OpenAI's New Deal

Today on the AI Daily Brief, OpenAI proposes a new deal.

Meanwhile on the headlines, Anthropics revenue has surged yet again to 30 billion annualized.

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, Blitzy, Assembly, and Zencoder to

get an ad-free version of the show, go to patreon.com slash AI Daily Brief, or you can subscribe

on Apple Podcasts if you are interested in sponsoring the show, send us a note at sponsors

at AI Daily Brief.ai.

Lastly, two other quick announcements before we move on.

As I mentioned yesterday, cohort 2 of our Enterprise Claw program is now open.

You can find out about that at EnterpriseClaw.ai and the latest AI pulse survey is out.

This is all about how you used AI in March.

This will now be the third month that we are doing this.

We're starting to get really good longitudinal results from this.

You can find the link at AI Daily Brief.ai, it's a big blinking banner right under the

menu items.

This will be open for a few days.

I would so appreciate it if you would go tell us how you used AI and of course the people

who contribute to the survey will get access to the results first.

Now, with that out of the way, let's talk some turkey.

We kick up today with a big update in the competition between the labs as Anthropic has

announced that they've now reached $30 billion in ARR.

It was actually tucked into a blog post about their new deal with Google and Broadcom,

which we'll cover in just a minute.

But that is a 3X increase since the end of last year and up 58% since the end of February.

Now according to the latest numbers that we have from OpenAI, that suggests that Anthropic

has flipped them to have a higher annualized run rate, although we've also heard in the

past that they don't calculate things exactly the same way.

And you better believe that if they haven't actually gone ahead of OpenAI in revenue,

we will hear from OpenAI about it very soon.

Now this all comes as the financials for both of these companies come under much greater

scrutiny as they head towards an eventual IPO at the end of this year at the beginning

of next.

On Monday, the Wall Street Journal published a deep dive into OpenAI and Anthropic's

numbers, sourced from financial disclosures around each company's recent fundraising.

The key focus was on training costs, which are sky high for both companies.

OpenAI expects to spend around 30 billion on model training this year, which is triple

what they spent last year.

Anthropics projected training costs are relatively more modest, but still almost triple to reach

28 billion by 2028.

Now while both training budgets are massive, it's notable that OpenAI is forecasting

costs to go up on a completely different level than Anthropic.

Because training costs are so high, both companies are providing an alternate counting of profitability

that excludes them.

Without training costs, both OpenAI and Anthropic are on track to eGout a small profit this year,

with that profit accelerating moving forward.

Not everyone loves this financial engineering.

Rah Malawalia sums up the feeling of many investors when he writes, OpenAI and Anthropic

are incredibly profitable, with you just strip out the training and inference costs.

This business model is equivalent to running a passenger airline except you need to replace

your jets every six months.

Bizarre to have another definition of earning simply because we don't like the costs.

Now in terms of top line revenue, both firms expect double revenue this year, and are

forecasting further doubling over the next few years.

Notably Anthropics revenue is almost entirely from enterprise customers, and they forecast

that to continue effectively and definitely.

OpenAI's revenue is more balanced than it used to be, but still skews towards the consumer.

They do expect enterprise and consumer revenue to balance out over time.

However for the moment, this means OpenAI is spending money on inference for a ton of free

users that Anthropic doesn't have to carry.

OpenAI expects it to take until 2030 for them to turn cash flow positive, while Anthropic

is forecasting a profit of the old well-edder stood variety by 2028.

Now the Wall Street Journal's analysis here is not particularly novel.

We've had the rough contours of these financials from other sources already.

What's more notable is that Wall Street is starting to analyze these companies as public

market behemoths rather than growth stage startups.

The journal had a very clear spin on the analysis summed up by this closing line.

Both OpenAI and Anthropic will burn through a giant amount of cash in the coming years

and are counting on their IPO investors to help buoy their businesses.

TLDR that is going to be the default narrative these companies fight against during their

IPO in over the next few years.

Still for many people, the big story here is this massive new Anthropic number.

Fleeting bits points out, Anthropic is growing at an annualized 97% percent.

This is the fastest revenue growth at this scale in history.

I don't know how to communicate the significance of Anthropics growth rate at this scale without

sounding hyperbolic.

