# Why Only AI Training Can Save the Economy — Transcript (2026-06-16)

https://aidailybrief.ai/e/2026-06-16 · Listen: https://pod.link/1680633614

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Speaker: [00:00:00] Hey guys, a quick note before we dive in. The episode you're about to hear was originally recorded as last weekend's Long Read Sunday. ~Now, of course, everything that happened with Fable on... ~ Now, of course, everything that happened between Anthropic and the US government and Fable being shut down on Friday night pushed that out

~And basically we're now still-- ~And basically we're now still waiting to see what the resolution of that should be. ~At the n- ~at the time I'm recording this on Monday night, ~it does not appear like we're g- it does not appear like we're going to get a quick resol- ~resol- it does not appear like we're going to get a quick resolution to this.

Although Anthropic is on site in DC, and it sounds like meetings were had today. ~Although there hasn't been s- ~although there hasn't been too much reporting about them yet

In the meantime I'm taking my seven-year-old daughter to a World Cup game today, ~for which I, to which I, ~for which I am super excited. ~And so I'm sharing with you th- and so I'm sharing with you the Long Read Sunday episode that I had origin- that I had orig- ~ and so I am sharing with you the Big Think style episode that we had originally scheduled for Sunday If some big news breaks, I will be back very soon with an update.

But for now, enjoy the show 

Speaker 4: Today on the AI Daily Brief, we are talking about why only AI training can save the economy

The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. ~ All ~All right, friends, quick announcements before we [00:01:00] dive in. 

Speaker 2: First of all, thank you to today's sponsors, KPMG, Section, 

Speaker 3: Assembly, 

Speaker 4: and 

OutSystems. 

To get an ad-free version of the show, go to patreon.com/aidailybrief, or you can subscribe on Apple Podcasts If you wanna learn more about sponsoring the show, send us a note at sponsors@aidailybrief.ai.

And while you're there, check out the new site if there is any specific part of a specific episode that you wanna share with someone Whether it's a number or some stat or quote, There's a good chance that it is now there cut up and shareable for you, so go check it out aidailybrief.ai. now today we're talking about a theme which has been pretty much ever present in my entire journey with AI

Which I think is now more existentially important, not just for the AI industry, but for the economy as a whole, ~that it's-- ~

~than it, ~

than it has ever been. I'm talking about AI training AI education, upskilling, whatever you wanna call it. The process by which we help people close the capability gap 

~between what AI, ~

between what AI could be doing for them ~and what they, ~the value that they are actually getting out of it

Now this is about as bombastic a title as you're ever gonna~ as you're ever gonna get on the l- ~get on the [00:02:00] AI Daily Brief But I'm gonna try to stand on business for this one

The short of the argument is that we are in a world

~where the relationship between, ~where the relationship between AI lab revenue growth

And AI infrastructure build out ~is the most re- ~

~is the defining, ~

~is the defining relationship of the, is the defining relationship of the American, e-~ is the defining relationship of the American economy

And where in that context, we will increasingly find ourselves caught between, on the one hand, 

~the AI lab, ~

the AI lab's need ~for ever-increasing, ~for ever-increasing growth in token usage, and on the other hand

Increasing scrutiny and limitations from enterprises

My belief is that the only way to solve the two, to provide both the labs what they need and the enterprises what they need, ~to keep the whole e- ~to keep the whole party going, is training

~So let's lay this out for you guys. ~ ~This is of course a Long Read/Big Think episode. So let's lay the... So let me lay out the argument. ~So let me lay out the argument for you guys

part one of the argument is that the American economy just is the AI trade. AI investment is not a sector story, it is the growth story. In Q1 of this year, GDP grew at 2% annualized with AI-driven investment contributing about~ about 75% of the increase. ~75% of the increase

AI data centers, hardware and networking hit 1.4% of US [00:03:00] GDP ~ ~tw- in Q1 2026

doubling from zero point seven percent And making AI infrastructure the leading driver of US private investment growth

