Okay, wait, hold on a second. I just need to
make sure I heard you read the pre -show. You
said Sam Altman's kids were supposed to run the
Mars colony. Yes, that is exactly what the leaked
email said. It's part of the whole lawsuit discovery.
Altman proposed this $80 billion project for
a Mars colony. And yeah, the text implies that...
the governance structure that involves his children.
Your biological children or his AGI children.
You know, knowing that whole circle of tech philosophy,
genuinely hard to tell. Maybe they see them as
the same thing eventually. But here's the real
kicker. Elon Musk's reply to this whole pitch
was just, it was brutal. I can only imagine.
He just replied with a single statistic. He called
the probability of success zero percent. Zero.
Zero. Okay. Welcome back to the Deem Time. I'm
really glad you're here with us. We usually spend
our time trying to filter out all the noise,
you know, find the signal in these huge data
dumps. But today. I mean, the noise is incredibly
loud. It is loud. But if you listen really closely,
there's a very distinct rhythm underneath it
all. Right. And we aren't just talking about
the usual chatbot headlines today. We're looking
at a fundamental shift in competence. We're talking
about AI putting on a lab coat at Stanford. Yeah.
To cure diseases. We're talking about AI literally
breaking out of the screen. To what? Fix your
dishwasher. And maybe most significantly, we're
talking about a massive geopolitical shift in
hardware. Something coming out of China that
just changes the entire board. It feels like
the theme for this dive is doing work. We're
moving past the era of, you know, generating
interesting text and into the era of executing
complex tasks from the biology lab to the factory
floor and then all the way to the global supply
chain. There's so much to unpack here. Let's
start with the scientists. Let's do it. So we
have this really fascinating study from Anthropic.
It's titled The AI Frontier, Scientific Research
and Physical Evolution. And I think for a lot
of people listening, this is going to require
a real mental shift about what an LLM actually
is. Ugh, a huge shift. Because usually when we
talk about large language models, you know, Claude,
GPT, whatever, we think of them as writing assistants.
Like, Claude, summarize this PDF. Or, hey, write
me a polite email to my boss. That kind of thing.
Exactly. It's a retrieval tool, a formatting
tool. It's basically a sophisticated autocomplete.
Right. But what Anthropic did was profile three
different labs, and these are serious, high -level
research labs, where they're using Claude to
do work that... That normally takes human scientists
months, and the AI is doing it in minutes. And
it's not just doing the grunt work. It's actually
doing the thinking. I mean, take Stanford's Biomne
project as the prime example. Biomne is fascinating.
They call this a research agent. Okay, before
we go further, we should probably clarify that
term. When we say agent here, we're not just
talking about a chatbot with a personality, are
we? No, definitely not. A research agent is a
system that doesn't just chat. It uses tools
to execute tasks. Think of it this way. A chatbot
is a brain in a jar. It can talk to you. An agent
is a brain that's been given digital hands that
can open files, run code, search databases. It
can execute a whole workflow on its own. So it
has agency. It can actually interact with its
environment. Exactly. And Biomni is working across
25 different scientific fields. It's building
this memory bank of skills. It learns expert
workflows so it doesn't have to start from scratch
every single time. See, that's the key differentiator.
It's not just retrieving data. It's figuring
out. how to do the science. Yeah. But the example
that really stood out to me, the one that really
made me pause, was from MIT. Oh, the Matsuram
Project. Such a great name, by the way. Scientists
love a good pun. They really do. So they were
using it for CRISPR screens. For anyone listening
who might not be a biologist, CRISPR is basically
gene editing. And these screens, they're usually
a brute force process, right? Oh, incredibly
brute force. You basically have to disable thousands
of genes one by one just to see what breaks.
It's tedious. It's expensive. And it's so prone
to human error because you're just staring at
these massive data sets for weeks. And this is
where the story gets a little wild. Because Claude
didn't just speed up the spreadsheet work. No.
This is the part that just stopped me in my tracks.