I asked Claw in the best comparison that I could find was in video, which grew at a

1240% annualized rate during its best individual quarter growth ever, which was Q2 in fiscal

year 24.

As John Arnold puts it, hard to believe that just 18 months ago, Anthropic was broadly

considered the odd man out of the AI race with an ambiguous business plan and no clear

funding model.

Not so anymore.

Now, on the back of soaring usage, Anthropic has signed a massive new compute partnership

with Google and Broadcom, Anthropic announced on Monday that they've expanded their existing

partnership to add multiple gigawatts of capacity set to come online from 2027.

The Wall Street Journal added that the precise number is 3.5 gigawatts.

Alongside the reveal that revenue had tripled to a 30 billion run rate, Anthropic also noted

that enterprise spend specifically is skyrocketing.

During their fundraising announcement in February, Anthropic boasted that 500 enterprise customers

had annual spends above a million dollars, less than two months later that figure his

double to a thousand customers.

Regarding the compute plans, Anthropic will build the majority of their new data centers

in the US.

The deal will expand in Anthropics commitment to deploying Google's TPUs, which are manufactured

by Broadcom.

Anthropic already began deploying TPUs in the fall and uses them exclusively for inference.

Their training clusters are exclusively developed and operated by AWS and that partnership remains

ongoing.

For Anthropic, the deal is obviously necessary.

Their capacity constraints have become a huge problem this year, so they need pretty

much every chip they can get their hand on.

Yet for Google and Broadcom, this is arguably even more important.

Google set out to build a new business around external TPU sales last year.

Many argued that they didn't have the sales or support staff to compete within video

or AMD and would face a hard slog to set up a new business line.

Yet now, in a single deal, Google has built a multi-billion dollar chip business around

a solo customer.

Broadcom, meanwhile, has guaranteed demand as long as Anthropic keeps growing.

Muhammad Hassan sums up, the AI arms race just turned into a full on power plant competition.

Speaking of Google, after releasing their new open source small model Gemma 4 last

week, the company has wasted no time in productizing it.

On Monday, they released an AI dictation app called Google AI Edge eloquent.

The product competes with things like Whisper Flow, allowing users to do live AI assisted

dictation on their phone.

Edge eloquent can filter out filler words, clean up phrasing to convey the intended message

and store custom jargon and keywords, much like the other AI dictation apps.

The big twist is that everything is run completely locally on device.

Users download the app with a packaged small language model and then can operate everything

without an internet connection.

Now although Edge eloquent probably isn't all that exciting, it does demonstrate a few

interesting things about Google's Gemma 4 family.

First, unlike previous small models, this doesn't seem to be a research project.

It is a commercially viable model for certain use cases and Google seems intent on building

products around it.

In addition, this could be the kind of local model Apple has been looking for to drive

Siri, which is expected to use Gemma 9 family models when it relaunches in the summer.

Gemma 4 doesn't seem to be quite there yet for driving a full offline version of Siri,

but you can see where Google is heading.

Now aside from commercial applications, Gemma 4 has seen a hugely positive response from

the developer community.

The Gemma 4 was downloaded 2 million times in its first week.

In contrast, Gemma 3 receives 6.7 million downloads over the past year, while Alibaba's

Qen 3.5 achieved 27 million downloads since its release in mid-February.

Something that went a little under the radar is that the entire family of models right

down to the 2B version have strong, agentic performance that could push the frontier for

mobile agents.

Philip Schmidt, a developer experience liaison at DeepMind, showed the model can query

with Kapedia using agent skills while running on an iPhone.

Obviously still very early innings, but it feels like Gemma 4 could lead to a breakout

moment for local models, especially once the open claw folks start tinkering with it.

Over in MetaLand, the company is preparing to release their new model and plans to offer

an open source version in the future.

Axios published new information about the model release on Monday, citing sources familiar

with the views of AICEO Alexander Wang.

They wrote that Meta wants to keep some part of the model proprietary during the initial

release to ensure it doesn't introduce new levels of safety risk, and this reporting

contradicts prior speculation that Meta would abandon their commitment to open source

models as part of this new release.

Axios added that an open source model aligns with how Wang sees Meta's position in the

AI race.

Wang reportedly views Meta as a democratizing force that can ensure there is a US-trained

option for open source developers.