Data from the St. Louis Fed suggests that AI investment accounted for thirty-nine percent of marginal GDP growth over the trailing four quarters, which is bigger than the tech sector's twenty-eight percent contribution at the peak of the dot-com boom

~What's more, ~ 

what's more, excluding these investments

Growth in the first half of 2025 would have been 0.1% annualized, a near standstill

Nathaniel Whittemore: ~In In this year alone, ~alone, in 2026 alone, big

Speaker 4: ~big tech, ~tech's AI CapEx spend will pass $800 billion

~Which some like AI's, ~ which some 

like 

AI's R David Sacks 

~have argued could, ~have argued could represent a two and a half percent GDP tailwind this year and a 3% GDP tailwind next

~Now this infrastructure, now this infrastructure, ~ 

now this infrastructure spend isn't coming from nowhere. It was justified initially by the belief in the importance of AI in the future, and as time goes on, it is increasingly justified ~by reve-- ~by specifically revenue growth from the labs. That's the contract. As long as token consumption [00:04:00] keeps rising and rising fast enough, the capital keeps flowing

In fact, if you think about the difference between Q4 of last year and the first half of this year in~ in terms of the popular financial nar- ~terms of the popular market narrative, last year from about mid-August all the way through December, the biggest discussion on Wall Street was about an AI bubble

And there were all sorts of different proximate reasons for that, comments from Sam Altman, 

~the MIT, ~

the MIT air quotes report that said that 95% of pilots were failing. But there was something much bigger underlying it ~that wasn't about, ~that wasn't about narratives, but was instead about math.

Specifically, seat math In short, 20 to $200 a month times the number of addressable seats among knowledge workers was not enough revenue ~to justify, to ~

~justify, ~

to justify trillions of dollars of infrastructure spending 

~The, the tam, ~tam,

the tam of AI when AI is sold as seats just wasn't gonna cut it

The shift, of course

is that seats ~cease to be the at- cease to be the core unit of-- cease to be, have ceased to be, have in the age of, have in the era of, ~have in the era of viable agents ceased to be the core unit that matters when it comes to AI economics

~The shift that we have all lived, ~lived, the shift that we have all lived [00:05:00] through~ the shift that we have all lived through is from an assisted, is from an assisted AI paradigm ~is from an assisted seat-based paradigm to an agentic usage-based consumption paradigm. Per person economics move from 20 to 200 bucks a month to potentially thousands of dollars

And the revenue evidence is clear

Anthropic had this 

~insane, ~

insane ADX surge ~that catapulted to 30 bill- that catapulted to a, ~that catapulted it to a $30 billion annual revenue run rate ~Which then jumped all the way up to, ~which then jumped all the way up to 47 billion by late May ~This was driven not by, this was driven of, this was driven not by all sorts of, this was driven not, ~this was driven not by all sorts of new people paying for ~ 20 or $200 a month Anthropic, ~$200 a month Claude accounts, but an insane amount of usage of Claude code

~And Anthropic wasn't the only-- ~wasn't the only-- And Anthropic wasn't the only AI company experiencing this shift

OpenAI's revenue also jumped significantly in the first quarter, ~aided and abetted by Codec-- ~aided and abetted by their Claude Code competitor, Codex

~which is their vehicle for delivering, ~which is their vehicle for delivering agentic tokens

~In the beginning of the year, the number of-- ~ In the beginning of the year for Anthropic, ~the number of company-- ~company-- the number of enterprises spending a million dollars a year jumped from 500 ~to a thousand, ~to more than a thousand in under two months

But of course, this came with consequences The big theme ~for the last, the big theme for the last six, the big theme for, the big theme for the last month or so, the big theme for the last month or th- ~for the last month or so on this show 

Nathaniel Whittemore: has been 

Speaker 4: the shift ~from the token scar- ~[00:06:00] the token subsidy era to the token scarcity era.

~Now, we don't know how much... ~ Now we don't know how much exactly the labs ~have actually been subsidizing, ~have actually been subsidizing their accounts, but recent estimates from SemiAnalysis

~Estimate that on the Claude plan, ~estimate that on the Claude $200 a month plan, the max possible spend was approximately $8,000 a month worth of tokens. 

