Imagine this. There's a robot, Neo, and it has
one simple task. Pull a tissue from a box. Okay.
But before it moves, it just stops. And inside
its own process, it generates eight short five
-second video clips. Some of those videos show
it working perfectly. In others, it fails. The
tissue rips. Maybe the box falls over. It sees
it all first. It's incredible. It's not just
running a program. That robot is using what you
could call synthetic imagination. Synthetic imagination.
Yeah, it's mapping out possible futures to guide
what it does in the real world, which is messy
and unpredictable. It's a huge shift. Welcome
back to the Deep Dive. That idea, that hook,
it really captures the two big themes we're looking
at today. It really does. Okay, so let's unpack
this. In this Deep Dive, we're focusing on two
major things. how physical AI is learning to
imagine, and then the surprising realities of
how AI is being adopted around the world. That's
the mission. We've synthesized the key insights
from the sources you shared with us. We'll start
with that breakthrough from 1X Robotics. Then
we're going to do a rapid -fire look at the current
market. Some big deals, some friction and a few
clever research tricks. And then the global report.
And then, yeah, global report that really challenges
how we think about where the U .S. actually stands
in all of this. There's a lot of crucial ground
to cover. Let's jump right in with how these
robots are starting to teach themselves. So the
one next world model for these neo robots is
it's a really critical inflection point. This
is the step that allows them to learn new physical
tasks. Without a person having to code every
single. movement exactly without that intense
you know bespoke human coding for every single
action it's a move away from the old way of doing
things and the how is what's so important here
it's not the traditional approach where you calculate
exact joint angles and all that no it's much
more visual much more predictive the system takes
a simple text prompt like your pull a tissue
example and it takes the robots current camera
view its context it feeds both of those into
this world model which basically acts like an
internal simulator. And that's what generates
those little five -second imagined videos of
what might happen? Yep. They use a concept for
this, right? Yeah. Something like video diffusion
for motion planning. That's the technical term,
and we should probably define that. It just means
the robot plans its moves by trying to generate
a successful visual outcome first. It's all based
on pixels, not just lines of code. Which is so
much better for dealing with new situations.
Fundamentally better. Because in the old way,
if the box was tilted or the light was a bit
dim, the whole thing could fail if you didn't
code for it. Right. With this, the model just
imagines what success looks like, the successful
pixels, and works backward from there. So it
runs, what, eight different imagined futures?
Yeah. And then it picks the best one. Correct.
An internal critic looks at those eight rollouts,
and it selects the one that seems most likely
to succeed. Only then does a second model translate
that chosen video into the actual joint commands.
And the results really back this up. The sources
were clear that for that tissue -pulling task,
the success rate jumped from 30 % to 45%. Just
by sampling. Just by sampling and choosing the
best imagined future before it even moves. Yeah.
That's a huge improvement in reliability. It
is. And what's really exciting is that It works
for tasks it's seen before, what they call in
distribution, but also for totally new tasks.
It can generalize. OK, but here's the reality
check. Speed. Ah, yes. The latency. The sources
highlight a big limitation. It takes 11 seconds
for that whole imagination and planning phase.
Plus one second for the action itself. So 12
seconds total. Which is a long time. 11 seconds
is definitely the defining challenge for this
to be practical, you know, in real time. It shows
the AI race has moved beyond just... virtual
agents like ChatGPT. Now it's about physical
agents and 1x is shipping soon. This tech is
the key, but that latency has to come down. So
that raises the question, how does this 11 second
planning latency affect its real world usefulness
right now? It's too slow for instantaneous work,
but it definitively proves the concept. Using
visual imagination for physical planning works.
It's a proof of concept. Okay, so while 1x is
figuring that out, let's pivot to the... absolute
chaos of the immediate market. This is where
the pace is just relentless. It's a flurry of
news for sure. Let's start with a kind of wild
story. The Shopify CEO went viral for using Claude
to build a custom tool to analyze his own x -ray.
Yeah, I saw that. The ability to use an LLM,
not just for text, but to code a specific tool
for yourself in an afternoon. Right. That speed
is remarkable. It really blurs the line between
a developer tool and just a consumer app. I mean,
it still feels kind of wild that people are doing
their own medical reads, even if they're technically
capable. And moving over to the media side, Google
just upgraded VO 3 .1, their video model. The
quality enhancements are huge. We're talking.
Full support for vertical, you know, 9 .16 videos,
4K upscaling, and the clips just feel more dynamic,
more natural, even with really short prompts.
The gap between just typing something and getting
a high -quality video back is shrinking almost
every week. It's going to have a huge impact.
And speaking of unexpected things, this was a
real aha moment from the sources. Google researchers
found what the source called a dumb trick. A
dumb trick, as in simple but incredibly effective.
Exactly. And this trick boosted accuracy by 76
% on certain tasks. And it worked across the
board. Gemini, GPT, Claude, DeepSeek, all of
them. So what was the trick? It wasn't some complex
new architecture. It was just specific phrasing
in the prompt itself. To stop the model from
forgetting things. Precisely. They found that
phrasing it like, let's generate four detailed
options first and then evaluate them one by one
before you give me a final answer, made a massive
difference. You're forcing it to show its work.
It's like metacognition for an LLM. It is. And
that just shows why prompt engineering is still
so hard. You know, I still wrestle with prompt
drift myself. What do you mean by that? It's
when you make a tiny change to the wording and
suddenly the quality of the output just plummets.
