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. Arena AI has just hit a $100 million
run rate. This is only eight months after they
launched paid evaluations. I want to break down
what Arena does, why they're special, why they're
unique, and why they're growing so fast. Palantir
is tapping NVIDIA's Nemotron open models for
US government AI. This is interesting and a lot
of drama is behind this story as well. Elizabeth
Warren and Scanlon are reviving a bill to ban
AI firms from selling health data. We'll get
into the details on that. Flexion Robotics is
training hundreds of humanoids to run office
errands autonomously. And China's CXMT is landing
a $3 billion memory supply deal with Tencent.
We know memory is one of the critical pieces
of the AI. infrastructure build out. And there's
a lot of issues going on with memory, increasing
the costs of basically all electronics today.
If you've ever been using an AI model like Claude
and have been frustrated that it doesn't create
images or audio or video, I'd love for you to
try the MCP connector for AI box. That's my own
startup. We essentially allow you with one link,
give it to Claude, and it can bring any of the
AI models that you use for everything else into
Claude. So you can use chat GPT's image generation
inside of Claude. You can use 11 labs audio generation.
of Claude or Google VO3's video generation inside
of Claude. So all of the capabilities of the
different AI models, there's 80 different ones
that we allow you to connect. You can bring them
all inside of Claude or ChatGPT or Gemini or
Cursor or any of your other places where you
really do all of your work, all of your workspaces
with AI. So if you want to check that out, it's
AIbox .ai slash MCP. It's a super easy MCP connector.
So you give Claude this link inside of the connectors
and you log into your AIbox account and now you
have all of these capabilities. I have a whole
website. where I explain how this works, how
you can do it. And it is $8 .99 a month to get
started with it. So it's super cheap. And I hope
that really unlocks a lot of creativity for you
to be able to get 80 different AI models and
capabilities inside of Claude or whatever else
you are building with. Okay, let's talk about
what's going on with Arena. This is a company
that is now at $100 million. They were originally
created in UC Berkeley. And it's basically an
AI leaderboard, right? Like this is the company
where you can go and test different AI models
against each other. They've hit this $100 million
annual revenue, and this is eight months after
they launched their paid evaluations, just back
in September. triple what they were doing in
January, which was 30 million, which honestly,
even in January, I thought this was really impressive.
So the company is now directly competing with
Scale .ai and Merkur for post -training dollars
because they're selling labs structured analytics
built on 10 million plus human model comparisons.
So basically what's going on is they've just
raised $250 million total across two different
rounds. So they did $150 million Series A in
January at a $1 .7 billion valuation. They did
that from A16Z. So the way that they actually
are making money is that the public leaderboard
is still free. So essentially, if you haven't
tried this before, you go to the site and it
puts two different AI model responses side by
side. A lot of people use this because you basically
get free AI usage out of it. Instead of having
to pay for ChaiGPT, you can go pay this and it
will give you, you know, two responses side by
side. You pick which one you like better and
you're helping, you know, tell it which ones
are the most popular. Those leaderboards are
then, you know, created so you can see, you know,
oh my gosh, ChaiGPT's new model is, you know,
beating the open, you know, the model from Anthropic,
whatever. So that's kind of where a lot of these
leaderboard come from. companies, how they kind
of run and why people use them, but how they're
actually making money is that they have an AI
evaluations product package, which is going to
show data basically to different AI labs. So
like OpenAI, Ananthropic, and Google, when they
have their models in there getting voted on,
they'll show them what areas their models are
losing on to competitors, right? So it's like,
hey, look, your model's good, but anytime a healthcare
question gets asked, yours is doing poorly. Or
anytime a finance question... or any type of
question related to this. So they're telling
them exactly where their models are lacking and
they're helping them to guide reinforcement learning.
So it's a, you know, it's a massive value for
OpenAI and Google. They're going to pay a ton
of money for that. It's a great value for people
that get free AI usage. And also it's interesting
for all of us to see what companies are doing
the best in the leaderboards. So Merkur's annualized
revenue, which is one of their competitors, hit
$1 billion this year and Handshake AI's training
army grew from $550 million to about a billion
dollars in... three months. So I think this just
kind of shows the scale of a lot of these post
-training markets, how much money they're actually
able to make. Let's talk about what's going on
with Palantir. So they have just basically selected
NVIDIA's Nemotron open models to use at the US
government for all of their AI. Of course, there's
a ton of drama with Anthropic and even OpenAI
right now, and the US government having to, you
know, pull their models or tell them like, hey,
you can't release your model till we give it
like an accurate assessment, whatever, right?
