Imagine needing the power of a nuclear plant,
a whole gigawatt. Right. And imagine needing
that much compute power every week just for AI.
Yeah, it's kind of staggering, isn't it? That's
the new reality Sam Altman is talking about.
He calls it abundant intelligence. Exactly. And
the response from industry, it was immediate
and huge. Welcome to the Deep Dive. Today we're
taking a calm. But I think really curious look
at the sources that are defining this huge AI
infrastructure race. Yeah, we're drawing from
a newsletter that really digs into, you know,
the hardware side, the software breakthroughs.
And maybe most interestingly, these new ways
people are trying to measure AI performance.
Who's really best? So our mission today is really
to unpack three main things. First, this literal
race, building the grid for AI. Second, the just
absurd speed of breakthroughs and what these
models can do. AI's capability. Yeah, the pace
is nuts. And third, a pretty revolutionary new
way to figure out which AI works best because,
spoiler, it's not the same for everyone. Let's
start with the Giga Ones then. Altman's big idea.
Right, this vision of abundant intelligence.
He basically argues that access to AI or intelligence
should be like a fundamental human right. That's
a pretty profound statement. It is, but making
that real, that takes scale. Almost unimaginable
scale. And that's where the numbers come in.
OpenAI's goal. Yeah. One gigawatt of compute
capacity per week. Exactly. And that demand just
kicked off this massive infrastructure build
out. Hyperscalers, VCs, everyone jumped in. The
investment scale is. Yeah. It's genuinely tough
to visualize. We hear big numbers, but. 5 .5
gigawatts. Yeah, that's Oracle. They're building
these huge data centers. Texas, New Mexico, the
Midwest. 5 .5 gigawatts. What does that even
compare to? Well, think about a major city like
Dallas, maybe. That's more than its base power
needs. So, yeah, it's massive. And they're talking,
what, 25 ,000 jobs just from that push? Wow.
Construction tech. Right. They're not just building
server farms. It feels like they're building,
you know, the next century's economic engine.
And soft banks in the mix, too, right? Yeah,
correct. Aggressively. Oh, yeah. They committed
1 .5 gigawatts and they want it done in 18 months.
18 months. That's incredibly fast. New sites
in Ohio, Texas. These aren't small steps. These
are like moonshots, big bets. So if you add it
all up, where are we heading? Well, the early
commitments, if you tally them, point towards
something like $500 billion. And 10 gigawatts
total by the end of 2025. Half a trillion dollars.
Yeah. 10 gigawatts. And they're already well
on their way. Like 400 billion and 7 gigawatts
are basically planned and funded already. It's
like stacking these incredibly advanced Lego
blocks, you know, but at warp speed. And then
you have players like Alibaba taking a slightly
different angle. Right. They talk about being
the electric company for AI, but they're also
going vertical. Super fast. Like dropping six
major product launches in one day. Exactly. It
highlights that speed, but also owning the whole
process from the chip all the way up to the app.
That seems key to their strategy. So why does
this infrastructure layer matter so much to someone
listening right now? Well, because this isn't
just about tech companies anymore. It's becoming
a race between like governments, big money VCs,
the cloud giants to own the fundamental infrastructure,
the plumbing for intelligence itself. It goes
way beyond just making chat bots a bit better.
It's foundational. OK, so the scale is huge.
The cost is astronomical. Does all this frantic
building actually mean that AI access gets cheaper?
and sooner for regular people. Yeah, I think
the signs point that way. This level of aggressive
competition, the sheer amount of money pouring
in, it suggests better, cheaper access is coming.
And probably fast. Right, because of that competition
and just the pace of the tech improving. Exactly.
Okay, so all this hardware is being built because
the software, the capabilities are demanding
it. Let's shift gears then from building speed
to intelligence speed. yeah the physical stuff
is really just trying to keep up with the software
breakthroughs the pace of improvement and models
and what they can do it's uh it's kind of absurd
right now we saw that play out recently didn't
we there's that live marketing showdown oh yeah
with the big llms chat gpt gemini perplexity
claude right and in a real business test one
of them just walked away with five out of the
six wins. That shows you like the immediate practical
value is already there. It's not theoretical.
And then there's the sheer size increase. You
mentioned Quinn 3 Max. Yeah. Alibaba's new model.
One trillion parameters. Whoa. One trillion.
Just try to imagine scaling that. A trillion
variables for the model to learn from. That's
a huge jump in complexity. It's a massive number.
And it's immediately showing results, beating
older models, even early GPT -5 versions on some
tasks. But the really stunning thing. What's
that? It's math performance. It scored 100 %
on the AME25 math benchmark. 100%. Wow. Okay,
that's the moment of wonder right there. That's
not just good. Perfect mastery on a really tough
academic test. Right. Models hitting expert levels
almost right out of the gate. And it's translating
to the real world, too, isn't it? The finance
exam. Oh, yeah. The CFA exam. Claude Opus. Gemini
2 .5 Pro. They both passed level three. Which
is notoriously the hardest level. Takes humans,
what, over a thousand hours of study? Usually,
yeah. Intense study. And these models did it.