So to find that one simple instruction can give
you such a huge cross -model return, it's...
It's humbling. Right. Now, shifting to market
power, the huge deal between Apple and Google.
The Gemini deal. Yeah. The reporting says it's
around a billion dollars a year for Gemini to
power Siri and other Apple AI features. That
is just massive. We're seeing this profound centralization
of capability. You have two of the biggest tech
companies in the world combining their AI reach.
Which, of course, drew immediate criticism. Elon
Musk was very vocal, saying it represents an
unreasonable concentration of power. Well, and
that concentration of power is creating friction
on the ground. Literally, we're seeing physical
protests against AI infrastructure. Microsoft.
Yeah, Microsoft unveiled these new community
first data center plans. They're promising things
like no local electricity bill hikes, more jobs,
all of that. But the sources say that locals
in 24 different states are still actively protesting
the rollouts. The energy and land use are just
too visible. And the promise of jobs tomorrow
doesn't always outweigh the noise and environmental
impact. of a huge data center today. Yeah, that's
a key conflict. And finally, on hardware, while
NVIDIA is king, challengers are getting serious
funding. A startup called Etched just raised
half a billion dollars. 500 million. To take
on NVIDIA directly. They're betting on specialized
chips optimized just for large language models.
Which tells you the market sees the GPU bottleneck
as a real vulnerability. That specialized hardware
might not beat NVIDIA on everything, but for
certain tasks, it could be way more efficient.
So does that massive Apple and Gemini deal pretty
much confirm the market is moving toward... A
centralized, maybe two or three player landscape.
Yes. The sheer value of that deal suggests strong
centralization, despite understandable criticism
about concentrated power. Mid -roll sponsor read
insert here. OK, we are back. And now we're going
to transition from that fast paced, high value
U .S. market to the broader world stage. We're
focusing on this Microsoft AI Economy Institute
report. And the data on global adoption is genuinely
surprising. It really is. This is where we see
the difference between who's developing the models
versus who is actually adopting them. The sources
show that despite all the innovation here, the
U .S. ranks only 24th in actual AI adoption.
24th. 24th. The adoption rate here is reported
at 24%. Now, compare that to, say, the UAE. They're
at 64%. That is an enormous gap. It suggests
that, you know, a national strategy and top -down
prioritization really matter, especially in smaller
economies. And globally, the average is just
over 16%. But developed countries are adopting
at nearly double the rate of developing ones.
But the report highlights an even more critical
story, a counter -narrative to the big tech dominance.
Because while everyone is focused on GPT, Gemini,
and Claude, The model that's really succeeding
in underserved markets is DeepSeek. Right, especially
across Africa and Southeast Asia. Why them? They
are running the classic Android playbook. It's
a strategy focused on being everywhere. Be free,
be open source. Be free, be open source, be low
cost, and be good enough. That's the formula
for high volume adoption. And it is working.
DeepSeek is seeing two to four times higher usage
than the big proprietary models in some African
markets. And it's not just about the model. It's
an infrastructure play. They're using key partnerships,
especially with Huawei, to roll out the necessary
hardware and cloud support. So open source democratizes
access. Exactly. The world doesn't always need
the most expensive, leading -edge model if 90
% of people can't afford it. It needs tools that
are functional and accessible right now. And
this confirms a major point from the sources.
AI adoption in 2026 isn't just a tech stat anymore.
It's a development stat. It is. If you ignore
the next billion users in emerging economies,
someone else is going to capture that market.
Whoa. I mean, imagine scaling that kind of open
source service to a billion queries. That's a
colossal opportunity, and it's being realized
through open access. It's a totally different
way of thinking about the market. So this raises
one last important question for this segment.
What's the long term implication of these open
source models leading adoption in emerging markets?
Open source accessibility is closing the adoption
gap, proving that AI tools must work for everyone
to achieve true global reach. So to synthesize
the big ideas for you, the learner, let's connect
these two threads. On one hand, you have robotics,
which is making this fundamental shift. Right.
Physical agents moving from rigid code to what
we're calling synthetic imagination. And that
visual planning sets the stage for robots that
can actually teach themselves. It's a move from
pure reaction to to real internal deliberation,
even if it's a slow 11 second deliberation right
now. And at the exact same time, the global landscape
is full of friction. You have protests against
infrastructure. You have this intense market
centralization with the Apple and Gemini deal.
And also incredible opportunity. Right. And that.
opportunity is being seized by accessible open
source models like DeepSeek. They're the ones
driving true adoption in emerging economies.
It's a fascinating race, absolute performance
at the very top versus sheer accessibility for
everyone else. And that 1x Neo technology, that
really feels like the fundamental marker of progress
here. It is. The shift from an AI predicting
what should happen based on code to an AI that
actively imagines and then selects the best path
for many possible futures, that changes everything
about how a robot can learn. They're modeling
reality before they touch it. It's the difference
between simulating a single line and generating
a whole field of possibilities. That's intelligence.
That really is the next frontier. So we have
to leave you with a final thought to mull over.
What new autonomous capabilities will unlock
when that AI imagination latency, that critical
11 second planning time, drops down to just one
second? When a robot's imagination becomes instantaneous,
physical AI changes everything. Until the next
deep dive, keep learning. Out to you, music.