What Happens When AI Solves Your Life’s Work

So far, the idea that AI would upend an undermine entire industries hasn't really come to fruition.

Certainly we're seeing how people do pretty much everything change,

coding is perhaps the most changed and yet demand for coders seems to be going up.

With a new set of mathematics results from OpenAI, however,

some are asking, is this the first field-level AI disruption actually happening in practice?

The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.

All right, friends, quick announcements before we dive in.

First of all, thank you to today's sponsors, KPMG, Harbor, robots, and pencils and Blitzy.

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note at sponsorsataiideally brief.ai. Uh oh. Financial figures for the AI industry are being

called into question, as OpenAI's revenue numbers are revealed to be significantly lower than

previously reported. The Financial Times reports that OpenAI has told investors that they hit roughly

50 billion in annualized revenue at the end of September. That's a fairly big gap from the 68

billion that was widely reported last month. So it's just just misreporting or something else

going on. Well, sources said that the $68 billion whisper number came not from OpenAI

itself, but from an OpenAI investor. Apparently, what was going on is that that investor was trying

to get an apples to apples comparison with Anthropic. Anthropic numbers are always quoted as

gross revenue before revenue sharing. In other words, when Anthropic tokens are sold through a third

party like Amazon or Microsoft, Anthropics, including that in the total revenue, even though by

the terms of their deal, some big chunk of that revenue is going straight to the pockets of those

third parties. The logic I imagine is to try to provide an overall number of the total expressed

demand in the form of Anthropic tokens sold. And that is a useful number to know. However,

now that these companies are going public, it's not really a convention that's likely to fly in

public markets. Meanwhile, OpenAI has for their part always used net revenue after those revenue

splits. To make matters worse, the FT noted that the OpenAI investor calculated gross revenue based

on a short time frame. It's unclear whether they extrapolated annualized revenue from a month,

a week, or even a day. The news was not all bad here. OpenAI also told investors that they achieved

77% run rate growth in the third quarter and 107% growth in their enterprise business,

but even that incredible growth was completely overshadowed by the $20 billion gap in revenue.

In many ways, Wall Street reacted as if OpenAI had missed on revenue during a earnings report.

Semiconductor stocks were down significantly after the FT published their report,

with Nvidia down 3%, Oracle down 5.5%, and the long tail of NeoCloud stock seeing even larger

drops. And yet, even if we recognize that OpenAI is not yet a public company, had not shared the

misreported number, and did not in fact misprojections on a earnings report, the reaction still brings up

some big questions around the Anthropic IPO. Once Anthropics-audited financials are available,

they are presumably going to be much lower than the numbers Anthropic has been sharing with

private market investors by the nature of using nonstandard accounting. Ultimately, the narrative

battle is growing more intense as Anthropic heads towards IPO. Amit is investing, I don't know

all the facts about this OpenAI revenue story, but what's becoming apparent once again is that

this entire semi-trade is levered against OpenAI in Anthropic and its fragile AF, any narrative

on slowing growth in the street loses their mind. Next up, some interesting results from the

information's latest subscriber survey. In terms of audience, the information has a very tech-forward,

tech-industry type of reader base. They found that two-thirds of their subscribers work at an

organization that develops AI applications. Perceived return on investment seems to be rising rapidly,

with 35% saying their organization is returning multiples on their AI spend. Another 17% said

the returns are positive but less than hoped. 30% said it was too early to tell and 8% said

returns were neutral, leaving just 8% who said AI spent had been a net negative.

The split between AI services is shaken up again, with OpenAI back on top. During the last survey

conducted during the summer, Claude had overtaken chat GPT and Google Gemini was also gaining.

In this edition, usage of Claude and Gemini both fell as the information's readers signed back

up with OpenAI. These three vendors are still in the clear lead with between 65 and 78% of readers

using their services. The next grouping includes Grock, Microsoft Co-Pilot and Proplexity,

each between 25 and 29%. The survey didn't include a specific section on recently released

personal agent use, but the information did find that OpenClaw use is still at 12%. Fascinatingly,

this is the high watermark for OpenClaw among subscribers of the information so far this year,

which means despite that hype train having died down quite a long time ago, the product itself

continues to resonate. Among other agent coding tools, 45% use Claude code, 33% use codex,

and cursor usage has doubled in recent months to reach 23%. The information noted huge changes

from a year ago. This time last year, 59% of readers said that they had never used an agent.