~And on the, ~

~and on the max ChatGPT, and on the max ChatGPT, ~ and on the max ChatGPT plan, the max possible spend was up at 14,000 a month Now, even if these numbers aren't correct, even if they're significantly off, you're still talking about just absolutely huge subsidy

~As the amount of AI, the-- as the amount of I-- as the amount of AI, ~AI, as as the amount of AI being consumed increases While those infrastructure projects

~Lag in their ability to increase the availability of it. Lag, lag, ~lag in their capacity to increase the availability of AI, both because of delays that they're facing, but also because it just takes a long time to bring that capacity online ~The tri- The tried and true rules of, ~the tried and true rules of economics apply

~pricings and market ~and market pricing and incentives start to shift

The end of April, beginning of May is really when we started to see this happen

~GitHub, GitHub, ~GitHub Copilot was one of the~ was one of the first ~

~to mo- ~

~to mo- ~first to announce a move to usage-based billing

specifically calling out the fact that agentic sessions

~were just fundamentally different than the, ~were just fundamentally different than the way that~ that they, than the type of usage that they had built the business, that they had built the pricing model previously, that b- they'd, ~they had built the previous pricing [00:07:00] model around

~At Google I/O, they announced a whole bunch of-- ~of-- At Google I/O, they announced a whole bunch of new pricing, actually bringing down the price of some of the premium tiers, but also adding usage limits for the first time after which you get shifted over to the API. ~In effect, hiding a u- ~in effect, hiding a usage-based shift behind a decrease in the~ in the base, behind a decrease in the base model pri- ~base plan price

One of the biggest dust-ups 

~between developers and, ~

between developers and Anthropic

~Happened when they started to shift, happened when they started to shift third party... Happened, happened when they started to shift all... Happened when they ha- ~happened when they started to shift all usage that happened on third-party harnesses, so basically anything outside of Claude Code or Cowork, ~to usage-based b- ~to usage-based billing as well

And very quickly, the consequences ~started to come to ho- started to come home t- started to come, ~started to hit home on the enterprise side of the equation as well

~We have been living through 2025 budgets meeting a 2020 meeting-- a...~

~We have been, ~have been,

we have been living through 2025 assisted AI budgets meeting 2026 agentic AI reality

obviously Uber~ has, ~

~obviously Uber has been the big, ~has been the big held up example First making news for blowing through their entire AI budget in the first four months

~And eventually moving to a $1,500 per month cap per employee. ~And eventually moving to a $1,500 a 

month, 

cap per employee

other advanced AI using companies like Walmart did something similar

This This is what led me to argue

~As I did on Twitter, ~as I did on Twitter at the beginning of the month, that every AI business is now and for the foreseeable future, in some way, shape, or form, a token efficiency [00:08:00] business

And you see so many examples of this

~One is you're seeing a, ~one is you're seeing a lot of the harness companies ~start to really emphasize, ~start to really emphasize model routing. 

~In other words, ~words,

in other words, a more sophisticated approach to routing certain tasks to cheaper, lower cost models, leaving the most state-of-the-art models and highest cost for only the most important tasks

~After Factory, after Factory annou- after Factory announced, ~ after Factory announced a new model routing feature at the beginning of June, Grinberg said that 13 million had been saved so far in the first 30 days of private preview

Now other companies are just~ are just shifting model ~

~are, ~

~are, now other companies are just shifting models enti- ~shifting models entirely

~DeepSeek came in, DeepSeek came in, DeepSeek came in DeepSeek came in as Ramp's~ 

Nathaniel Whittemore: DeepSeek came in as Ramp's 

Speaker 4: top trending SaaS vendor

~As companies like, as companies like Linde, as companies like AI, ~as companies like AI startup Linde~ have shift- ~have shifted off expensive American models and towards those cheaper Chinese alternatives

In addition to those cheaper Chinese alternatives, ~you're also seeing a lot of company-- ~You're also seeing a lot of companies experiment with tactics like post-training ~to ~

~roll their, ~

~to roll their own, to roll their own alternatives, to roll their own alternatives, to roll their own alter- ~

~to roll, ~

to roll their own alternatives with a specific industry or functional focus.

