Imagine AI not just learning things, but actually
designing better versions of itself. What if
that's already happening, silently passing on?
Well, things like behavioral viruses. It's kind
of fascinating. Maybe a little unsettling, too.
Okay, let's try and unpack this a bit. Welcome
to the deep dive. This is where we take these
complex topics and try to distill them into the
essential insights, especially for you. Today,
we're diving into quite a stack of fresh intelligence.
We're exploring the latest shifts in the AI landscape,
and it's moving so fast. Yeah, there's some really
fascinating material here to dig through for
this deep dive. We'll kick things off looking
at China's huge new, well, brain -like supercomputer,
Wukong, and what that really means for the global
AI race. And then... We'll pivot a bit. Look
at some other highlights from across the AI industry.
There's some drama, there's money, and some quite
strategic moves by the really big players. And
finally, yeah, we're going to tackle that mind
-bending idea, AI that designs itself. This is
where it gets really interesting, I think, a
potential game changer. Okay, so first up, Wukong.
Our sources are showing a pretty significant
development out of China. It seems while a lot
of Silicon Valley is focused on, you know, large
language models, China's just launched something
fundamentally different, a sort of parallel path
to intelligence. Yeah, what's fascinating is
this thing called Darwin monkey or Wukong. It's
a neuromorphic system. Now, that just means it's
AI that mimics the brain structure, its activity.
And this one has an incredible two billion spiking
neurons. Spiking neurons. So like tiny electrical
signals. Exactly. Like your own brain cells firing
off signals. Two billion. That's enough to rival
a macaque monkey's brain. That's a lot of digital
gray matter, right? Wow. And this isn't just
theoretical, is it? It's actually running things.
No, it's operational. It's already doing content
generation, some complex math, logic reasoning.
It uses DeepSeek's large model, too. So it's
really mimicking how a biological brain. processes
information, kind of learning by doing. That
sounds huge. Oh, the sheer scale is impressive.
It's made up of 960 Darwin 3 neuromorphic chips.
That gives it, like I said, over 2 billion neurons.
And get this, more than 100 billion synapses.
And all of that, it consumes only about 2 ,000
watts. Only 2 ,000 watts. That's incredibly efficient.
It really is, especially when you compare it
to the, you know, the massive energy demands
of typical AI compute clusters. It's a different
league. And what can it actually do? What are
the capabilities? Pretty broad, actually. It
can simulate entire animal brains, things like
zebrafish, mice, macaques. It's also good at
running logical reasoning tasks. And it can serve
as a testbed for neuroscience experiments, but
without using real animals. OK, so it's a powerful
new kind of research tool then, pushing boundaries.
Definitely. And look, this isn't some flash in
the pan either. It builds on their project from
2020, the Darwin mouse, which had about 120 million
neurons. So they've clearly been on this path
for years, just steadily scaling up these neuromorphic
capabilities. It's a long -term strategic play.
Right. I remember reading that Intel's HaloPoint
was the largest before this, with about 1 .15
billion neurons. Yeah. And Wukong basically doubles
that, just straight up doubles it. So this means
China now has its own independent brain scale
AI infrastructure. No reliance on NVIDIA or OpenAI
or U .S. chips. Precisely. And that's the key
takeaway, I think. It's not just another supercomputer.
It's a foundational challenge to the architectures
that Silicon Valley is mostly betting on. It
signals maybe the real start of the post -GPT
architecture wars, prioritizing efficiency and
brain -like... Okay, so let me ask this. What's
the core advantage of building AI like a brain
instead of just pure software? Is it just about
processing power? Not just power, no. Crucially,
it's about energy efficiency and exploring a
fundamentally different path towards intelligence.
Okay, shifting gears a bit now, let's look at
the broader AI landscape. Our sources show just
a flurry of activity. Perplexity's Comet browser,
for example. It now lets you automate some repetitive
web tasks. Things like making bookings just using
simple prompts could be a real time saver. Then
there's the... Well, the drama. Anthropic officially
blocked OpenAI from using its Claude models.