The use of OpenWatemodels is also climbing, with 72% of survey respondents now using OpenWatemodels

with some regularity. 20% said that OpenWatemodels now drive the majority of their organization's workload,

10% said usage was about even with closed models, and 42% said OpenWatemodels are deployed to

a minority of workloads. Interestingly, although 46% of respondents said that they were using OpenWatemodels

for their work, only 21% said that they were using Chinese OpenWatemodels. Suggesting that most

organizations, even these very forward startup type organizations, are still a little hesitant to

rely on foreign models. Finally, the information found a little evidence that AI adoption has shifted

hiring patterns. Only 22% said that they were hiring less, 10% said that they were hiring more,

and 21% said that this was too early to tell. This left almost half saying that AI hasn't altered

their hiring practices at all. Of course, this is all just a small snapshot of a highly

in-franchised set of AI users, but still provides some really interesting patterns nonetheless.

One update that is getting a lot of chatter on social media, in a change to their terms of

service, and Thropic will now ban users for being too mean to clot. According to the new policy,

users are prohibited from, quote, sustained and needless abusive or cruel behavior.

And Thropic said that the policy is only meant to apply in extreme cases, and should not interfere

with, quote, common versions of user frustration and pushback. Thank goodness because the number

of times I find myself asking, clot or GPT, what the heck is wrong with it in much more

color for language, would most certainly get me banned. Now, the discussion from there mostly went

into AI Model Wellfare and AI Model Consciousness, which is way beyond the scope of the headlines.

But for a takeaway from the AI Realist perspective, Bucks is Aaron Levy writes,

this sounds weird, but it is actually probably a good policy. Even if you don't believe AI is conscious,

I don't, it stands to reason that you don't want future models trained on endless content of

humans being root to models. The models only understand the data they've been trained on,

or what they run into in their interactions. So if you want safe and aligned models,

we probably want AI to have lots of good interactions in their training data.

For those just here for the features, a more interesting update for Claude was the introduction

of Claude dashboards and Claude motion. Dashboards allow users to connect Claude to a dataset,

such as the CRM or Accounting software, and generate a custom dashboard for visualization

of the data. Users can also query the data in natural language to gather further insights.

The dashboards are also configured to automatically update as new data comes in.

Now, this kind of functionality has been technically possible for a while, but Anthropic

is packaging it as a simplified feature accessible through simple prompts. Motion meanwhile

allows users to transform the data into short animations suitable for presentations.

The animations are written in code rather than video frames, so you can easily modify words

and numbers without needing to start over with each edit. This seems to be the practical use case

for the flashy animations highlighted in the release of Opus 5.5. Along with OpenAI's introduction

of Intelligent UI earlier this week, we're seeing a massive expansion in how AI can be used to

communicate and interact with data. The folks in Anthropic meanwhile suggest pushing this feature

as hard as you can, with Robert Biposting, try giving it bigger briefs than you think it can handle.

It will seriously impress you, and indeed early users are certainly impressed by what they see.

Drew Fallon of Iris Finance made an animated product demo commenting,

a year ago this would cost tens of thousands of dollars. Insane. If you haven't yet

decided what you were going to spend your hacking time on this weekend, Claude motions seems

like a pretty good place to play. For now though that is going to do it for today's headlines,

next up the main episode.

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Every episode I talk about the competition between OpenAI and Anthropic, SpaceX AI, Google, and Meta.

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Welcome back to the AI Daily Brief. Earlier this week, OpenAI released a

tone of pioneering mathematical proofs that completely upended the academic field.

The results were published on the GitHub repo and included 372 novel results and 722

supporting papers. OpenAI said that the results were generated by an internal frontier model

that hasn't been released to the public. Likely the same model that produced the Navier-Stokes

solution last month, which kicked off a massive round of discussion. This time around,

OpenAI coordinated the release with their newly appointed mathematics advisory committee.