Cursor's Composer 2.5 is performing at Opus 4 seven and GPT-5 five type of levels at a tenth of the cost

And even vertical companies like Harvey are experimenting with more complex [00:09:00] structures that can use post-trained versions of open models like Kimi K 2.6 

in concert with more advanced~ more advanced models like, ~models like Opus to perform both at a higher level and at lower cost

Nathaniel Whittemore: ~Now this got us to the freak out that I... Now this got us to the freak out~

Speaker 4: ~Now all of this got us to the freak out that I... Now all of this got us to the freak out that I... Now all of this got us to the freak out that I... ~ Now all of this got us to the freak out that I focused on for Friday's episode. The Citadel Securities note which showed the Silicon data LLM token expenditure index Starting to roll over and point downward

Now, as I explained on Friday, this chart actually isn't showing what~ what people s- ~people thought it was. specifically, it has nothing to do with overall demand

or overall token volume or overall token expenditure. Instead, the index tracks the average price that customers are paying for a million tokens

~But because, but because, ~ 

but because their data comes entirely ~from router com- ~from third-party router companies, i.e., not the labs themselves, it's biased towards companies that are actively seeking out lower cost alternatives. Still, it does tell part of the same story where companies, especially leading companies, are looking for cost advantages for the first time

And so we get to this equation. What the labs need versus what enterprises will pay

[00:10:00] 

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Now already, ~even w- ~

Speaker 4: ~even in, ~

even in their private market context, ~the relationship between ~

~the, ~

the relationship between the labs' continued growth and token consumption expressed as revenue and the amount of capital that is available for the infrastructure build-out is incredibly important. However, ~when Anthropic and OpenAI go ~and OpenAI IPO, the intensity of the public market pressure to show every single quarter massive, and 

I mean 

massive growth in token consumption is hard to overstate.

Already we live in a world where it doesn't matter ~that NVIDIA grow- ~that NVIDIA grows its revenue by a significant amount. ~It has to so dramatically outperfo- ~it has to dramatically outperform market estimates 

~Or else its, ~

~or else its stock price tends, ~or else its stock price tends to go down 

~after, after, ~quarterly reporting That sort of phenomenon is~ is going to be-- That sort of phenomenon and scrutiny ~going to be even more [00:14:00] aggressive ~ around, ~around the leading labs

~Meaning that they will, ~ meaning that they will have to do whatever it takes to get people consuming more tokens

~Now it's, ~ now on the other side of the equation, it's not that enterprises don't want to spend money on tokens

~It's not that they want to-- ~It's not that they don't wanna get value out of AI

~But increasingly, if the priority bec- ~but increasingly, if the CFO set 

~becomes, ~

becomes the most important force in the decisions being made around AI, there are some ~pretty serious and ~pretty serious implications for the AI company's ability to achieve that never-ending growth that is so important 

not just to their bottom line, but~ but to the overall, ~to our overall economy

Now already, you've seen the labs acknowledge 

~that, ~

~that their initial th- that their initial, ~

~that, ~

that their initial ideas that agents were just going to show up and all of a sudden take over a huge portion of knowledge work ~haven't play--~ have kinda slammed up against the reality of human institutional inertia perhaps the best expression of this ~Perhaps the best expression of this is that in-- is that over the last, ~is that over the last six weeks, both OpenAI and Anthropic have launched ~major consulting author- ~major consulting efforts focused around forward deployed engineering

Now, I applaud those efforts. I think they're a great step

~But I think that they're only part of the transformation in the way that-- ~that-- But I think that they're only part of the transformation we're going to [00:15:00] see in how the labs think

in short, there are two big realizations that are going to be hurtling towards OpenAI and Anthropic specifically. The first is that as they dig in with all of their FTEs

they will discover that a huge portion of the value of AI ~is not going to become-- ~is not going to come from a set of centrally planned agents built by the FDEs in concert with the software engineers inside enterprises. Instead, the value will come from many diverse knowledge workers of all different stripes ~ even buil-~ building and using agents well

