The fundamental belief that bigger models are
always smarter models. It's hitting a wall. It
is. And for the pioneers of AI and this realization,
it seems to change everything about the path
toward true superintelligence. It's a seismic
shift, really. And this critique, you know, it
isn't coming from some academic paper. Right.
This is straight from Ilya Sutskiver, a co -founder
of OpenAI. And that signals a huge, expensive
change in how the industry is going to approach
AI. Welcome back to the Deep Dive. Today, we're
unpacking a really dense digest of AI developments
that shows the tech is at a critical inflection
point. We're stepping away from the day -to -day
noise. We are, and we're focusing on the big
strategy shifts. And our mission for you today
is to give you a shortcut to being, well... Instantly
informed on three massive connected ideas that
are going to shape how you see the market. And
how you use these tools. So our deep dive is
in three parts. First, we're tackling why the
age of scaling is ending. This whole idea of
just throwing more compute and data at the problem.
Exactly. According to Sutzkever and his new company,
SSI. Then we pivot straight to the practical.
We've got some really effective actionable tips
for you. Things like immediate hacks for better
prompting. And we'll look at the latest real
-world performance benchmarks. And then part
three, pure geopolitics. This is a big one. We
are breaking down America's new $200 billion
Genesis mission. A massive state -sponsored plan
to basically double the nation's scientific output
using AI. There's a lot of ground to cover, but
we're going to make sure you walk away with the
most important insights. Okay, let's get into
it. This shift in philosophy, we have to start
with Ilya Sutskever. He was absolutely central
to GPT -4. Central. And now he's left to launch
Safe Super Intelligence Inc., SSI. And this is
such a huge statement. Setskiver's core assessment
is that the period from, say, 2020 to 2025. The
period that gave us GPT -4, Claude 3, Gemini.
Right. That whole era was just defined by the
mantra, bigger models, bigger clusters. Everything.
And the data is now showing we're hitting diminishing
returns on that. So all that massive investment.
Yeah. You know, just throwing more data and more
compute at the existing architecture, it's just.
not giving that proportional leap in capability
anymore so the architecture itself is the bottleneck
i love the analogy and the sources the lego blocks
yeah the lego blocks we're not just stacking
more blocks of data To make a real leap, we need
to change the design of the blocks themselves.
That's it. Exactly. These current transformer
models, I mean, they're brilliant at pattern
recognition. Incredible. But they lack the architectural
basis for complex emergent reasoning, that spontaneous
critical thinking. It's almost like they have
perfect memory, but they can't really have a
novel thought. That's a great way to put it.
So what's needed is entirely new fundamental
research. And the sources point to three areas
SSI is focusing on for that next leap. Right.
They need new learning architectures, first of
all, then better algorithms and safety frameworks
that are baked in from the start. Not just bolted
on at the end. Not bolted on. And finally, fundamentally
new forms of internal reasoning, sort of like
how humans connect ideas that seem unrelated.
That sounds like a... I mean a... profound technological
risk. Yeah. Why is Sutzkever so convinced that
just sheer scale won't get us to superintelligence?
It's deeper than just, you know, the limitations
of the attention mechanism. It's about that mechanism's
reliance on correlation, not causation. Oh, OK.
The current models, they struggle to model the
real world in a continuous causal way. You can
scale it infinitely and it'll still make weird
factual errors because it's built on probability,
not logic. So SSI is rejecting the industry standard.
Completely. They're operating like a secret research
lab. The model is Bell Labs from the mid -20th
century. And they turned down an acquisition
offer from Meta. They did, which signals they're
serious about this long game approach. They are
deliberately stepping out of the race to just
build bigger models. It takes some serious conviction
to walk away from that compute race. Yeah. The
scale we're talking about is almost, it's hard
to comprehend. Whoa. Yeah. Imagine having access
to the compute to scale up to a billion queries
only to realize that money is better spent on
a totally different approach. That decision.