So one thing that's interesting, I think in particular
with the Department of War and a lot of the beef
that they had with Anthropic and them, you know,
classifying Anthropic as a supply chain risk
because they wouldn't let them do all the things
that they wanted to and stuff. Because of that,
Palantir, who does a lot of the development for
the US government, a lot of software development,
has selected to use an open source model. So
NVIDIA has their Nemotron model. They're using
this open source model and they're using it to
run a lot of stuff. Now, a lot of AI, you know,
work inside of the US government. And the reason
why an open source model is great for this particular
use case, or why the US government would be so
happy to use it is because there is no centralized,
you know, servers that the models got to go back
to, there is no one that can say, hey, you can't
use it for x, y, z reasons, it's an open source
model, you can do whatever you want with it.
So in basically everything that they're doing,
they will have, they won't have to go and get
it signed off. I remember this kind of this famous
conversation between the head of war and the
CEO of Anthropic Dario, and they were saying
like, hey, we need it to be able to do XYZ tasks.
And Dario's like, oh, well, if you're going to
do that, like technically that's against our
terms of service. And then they're like, okay,
well, what if like there was a moment when we
really needed to make some sort of, you know,
critical decision on this? And Dario's like,
oh, well, you could just call me and like I could,
if there's like an exception to like a rule,
just call me and like I'll unblock something
for you. And they're like, okay, well, we don't
want to call you and unblock something if we're
in the middle of like a confidential. you know,
battle thing, blah, blah, blah. So I mean, a
lot of people have a lot of opinions on this,
but these are the two arguments that we hear
on this. And so this is why it looks like Palantir
is going to be using this to run a lot of US
government agencies. And beyond all of the drama,
I just think for keeping classified data without
having to send that to an outside network, I
think a lot of agencies will want to use that.
So agencies also get to keep the customized model
weights that they train, which I think solves
basically the core problem that's kept a lot
of secure AI adoption stuck. and being very slow
in the past. And in particular with Nemotron,
this is an open model and it's going to run inside
of Palantir's Sovereign AI operating system.
It's going to be all run on NVIDIA's accelerated
hardware. There's going to be handling data authorization.
There's going to be isolation and all of the
auditing is all going to be in a closed loop.
I'll also say this isn't too crazy. Like two
thirds of companies right now already use open
models. And I think a lot of them say they're
doing this because of cost efficiency. NVIDIA
for frames this as kind of the template for scaling
AI in regulated environments, sensitive environments,
so like finance and healthcare and government,
obviously. In AI regulation, Elizabeth Warren
and Scanlon are reviving their Health and Location
Data Protection Act. They're adding a bunch of
new things to it. It now explicitly bans AI companies
like OpenAI, Anthropic, and XAI from selling
health and location data users enter into ChatshipT,
Claude, and Grok. The bill is going to allocate
$1 billion to the FTC over 10 years to enforce
this. And basically this is filling a regulatory
gap because a lot of these AI labs are racing.
to get users. And I mean, even myself, you have
ChatGPT, they just added ChatGPT Finance, which
of course I, my wife like hates this kind of
thing. She hates giving data to AI models, and
it's probably for good reason. Myself though,
I went and added basically all of my credit cards
and bank accounts to my ChatGPT. It does the
integration through Plaid, but it's been super
useful to be able to ask it questions about where
we're spending, what our recurring expenses are,
income from businesses. I mean, there's just
so much interesting data you can get just chatting
with this thing. So I love it. I know a lot of
people use this for health things as well. And
there's a lot of benefits, but a lot of people
are concerned about, you know, giving all of
these financial and health data to these companies.
What are they going to do with it? So the FTC
has to write implementation rules within 180
days of this bill being passed. If it is passed,
the agency state and attorney general and individuals
can all sue for any violations on this. There's
some co -sponsors on this. We have Senator Ron
Wyndon, Bernie Sanders. The original bill is
from June of 2022, but it only covered data brokers.
It didn't have AI companies and kind of how they're
collecting data upstream. Chatbot health transcripts
are unusually rich, right? Because users are
going to be pasting in their lab results, or
they're going to upload scans, they're going
to ask follow -ups, all of that. And all that
can create a lot of really complete medical profiles
that no other health... you know, traditional
app would actually be able to capture it. People
are uploading pictures of their x rays and so
much more. So anyways, I do love all of this
kind of data protection, not being able to sell
that. That makes a lot of sense to me, even though
I don't agree with everything that Bernie Sanders
does or says when it comes to AI in particular,
banning AI data centers from New York. I mean,
I just think that that's kind of counterintuitive
to a lot of what progress and the benefit that
these data centers and well, the benefit that
a lot of this AI can have on people. But anyways,
all of that aside, this is one where I would
definitely say I'm agreeing with Elizabeth Warren
and Bernie. Sanders on the face of this bill.