There is a bottoms-up agent experimentation that is going to absolutely be required ~for companies to get the high, ~for companies to get the most value out of AI, and that's not going to come from FTE efforts alone. Now, the second realization, if~ if we're being a little bit more cynical, ~we're allowing ourselves to be a bit more cynical for a moment, is that even if some folks inside labs don't believe realization one

And don't think that the right paradigm is every knowledge worker off experimenting with their own agents

there's a fairly decent chance that the token growth pressure will [00:16:00] force them to act as if that's true anyway

In other words, 

~in other words, they'll figure out that you-- ~they'll figure out that they won't be able to hit quarterly token growth with a strategy that only gives leverage to a select few, ~and that demand expansion will require everyone-- ~and that demand expansion will require everyone building and using agents

My prediction then is that over the next six to 12 months, we will see dramatic increases in lab investment ~in enable, ~in enablement training and expanding the user 

base and depth of usage

Nathaniel Whittemore: Put 

Speaker 4: differently, 

~if everyone copies, ~

~if everyone copies Uber, ~if everyone copies Uber and sets spending limits at $1,500 a month per employee, then the labs have to do whatever it takes to get every employee spending $1,500 a month and then having the impact of that $1,500 be so high ~that it makes sense, ~that it makes sense for the enterprises to increase those limits

Now, this is not going to happen on its own.

One of the things that I am most worried about when it comes to enterprises 

~moving into this, ~

moving into this token efficiency period 

~is that, ~

is 

that 

that type of thinking and those types of caps come with a hidden cost

Caps [00:17:00] don't just limit spend, they shape what gets attempted. Budget scrutiny, even if completely understandable will push enterprises and individuals within enterprises towards basic productivity type of use cases and away from the big unseemly experiments that are required for the next generation of economic value to be created

~I think you could refer, ~I call this the known ROI bias

If people aren't given permission and structure and sandboxes and encouragement ~to go out and see what a fleet of ag-- ~to go out and see what new things they could create with a fleet of agents. They're just going to try to do today's work a little bit faster, or a little bit cheaper

This will not unlock the full value of AI. In fact, it will limit severely the value that the world gets out of AI

~And perhaps more pertinently for my, and, ~and,

and perhaps more pertinently for my predictions for the next six to 12 months, ~it will limit the amount of, ~it will limit the amount of tokens that the AI labs can sell

And so we come back to this equation and my argument that ~the only thing, ~the single and only thing that can solve for the needs ~of both of the party, of the both ~

~of, uh,~ 

of both of the categories of parties on either side of [00:18:00] this equation

is AI training

resources at mass scale and high quality ~to move people from assi-~ to move people from assisted to agentic AI ~And to help them, and to help them, ~and to help them learn to use AI,~ and to s- and to help them learn to use ~

~agentic AI, ~to uncover the next generation of use cases that unlock value that make the input costs seem negligible

~Unfortunately, ~Unfortunately,

unfortunately, the state of AI education is abysmal

~It is actually just an insane in... ~It is actually just an insane market failure

how little high quality AI training and education has come about in the last few years

An EY AI ~survey from this year said An EY ~survey from this year found that only 28% of organizations 

~have managed, ~

have managed to empower AI employees 

~to utilize AI, ~

to utilize AI to actually change any sort of business processes

DataCamp did a survey of~ of 500 enter- ~more than 500 enterprise leaders and found that while video courses are the most~ are the most tr- are the most common tr- ~common AI training format, 

~they produce, they produce, ~

they produce, as DataCamp puts it, awareness without confidence and adoption without judgment

The World Economic Forum notes That the half-life of skills is at a critical point

Meaning that when it comes to AI [00:19:00] education, content decays before a course catalog can even ship And guess what? ~Things already, ~things were already tough when we were just talking about prompt engineering. ~Now that we're in the world, ~now that we're in the world of agents and agent management and agent building, it is massively more complicated.