Some of their suggestions included releasing reasoning traces alongside the proofs,

which OpenAI has done for a sample of 10 problems. The committee also suggested it would be

useful to know the resources committed to each problem. In that regard, OpenAI disclosed that

the average result used compute equivalent to roughly three hours of chat GPT pro-thinking.

But the real story here is how this release changes the field of mathematics. In one fell swoop,

OpenAI has solved a significant chunk of the most difficult outstanding problems in the field.

We did have a few progressive warning shots over the past year. Last summer models from Google

and OpenAI achieved goal-level performance in the International Math Olympiad, the top contest

for high school mathematicians. Then earlier this year models from OpenAI and Anthropic

disproved several Erdos conjectures, which are famous in longstanding problems in geometry.

Then last month, OpenAI released a proof for the Navier-Stokes equation, an almost 200-year-old problem

in fluid dynamics. Navier-Stokes is one of seven millennium prize problems which have a million

dollar prize attached. Only one has been solved by a human, and merely making significant progress

on one is enough to guarantee a field's medal often compared to a Nobel Prize. If you are currently

in your head hearing Robin Williams and Stellan Scarsgard argue about a field's medal,

in good will hunting, you are not alone. In any case, the Navier-Stokes problem was one of the most

significant scientific results produced by an AI model, and yet it was just tiny precursor for

what OpenAI released this week. The best way to understand the scope of OpenAI's proofs is to

refer to a list of the top 500 open problems in mathematics produced by Proof Atlas.

This is an AI-generated list, but it's a decent representation of the most well-known problems in

the field. OpenAI fully solved 90 of the top 500 problems. There's also dozens of partial proofs

that would qualify as significant contributions to the field. Among them were partial solutions to

two additional millennium prize problems, the Reeman hypothesis and the Hodg conjecture.

For the purposes of this episode today, I'm not going to get deep into what these problems actually

are, as they all involve postgraduate level theoretical mathematics that is basically impenetrable

to a layman. Just to make that point, the Reeman hypothesis is arguably the most important problem

in number theory. It states that the Reeman-Zeta function which concerns the distribution of prime

numbers has zeroes only at even integers and complex numbers with a real component of one half.

Instead, it's easier to gauge how big a moment this is by observing how professional mathematicians

reacted. Alex Conturovich, a Rutgers professor, wrote,

Quasai-Reeman hypothesis, are you kidding me? If a human did this, it would be an instant

field's medal. No questions asked. Indeed, most of the initial takes came from mathematicians

that have been relatively positive about AI's contribution to the field. Darren Lit from the

University of Toronto framed this moment as being about mathematicians shifting from working

on problems for decades to verifying proofs produced by AI. He commented,

Fun! Looks like mathematicians have a lot of exciting work to do. If I understand correctly,

one of the results is a very special case of the conjecture of mine, which is also a consequence

of stronger work and progress by a student of mine. Mathematician Michael Warren wrote,

I didn't see anything particularly unexpected. Labs can solve math problems now,

so they just spend a bunch of money and solve a bunch of problems.

Historically speaking in baseball terms, today would be the day that Barry Bond's blasted

his 756th home run. If you would have told Bond's this in 1987, it would have been unbelievable.

But in 2007, still, there was a sense of awe from those who had worked at the intersection of AI

and mathematics over recent years. Anthropic researcher Levent Alpagy, who had been working on

Navier Stokes before OpenAI released their result, called this, obviously the most significant

moment in mathematical history. Former OpenAI researcher Acer said the quasi-reamment result was

his personal move 37 moment for mathematics. Move 37 refers to Alpha Go's unintuitive move that

helped it defeat Go World Champion Lee Cedal in 2016. Even for those without mathematical

training, the results were fairly mind blowing. Responding to his first read through the results,

Chabion X wrote, the list is absurd, a zero-free half-plane for the Zeta function, which is the first

result of its kind and over a century. Hilbert's 10th problem over the rationals, the Hodg conjecture

for CM Abelian varieties. Irrationality at Catalan's consent and dozens more, any one of these would

normally be a career, but the number that many aren't seeing is the following. It's three.