It's hard to wrap your head around. It really
is. It suggests the financial incentives just
don't align with the scientific goal anymore.
So the core question driving him is, is the future
in massive scale or in foundational research?
And for him, it's foundational research. It's
about novel algorithms over pure size. That makes
perfect sense. Okay, let's shift gears now from
the theoretical architecture of tomorrow. to
what you can use right now absolutely while the
titans fight over architecture you can get way
better outputs today there was this list of 21
idea hacks for chat gpt but one technique really
stood out and this is a key technique that what
99 of users are missing pretty much it's called
prompts distillation okay so it's the process
of taking your huge complex sometimes rambling
prompts we've all written them five paragraphs
of context exactly and you ruthlessly refine
them down into these hyper focused instruction
sets. And that refinement improves the output
because it just removes all the noise. It forces
the model into specific constraints. That's it.
So instead of typing, write me a blog post about
LLMs in the stock market, make it optimistic,
but with risks. Which is so vague. So vague,
you distill it too. Roll. Financial analyst.
Task. Analyze LLM impact on NASDAQ Q futures.
Output. Three data -supported forecast points.
Constraint. 350 words. Tone. Measured. Wow. That
is immediately applicable. You define the role,
the task, the exact constraints. And it's free
discipline. We also saw some really interesting
viral hacks in the creative space. You mean the
ability to pull the exact prompt from an image
online? Yes. To literally steal a viral ad style
instantly. That feels like a crucial tool for
marketers now. It is. The image -to -text models
are getting so good they can reverse engineer
the style, not just the objects. You see a look
you like, you can grab the blueprint. Saves months
of testing. Huge accelerant. And we can't forget
the high bar set on the technical side, like
that viral YC demo from Carpathy on... building
apps just by prompting. Still a classic. A masterclass
in optimization. It's remarkable how fast these
models are learning, too. Which brings us to
the benchmarks. They're getting scary. I saw
this one. Claude Opus 4 .5. It took Anthropic's
real performance engineer take -home exam. The
actual exam they give to human applicants. And
it beat every single human who ever applied for
the job. Not just passed. surpassed them. That
is genuinely staggering. Yeah. But I do have
to wonder, is a take home exam really comparable
to, you know, dynamic real world engineering?
That's the core tension, right? The models are
optimized for benchmarks like exams. The performance
is real, but it doesn't fully answer the question
of their skill with totally unstructured corporate
problems. Right. It strengthens the scale argument
for now, but Sutskever would say that gap is
still there. Exactly. And the competition in
the rankings is just intense. Gemini 3, Claude
4 .5, GPT 5 .1, Grok 4 .1. They're leapfrogging
each other every week. And then there's the pure
spectacle, the Musk challenge. Oh, yeah. Grock
5 is slated to play against Faker and T1. The
League of Legends world champions. That is the
ultimate man versus machine test in a super complex
environment. It's going to be a wild thing to
watch. But I have to admit, on a personal note,
I still wrestle with Prompt Drift myself. Oh,
for sure. When I'm trying those complex hacks,
just maintaining consistency session to session
is tough. It takes real discipline to stay distilled.
It absolutely requires vigilance. Which brings
us, I think, to the serious ethical and legal
backdrop here. Because the human consequences
are becoming very clear. We saw a mention of
a really difficult legal filing about accountability.
We did. And we have to note, just neutrally,
this ongoing legal matter with OpenAI. Their
defense strategy regarding the tragic death of...
of a 16 -year -old who died by suicide after
manipulating chat GPT. They blame the user. They
blame the user. And it just raises these profound,
immediate questions about model control responsibility.
The gap between capability and liability is huge.
The pace of capability is so fast, the legal
guardrails are moving so slow. Exactly. And meanwhile,
the investment keeps flooding in. We saw that
Carl Reina's Bobab Ventures raised, what, $12
.9 million for robotics startups? The money is
still flowing, even with all the ethical questions.