I mean, whoa. Yeah. Get this. Claude actually
analyzed the data and it spotted an RNA modification
pathway that the human scientists had completely
missed. Wait, hold on. Let's pause right there.
It found a biological reality that the human
experts overlooked. Yes. When they benchmarked
it, you know, against other models and against
the human analysis, Claude identified a pathway
that wasn't obvious. It saw a pattern in the
noise that the humans just didn't see. That feels
like a pivotal moment. It's one thing to say
AI can calculate faster than me. We're used to
that. It's a totally different thing to say the
AI noticed something about biology that I didn't.
It completely changes the value proposition.
It moves from assistant to collaborator. And
there's a third example, right? It touched on
the economics of all this. The Lindbergh lab
at Stanford. Right. This is all about cost. Gene
screens are incredibly expensive. We're talking
something like $20 ,000 per screen. 20 grand.
Yeah. So you can't just test everything. You
have to be picky. You have to place your bets
very, very carefully. Usually, scientists use
a mix of spreadsheets, some intuition, and existing
research to guess which genes to target. It's
an educated guess, but at $20 ,000 a pop, it's
still a big gamble. So how did the agent change
that gamble? Well, they had Claude navigate a
molecule relationship map. So instead of just
looking at lists, the AI analyzed this whole
network of connections between molecules, and
it suggested the targets based on those relationships.
So it's replacing intuition with network analysis.
Precisely. And the study emphasizes this point
over and over again. None of these breakthroughs
came from just chatting with the bot. Right.
That goes back to your agent definition. Yeah,
the breakthrough is connecting Claude to tools,
giving it a custom workflow, adding guardrails
so it doesn't just hallucinate and make things
up. It's like the difference between asking a
carpenter to describe a table and giving a carpenter
a hammer and wood and saying, build it. That's
a perfect analogy. So this brings up the big
question for me here. If AI can cut the time
from months to minutes and find things that we
miss, does this make science cheaper? Or does
it just mean we start asking much, much harder
questions? It removes the grunt work so brains
can focus on the breakthrough. So we just level
up the difficulty because we have a smarter teammate.
We have to. All the easy problems are already
solved. All right. Let's move from the sterile
lab environment to something a little messier.
The kitchen, the garage, the factory floor. The
real world. Exactly. We're talking about physical
AI. Yeah. Now, I have to admit, when I first
saw that term in the source material, my marketing
alarm bells went off. Physical AI just sounds
like a buzzword somebody made up to sell more
chips. It definitely has that ring to it. I get
it. But if you look at the actual capital expenditure,
the money is telling a different story. The source
material is adamant about this. 2026 won't be
on screens. That is a very bold claim considering
how much of our lives are on screens. It is.
But look at the investment stack. We're looking
at a $123 billion ecosystem. Yeah. NVIDIA, Tesla,
the entire robotics supply chain, they are all
racing towards this. So when they say physical
AI, what are they actually building? It's really
about embodiment. For the last few years, AI
has been a brain in a jar, right? It's a server
farm in Virginia. It outputs text or images.
Physical AI is about giving that brain a body.
We're putting that brain inside the bodies we
already have. You know, our cars, our appliances.
Right. The source material had the slightly hilarious
example of a future where your fridge is negotiating
with your dishwasher. I saw that. Yeah. And honestly,
it sounds like a bad cartoon. Why do I need my
appliances to gossip with each other? I know,
it sounds ridiculous. But think about the internet
of things. We've been hearing about IoT for a
decade. Your toaster will talk to the internet.
And for 10 years, all that really meant was that
your toaster needed a firmware update and had
a security vulnerability. It was just dumb connectivity.