That's the average hours of ChatchyPt pro compute per result. A month ago, Navier Stokes took them

around 10,000 agents in 88 hours. That efficiency gain is within just a few weeks. Math Twitter

obviously is shocked. Again, this is literally the intelligence explosion happening right now.

2027 will be the year of superintelligence. I'm now convinced of that.

Still, one big wrinkle in the release is the difficulty in understanding exactly what's been

published. The Navier Stokes result published last month is still in the process of being

verified by mathematicians, and this is hundreds of new results to pour over. Mathematician Francesco

Maggie pointed out the obvious issue with high volume AI mathematics writing, Mathematics does

not simply grow by accumulating correct statements. Results have to be understood, connected,

explained, challenged, reused. Until that happens, they risk remaining letter of morta.

Now imagine adding 400 more AI-generated proofs. For each of them, until some human mathematician

picks it up, studies it, and connects it to the existing mathematical culture, isn't it in

essentially the same position? So my question is genuinely, what is the intended mathematical

value of releasing hundreds of proofs at once? What happens if there simply aren't enough people

willing or able to read them? To be clear, I think mathematicians should discover mathematics by

any means necessary, including scavenging through artificial alien-looking mathematical material,

since there may be extraordinary things to find there, but humans are not machines.

As attention, understanding, taste, and mathematical culture are scarce resources.

What happens if the rate at which mathematics is generated becomes much greater than the rate at

which mathematicians can absorb it? Physicist Steve Sue took it a step further asking,

what happens when a single day of AI research produces more mathematics than humanity can absorb

in a century? Imagine millions of superhuman research agents at work. Formal systems may verify

the proofs, but humans won't have enough context to understand the underlying web of machine-invented

concepts. In his view, we could quickly reach a period where fields like mathematics and physics

lose interpretability. At that point, he continued, AI is no longer a scientific tool.

Humans are receiving selected explanations from a much larger intellectual civilization they

cannot independently grasp. Another big strand of the conversation was speculation on what this could

mean for mathematics as a field of academic study. Dan McCattier wrote,

open AI is downplaying this for PR reasons. If you're a mathematician, you must feel like a nuclear

bomb hit and you're at ground zero. Reasoning models are two years old. In that time, they went from

incapable of basic arithmetic to solving problems humans couldn't solve for decades.

Math is only the beginning. AI will revolutionize the entirety of the human scientific endeavor.

Most of us don't appreciate what that means. And you can feel a bit of that shell-shock that Dan

is talking about, especially among mathematics students still trying to find their place in the

world. Mostly harmless grad students asked, what can I pivot to now that AI has taken away math?

They later explained that this released quote,

kind of nukes basically all the problems and projects I'd want to contribute to or work on in my

program. And yet, if there were many lamenting over the apparent end of a field, some mathematicians

were incredibly optimistic. Shrirum Kanan, a former math professor at the University of Washington,

it's a great day for humanity and math. If you asked a mathematician anywhere from 5000 BC to

January 2026, whether they would like a math genie to exist, 100% of mathematicians would have

said yes. Mathematicians give up significant opportunity costs to help advance human knowledge.

It's a no-brainer that they will eventually adjust to this new world and be glad this happened.

It's just an extreme but temporary shock to, I am world-class at math as an identity.

Another very different topic of conversation was how results in any of these theoretical

mathematics problems applied at the real world. The Airdos problems are mostly just intellectual

exercises with no clear application. Even the Navier Stokes proof, while very impressive,

doesn't really have an obvious impact in the real world. Engineers working on fluid dynamics problems

like aircraft design get by just fine using a simplified version of Navier Stokes.

Most of the problems that open AI solved in this batch are similar, very impressive,

long-standing, but ultimately not all that practical. Still, there were a few examples

that could make an impact in the real world. One was a dramatic improvement to the theoretical

efficiency of matrix multiplication, the math that underpins AI inference. Cornell math professor

Steven Strogat's commented, many staggering results here, but this is a particularly amazing one.

