Welcome to the AI Chat Podcast. I'm your host,
Jaden Schaefer. Every day I cover cutting -edge
AI news and talk with the leaders behind it,
breaking down what it means for your life and
business. OpenAI has just bought back $7 billion
worth of employee shares at about an $852 billion
valuation. Anthropic's unreleased model has advanced
the Riemann hypothesis. They spent 31 million
tokens to do this. OpenAI's Astra model has solved
a 10 long open math problem for $2 ,000 worth
of tokens. So it wasn't cheap, but it was able
to get it done. Alibaba released Quen 3 .8 Max,
which is a 3 .4 trillion parameter model. It's
basically competitive with Claude. Researchers
right now are extracting hidden reasoning from
Claude, ChatGPT, and Gemini. They're using a
trick with the API, and they're comparing the
reasoning of different models with the open source
or open weight model. that are Chinese versions
to see if they ripped off those models. Almost
everyone I know is paying $20 a month for some
AI tool, whether that's ChatGPT, Cloud, Gemini,
Canva, there's so many out there. And the one
thing that I will say to all of them is I would
love for them to check out... AI box .ai, which
is my own startup that lets you access 80 different
AI models all on one platform. Our cheapest tier
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access to over 80 different AI models. There's
image models, there's audio models, there's video
generation models like Google VO three. And of
course you have all of the text and reasoning
models like cloud chat, GPD and Gemini. If you
want to get access to all of that in one place
and also have an MCP, which basically is a link
that lets If that's your main model that you
use, generate images or let's cloud generate
audio or let's cloud generate video. So you can
pull any model into any model. If you want to
check that out, it is AI box .ai. I'll leave
a link in the description. OpenAI has just completed
a $7 billion buyback of employee shares. They
did this, which I think a lot of people are kind
of shocked by, at an $852 billion valuation.
Now, if you remember, this is the exact same
valuation that they had back in March. We are
now many months later. And if you look at a company
like Anthropic, which month over month is adding
like $100 billion to their valuation every single
month, basically, this is not, I mean, you could
look at this a couple different ways, but like
technically that's not great. Their valuation
hasn't grown since March. I think this is basically
they're trying to keep the staff from leaving
and going to Anthropic or going to Gemini. But
also, I think this is basically showing they're
not in a big rush to go public anytime soon.
This $7 billion tender offer is one of the largest
employee buybacks a private company has ever
done. I think that's no shocker. But this is
very consistent with what OpenAI has done. They
have a pattern of doing really regular secondary
sales. They've done this for the last three years
where they'll let the employees sell at a regular
basis. Sam Altman told the staff last month that
OpenAI missed a bunch of internal financial targets
over the last year, but he expects that the next
year is going to be stronger. I mean, there's
the elephant in the room and the no surprise
is that Anthropic just absolutely mopped the
floor with their growth. And OpenAI, I don't
think has actually shrunk. They probably just
went stagnant or they kind of plateaued for a
minute. And the reason why is just because Anthropic
grew so fast, they just took a lot of the oxygen
out of the room. Is it going to stay that way
forever? I personally don't. think so. I'm heavily
testing both Claude and chat GPT. And right now,
my main tool that I'm using is chat GPT work,
I switched from Claude co work about two weeks
ago. And I've been really impressed Claude co
work or chat GPT work can do a lot of things
that Claude co work didn't, it feels like opening,
I felt pretty threatened, and they put a lot
of time and energy into getting this right. So
I love a good healthy rivalry, I think it was
going to go back and forth. I'm excited if we
have other competitors, if it feels like Google
gets a little bit more in this space. You have
perplexity in there. But overall, I think OpenAI
has a superior product right now. And so I think
they might start getting back a little bit of
that enterprise that they've lost. Anthropic
was reportedly profitable earlier this year.
I think that's definitely going to threaten OpenAI
as far as if Anthropic can reach the stock market
first. If they can do their IPO first, they're
going to get a lot of... a lot of buyers that
would possibly have invested in OpenAI if not.
So I think definitely there's a timing issue
or there's a motivation to get to an IPO first
for OpenAI and Anthropic. But right now, if they're
holding their valuation flat and they're doing
their tender offer for all their employees now
rather than waiting for the IPO, OpenAI is basically
buying time to prove that their business model
is going to work at scale, especially for enterprise
and their API products that have real... recurring
revenue also i mean if you want to be like sort
of pessimistic maybe you could say look, perhaps
Sam Altman believes that the company is worth
more and it's growing, but he just wants to buy
back all the shares at a really good deal so
he can go buy $7 billion worth of shares from
the employees. And when they IPO, they can bump
up the IPO price 50 % and now those $7 billion
of shares will be worth $14 billion. The company
that's worth over $850 billion, I don't know
if that's really the main incentive here, but
there's definitely a case to be made that perhaps
those shares are at a discount. I think more
like - Likely he's just trying to keep his employees
happy and keep them around. But, you know, it's
got to make you think that an IPO is not just
weeks away if they're able to or if they're going
to be buying back these shares from their employees.