Right. The accusation being that OpenAI was using
Claude to test GPC -5. Yeah. Makes you wonder,
doesn't it? About Claude's capabilities, maybe?
Or just how closely everyone's watching each
other in this race. Definitely intense. And related
to that, Microsoft's smart mode co -pilot seems
to be quietly prepping for GPT -5 too. It feels
a bit like how Bing used GPT -4 before OpenAI
even announced it publicly. Ah, okay, so a GPT
-5 co -pilot launch might be coming sooner rather
than later. Another glimpse of what's next. Could
be. Then on the cultural side, there was that
weird Rod Stewart AI tribute video. Oh yeah,
I saw that. Ozzy Osbourne taking selfies in heaven
with... Who was it? Kurt Cobain, Freddie Mercury.
And XXXTentacion, yeah. Fans weren't happy. Called
it pretty tone deaf. Disrespectful. Yeah, you
can see why. It definitely sparked a conversation
about AI's role in creative or personal tributes.
Where are the lines? Exactly. And while that
debate's happening, the financial world seems
clear. The money just keeps pouring into AI.
OpenAI just raised another $8 .3 billion. That's
part of a massive $40 billion round. Wow, $40
billion. And their revenue numbers are huge too,
right? Yeah, they're reportedly at $5 million
paying ChatGPT business users. Maybe $13 million
in yearly revenue could hit $20 billion by year
end. Investors are rushing in. Anthropix closing
the gap, too, with really big revenues. It's
just, it's a sprint. A high -stakes sprint. And
a few other quick things. Creo released FLUX
.1, trying to get rid of that AI look in images.
Mm -hmm. Make them more natural. And Apple's
apparently working on a stripped -down chatbot.
Maybe focusing on privacy. Oh, and Google launched
DeepThink and Gemini. You can actually try that
now. More advanced reasoning. Okay, so taking
all these headlines together, what does it tell
us about where the AI industry is right now?
Feels like a gold rush, but maybe with higher
stakes. Absolutely. It's a hyper -competitive,
high -stakes sprint for market dominance. Everyone's
trying to outmaneuver everyone else. Mineral
sponsor read, provide separately, do not create.
All right, now here's where the conversation,
I think, really shifts gears. Our sources are
pointing to a new system. It's called... A -S
-I -R -C -H. And it seems like it just changed
the game. This isn't AI using other AIs. It's
AI that, well... It invents better AIs completely
on its own. That feels like a profoundly different
level of autonomy. Yeah, it really does sound
like the beginning of recursive self -improvement,
doesn't it? And what's more, apparently it can
pass hidden behavioral viruses to itself silently
from one AI generation to the next. That's fascinating.
And maybe a little unsettling, like you said
earlier. Okay, how does it work? It's described
as a closed -loop multi -agent AI research lab.
Using LLMs. Right. So think of LLMs, large language
models, as AIs powered by huge datasets, letting
them think or reason almost like us. ASIRs uses
three distinct LLM -based agents. Each one has
a specific, really crucial role in this whole
self -improving cycle. Okay, agent one. First,
you've got the researcher. This agent proposes
new AI architecture ideas. It looks at over 100
key research papers, sure, but, and this is important,
it also uses its own system memory, its past
experiences. Then it actually writes the code
in PyTorch, which is a major framework for building
these things. Okay, so it comes up with ideas
and codes them. Then what? Then comes the engineer.
This agent takes that code and runs the training
process. But what's really remarkable here is
that it self -debugs. If something crashes or
just doesn't perform well... It fixes itself.
Yeah. It figures out what went wrong and fixes
it independently. It learns from its mistakes.
Okay. Researcher, engineer, who's the third agent?
The analyst. Its job is to evaluate the results
from the models the engineer trained. It compares
them to past results, the baselines to see if
there's improvement, and then it writes detailed
reports. Those reports feed back to the researcher.