The exponent for matrix multiplication is no more than 2.25. The previous world record had been

something like 2.37. This leap in progress is like Bob Beeman's long jump.

For some, what was most interesting was not always there, but what was missing. Bitcoin security

researcher Justin Drake noted a quote, striking underrepresentation of cryptographic breakthroughs

among the 722 mathematical results open AI published. I've witnessed firsthand the U.S.

government censoring academic quantum cryptanalysis results. Backroom interventionism is my base case.

University of Texas computer science professor Scott Aronson added on his blog,

My sources tell me that AI companies have now started gingerly and discreetly,

investigating whether their latest internal models can break important cryptographic protocols

and primitives. If they can, then it would certainly be nice to get ahead of things before

the rest of the world figures out the same. In other words, if open AI's internal model can

break major cryptographic schemes, that's not just a problem for Bitcoin. It's a problem for

the cryptography that underpins pretty much all of the digital world. Still, with all of this,

maybe the most resonant conversation was whether we can expect the same thing to happen to

other fields. Arpeggupta, an associate professor of finance at NYU Stern wrote,

you're deluding yourself if you think AI advances like in math aren't coming to economics and other

social sciences. Or an economics professor Ethan Mollick is already starting to see it, commenting,

after a brief but institution eroding slop science era, it increasingly looks like we are going

to have two revolutions from AI. One, everything ever published will be re-read and re-judged in ways

that human scientists never anticipated. Two, novel discoveries will start to come fast. I'm seeing

rapid increases in the ability of AI to do novel work in my field of economics sociology,

with nearly autonomous research getting to top journal level. Still, a big question is whether

all other fields are able to be approached in the same way as mathematics. By and large,

these math proofs were not generated by reasoning into novel ideas. They were largely within the

domain of problems that could be solved with big data approaches coupled to pattern recognition.

It would be reductive to call them brute force, but these are all verifiable problems that fit

neatly into the reinforcement learning paradigm. Pedro Domingo, so computer science professor at the

University of Washington summed it up by commenting, mathematics is a classic example of more of

X paradox, hard for humans, easy for machines. Unpacking his roadmap for where this is all going,

Google Distinguished Scientist Payment Millenfar wrote, Speed of Automation is an inverse

proportion to the entropy of the subject domain. Math and coding are structured and orderly,

which is why AI is able to do them so well. In order of difficulty, the hard sciences are next.

They may be governed by strict physical laws but are complicated by dependence on noisy

measurements and laboratory experimentation. Financial markets are more difficult still.

They are complex, adaptive system subject to human psychology. While past data is abundant,

the underlying statistics are non-stationary and highly variable. Clinical diagnostics and

decision making in medicine are far more difficult than most people realize. It's very high stakes,

it requires synthesizing very fragmented signals, and dealing with idiosyncratic human behavior

in biology. It's a nightmare of incomplete information. The law, jurisprudence, and legal

interpretation operate on structured text and precedent most of the time, but rely heavily

on ambiguous language, evolving societal norms, persuasive rhetoric, and human judgment.

They won't be automatic anytime soon. Culture, art, and strategic leadership are an odd mix,

but I think all of them are quite safe from automation. They are the pinnacle of disorder and

human creativity. They rely on tacit knowledge, intuitive perceptions of the world,

emotional intelligence, understanding of cultural consensus, and navigating complex moral

trade-offs. So rejoice, we'll have artists, lawyers, doctors, and CEOs for a long time yet.

In fact, I think from here there are two incredibly fascinating paths to watch. One is what

Payman was speculating on, which fields this sort of disruption comes to in what ways,

or alternatively shows us that there are human or systemic inertia road bumps that make this

disruption not as inevitable in other areas. But the second thing that I think will be interesting

to watch is what mathematicians do with all of this. To use the words of the previous commenter,

what happens when every mathematician has a mathematical genie? Does it negate all of their hard

work and learning, or does it allow them to do things that are undreamed of as of yet? Over the coming

months, that is what we will begin to see. For now that is going to do it for today's AIDLI Brief,

I appreciate you listening or watching as always, and until next time, peace!