure build-out in the form of all these compute deals and all the things that we heard about throughout the back half of 2025 

That 

starts to look very [00:04:45] different when you see the type of growth numbers, and frankly, the type of pure raw revenue numbers that companies like OpenAI 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 [00:05:00] 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 [00:05:15] 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, "How Anthropic Got So Big So Fast". They closed the month with a sixty-five million dollar [00:05:30] fundraising round, valuing them just under a trillion And there was also a competitive dynamic to this, with the month seeing Anthropic racing out ahead of competitor OpenAI when came to business adoption according to statistics from Ramp

And we even got what Anthropic [00:05:45] anticipates to be not only their first profitable quarter, but the first profitable quarter for any of the big foundation model labs

Once again, the psychological impact

of achieving profitability with this type of growth rate, with this type of expenditure, [00:06:00] 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

up with an you could sum this up with a recent Axios article AI sticker shock hits corporate [00:06:15] 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, was all about starting to realize what that meant and what it was going to [00:06:30] look like

Going back a couple months previously, Uber made headlines in April when its CTO shared that the company had burned through its entire 2026 AI budget in just four months. Now, on the one hand, when I saw this article, it didn't seem all that [00:06:45] surprising to me

in the sense that you have to think that those token budgets were being figured out, probably not even in the November or December period where the Opus 4.5 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 [00:07:00] 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 where some company or another

was creating some sort of [00:07:15] internal leaderboard incentivizing people to consume as many tokens as they possibly could whole s- I did a couple of episodes about this whole token maxing idea,

actually providing more of a defense for it Even though I think that a lot of people's first instinct, which is completely reasonable is to point out the truism [00:07:30] of Goodhart's law 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 thecontext of token maxing, you're measuring an input rather than an output when inherently and in the long run, [00:07:45] 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 [00:08:00] maxing that would rear their ugly head 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 

[00:08:15] just just a few months

This got reinterpreted and reducedto headlines like The Information's

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 [00:08:30] 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 [00:08:45] 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 $100, $200, $300 a month type of [00:09:00] subscriptions 

While 

perhaps being profitable for some portion of users, were very, very frequently very unprofitable. We don't know for sure, but estimates around the actual value of the tokens that you could theoretically consume on one of those $200 a month [00:09:15] plans could sometimes be 10 or 20 times that $200 value.



most, in other words, the most active power users of those max plans were sometimes getting 2,000, 4,000, 5,000, even of value out of just $200 a [00:09:30] month, and that was the AI subsidy really-- Now, 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 paused to consider the financial implications of any dumb idea I wanted to try on GoDex or Claude Goat. I [00:09:45] just start building it. I just start releasing it

we have been in a let her rip kind of place

Now 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 [00:10:00] On both sides of the equation. For the provider companies, there's only so long they can subsidize that type of usage to the tune of 10 or 20X 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 [00:10:15] do And that shift in business model is the first big implication of the AI subsidy era ending

Over Over the course of the month, we had a number of different companies announce that they wereshifting from a flat seat sort of model to more usage-based billing. One of the first of [00:10:30] those was GitHub Copilot, who actually made the announcement at the very end of April

In their announcement post, they wrote: " Copilot is not the same product asit was a year ago. 

It 

has evolved from an in-editor assistant into an agentic platform capable of running long, multi-step coding sessions [00:10:45] using the latest models and iterating across entire repositories. Agentic 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. GitHub has absorbed [00:11:00] much of the escalating inference cost behind that usage, but the current premium request model is no longer sustainable."

a couple week, 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 [00:11:15] 200, and they also introduced a new $100 plan. But they also introduced on top of that usage limits and usage-based 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 [00:11:30] 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 Claude Code, as soon as you move to a different type of harness or a different environment that's [00:11:45] notowned by Anthropic, 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 [00:12:00] beginning. But it's not the only one

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[00:15:30] 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 agentic AI 

well

Companies are just [00:15:45] 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 [00:16:00] 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 OpenAI and Anthropic [00:16:15] 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 [00:16:30] clients.

While on the Anthropic front, they partnered with Blackstone, Hellman & Friedman, and Goldman Sachs 

to also launch a separate as yet enterprise 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 [00:16:45] smaller stake 

than OpenAI 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 [00:17:00] 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 [00:17:15] 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. It's also because of those shifts that we just saw in [00:17:30] the business model where it's just too expensive to token max now

The 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 [00:17:45] 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. Cursor [00:18:00] announced the next generation of their, 

Composer model, Composer 2.5. And not only is it performing well, it's doing so at a much lower cost than Opus 47 and GPT-55 



so part of response to the token shortage is market-based innovation to bring the cost of [00:18:15] tokens down without sacrificing performance

Google seemed to recognize that this might be part of their play as well, 

giving lip service on the main stage at Google I/O this month to the idea of Gemini 3.Flash being a way for enterprises to cut [00:18:30] costs. However, in practice, that's not really how it's playing out.

As Artificial Analysis points out Gemini 3.5 Flash costs about five times as much as Gemini 3 Flash both based on higher token prices as well as higher token [00:18:45] usage

have... 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. Their Gemma series of models, which are their smallest and cheapest models, are actually seeing really fast adoption, with the adoption of those models [00:19:00] outpacing similar Chinese models

in 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 warring going on

with China running its tried and true playbook of artificially keeping prices low to create a competitive [00:19:15] advantage

which it seems like might be happening around DeepSeek, who has just made a recent temporary seventy-five percent price cut 

on their V4 model permanent

To be clear, that's not because DeepSeek has figured out some way to

serve those tokens at seventy-five percent of the cost

It's because in a world of token [00:19:30] shortage, a lot of companies around the world are going to be forced to look away from the 

state-of-the-art OpenAI 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 [00:19:45] infrastructure is, as Swyx Shawn Wang put it, going vertical. Inference provider Base10 is raising a billion dollars at an $11 billion valuation, more than doubling its valuation from just one quarter ago.