Sources suggest that Wang believes that open AI and Anthropic are increasingly focused

on developing AI systems for governments and the enterprise, while Meta is focused on

the consumer.

Right Axios, Meta wants its models to distribute it as widely and as broadly as possible around

the world.

This is the first news we've had on the forthcoming model, codenamed Avocado in several

weeks.

In early March, the New York Times reported that Avocado had been delayed and couldn't

match Gemini 3 on benchmarks.

Talk of safety concerns could imply that model performance has improved with another

month of post-training, still sources say that Meta knows that its models won't be

competitive across the board, but believes they will have certain strengths that drive

consumer appeal.

Meanwhile, while Meta's own model is getting close to release, Meta engineers are still

using Clawd, a whole lot of Clawd.

The information reports that Meta employees have set up an internal leaderboard to see who

is turning through the most tokens.

The leaderboard is dubbed Clawd Enomics and aggregates the top 250 token users among Meta's

85,000 employees.

Top-ranking token users can earn the rank of session immortal or token legend.

The information argued this is a new type of conspicuous consumption in Silicon Valley

known as token maxing.

The thought is that token consumption is a good proxy for AI enhanced productivity, so

engineers want to climb the leaderboard.

Now the flaw in thinking is immediately obvious with the information also reporting that

Summit Meta are running large numbers of agents in parallel, with the goal to rip through

as many tokens as possible, not necessarily be as productive as possible.

And while token maxing could be the new version of judging engineers by counting how many

lines of code they write, the culture is being driven from the top.

Last month, Nvidia CEO Jensen Huang said he would be deeply alarmed if an engineer

on a $500,000 salary wasn't using $250,000 worth of tokens annually.

That's also the view at Meta, with CTO Andrew Bosworth boasting in February that one

of his top engineers is spending the equivalent of his salary on tokens to generate a 10X

efficiency boost.

Bosworth commented, this is easy money.

Keep doing it, no limit.

Now there is a lot of chatter on this, with many feeling like Joe Weisenthal who writes,

how does measuring productivity by total token consumption make any sense at all, comparing

it to Chairman Mao requiring peasants to smelt steel in their backyards during the

Great Leap Forward, which of course led to tons of useless low grade steel, Joe continues,

real backyard steel furnaces vibe in my opinion.

Interestingly, Meta critic Capital on Twitter makes a different China comparison to argue

why this actually makes sense.

He wrote, very early in its development, the party would set GDP growth goals for each

province in China.

As you know, you can do lots of silly things to boost GDP growth, and party officials

certainly did, but the opportunities for China's development were so vast that simply

putting a GDP growth target was enough.

It took decades for Good Hearts Law to catch up with them.

Same goes for tokens.

Meta is spending 90 million tokens per developer per day.

Meta opus 4.6 rates, Meta would be spending in the zip code of 4 to 5 billion dollars

per year.

I think all but 5 corporations on earth can spend that much on AI.

It's a massive feat of engineering from wall to wall to be capable of spending that many

tokens.

TLDR, the cost of token maxing is small because token maxing is extremely hard.

You can safely expect that over the next 18 months, 98% of corporations would be better

off token maxing.

Interesting thoughts there, but for now, that is going to do it for today's headlines.

Next up, the main episode.

Alright folks, quick pause.

Here's the uncomfortable truth.

If your enterprise AI strategy is we bought some tools, you don't actually have a strategy.

KPMG took the harder route and became their own client zero.

They embedded AI in agents across the enterprise, how work it's done, how teams collaborate,

how decisions move, not as a tech initiative but as a total operating model shift.

And here's the real unlock.

That shift raised the ceiling on what people could do, human stayed firmly at the center

while AI reduced friction, serviced insight and accelerated momentum.

The outcome was a more capable, more empowered workforce.

If you want to understand what that actually looks like in the real world, go to www.kPMG.US-AI.

That's www.kPMG.US-AI.

You've tried in IDE co-pilots, they're fast but they only see local silos of your code.

However, these tools across a large enterprise code base and they quickly become less effective.

The fundamental constraint?

Context.

Blitzy solves this with infinite code context.

Understanding your code base down to the line level dependency across millions of lines

of code.

While co-pilots help developers write code faster, Blitzy orchestrates thousands of agents

that reason across your full code base.