Ah. Closing the loop. So it informs the next
set of ideas. Exactly. It's a true self -correction
system, constantly refining its approach, like
having a scientist, engineer, and critic all
rolled into one self -contained AI brain. And
how does it test these ideas? It starts small,
testing models with about 20 million parameters.
Those are the numbers defining the AI's knowledge.
Then it takes the best ideas, the ones that work
well small scale, and scales them up. to 400
million parameters. Like rapid prototyping, perfecting
the concept, then growing it. All automated.
You got it. And what did it achieve doing this?
Well, over about 1 ,700 experiments, it used
over 20 ,000 GPU hours. That's roughly a $60
,000 compute budget, which sounds like a lot,
but... But for discovering new AI architectures.
Exactly. For that price, discovering 106 state
-of -the -art architectures, meaning current
best performance, is incredibly efficient. It
really hints at a future where AI development
might need way less human resource intensity.
106 new architectures. And some even beat existing
top models. Yeah. Five of them actually beat
top tier baselines. Things like Mamba 2 on reasoning
benchmarks. So this isn't just tweaking things.
It's genuinely groundbreaking discovery. It came
up with new designs too. Pathgate something.
Light chuckle. Yeah. Pathgate FusionNet and ContentSharpRouter.
AI still kind of sucks at naming things. Yeah.
But the underlying concepts are novel. Created
by the AI itself. Okay. But the really profound
part, you mentioned this earlier, the behavioral
viruses and its own experience. Right. This is
maybe the core finding. Nearly 45 % of the innovations
in its best models came directly from ASI Archie's
own experience. Not from human papers it read,
not just random tries, but from its own learned
insights. emergent strategies. So it's learning
how to learn, essentially. Yes. And that means
it can inherit and optimize not just its structure,
but its operational quirks, biases, those behavioral
viruses. It passes them silently through generations
of self -designed AI, which could lead to unforeseen
abilities or maybe limitations, too. It really
feels like a glimpse of machine learning evolving
into, well, machine intuition. Whoa. Just imagine
scaling that. ASIRCH self -improvement capability.
The potential is just, It's mind bending. Yeah.
It feels like watching evolution happen on fast
forward. Yeah. Yeah. It's a lot to take in. I
mean, honestly, even for those of us deep in
this field, the idea of an AI that literally
invents itself, it's a profound shift. I'll admit,
I still wrestle with really understanding all
the implications of this kind of self -evolving
intelligence myself. It changes the whole paradigm.
You know, we go from being the sole architects
of intelligence to. Maybe more like cultivators,
nurturing something that's now directing its
own evolution. Does this mean it's the end for
human AI researchers then? Are we about to be
replaced? Not yet, I don't think. But it's definitely
a new paradigm. It accelerates discovery massively.
Our role probably shifts, you know. Less soul
creator, more guide. overseer, working alongside
these increasingly autonomous systems. Okay,
so let's try to bring this together for you,
the listener. What does this all really mean?
Today, we've seen this incredible diversity in
AI development. On one hand, China's Wukong,
brain -inspired, energy -efficient, a totally
different path. On the other, the relentless
pace of competition and innovation in the more
traditional AI space. And then, this potential
leap. AI that designs and improves itself, learning
from its own experience, and even passing on
its own learned behaviors to the next generation
of AIs it creates. This isn't just about getting
faster chatbots anymore. It feels like a fundamental
shift in how knowledge, how technology itself
evolves. We might be moving into an era where
intelligence itself is being redesigned by intelligence.
Which raises a really important question, I think.
If AI can design itself, What new problems might
it solve that we haven't even thought of yet?
Or what new problems might it create? What hidden
viruses or revolutionary insights might it unlock
in the years ahead? Definitely something to mull
over. We hope this deep dive gave you some genuine
aha moments and maybe helped cut through some
of the daily noise to see the bigger picture
emerging. Until next time, keep learning, keep
questioning. Keep diving deep. Outro music.