In minutes. In minutes. The implication there
for, like, knowledge work, professional training,
it's just huge and immediate. You have to wonder,
what's the value of that certification if a machine
can ace it instantly? Exactly. And look how fast
it gets integrated. Microsoft already plugged
Cloud into 365 Copilot. That's the first external
AI model inside the main Office tools, right?
Yep. The speed from breakthrough to application
is relentless. It really is. Okay, here's maybe
a vulnerable admission. Uh -oh. Even trying to
follow this closely, I still kind of wrestle
with prompt drift. Oh, absolutely. You're definitely
not alone there, where you ask the same thing
or use the same image prompts like two weeks
apart. And the results are completely different
because the model changed underneath without
you knowing. Yeah. It's a real challenge, especially,
like you said, with visual tools. That Redditor's
game, real versus AI images. It's getting almost
impossible to tell consistently now. For anyone.
We're struggling to keep up with the tools, really.
But the tools just keep coming. They do. Quick
hits here. Cling 2 .5 Turbo. Getting really good
at realistic images, video. Pushing towards that
believable synthetic media. And that AI bandage
thing. That sounds wild. Isn't it? An AI -powered
smart bandage monitors the wound, adjusts things.
They claim it heals 25 % faster. And maybe less
exciting, but relevant. Ads are probably coming
to free chat GPT by 2026. Ah, ties back to the
compute costs, I guess. Pretty much has to, yeah.
Okay, so with these models acing things like
the CFA exam in minutes, what does that tell
us about where the ceiling is? Is there even
a ceiling for these current models? That immediate
mastery, it really shows we have to constantly
reevaluate. Even models that were top tier just
months ago, the ceiling just keeps rising perpetually.
Okay, so if the capabilities are moving that
fast, the way we measure them needs to change
too, right? Exactly. Which brings us to the metrics,
how we decide what's good. Traditionally, we've
had leaderboards. El Marina, places like that,
they've been the standard. Yeah, but they had
this huge blind spot, a really unavoidable one.
They basically treat every user the same. Doesn't
matter who you are, where you are, why you're
using it. An engineer in Tokyo, a lawyer in London.
Or a student in Buenos Aires, yeah. The leaderboards
just give you a raw performance score, undifferentiated.
That doesn't really tell you if it's useful for
your specific need, though. Not at all. So the
new idea is from Scale AI. They launched Seal
Showdown. Seal Showdown. Okay, what's different?
It completely flips the ranking method. Instead
of just raw speed or whatever, it ranks models
based on real user preferences, and it connects
those preferences to demographic info. Oh, okay.
So it's subjective based on who's using it. Exactly.
Think about, say you're a bank choosing an LLM
for your Spanish -speaking customers in Miami.
El Marina, useless for that decision. But SEAL
Showdown could actually give you useful data
because it breaks down results by age, education,
language, country. So you can pick the model
that that specific group actually prefers. Precisely.
That's actionable utility. How are they getting
the data? Globally. They say preferences from
100 countries, 70 languages. Try and get a real
global snapshot. And how do they stop people
from gaming the system? Like model creators trying
to boost their scores. Seems like they've thought
about that. Voting is voluntary, anonymous, and
they hold back the results from public view for
60 days to prevent that kind of manipulation.
Okay, so it's a big shift. El Marina gives you
raw speed, like engine horsepower. SEAL Showdown
tells you which car people actually like driving
in their specific neighborhoods. That's a great
analogy, yeah. Which tool is perceived as best
by your users? That creates real market pressure.
So you'd expect the big players, OpenAI, Anthropic,
Google. They'll have to react somehow. Oh, definitely.
They'll likely try to build their own versions
or maybe just plug into scale system. You can't
really ignore that kind of specific customer
feedback. So what's the core assumption about
AI performance that this new benchmark really
challenges? It fundamentally challenges that
old idea that there's one single best model,
one size fits all, that it can serve every single
user everywhere equally well. SEAL says, no,
it's more complicated. Okay, let's try to wrap
this deep dive up. Quick recap of the big layers
we talked about. Sure. First, the foundation,
the infrastructure, this gigawatt race heading
towards $500 billion, all chasing that abundant
intelligence vision. It's a battle for the means
of production, really. Second, the capabilities,
just stunning growth, models mastering complex
skills like that CFA exam almost instantly. It's
constantly pushing the performance ceiling up,
impacting work and education. Yeah. And third,
how we measure it all. Shifting towards personalized,
user -focused benchmarks like SEAL Showdown.
Moving past Rossby to ask, who is this really
best for? It brings us right back to Altman's
original claim, doesn't it? Yeah. If AI access
becomes a fundamental right, how does that change
global power dynamics? When only a handful of
massive companies, Oracle, SoftBank, Alibaba,
can actually afford to build the 10 gigawatts
needed to deliver that right. That's the billion.
Maybe trillion dollar question, isn't it? Who
owns the intelligence grid and what responsibility
comes with that ownership? That's the core geopolitical
tension underneath this whole race. Something
to think about. We'd encourage you listening
to consider the sources we unpacked. Maybe think
about how your own use of AI tools or how you
judge them might be skewed by those old metrics.
Are you using the fastest model or the one that
actually works best for you? Good question to
ponder. Thank you for joining us on the Deep
Dive.