Allow Blitzy to do the heavy lifting, delivering over 80% of every sprint autonomously with

rigorously validated code.

Blitzy provides a granular list of the remaining work for humans to complete with their code

pilots.

While feature additions, large scale refactors, legacy modernization, greenfield initiatives,

all 5X faster.

See the Blitzy difference at Blitzy.com, that's B-L-I-T-Z-Y.com.

One of the trends that I follow most closely when it comes to AI is around voice.

Today's episode is brought to you by Assembly AI, the best way to build voice AI apps.

The company has been moving with extreme velocity lately, shipping major improvements to their

speech to text models that go way beyond just better transcription.

Specifically, they are getting to an accuracy level that can reliably capture the type

of things that used to break every other speech to text model.

Think credit card numbers read aloud, email addresses spelled out, complex medical terminology,

financial figures.

All of these things, in other words, that it really matters to get right.

So for anyone who's building in FinTech, healthcare, sales intelligence, customer support,

getting those things wrong isn't just annoying, it's a liability.

Their speech understanding models are also really good at things like identifying speaker,

focusing key moments, and uncovering insights from voice data.

And all of that happens in a single API call.

The proof is in the pudding and assembly power some of the top voice AI products in the

market today like granola, dovetail, and ashbee.

Getting started is free.

Head to assemblyai.com slash brief to test it live and get $50 in free credits.

No contract, no upfront commitments, that's assemblyai.com slash brief.

If you're using AI to code, ask yourself, are you building software or are you just playing

prompt roulette?

We know that unstructured prompting works at first, but eventually it leads to AI slop

and technical debt.

Enter Zenflow.

Zenflow takes you from vibe coding to AI first engineering.

It's the first AI orchestration layer that brings discipline to the chaos.

It transforms freeform prompting into spec driven workflows and multi agent verification,

where agents actually cross check each other to prevent drift.

You can even command a fleet of parallel agents to implement features and fix bugs simultaneously.

You've seen teams accelerate delivery 2x to 10x.

Stop gambling with prompts.

Start orchestrating your AI.

Turn raw speed into reliable production grade output at Zenflow.free.

Welcome back to the AI Daily Brief.

Today we are looking at a policy document from OpenAI and it comes at the convergence of

two moments in and around the industry.

The first moment is what we were discussing on yesterday's show.

It's growing indication from the labs that the next jump, the one that we are on the

verge of with the next set of models, represents a really big one.

Remember at the end of March, we got the leak about Anthropics Mythos model, which it

said represented a step change their words and capabilities.

In fact, what we got with the leak was a blog post saying that the model was so powerful

that they were going to slow roll it a little bit, rather than a full announcement and

a release of the model as we've gotten in the past.

On the OpenAI side, the company has been heavily teasing their new Spud model, actually

doing more to hype it up than to tamp down expectations, reversing the trend that they've

had all the way since back when GPT-5 underperformed.

So on the one side, we have this moment of precipice, where the next set of models could

represent a very big jump.

Then on the other side, we have the continued and frankly increasing reality of dreary American

sentiment when it comes to AI.

A new poll from Quinnipiac suggests that sentiment is going from bad to worse.

The 5% of Americans now believe that AI will do more harm than good in their day-to-day

lives.

That's up 11 percentage points from a year ago and tips to the majority for the first

time.

70% believe that AI will reduce job opportunities, which is up 14 percentage points.

A mere 7% of respondents believe that AI will increase job opportunities.

In other words, Americans believe by a 10-to-1 ratio that AI will reduce rather than increase

jobs.

3% said that they were either very or somewhat concerned about AI making their job obsolete,

and yet this is all despite adoption rocketing forward.

The majority of people are now using AI to research topics they're curious about, rising

from 37 to 51% over the past year.

Analyzing data and creating images, each increased significantly as use cases as well, both

rising from around 16 to around 25%.

The number of Americans who said they had never used AI was down from 33% last year

to 27% this year.

Camila Trientoro, an associate professor at the Quinnipiac School of Business noted,

younger Americans report the highest familiarity with AI tools, but they are also the least optimistic

about the labor market.

AI fluency and optimism here are moving in opposite directions.

This is also not just one poll.