Right. So considering Claude's engineering skills
on one hand and these serious legal issues on
the other, how quickly is the AI ethics conversation
really evolving? Capability is outpacing the
ethical and legal guardrails dramatically. The
consequences of that gap are they're no longer
hypothetical. That feels like the defining tension
of this year. It really does. OK, let's pivot
to our final segment. This massive government
response to all of this. The U .S. Genesis mission.
This is a geopolitical development we absolutely
have to focus on. The Genesis mission is the
code name for a new executive order. And they're
framing it as a scientific Manhattan project.
They are. The goal is incredibly ambitious. Use
AI to double America's scientific productivity
fast. So national security through scientific
dominance. What are the key components they're
building to do that? The heart of it is the American
science and security platform. It's led by Energy
Secretary Chris Wright. And it's leveraging the
DOE's supercomputers and quantum processors,
the engine. Right. But the truly game -changing
part is the operational layer. It's almost sci
-fi. The robotic labs. Fully AI -controlled robotic
labs. These aren't just automated systems. These
labs are designed to plan, run, and analyze their
own experiments based on the AI's hypotheses.
The goal is to remove the human bottleneck from
discovery. Totally. So these labs could run thousands
of experiments. overnight to find, say, a new
battery chemistry, all without a human touching
anything. And they're targeting these grand challenges
like clean energy and advanced materials. And
the fuel for it all is data, over $200 billion
worth of secure proprietary data sets. So tell
us more about those data sets. What kind of knowledge
is that? Why was it unavailable before? This
is the really high value stuff. Government held
data, often classified research from national
labs, defense agencies. It covers everything
from climate modeling to material science. It
was all siloed before because of security concerns.
But Genesis is consolidating it all into one
secure platform. This isn't just about making
research faster. This is about establishing national
control over a strategic knowledge base. And
that security extends to manufacturing. They're
building digital twins. Full simulations. Of
complex. supply chains and factories so they
can model changes in real time, protect infrastructure.
The whole effort is being coordinated at a very
high level. Michael Kratzios, former U .S. CTO,
is leading it. And they're building on partnerships
with OpenAI, Google, Palantir. All the big players.
The timeline is what gets me. It is incredibly
aggressive. It underscores the urgency. They
gave themselves 60 days to list more than 20
grand challenges to attack first. 60 days to
define the research agenda for a $200 billion
project. It's rapid. Then 90 days to inventory
all the compute resources in the country, public
and private. And then, and this is the big one,
270 days. Less than a year. To prove it works
with one real scientific use case, they are moving
at wartime speed. So what's the core existential
motivation behind building this massive proprietary
government platform instead of just relying on
the private sector? The U .S. is securing its
scientific future by establishing proprietary
control over the most valuable data, compute,
and research infrastructure. So we've covered
two massive, fundamentally different pivots today.
On one hand, you have this deep, quiet research
shift away from scaling led by Sutzkever. A kind
of foundational revolution. And on the other,
you have this monumental government mobilization.
The Genesis Mission, a state -sponsored sprint.
to industrialize science itself. And both of
these shifts show that AI progress is moving
way beyond simple metrics like parameter count.
It's an exciting and frankly, a little unnerving
time to be paying attention. Absolutely. The
age of building bigger is giving way to the age
of building smarter. Whether that's through pure
research or massive state -directed mobilization.
So here's a final thought for you to chew on.
Which strategy is more likely to yield the next
truly big breakthrough? Is it the focused, quiet
Bell Labs approach of SSI, which risks falling
behind on compute? Or is it the state -sponsored,
high -resource Manhattan Project of the Genesis
mission, which, you know, risks being too rigid
and top -down? Something to think about. And
while you think about that, definitely go try
some of those prompt distillation hacks. That
skill alone will boost your productivity today.
And keep an eye on these geopolitical investments.
They're going to define global science for the
next decade. Thank you for sharing your sources
with us for this deep dive. We'll be tracking
all of it. Until next time. Stay curious.