Exactly. Physical AI is about competence. It's
not just connected. It has agency. It's about
the fridge, analyzing what's inside, realizing
you're out of milk, checking the dishwasher cycle
to coordinate energy use to the cheapest time
of day, and maybe ordering groceries based on
your diet plan. So it's the fulfillment of the
smart home promise, but like actually smart this
time. And it's about the supply chain. If you're
a consumer or a creator, you are now part of
this loop. This isn't just about cool robots
doing backflips. It's about the entire physical
world getting a new operating system. That is
both exciting and, frankly, a little terrifying.
I'm just picturing my fridge judging my late
-night snack choices. Oh, it absolutely will.
I'm sorry, Dave. I can't open the door. You've
exceeded your calorie limit for the day. See,
that's where I pull the plug. But a serious question,
then. Yeah. Are we ready for our appliances to
have agency? Or do we just want better automation?
We want them to do the dishes, not argue about
the soap. Right. We want the labor, not the debate.
Exactly. Okay. Let's pivot from the domestic
to the geopolitical. Because while the U .S.
is focusing on robots, there's this massive story
coming out of China about the chips that run
them. Hardware wars. We have a story here about
Zippo AI launching a new model. It's called GLM
Image. And on the surface, okay, it's an image
generator, like Midjourney or DALI. Right. It's
a 16 billion parameter model. Decent size, pretty
capable. But the specs of the model aren't the
real headline here. The headline is the silicon
it was trained on. Exactly. Usually when you
read a paper like this, you scroll down to the
hardware section and it always says, you know,
trained on NVIDIA H100 clusters. Industry standard.
But this one, no NVIDIA, no AMD, no US tech at
all. This was trained entirely on Huawei hardware.
That is a huge, huge signal. We've been talking
about the chip bans for years. The U .S. government
placed these really strict restrictions to try
and limit China's access to high -end AI training
chips. And the prevailing theory in Washington
was that this would, you know, cripple their
ability to train foundation models. It was supposed
to be a chokehold. But Zipu AI just proved that
that chokehold might be slipping. They build
a full domestic stack. This is what China has
been chasing for years. Independence. Let's look
under the hood for a second, because the way
they did this is technically pretty interesting.
It's not just a copy paste of a U .S. model.
No, the architecture is quite clever, actually.
They use a two stage system. OK. Bring that down
for us. So stage one uses what's called an autoregressive
transformer. All right, jargon alert. Let's define
autoregressive transformer for everyone listening.
Yeah, the simplest definition is it's a model
that predicts the next piece of data in a sequence.
Kind of like how chat GPT predicts the next word
in a sentence. Exactly like that. But here it's
predicting the layout of an image. It's kind
of acting like an architect. So it creates the
blueprint first. Right. It predicts what they
call semantic VQ tokens. Basically, it figures
out the meaning and the structure of the image
first. That's a nine billion parameter model
doing the planning. And then stage two. Stage
two is a diffusion transformer. This is the painter.
It takes that blueprint and it renders the final
pixels. So logic first, then aesthetics. Yes.
Because of this split approach, it's really good
at text -heavy images, which has been a weak
point for some other models. Now, the source
material does mention that in terms of pure artistic
vibes, it might not beat the absolute top -tier
Western models yet. You know, models like Nano
Banana or Seadream. Maybe not yet, no. But it
proves the concept. It proves you can train a
massive functional commercial -grade model without
ever touching American silicon. So if the hardware
bands were meant to stop them, did they just
force them to accelerate their own development?
Necessity is the mother of invention. We force
them to build their own. And now that stack exists.
And once it exists, you can't un -invent it.
The divergence is real now. We basically have
two separate hardware ecosystems developing in
parallel. Which complicates, well, everything.
All right, let's take a quick breath. That was
a lot of heavy lifting. Science agents, robotic
supply chains, and semiconductor geopolitics.
The trifecta of modern anxiety. Let's move to
our rapid fire segment. Culture, chaos, and what
we're calling cursed phrases. Oh, I love this
list. First up, Leo Tolstoy. Poor Tolstoy. Can't
even rest in peace. People on X, formerly Twitter,
are accusing Leo Tolstoy of using AI. Because
of the em dashes. Apparently using em dashes,
that long dash you use for a break in a sentence,
is now considered a sign of AI writing. Never
mind that War and Peace was published in the
1860s. Right. Or that, you know, he died in 1910.