We're seeing AI being blamed for increasing electricity prices, opposition to data centers

growing, and in one dramatic example of just how negative the perception around AI is,

it has worse PR right now than the extremely controversial ice.

Into that environment, open AI released the new document industrial policy for the

intelligence age.

The document is framed not as some complete policy statement or comprehensive anything,

but instead a way to try to nudge the conversation around important policy topics forward.

They divide their policy discussions into two areas, first building an open economy,

and second building a resilient society.

And I think that the document needs to be judged in two different ways.

One is from a PR lens, and what it does for open AI and the AI industry in general when

it comes to public perception, and second in terms of what one might think about the

policies themselves.

Now to be fair to open AI on the first way of judging this as a PR document, it obviously

isn't intended to be that primarily.

It feels like it's much more designed for perhaps a Washington insider audience, and

that if it was a document for general public consumption, maybe it would look a little

bit different.

At the same time, the reason I won't give open AI a pass here, the reason I'm not interested

in giving open AI a pass on that front is that at this point, with where they sit in

the industry, and especially when they pair this with big premier interviews with the founders

of media companies like the ones that I'm often with Axios, they clearly recognize that

everything that they say is whether they would like it to be or not a public relations

statement as well as whatever it is supposed to be to be completely transparent.

I very, very, very much dislike this document.

It exists in this strange uncanny valley where it is so technocratic down to the narcolepsy

inducing name industrial policy for the intelligence age that it is inevitably going to fail in any

sort of PR goal, but at the same time, not robust enough from a policy perspective that

it feels likely to do a particularly good job at advancing any of these policies as well.

It is a document in other words without a clear home or purpose, or one where its home

and purpose is so confused that it makes it at least in this current form not all that

useful to anyone.

Now, we are going to go through the policy proposals because there are some interesting

and important discussions that are started there, and I want to take this idea of being

a conversation starter in good faith, but I do have to say a couple more things about

the PR impact right now.

I don't know that I've ever seen an industry that is so fundamentally unwilling to spend

any time at all, articulating why it deserves to exist as the AI industry.

Every single document like this, every single statement that comes out of Dario or Sam's

mouths, is so focused on affirming the negative and validating people's concerns that literally

no time is spent actually explaining how this is going to make the world better.

Every discussion is this incredible quick pass through where a bunch of theoretical benefits

in the future are listed in short order without actually articulating how we get there.

With the impact of those changes will be on people's lives, all along the way to getting

to what seems to be the core point, would you get it validating all the bad things?

We get these hand-wavy statements like this one, we strongly believe that AI's benefits

will far outweigh its challenges, then only to have the next three lines be all about

how clear-eyed about the risks they are.

This does not come off as being reasonable.

It does not come off as being sober or thoughtful.

What it does is make people ask why the hell are we doing this in the first place, then?

You know how when you see an ad for some numerical drug on TV?

The last 10 or 15 seconds of the 60 second spot is always them disclosing all the risks

inside effects.

The way the AI industry communicates is as if they flip that ratio around and spend three

quarters of the ad talking about all the side effects and negatives and only a tiny little

bit on why the things should actually exist in the first place and what all of these

risk descriptions, these sober, thoughtful risk descriptions fail to engage with, is

the thing that seems incredibly obvious to most average people, which is that AI doesn't

have some mandate from heaven to exist.

When open AI or anthropic or anyone else in the AI industry talks about mitigating these

serious risks, many of which sound absolutely horrible, the response of many normal people

is to say, well then why are we doing this in the first place?

And those companies answer is, well, it's happening one way or another and don't respond

when people say, wait, but why?

The people are left to assume that the answer is because it's going to make some people

rich.

That is the default understanding in the absence of a better answer.

And of course, that default understanding just makes people angrier.

If the answer is because China is going to do it if we don't, maybe for some, that's

a little bit more understandable, but it remains incredibly abstract.

The only possible satisfying and only possible viable answer must be that the benefits of

AI are higher than the costs.

And just saying that in this hand-wavy way, we think the benefits are higher than the costs

and no longer cuts it.

It never cut it, but it really doesn't anymore.

Right now, with where things are, every single time, any leader or senior official from

any major lab speaks, they are either contributing to the strong sentiment that we see in the

sea and all of these polls that AI is likely to be worse than it is good or they are doing

work to reverse that sentiment.

