So for the last, what, 18 months, we've just
been buried in these staggering numbers about
AI adoption. Oh, yeah, nonstop. But this new
open AI enterprise report, it just dropped this
huge, almost paradoxical insight. Right. There's
this immense gap opening up. A huge chunk of
companies haven't even touched the basic AI features,
even while the top tier is just, you know, exploding.
It's the great AI divide. Yeah. And you really
need to look at the metrics here because. The
financial stakes are just enormous. We all know
the 800 million weekly chat GPT users, but in
the corporate world, the difference between these
frontier firms and everyone else is already creating
this huge divergence in shareholder returns.
Welcome back to the Deep Dive. Our mission today,
it's built entirely from the sources you sent
us, really trying to cut through the noise and
give you the most critical, actionable stuff.
And we have a pretty clear roadmap for this.
First, we're going to dissect this great AI divide.
Look at the user habits that are translating
directly into real financial gains. Second, we'll
scan the landscape for 2026. So, monetization
trends, hardware shifts, and this crazy accelerating
model race. And finally, we have this just ultimate
underdog story. How a tiny six -person team just
smashed a critical AGI benchmark. Yeah. And they
proved that smart system design is maybe more
valuable now than just raw computational muscle.
Okay, let's untack this. When you just look at
the raw growth metrics from this report, the
numbers, they almost feel unreal. Right. Enterprise
usage is up nine times year over year. Nine times.
And the number of weekly messages inside these
companies, that's up eight times. This isn't
some slow rollout. No, it's an absolute stampede,
at least for the companies who've bought in.
But that's just the volume side. What's really,
I think, insightful here is the quality of the
usage. The quality, how so? Reasoning token usage.
Yeah. Which is basically the model's internal
thought process, right? Its ability to do complex,
multi -step problem solving. That's up a staggering
320 times since last November. 320. I mean, that's
a number you have to sit with for a second. Yeah.
It suggests businesses are not just using AI
for, you know. drafting a quick email anymore.
Not at all. They're giving it deep, complex,
strategic work. They're running analysis that
needs the AI to connect a bunch of different
dots and follow this long chain of logic. Right.
And this kind of deep reasoning. It only really
became commercially viable like 16 months ago.
So this explosion is a very recent and I think
profound shift. And that shift is leading to
a productivity payoff that seems undeniably real.
It is. The average user saves, what, 40 to 60
minutes a day, which is great. Yeah, that's significant.
But the heavy users, the ones who are really
embedding it in every single workflow, they're
saving 10 or more hours a week. That's a full
extra workday. It's like buying a full extra
workday without a new hire. And on top of that,
75 % of workers now say they're doing things
they just couldn't do before, like complex coding,
data modeling, building custom automations. It's
letting non -technical people jump straight into
the technical deep end. I have to admit, I still
wrestle with prompt drift myself sometimes, you
know, finding just the right conversational path
to get the result I want. But seeing people who
a year ago wouldn't touch a line of code. And
now they're building actual functional applications.
It's astounding. It's just flattening that technical
skill curve so dramatically. Yeah, but here's
where it gets really interesting, right? Yeah.
How do those frontier users, the ones driving
all this, how do they actually do it? It seems
to come down to deliberate. high volume and what
you call high leverage interaction. Right. They
send six times more messages per employee than
the average company. Six times. But here's the
critical metric. The top analysts in these firms,
they're using AI tooling 16 times more often
than their peers. 16. That intensity just creates
this feedback loop that the non -adopters, they
just can't compete with it. And that intensity
maps directly to the bottom line. The firms operating
at this level are seeing 1 .7 times faster revenue
growth. But the kicker is the shareholder return,
3 .6 times higher. 3 .6. So if you're a company
that hasn't even touched basic AI yet, the data
is screaming that you are already way behind
the curve. So this raises a big question for
me. We see this data and it correlates financial
return with this high volume usage. Is this adoption
gap we're seeing fundamentally a failure of management,
a failure to encourage that kind of... deliberate
interaction the data certainly suggests management
is key financial returns track directly with
that deliberate high leverage ai usage so leadership
has to be a factor so while the big players are
winning today with sheer usage we've got to look
at what they're prepping for tomorrow let's uh
scan the immediate landscape the pace is just
It's a racing heartbeat. We're seeing utility
tools just explode. Like, developers shared 25
pre -built cloud skills. They're kind of like
custom GPTs that are just copy -paste. Which
massively simplifies things for teams. Totally.
It simplifies rapid deployment, and people are
hunting for these high -value shortcuts. Right,
like that thread with 15 ,000 bookmarks. Yeah.
The one that claims six prompts are better than
Duolingo for learning a language. Exactly. People
want to shortcut expertise instantly. Meanwhile...
You know, the big model heavyweights are accelerating.
There are these reports that OpenAI's code red
might have led to them panic releasing GPT 5
.2 like this week. And Elon's always teasing
Grok 4 .20. Of course. And the investment side,
it really shows where the long term money is
going. Physical AI. Embodiment. Yeah. SoftBank
and NVIDIA are reportedly about to put over a
billion dollars into skilled AI. They build the
robot brains, right? They build robot brains
using human like AI. And that company's valuation?