You know, if an IPO is around the corner, they
probably just let them all sell on the open market.
But if it's going to be months away, they probably
will just buy them and wrap that up now. There's
an unreleased anthropic model that is advancing
the Riemann hypothesis. So basically this is
a 150 -year -old math problem. There is a $1
million bounty on this math problem, by the way,
which is kind of cool that this exists. But by
extending the range of numbers for... which it's
been verified. This new model from Anthropic
ran itself for a day and a half. It organized
60 subagents to test 650 different ideas and
formalize the results in a way that mathematicians
could check because not only do you have to solve
these problems, but you have to explain how you
got it. So this is a big part of it. The model
orchestrated its own workflow without a human
mathematician directing it. It was assigning
tasks like idea generation, validation, paper
drafting to different subagents. It used about
31 million tokens to produce this. Two anthropic
mathematicians confirmed the results, which was
then formalized in Lean, which is an open source
proof assistant that lets other researchers verify
it step by step. OpenAI's internal Astra model
proved 10 major results this year and a separate
anthropic effort disproved the Jacobian conjecture,
which is a 1939 problem. So I think we're seeing
a big shift in how AI is tackling big problems
in math right now. I think AI is producing verifiable
mathematical progress on a bunch of unsolved
problems, but there's a lot of people in the
math field that are debating what it means for
credit and accountability when a machine is coordinating
all this work. instead of a person, but also
you can imagine, well, what if a person just
used? You know, something like open AI, like
right now, this is, you know, being done by the
actual researchers are anthropic. But what would
happen if, you know, they put out this unreleased
model that's good at math and a regular person
or a regular mathematician uses it to solve one
of these problems? Who gets the credit for it?
And maybe they say like, oh, no, I did it all
by myself and I never even used AI. Would they
get credit for it for solving these really hard
kinds of problems? This is what everyone in the
math field is currently debating. Now, I mentioned
that. problem or those 10 problems that Astra
solved. It basically the way this worked is this
is also an unreleased model, by the way. So OpenAI
and Anthropic, it's so funny. It feels like they
do all their PR stunts like in at the same time.
So it's like OpenAI goes and hacks Hugging Face
and Anthropic's like, we hacked people. And then
Meta's like, we hacked people too. And then Quinn's
KimmyK3 is like, we hacked people or not Quinn,
Moonshot's KimmyK3 is like, we hacked people
as well with our unreleased model. And now we
have like, I don't know, all of these different
models are like, we solved, you know, these unsolvable
hundreds. year old math problems. And they're
like, Oh, we did too with our with our unpublished
model. Anyways, I find it funny. But I mean,
this one's pretty cool. So in this case, though,
the Astra model, so not released, it solved 10
long standing math problems that mathematicians
had worked on for decades. It published a 250
page proofs that verified with formal proof checking
software, the solution like to actually get the
solution to this cost about $2 ,000 in tokens
to generate it. And on the one hand, I'm sure
people are like, well, that's a lot of money
to solve, you know, solve these math problems.
I was like, if these are like famous math problems
that have never been solved, they've been around
for like hundreds of years, I mean, 2000 bucks,
that seems pretty cheap, especially for a PR
stunt like this. I mean, this is worth a lot
more than $2 ,000 to open AI, this is probably
worth, you know. uh, like 50 or a hundred million
dollars worth of good PR for them. So it makes
sense why they would do this. Um, but yeah, these
are, the math is getting cracked by, um, these
AI models. And this is stuff that's been, you
know, pretty tricky for a very long time. Alibaba
has just released Quen 3 .8 Max on Monday. This
is a huge AI model. It has 2 .4 trillion parameters.
It is on a bunch of benchmarks. It is right behind
Anthropix Claude Fable 5 on the public leaderboard.
So it's, I mean, it's right up there as number
two. The company said that they're going to release
the model's code and weights next week, making
it free for developers. So any developer can
go and download it. They can go modify it. I
mean, the thing that's cool here, it's not like
they're like, oh, we released a number two. model,
that's great meta just released like a number
four model yesterday or whatever. The thing that's
amazing about it is that it's a number two model,
and they're going to release the codes, open
weights and like give this to anyone to go and
download and use. on arena .ai's text leaderboard.