I think that we in the AI industry should be judging every communication on the basis

of on whether it reinforces that negative sentiment or whether it actually combats it.

So as I said, giving credit to the people who wrote this, I do not believe they were thinking

about it first and foremost as a PR document, but unfortunately in the world that we live

in and in the world that open AI and all these companies operate in, it is that whether

they want it to be or not.

Now as you might imagine, I am far from the only person who has some negative feelings

on that side.

Daniel Jeffery's writes, please, please, please, I'm on my knees begging every AI exec

on the planet, just stop with this stuff.

Just give us models.

Let the collective distribute intelligence of people figure things out in real time like

we always do.

Let people adapt.

It's what we do.

We are not giving birth to magic super miracle machines that suddenly invalidate every single

pattern of the entirety of human history and technological development.

We're not really.

AI is amazing.

It's wonderful, but it's not magic.

Can we please just let AI be cool and useful and problematic in realistic ways instead

of all this crazy talk?

Meanwhile, others point out that there is something discordant about where AI actually

is and all of this talk of world changing superintelligence.

And by the way, this is not just the Gary Marcus' of the world who are desperate to

convince you that AI is in all that powerful.

These are people who are totally bought in.

Cheyenne Zhao, whose literal handle is Gen AI is real, posted the companion Altman interview

and said, the replies are more insightful than the interview.

Someone pointing out that GPT 5.4 has been spinning in circles on a web hook for four

hours, while Sam talks about superintelligence captures everything wrong with how AI is being

discussed right now.

The models are genuinely impressive and improving fast, but calling this superintelligence devalues

the word and makes it harder to have serious policy conversations when we actually need them.

They're in the extremely capable tool era, not the new social contract era.

Buco Capital Bloke put it a little bit more bluntly last week, speaking in general not

about this specific document.

He writes,

You must understand that every tech executive has AI psychosis.

They're puking out claw-generated markdown files full of hallucinations, asking if this

means they can fire 500 people.

Aaron Levy from Box actually responded and said, the worst thing you can do is just dabble

with AI a little bit.

That's the spot where you see its capability, but overgeneralize on the use cases and

how easy the automation is.

You almost have to use it too much, develop psychosis, then get to the other side and realize

how much care and feeding and management of the agent work flows is required.

On the other end, you realize you actually need to probably hire more or new people to

then do all the new things agents can do.

But let's talk about some of the policy proposals.

I'm going to spend a lot more time on section one, the open economy than I am on the second

part, resilient society.

The first thing they discuss is the importance of including worker perspectives in the AI

transition.

Right, give workers a voice in the AI transition to make work better and safer, including

a formal way to collaborate with management to make sure AI improves job quality, enhances

safety, and respects labor rights.

This is something that I do think is extremely important, but also reveals one of the biggest

challenges with this document overall, which is the thing identified by Will Manitas in

his response, I say, no new deal for open AI, which is that basically this document is

absolutely chock full of pretty sentiments that at least in the way that they are described

right now seem to wholly ignore the political reality and the political history that they

operate within.

We've discussed this worker management thing numerous times in the past on this show.

And what is happening and will happen is a wholesale shift in the relationship between

employees and management in lots of different ways.

On the one hand, managers have much more power because they feel like they can do things

with fewer people.

On the other hand, the end worker who is actually using the AI kind of negates the need for

a lot of layers of middle management, but then there's also issues like the fact that

in many cases workers are training their own replacements, the point being that what's

happening here, what will happen and what needs to happen is not some policy that can

be enacted.

It's going to be a total new labor movement.

Open AI doesn't use the word union here, which is one of Will's biggest beefs with

Will pointing out that the new deal was not some benevolent meaning between the capital

class and labor class facilitated by FDR, but the byproduct of decades of political violence

and a labor movement that was willing to fight and literally die for change, not to mention

leadership that had an actual mandate, the likes of which no one in American politics

has had for a very long time.

Still to the extent that we are talking about conversation starters, yes, we do need

to have the conversation about this shift in the relationship between employees and management.

Next up, we have AI first entrepreneurs.

Now the critique of this one is that telling a displaced customer service agent to go start

some small business that competes with their former employers feels at best tone deaf.

But of course, that's not the actual point of pro entrepreneur policy.