Now, I don't know if there's secret sneaky things
stuck in there, but overall, don't sell my health
data to outside companies that I give you. I
am definitely in agreeance with that one altogether.
Okay, let's talk about what's going on with Flexion
Robotics. They are training humanoid robots to
run office errands. They're helping them do this
all autonomously. This is a Swiss startup. It
was founded by ex -NVIDIA researchers that built
a software stack essentially that's letting these
humanoid robots do multi -step office tasks so
things like and oh and by the way you can do
this all by just talking to them so i think i'm
really used to seeing those like dog robots from
boston dynamics and there's like remote controls
where you can kind of control them i've seen
them at like conferences and stuff this will
be different this is you're literally going to
humanoid robot and like hey go get me a coffee
although i'm sure there's you know better things
than telling it to go get you a coffee it'd probably
be like walking around and getting things faxed
or scanned or dropping things off different people's
desks. I don't know, right? This could actually
be quite useful. I almost imagine this like if
I was in an office setting, everyone would have
like a little microphone button maybe that like
summons this thing. And you could like think
of how you kind of use like Claude or I guess
how I use Claude is I hit like my voice to text
button. I give it an instruction and it runs
off doing it. Let's say there's one humanoid
robot for the entire office. Not everyone needs
their own, obviously. So you could have like
one or two, maybe one per floor or something
like that. You push a button, you give it its...
command you tell what you want it to do it comes
over to your desk it grabs the document grabs
the paper grabs the you know whatever you need
it to move or do and then it goes and executes
it it's like a real world version of claude i
love this if you can't tell basically this is
a modified unitry humanoid robot and it's running
all of their systems it's um you know they're
getting it to like go retrieve like mail and
stuff Apparently, it can go up and down stairs.
It can go in the elevator. It's super capable.
And they have it stocking a shelf in a recent
demo that they released. So it's very capable.
Right now, they're using reinforcement learning
at basically every layer of what they've built.
They have a master planning. They have simulation
environments. They have motor controller. If
you look at the overall market, ABI Research
is projecting that robot foundational model markets
are going to hit about $150 billion by 2036.
Flexion is basically trying to position some
of the software as the product, and they're saying,
look, the hardware is a commodity. There's going
to be a ton of these companies that are building
these humanoid robots, maybe Tesla. We're not
going to compete on there, but we're going to
compete on the software that helps train these
robots to do things in your environment, in your
office, and how you guys actually control that.
So honestly, I kind of love that. It's moving.
It makes the humanoid robot feel more like an
LLM, and there's all of the stuff getting built
on top of it. Flexicon is hardware agnostics.
So they're saying they work with a whole bunch
of different humanoid robot makers. They're not
going to build their own robot. They're going
to be able to let you use Unitary, Figure, X1,
and a bunch of other platforms. China's CXMT
has landed a $3 billion memory supply deal with
Tencent. This is the largest commercial contract,
I think, in basically domestic DRAMs makers in
like their entire history of their company. The
deal right now, I think, is showing that China's
largest cloud operators are now actually willing
to source AI memory from local. suppliers, because
a lot of the US export controls are tightening
around basically all of the advanced chips, right?
So Tencent is a huge customer, they need so much
of this. And traditionally, they have bought
a lot of this from American companies. But it
seems like they're able to get this from Chinese
companies now because of these export controls.
Something interesting about this company in particular
is that CXMT is kind of moving from like smartphones
and PC OEMs to now hyperscale AI customers. And
I think it's kind of the first time for them.
It's very similar to what SK Hynix did, and they
kind of hit this dominance path because they
were getting customers like NVIDIA to buy from
them. I'll be interested to see if CXMT is able
to move outside of just China and really get
more global dominance, or if it's something that's
just going to be geographically landlocked because
of kind of the sensitive nature of everything
happening with cloud and AI and all of the data
center buildouts. This podcast was a lot of fun
today. I'm actually sitting here on a yoga ball
holding my baby. I got a new baby as of a couple
weeks. ago and hopefully you weren't able to
hear him grunting too much. He's sleeping, so
he should be pretty happy. But anyways, I'm going
to keep getting these podcasts out for you. There's
so much happening in AI news. I love this stuff.
I'm super passionate about it, but lots of fun
going over. But excuse any little sounds you
might be hearing in the background as we're recording
in the studio with Clay. So anyways, if you wouldn't
mind leaving a review, it would help the show
a ton. I appreciate them all. I read them all.
But anyways, thanks so much. And make sure to
check out AI box dot AI, the MCP in particular,
if you want to get access to all of the different
AI models, there's about 80 of them in one platform,
or you can build them all into Claude. So you
get image, audio, video all inside of Claude.
All right, you guys already know the links in
the description. I'll catch you in the next episode.