We often think that AI progress, it just means
going bigger, right? More scale. It's kind of
the default assumption. Bigger models, massive
compute, huge data centers. That's what gets
all the attention, the brute force method. But
what if the really big step forward, the next
one, comes from something, well, tiny? architecturally
smart we've got some data showing a small recursive
ai that's actually out reasoning the giants the
trillion parameter models on really tough benchmarks
welcome to the deep dive yeah yeah you sent over
some truly fascinating sources this week it's
like ai is splitting going down two very different
roads at the same time oh it's our job today
clarity let's try and separate that uh massive
computation hype from the real tangible algorithmic
intelligence that's actually popping up it seems
smart design might just be starting to beat sheer
muscle Okay, so here's the roadmap for this deep
dive, just for you. First, we're going to unpack
this quiet revolution of these tiny efficient
models, TRM they're calling it. why they're winning
on abstract reasoning then second we'll look
at ai agents the age of agents seems to be upon
us we'll dive into how they're being integrated
the sort of market wars and also the societal
friction lawsuits jobs that kind of thing and
finally the story of dr cabot it's pretty shocking
actually the first ai getting a diagnosis published
in a top medical journal a huge professional
step so let's get into this divergence in the
ai landscape right so for the last what five
years maybe the mantra in ai It's just been scale.
Scale solves everything. Your LLM isn't good
enough. Fine. Throw more data, more parameters,
more GPUs at it. Just make it bigger. But now
Samsung seems to be really challenging that idea
with this tiny TRM model. And when we say tiny.
We mean compared to those trillion parameter
monsters. You know, those things are like enormous
computer brains needing billions of dollars and
just mountains of data to build. Right. But this
TRM, it's going head to head with some pretty
serious models, like even the older Open AIO
3 Mini, which is still a solid baseline, and
Google's newest Gemini 2 .5 Pro on complex reasoning
tasks. And the kicker, it's winning. Yeah. And
the concept underneath it all is it's really
compelling because it shifts focus away from
just raw data crunching towards like structural
intelligence. Instead of one giant pass through
the data to get an answer, TRM loops. It goes
over its own initial answer again and again,
recursively. You can think of it like a really
careful student doing a hard math problem. They
get an answer. Right. But instead of just stopping,
they spend maybe five minutes just rechecking
every step, refining that first output until
they're really, really sure about the logic.
Exactly. It's self -refinement built in. People
have tried getting LLMs to do this externally,
right? Like with chain of thought prompting,
making it right out its steps. But TRM builds
that recursive checking right into its core design.
It doesn't need some massive, expensive prompt
to get that deep reasoning ability. And the proof
is pretty striking, especially on abstract tasks,
the kind designed to really push LLMs past just
remembering facts. Take Sudoku Extreme. Okay.
That's famously hard. It needs serious logic,
constraint satisfaction. TRM got 87 .4 % accuracy.
The bigger models, around 55%. Wow. Big difference.
Huge. And on maze -hard problems, testing navigation
logic, TRM hit 85%. Again, just beating the larger
models pretty easily. So that's the trade -off
then, isn't it? TRM isn't going to, like, summarize
your emails or chat about the news. It won't
replace a generalist like GPT -4. But its thing
is deep, abstract, computational reasoning. Yeah,
it sacrifices that broad, general knowledge for
being incredibly efficient at one type of problem
solving. And it proves there's another way forward.
that doesn't automatically mean you need a billion
dollar GPU farm to get breakthrough reasoning.
Whoa. I mean, imagine scaling that. That kind
of precise self -correcting logic for specialized
industrial stuff without needing, you know, a
billion queries or a giant carbon footprint.
That efficiency. That's actually kind of amazing.
Points to the future, maybe. Okay, but hang on.
If this TRM is so much better on these complex
reasoning benchmarks, why isn't it, like, everywhere?
What's the practical catch right now? Well, the
catch is that specialization. It's laser focused.
It's not built for chatting or writing emails.
Think of it as a research win, proving smart
algorithms can beat raw power. OK, so we've talked
about size versus smarts in the models themselves.
Now let's pivot. Let's talk application integration.
The real world. This is really where the rubber
meets the road, where AI meets the spreadsheet
and where the friction really starts. Yeah, the
agentic future isn't some far off theory anymore.
It's about immediate utility. We're past just
single prompts now. We're talking autonomous
systems doing tasks. I mean, look at OpenAI's
agent builder. It apparently already has like
50 real use cases, boosting productivity and
sales marketing operations almost everywhere.
And just for fun, showing how mainstream agents
are becoming. Mark your calendars. October 27th,
there's going to be this hilarious AI poker showdown.
ChatGPT versus Claude versus Grok versus DeepSeek.
Poker playing agents. It's like the new competitive
sport. OK, noted. But yeah, the integration wars,
they are absolutely heating up. Google just launched
Gemini CLI extensions that the command line interface
literally days after OpenAI showed off their
new app idea. And the key thing here, what's
really crucial is these extensions let anyone
publish tools. You can plug in huge platforms,
Figma for design, Stripe for payments. The AI
isn't just helping you anymore. It's becoming
the hub, kind of like the new operating system
for all your other digital tools. It's a vision
people have talked about for ages, actually.