The paranoia is real. People are seeing ghosts
in the machine everywhere. It's becoming a witch
hunt. If you use good grammar or complex punctuation,
you must be a bot. Which leads us right to the
next item, the cursed phrases list. Ah, yes.
There's a new list of words circulating that
supposedly out you as an AI. Words that the models
are statistically more likely to use. I actually
checked this list before we started recording.
And did you pass? I have to be honest. Yeah.
I failed miserably. I used Delve and Tapestry
all the time. You're a bot, confirmed. I know.
I felt so called out. But it's a real problem
for writers now. I actually still wrestle with
this. I find myself pruning my own vocabulary,
deleting words I love just because I'm afraid
someone will think I use chat GPT to write it.
It's this weird feedback loop of anxiety. It's
a crisis of authenticity. We're stripping down
our language just to prove we're human, which
is so ironic. Item three. We touch on this in
the intro. Elon versus Sam. The lawsuit that
keeps on giving. We mentioned the Mars colony
email, but there was another detail that really
stuck out to me. Elon Musk. in that correspondence
called OpenAI 0 % likely to succeed before he
left. That is such a definitive statement, not
unlikely, zero. It really just highlights the
split in vision, doesn't it? You have Altman
with this expansive, almost hallucinogenic optimism,
and then Musk with this brutal probability -based
skepticism. And yet, strangely, they're both
pushing the frontier harder than anyone else.
True. Item four. Energy. The elephant in the
room. The Trump administration is reportedly
asking tech giants for $15 billion. For power
plants. Power plants they might never use. They
want an upfront commitment. And the tech companies
aren't thrilled. They say they weren't even consulted
on the price tag. But it just shows the scale
of the infrastructure problem. We're not talking
about coding anymore. We're talking about AI
consuming electricity on a nation state level.
The constraints aren't software anymore. It's
physics. It's electrons. And finally, the bear
case. Michael Burry. The big short guy himself.
He's quoting Warren Buffett, and he's predicting
the AI stock boom, specifically NVIDIA, is going
to end badly. He thinks it's a bubble. He thinks
the hype has just completely outpaced the reality.
So is Burry right, or is he just early again?
He's betting against the momentum of the entire
world. That's a bold move. Very bold. OK, we're
back. Let's try to pull all of these threads
together before we go. We covered a lot of ground
today. We did. But I think there's a pretty clear
through line here. I agree. It starts with competence.
That's Claude. It's not just chatting. It's doing
real science at MIT and Stanford. It's discovering
biology that we missed. Then you have embodiment,
the whole physical layer. That $123 billion stack,
the fridge, the car, the factory. AI is growing
legs and entering the supply chain. And then
divergence. China building a non -U .S. stack
with Zipu and Huawei. The hardware world is splitting
in two, and it's proving that bands might just
accelerate independence. And finally, hype versus
reality. Mars colonies run by kids versus the
reality of crumbling power grids and these $15
billion demands for electricity. It really feels
like the play phase is over. Yeah, it's not a
toy anymore. Now the hard work and the hardware
begins. So here's my question for you, listening
to this. We talked about research agents and
physical AI. Are you using these tools to chat
or are you using them to build workflows? That's
the shift you really need to make. Because if
you're just chatting, you might be missing the
whole point. Oh, and definitely check the show
notes for that cursed phrases list. Save yourself.
Don't be a tapestry person. Please don't. I want
to leave you with one final thought. We spent
a lot of time on the code and the chips today.
But think about that $15 billion demand for power
plants. It's a massive number. The ultimate constraint
on AI isn't code anymore. It isn't even really
chips, as China just proved. It's electricity.
The smartest model in the world is completely
useless if you can't plug it in. Keep diving.
See you next time.