It tripled in less than two years. The big money
thinks the next frontier is physical. So looking
out a bit, to 2026, Microsoft has outlined these
seven big trends. And two of them really jump
out. First, monetization. Google finally confirmed
it. Ads are coming to Gemini in 2026. The end
of the ad -free era. It was always going to happen.
It was inevitable. But that's a huge psychological
shift, isn't it? For enterprise users, once ads
are in the ecosystem, the whole trust model changes.
For sure. Are you really going to build your
core workflows on a tool where the attention
economy is now a feature? It creates a friction
point, for sure. And the second major trend is
hardware integration. We're seeing this massive
shift from screen -based chat to something more
ambient. Google's new AI glasses, also coming
in 2026 with Gemini built right in. Yeah, that's
a direct shot at Meta's Ray -Bans. It signals
that AI is about to become deeply integrated
into how we actually perceive the world. So what
really stands out to you in all this? I mean,
are we really moving past AI as just a chat box,
a tool we open, to AI as integrated hardware
that's just... always on. Yes, I think the focus
is absolutely shifting to deeply integrated AI
and hardware, making the technology ambient and
constant. Mineral sponsor, Geek Placeholder.
All right, let's pivot to what might be the breakthrough
that really shocked the AI world this week. We're
talking about an incredible underdog story. Oh,
this is great. A six -person startup called Poetic.
They just beat giants like Google DeepMind and
OpenAI on one of the toughest reasoning benchmarks
out there. The ARC -AGI -2. And this is a huge
deal, a huge win, for a couple reasons. First,
the ARC -AGI -2 is a genuinely difficult test.
It's not about spitting back facts. It's about
abstract visual patterns, fluid reasoning. You
have to actually understand structure and logic.
Exactly. And Poetic was the first system ever
to cross 50 % accuracy. They hit 54%. And here's
where it gets really wild. Poetic didn't do what
everyone else does. They didn't sink billions
into training some giant new model from scratch.
No, what's so fascinating is they just used Google's
own Gemini 3 Pro as their base engine. They rented
it. They essentially rented the foundational
capability from their biggest competitor. So
how does a tiny team win the race when they're
using the very engine built by the people they're
racing against? They built a smart, self -improving
system on top of it. A refinement layer. Okay,
so break that down. A refinement layer. Think
of it like this. The foundational model, Gemini,
it's like a massive, powerful jet engine that
you can just rent. Okay. The refinement layer
is the brilliant, custom -built flight computer
that makes sure that engine is always running
at peak performance, maximizing efficiency for
whatever specific job you give it. That helps
a lot. So it's not about brute force power. It's
about elegant, high -leverage engineering. Precisely.
And this layer has... Basically, three jobs that
let them win. First, it's a smart traffic cop.
It picks the right base model for the task. Second,
it guides and improves the output. It's almost
like it's teaching the rented engine in real
time until the answer is good enough. And third,
it is a self -auditing mechanism to verify the
quality before it ever spits out the final result.
And I'm guessing this method is a lot cheaper.
Oh, significantly cheaper. Yeah. And way more
flexible. Poetic's approach cost about $30 per
task. Google DeepThink, which only hit 45 % accuracy,
cost $77 per task. And that efficiency, that
changes the whole economic model. Their system
can adapt to any new base model from any company
in a few hours. No costly retraining. And it's
open source. Whoa. I mean, imagine scaling that
refinement layer to a billion queries. Right.
That completely changes the economics. You're
not bottlenecked by the insane cost of building
the foundational model anymore. The value just
shifts. So what does this all mean then? Does
this small team success show that smart system
architecture, that elegant design? Is that the
new moat now? Is it displacing raw data and compute
as the main barrier to entry? I think this paradigm
shift proves that smart systems, these refinement
layers, they can outperform raw training scale
and compute costs. It's a huge win for efficiency
and innovation over just pure capital. You know,
this whole deep dive, it really boils down to
two core takeaways for you, the listener. First,
that massive productivity and financial gap we
talked about. It's driven by intense, deliberate
usage. You have to adopt that frontier mindset.
Using AI. tooling 16 times more than your peers.
To see that 3 .6 times shareholder return, it's
a choice. And second, the race for AGI. It might
now be won by the smart architectural layers.
That six person startup proved that system design
and refinement can be more powerful than just
having the biggest training budget. The value
is shifting from the size of the engine. To the
brilliance of the engineering team that's customizing
it. Yeah. So we'd encourage you to adopt that
frontier mindset. Just look at your own knowledge
gathering. How can you increase the leverage
of your tools? How can you add your own refinement
layer to how you approach problems? And here's
a final thought. If an open source refinement
layer can outperform these proprietary foundational
models on the world's hardest AGI benchmarks.
What does that imply for the security and the
future market value of those incredibly expensive
proprietary models? Something to mull over. We'll
catch you next time. Thanks for joining us for
this deep dive Outero Music.