In other words, the point is not that every worker who is displaced by AI is going to

all of a sudden go be an entrepreneur now.

It's to ask what sort of policy interventions and support structures could increase the

successful small business entrepreneurship rate by 50% or even 100% from where it is today.

There is not going to be one single policy silver bullet for the amount of change that's

going to happen.

Pro entrepreneurial policy is one part of a much larger toolkit and in that I'm completely

supportive.

Now, I'm not totally sure what the right policy interventions are with the right type

of entrepreneurial support is, but I do think that this is going to be a part of the

solution because in a vastly adapting future for many, the only secure future will be the

one they secure for themselves.

Next up, we have the right to AI and this is something that open AI has talked about

before.

That we need to treat access to AI as foundational for participation in the modern economy, similar

to mass efforts to increase global literacy or to make sure that electricity and the internet

reach remote parts of the globe.

And what I would say here, which to be fair, they give at least mention to, is that access

to AI is going to be meaningless without the agency to actually use it.

What I mean by that is that we can't just give everyone a free chat GPT account and hope

it works.

The amount that companies are spending on AI infrastructure right now is based on studies

that we've found more than 12 times bigger than the amount that they're spending investing

in people's capability to use these tools.

And that's within the companies who have a direct financial incentive to have their

people use these tools well.

We need a mask scale infrastructure mobilization to help people figure out how to use the new

tools of the new economy, call it whatever you want, a martial plan for education.

We need to be thinking in those big, massive terms because without it, any right to AI

is just a pretty notion on a piece of paper.

Next up, open AI calls on us to modernize the tax base.

And this is actually an area where I think we are inevitably going to see some of the

biggest shifts.

And frankly, I think that we are going to see some breakdown of traditional conservative

and liberal lines when it comes to tax policy.

The logic is that if the balance of the economy shifts from labor to capital, there just

literally has to be some commensurate change when it comes to taxation.

How doing that well is going to be massively challenging.

But I think based on the trajectory of both the economy and the larger political conversation,

some version of this is inevitable.

Maybe it's policies that have a lot of support in liberal circles already like higher taxes

on capital gains.

Maybe it's new types of taxes on automation.

But basically, I think something has to give here.

And I think you will likely find some very strange bedfellows when it comes to figuring

out how to do it well.

Now luckily, when it comes to an inside the AI industry perspective, this sort of shift

in how we think about taxation, likely as the benefit of being extremely good politics.

The next idea from OpenAI, which is getting a lot of coverage, is a public wealth fund.

They write, while tax reforms help ensure governments can continue to fund essential programs,

a public wealth fund is designed to ensure that people directly share in the upside of

that growth.

Policymakers and AI companies should work together to determine how to best seed the fund,

which could invest in diversified long-term assets that capture growth in both AI companies

and the broader set of firms adopting and deploying AI.

Ideas from the fund can be distributed directly to citizens, allowing more people to participate

directly in the upside of AI-driven growth, regardless of their starting wealth or access

to capital.

I seem to be a little bit more skeptical of the ultimate importance of this than others

out there.

I certainly don't think it's bad.

I think it would be good to have people rooting for the success of these companies.

But I think I have a little bit more skepticism than many others around things where everyone

gets a little share of them.

And again, that's not because they're bad, but because I think maybe the central challenge

of American politics is that people don't want the average of what people have.

They want and feel like they deserve the exceptional.

We live in a world where it feels like we are constantly confronted with people who have

more than us, whether that's in Instagram posts, whether they're real or not, or having

a walk through first class to get to our section of the plane.

Now, it's not necessarily AI's job to deal with that.

In fact, it may not be a policy remediation at all.

But my concern about a public wealth fund is that I think it could be a very window-dressing

e-exciting to write about type of thing that doesn't really move the needle when it comes

to core sentiment.

On the other end of the spectrum, I'm much more enthusiastic about things like open

AI's discussion of accelerating grid expansion, except I would take it farther and not just

think about how to accelerate grid expansion in ways that don't cost individual people

money, but actually have the benefits accrue to those people first.

Basically, rather than these pretty pledges to ensure that the data center build out

doesn't increase people's electricity prices, we should be actively making their lives

cheaper.

Just keeping it the same, I think that as an incredible amount of wealth accrues to the

AI companies, we are going to need ways for that to flow back to the rest of the world.