It's also incredibly more important Even if you don't agree with me about the intrinsic economic importance of AI training, I think at this point it is very hard to deny that we aren't in the midst of a secular shift in what knowledge work consists of. Simply put, we are moving from a paradigm in which we do things to one in which increasingly 

~we oversee, ~

we oversee synthetic intelligences that do those things for us prompting assisted AI was a new skill, but it was not a new knowledge work primitive ~Managing agents, man- ~man- managing agents on the other hand, is a new knowledge work primitive that every single knowledge worker in the future will need to be skilled in

~This is a lot, this is a lot closer to management training than it is to, than it-- ~than it... This is a lot closer to management training than it is to software training

Now obviously, if you've been watching my moves this year

~It's been pretty clear that I-- It's been pretty clear that I'm thinking about-- ~It's been pretty clear that I'm thinking about [00:20:00] AI education and training a lot ~I've now released three different, ~different, I've now released three different free self-directed programs: the AIDB New Year's program, ~Claw Camp in the midst-- ~Claw Camp in the midst of the Open Claw craze, and Agent OS, which is 

sort of 

an updated 

~agentic operating system, ~

agentic operating system program ~that takes a lot of, ~that takes a lot of the pieces of Claw Camp ~and moves them into the Claude Code and Codex, ~and moves them into the Claude Code Codex type of paradigm

Part of the reason why I've wanted to experiment with these sort of free~ free self-paced programs, these ~self-directed programs ~is that I think that the scale of the edge... Is that I think that the scale~

is that I think that the scale of the need for this training

is mass scale. ~there need to be more, there need to be more, ~there need to be more free programs, there need to be more paid programs, there need to be more programs of every stripe in between

And to be clear, there are some good educational resources out there

AI entrepreneur and educator Riley Brown is constantly pumping out really, really great how-to videos. 

~I just recommended, ~

~I just recommended his, ~ ~I often recommend his, I occ- I often recommend his video about, ~I often recommend his video about 

~learning, ~

learning 95% of Codex in just half an hour as a~ as a great place for people to start with that soft- ~great place for people to start with that tool

~And you do have, ~ and you do have some companies 

~like, ~

like 

AIDB Sponsor Section ~who are doing their damnedest to try to close, who are try- who are doing this, ~who are doing their damnedest to try to close the capability gap

~But it's ju- ~ but it's just not enough, and we're gonna [00:21:00] need more

So what to do with this? 

Well, 

for my part

~Well, for my part, you'll be hearing a lot more about some-- you'll be hearing a lot more-- You'll be hearing a lot more about some-- ~You'll be hearing a lot more about some new initiatives coming soon

Particularly with superintelligent ~where we're-- ~

~r- ~

~particularly with superintelligent where we're gonna be returning to our roots, ~where we're going to be in some ways returning to our roots quite soon

~And more broadly, and more broadly, ~ and more broadly, this is just a drum that I'm going to be beating a lot more

I think everyone has a role to play in this

~But I think that the most critical role-- ~But I think the most critical role, as you can probably tell, is going to have to come from the labs themselves. ~So Anthropic, OpenAI, ~

~so, so, ~ 

so Anthropic, OpenAI leaders, if you are listening to this

~Whether it's in, whether it's now, ~whether it's now or three months from now or six months from now, I can almost guarantee these are conversations you're going to be having

If you want some ideas for what you can do with this and how you can in the process not only help your companies, but yes, save the entire American economy, 

well, 

you know how to reach out

For everyone else, keep being the shining examples that you guys are for the communities that you're a part of. People's sense of the possible is shaped by what's around them, ~and AIDB listeners are the por-- ~and AIDB listeners I have found over and over again 

~are the portion, are the portion, are the portion, ~

are the portion of the population who are helping everyone else

see how [00:22:00] powerful and exciting and dynamic and good AI can be ~So keep it up. ~So keep it up And I'm here to help. For now, that's gonna do it for today's AI Daily Brief. Appreciate you listening or watching as always, and until next time, peace. 

​ 

Nathaniel Whittemore's audio recording: ~Quick note before we get into today. A ~