It is behind Fable 5 and 3 Cloud Opus models.
It's ahead of a bunch of other ones. Alibaba
is coming to the openweight distribution. They
had a little bit of a proprietary pivot earlier
this year, which was kind of showing how a lot
of Chinese labs like Moonshot and ByteDance were
ever, there's like kind of this weird, and it
feels like it happens in sync, but there's this
weird moment for a lot of these. companies that
are making these open weight models it feels
like if they fall a little bit too far behind
then they're like they go proprietary they fall
a little bit behind then they're like okay now
that we're getting caught up we got our mojo
back we're going to release an open weight model
even meta did that moonshots AI's Kimi K3 released
last week had a 2 .8 trillion parameter model.
So, I mean, this is 2 .4 trillion. It's very
similar, but it's definitely, you know, Kimi
K3 is a little bit more than Quen 3 .8 max. But
the parameters don't 100 % matter, especially
because this is currently ranking above it or
on a bunch of different benchmarks. Researchers
have found a way to extract hidden reasoning
from Claude, GPT, and Gemini. This is a really
interesting one for me. What they're doing is
they go and trick smaller AI model variants into
decrypting what larger models keep secret. So
inside of any time that you ask an AI model like
ChatGP or Cloud a question, it's not just running
it to one model and spitting back the results.
It takes your question and it gives it to like
16 or a whole bunch of different sub models that
all review it and they all come up with their
own response. And then it sends that to another
model that looks at all the responses and it
can solve Anyways, there's like these orchestrations
of models in the background looking at it and
reasoning through it and trying to come up with
the best response possible for you. But apparently
that is the vulnerability. So the same flaw has
also leaked API keys and passwords before the
companies patched it last month. So essentially
the main model was kind of better protected,
but these little sub models where they could
actually go and exploit them. This particular
attack exploits shared decryption keys between
large and small model versions. So you're feeding
encrypted reasoning to a less restricted. sibling
model and then you're making it decrypt and expose
the hidden chain of thought from the bigger and
better model that is you know more encrypted
or you know higher security but the little models
like you're using their own models against them
kind of moonshots open weight model kimmy k3
produced reasoning outputs that were very similar
apparently to claude opus 4 .8 and gpt 4 .0 their
reasoning traced across 90 different test questions
so basically the idea here is that kimmy k3 probably
use model distillation. I mean, this isn't a
shocker. Why wouldn't they? They obviously use
model distillation on Opus 4 .8 and GPT -4 .0.
OpenAI, Anthropic, and Google all patched that
particular vulnerability after they were notified.
Researcher Alexander Panfulov says that some
reasoning content can still be reconstructed
and closing the hole entirely would require rebuilding
how APIs handle offloaded computation. So in
some of these cases, it's really, really hard
to patch this, this kind of exploit. And, you
know, they'd have to really rebuild a lot of
their infrastructure and rebuild how APIs handle
stuff altogether, which, you know, obviously
that's a mess. problem. I think this exposes
a really big structural weakness in how frontier
models offload reasoning. The companies share
infrastructure for cost reasons, but that shared
infrastructure becomes a backdoor when alignment
training is different. So the distillation angle
matters most for policy. I think when we're talking
about distillation, if U .S. models are being
systematically copied, you know, by all these
open weight competitors, which some of them are
American, right? We have Meta doing that with
Meta's Muse Spark. And then, of course, we have
K3. But basically, this is a technique that is
this particular. method is a very plausible avenue.
A couple other interesting things that happened
this week. Brad Lightcap, OpenAI's longtime COO,
is leaving after eight years to start something
new. And Anthropic is going to start watermarking
claw -generated text. They have to do this to
comply with the EU AI Act. So there's a lot going
on. Thank you so much for tuning into the podcast.
If you enjoyed this episode and you haven't left
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So I just appreciate hearing from you guys. If
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I really appreciate it. Also make sure to check
out AI box .ai. If you want to get access to
over 80 different image, audio, video, and text
models, all in one platform for $8 and 99 cents
a month. We also have 20 % off annual subscriptions.
And if you want to get more tokens, there's a
bunch of different tiers. You could do some really,
truly incredible. things with AI box dot AI,
especially our MCP that lets you connect AI box
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and audio all inside of Claude, which is what
I use it for a ton. All right, catch you guys
all in the next episode.