Private financing of public utilities may end up being part of that equation.

Another area that's seeing lots of discussion is the incredibly poorly named and framed

efficiency dividends, by which open AI is basically talking about reinvesting the

realized value of AI back into regular people's lives.

Now again, to be fair to them, they are not planting their flag heavily in one or another

policy, but they're coming back to ideas which have been floating around for a while now

like the 32 hour or four day work week.

This is something that before he decided to go full front to the assault on the data centers,

Bernie was putting in his AI policy back last summer.

I tend to be a little bit more skeptical of things like the 32 hour work week, because

I think people view them as a panacea, when really a lot of people are just going to

work more anyway, but there are plenty of other ideas that have the same principle of

reinvesting AI's realized value back into people that I think could be a really important

thing.

Both on the individual level, i.e. things like retirement matches or covering a larger

share of health care costs, but it also could be on that more global societal level.

Later on in the document, they talk about portable benefits, i.e. things like health care

retirement savings and skills training that aren't solely connected to a single private

employer, and the efficiency dividends could go to pay for that.

They also talk about pathways into human centered work, and to the extent that there need

to be things like free training programs and better support infrastructure around some

of these industries that are historically taxed on resources like, for example, elder

care.

Again, those efficiency dividends could go to pay for that.

To not dance around it, there is going to be some redistribution of AI-generated wealth,

and I think some of these types of programs could be more politically palatable than just

handing people money directly.

One idea that is very technocratic, but also interesting, and I think worthy of a lot

more conversation, is some of the ideas of adaptive safety nets that open AI is proposing.

One of the things that they're suggesting is investing in much better, more direct

measurement of how AI is impacting things like work wages job quality, and then use those

things to inform automated and dynamic social safety net programs, and honestly, holding

aside the AI context, what they're basically saying is that the tools we have at our disposal

allow us to potentially make much more targeted, narrow, and specific interventions rather than

having these big cumbersome programs which can buckle under their own weight over time.

So again, as you can see, although I have a lot of specific thoughts around each of these

areas, I do think there's a lot of good fodder for discussion here.

I'm just not sure that this type of document is the right way to actually start those discussions,

and I think in the context into which it is arriving, it might actually in some ways

be counterproductive.

The biggest applied critique that I've seen is that one of the things that is noticeably

absent from the document is any sort of even hint of a commitment from open AI to programs

or initiatives or policies that would cost them anything.

As Wilma Needis writes, the document proposes that policy makers might consider higher

taxes on capital, open AI could commit to paying them.

The document proposes a public wealth fund, open AI could seed it, the document proposes

the data centers pay their own energy costs, open AI could accept voluntary rate separation

today in every jurisdiction where it operates, the document proposes that frontier AI companies

adopt public-benefit governance, open AI could reinstate the profit caps it dismantled

six months ago, none of these things are in the document, the only things in the document

are a workshop, fellowships paid in the company's own product, and an email address that routes

to no one.

Alexander McCoy puts this sentiment a little more cynically writing,

Good ideas, Sam.

I know some members of Congress who can get right to work on writing the legislation.

Some quick questions.

How much equity in open AI should we plan on you contributing?

Will it be your own equity, a delusion of existing shares, or is your idea that the

federal government will buy shares using taxpayer dollars once you IPO?

Two, how many tens of millions of dollars of your own money are you pledging to commit

to pass these policies you say are necessary?

How are you going to counter the hundred million dollars of leading future AI political

spending, which opposes these policies, which is funded by your own investors and fellow

executives?

Three, how are you directing open AI chief of policy, Crystal Hain, to redirect open AI's

massive lobbyist and public affairs resources to support this agenda, which they currently

actively oppose?

Now, this is coming from someone who in their Twitter bio says that they are fighting the

power of big artificial intelligence corporations, so you need to view it through that lens, but

I think that that would be a more prominent and common sentiment than you might think.

Effectively, where I agree with open AI wholeheartedly is that we need to have these conversations,

but what seems to go unrecognized is that in the context of both the changes that they

say are coming and the grave state of public opinion on AI in America, 13-page policy PDFs

with no actual commitment or direction, ain't it?

For now, that is going to do it for today's AIDLY brief, appreciate you listening or watching

as always, and until next time, peace.