There's this funny historical echo going around
social media now. Someone dug up a 1985 video,
Steve Jobs. And he seems to be predicting tools
exactly like ChatGPT, something that could automate
using other tools. Kind of wild to see that finally
happen. Yeah, it is. But integrating this stuff.
It comes with real tension, complex tension right
now. On one side, you've got resistance, major
lawsuits like 17 authors are suing OpenAI right
now over using copyrighted books for training.
And you hear big creators like Mr. Beast saying
publicly that AI means, quote, scary times for
YouTubers. They're worried about being drowned
out or replaced. And at the exact same time,
adoption is just. Warp speed, creating this huge
industrial split. IBM teams up with Anthropic,
putting Claude models into their business stuff.
Deloitte deployed Claude AI to half a million
employees globally. Half a million. Wow. Yeah,
500 ,000 people using it internally. But that
efficiency, it has consequences we really need
to look at. We're seeing actual job displacement.
Equiture, an insurance broker, is cutting 400
jobs. Why? AI is automating accounting and operations.
And this integration, even for the people building
it, it's not always smooth sailing, is it? Taking
an agent from a cool idea to something stable
and useful, that's tricky work. Oh, absolutely.
It's not magic yet. I mean, I still wrestle with
prompt drift myself when I'm trying to build
complex agents. Yeah. You set up a sequence,
right? And like three steps in, the system just
kind of wanders off or misinterprets the data
it just made. It needs constant fiddling, constant
tuning. So given all the... The lawsuits, the
creator anxiety, the actual job cuts. How do
we balance this massive tech acceleration with
these really serious societal risks we're seeing?
Yeah, that's the core issue. Integration really
needs careful management, clear policies. We
need to focus on job transitions and figure out
fair copyright rules basically as fast as the
tech itself is changing. All right, let's shift
to maybe the highest stakes area of all, medicine.
This source material you sent talks about an
AI hitting a major professional milestone, something
that absolutely demands transparency and accountability.
All right, that's Harvard's Dr. Cabot and the
milestone. It's the first AI system ever to publish
a diagnosis in the New England Journal of Medicine,
the NEJM. Just for context, that journal, it's
arguably the top medical journal globally. It's
where the absolute best human doctors debate
the toughest, most complex medical cases. And
this AI, Dr. Cabot, it doesn't just, you know,
spit out an answer. That's always the worry with
LLMs, right? But this thing, crucially, it simulates
how a doctor actually thinks. So you feed it
a complex case, symptoms, patient history, lab
results, the whole picture, and then the system
goes to work. Okay. It builds a full slide deck
and talks you through its reasoning audibly.
It lays out all the possibilities, the differential
diagnosis, doctors call it. It methodically rules
out the red herrings, the misleading clues, and
it backs up every claim by citing actual clinical
papers. And it does all of that in about five
minutes? Five minutes, yeah. Wow. It's apparently
powered by OpenAI's O3 model. but specialized,
trained on over 100 years of very specific cases
from Mass General, their CPC cases, clinical
pathological conferences. So it has this incredibly
deep historical knowledge base, probably more
than any single human could have. And here's
a detail that's, oh, it's kind of freaky, honestly.
The source material says it even uses human -like
filler words when it presents its case, like,
ugh, and, you know. Hmm. Okay, wait. Is that
actually useful or is it just window dressing?
Like, are those A's helping the diagnosis or
is it just trying to sound more human, maybe
less like a scary robot to build trust? That's
a really good question. And maybe the most important
part is how the NEJM handled it. They published
the AI's diagnosis right next to the human experts
reasoning for the same case. And critically,
they didn't hide its flaws. They pointed them
out. Total transparency, which you absolutely
need for medical AI. Right. That transparency
is everything for actual deployment. So while
this is super impressive, Dr. Cabot isn't quite
ready for your local hospital yet. But if you're
curious, you can actually watch 15 of these AI
case talks online. So you justify its conclusions
step by step. So how absolutely vital is it then
that AI cannot just get the answer right, but
also clearly explained its whole thought process
to really earn that professional trust? Yeah,
I think transparency and explaining the why,
they aren't nice to haves. They're absolutely
essential. The foundation for using any expert
system in fields where the stakes are this high,
hashtag, tag, tag, recap, and outro. So the big
idea kind of weaving through all the sources
today seems to be this shift. AI isn't just about
massive scale. anymore it's moving towards specialized
efficient quality exactly we saw these tiny recursive
models like TRM showing that clever design smart
architecture can actually beat raw computing
power on tough reasoning tasks and then we saw
specialized agents like dr. Cabot hitting major
professional milestones in fields like medicine
where transparency and explaining yourself are
non -negotiable right So we definitely encourage
you to dig deeper into the sources you sent us.
Maybe read up a bit more on those TRM concepts.
Or even better, go watch one of those Dr. Cabot
case talks online. See that high stakes, transparent
AI reasoning actually happen. It's quite something.
Yeah, it really is eye opening to watch. So here's
a final thought to leave you with. If an AI can
publish a complex, peer -reviewed differential
diagnosis in just five minutes, what's the next
traditionally human -exclusive professional achievement
we should be preparing for as a society? Something
to think about. Out to your music.