OpenRouter, which can help developers 

[00:20:00] automatically 

toggle between models that have different trade-offs in terms of cost, efficiency, performance, et cetera. Raised $113 million 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 [00:20:15] 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, is Elon shifting into a very different type of role vis-a-vis the AI industry

Up till now, 

Elon's [00:20:30] headliner role in the AI space has been as one, cheerleader of Grok, 

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 OpenAI 



now Elon's lawsuit against OpenAI [00:20:45] 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 Grok sat relative to the models, or simply wanting to [00:21:00] stick it to Sam Altman, 

Elon decided to team up with Anthropic

The first announcement, 

was that SpaceX AI, which is the new X AI inside of SpaceX, basically SpaceX's AI division, would allow Anthropic to use Colossus-1 to provide additional [00:21:15] capacity to Claude. Now, Anthropic has been severely compute constrained throughout the year, causing major headaches for users, so this was very welcome news to Claude users and started to show this realignment happening.

However, then just a couple weeks later, we found out that [00:21:30] not only would Anthropic be using Colossus-1, fir-- which was SpaceX/X AI's first big data center that they spun up at the end of 2024, but at least on a temporary basis, Anthropic would also be using Colossus-2

In In the span of just a couple of weeks, [00:21:45] SpaceX became a Neocloud With absolutely massive implications for the upcoming IPO

this, 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 [00:22:00] better than just about anyone, which is building big ungodly physical infrastructure, using that as his way to leverage and influence the AI by virtue of being a self-appointed czar of compute.

And by providing a clear line pathway between [00:22:15] SpaceX as neo-cloud provider right and future orbital data center provider just makes the SpaceX IPO make so much more sense in context. First of all, it allows investors to get excited about adifferent part of the AI stack, an increasingly important [00:22:30] infrastructure part of the AI stack, as opposed to just investing in an also-ran in Groq And by the way, for those of you who love Grok, 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 I just think that the trend line is pretty [00:22:45] clear at this point

And 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 Hynix [00:23:00] 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 [00:23:15] because if they can go sell back the $130 billion or whatever worth of compute that they're investing in 

at a premium, it significantly de-risks big CapEx spend

I think the theme of the compute build-out as a response to the token shortage era is [00:23:30] 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 [00:23:45] 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 [00:24:00] 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 wanna point out as well It was relatively quiet in terms of new model releases.

We did just at the end of the month get Claude Opus 4.8. But part of what was interesting about that [00:24:15] to me was how much the emphasis has shifted from models alone to the harnesses they sit in

Creator and entrepreneur 

Riley Brown wrote, " Unless it's a major breakthrough in model capability, I'm much more excited for super app updates like Codex and Claude Desktop. There's so much to be unlocked [00:24:30] by making those surfaces better." That was his response to the release of Claude Opus 4.8, basically saying, "I'm not interested if it doesn't come with a Claude code update."

Greg Greg Eisenberg went farther, saying, "I didn't cover Claude Opus 4.8 on my pod because I don't think it's meaningfully better than GPT 5.5 as of May [00:24:45] 29th. 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. 4.6 to 4.7 to 4.8, each one is a little different. Nobody can agree if it's better or worse. [00:25:00] The benchmarks say one thing, the vibes says another." The thing that actually matters right now is what's happening around the models.

Claude 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 [00:25:15] this new dynamic workflows approach as part of the Opus 4.8 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 Claude Code as well. If you wanna learn more about 

slash /goal, 

go check out the episode we

did for this [00:25:30] week's Long Read Sunday, which is a primer on how to use slash /goal, 

especially for knowledge work

On On the narrative side, May might go down as the month where Sam Altman and Dario Amodei finally figured out that they probably shouldn't tell everyone that the thing that they were building

And which was [00:25:45] making them unfathomably rich, was going to take everyone's jobs and livelihoods s- 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 

to articulate why [00:26:00] 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 conversation around AI on a [00:26:15] 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. [00:26:30] You've got the Bernie and AOC wing who are calling for data center moratoriums, but you now have Elizabeth Warren coming in basically saying, "Let's not stop AI with data center moratoriums

our, 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 [00:26:45] conversation about novel taxation structures like token taxes 



is going to become a more prominent theme in the monthsto come

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 [00:27:00] or non-release of Anthropic's Mythos. And what's interesting about this, bringing it back to the token shortage meta theme 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 [00:27:15] of April and the very beginning of May that part of the reason that they were opposing Anthropic'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 wanna use

So what comes next? certainly a huge theme this month is going to be the [00:27:30] SpaceX IPO It's also likely that we'll get another OpenAI 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

But But overall, I think that 

a lot of the immediate term next period is going to be [00:27:45] all about how to recalibrate for an era of token shortage

From 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 [00:28:00] 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 gonna do it for today's AI Daily Brief

I'm back [00:28:15] from traveling tomorrow and we will be back with our normal formats. For now, however, appreciate you listening or watching, and until next time, peace 

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