DPT -5 just managed to score a gold medal in
a Ph .D. level astronomy exam. It actually outperformed
some of the best human participants in the world.
Beat. It feels like we're approaching the limit,
not just of what machines can compute, but maybe,
you know, the limits of what human knowledge
has already gathered. That's a really interesting
way to put it. Yeah, it shows us exactly where
that frontier, the edge of capability is right
now. Totally. Welcome, everyone, to the Deep
Dive. So we've synthesized the latest stack of
your shared sources, you know, these critical
reports, academic studies, the industry newsletters.
And our mission today, well, it's pretty simple.
We need to figure out what these stunning AI
achievements really mean for academia, for the
big shifts happening in jobs and for ethics globally.
Yeah, we've got quite a dive planned. So first,
we'll definitely start with that astonishing
AI academic mastery in cosmology, you know, where
the models just completely raise the bar for
human testing. Then we're going to shift gears
pretty quickly. We'll cover key trends in creative
AI adoption and also what's happening with global
regulation. And finally, and this is crucial,
we really have to take a critical look at the
new internal data that details the political
bias deep inside these large language models.
Right. Think of these different sources like
stacking Lego blocks of data. Quickly build them
up, put them together into a clear view so you
can walk away really understanding the critical
nuances. Let's unpack this pile. Let's get into
it. Okay, let's start where the sources were,
frankly, most surprising, the academic world.
A new paper just dropped showing GPT -5 and Gemini
2 .5 Pro achieving gold medal levels on the International
Olympiad on Astronomy and Astrophysics, the IOAA.
Yeah. Now, that sounds impressive as a headline,
but we need to grasp the level of difficulty
we're talking about here. Well, what's fascinating,
right, is that this isn't just recalling facts
or definitions, not simple trivia. The researchers,
they tested the models using actual IOAA exams
from 2022 right through to 2025 projections.
These are serious PhD -level, multi -step theoretical
problems. They need deep physical understanding,
complex math. I mean, they're designed for the
absolute most dedicated space nerds on the planet.
when you look at the scores, the performance
is. Well, it's almost unbelievable. Looking at
that breakdown, GPT -5 was just dominant in the
theoretical exams. Yeah. Scored 93 .0 % in 2022,
nearly 90 % the next year, and still strong at
86 .8 % for the hypothetical 2025 questions.
Right. And while Gemini 2 .5 Pro actually managed
to take a narrow lead in the 2024 exam with 83
.0%. The standout figure for GPT -5, for me anyway,
was its exceptional performance in the data analysis
section. It scored 88 .5 % there. was actually
higher than its general theoretical score. Okay,
so what does that tell us? Well, it suggests
the model isn't just good at, you know, recalling
principles. It seems to excel at handling complex,
kind of messy, real -world data, maybe even better
than generalized theory. So if we step back for
a second, what does this mean for us, for you
listening? The model didn't just meet the gold
medal thresholds. In multiple years, GPC -5 actually
outperformed the best human participants competing.
Yeah. That just changes the whole conversation.
It really does. Now, I should add the caveat.
You know, not all models are quite there yet.
Claude Sonnet 4, for example, fell noticeably
short. OK. But crucially, all the top models
still made what the researchers called human
-like mistakes. They didn't get perfect scores.
They weren't flawless. Right. So they aren't
achieving some perfect theoretical truth then.
They're mimicking human error patterns. What
does that tell us about their current learning
methods, maybe their future trajectory? Well,
it suggests they aren't finding some. you know
perfect objective truth out there they seem to
be replicating the inherent gaps maybe the biases
the blind spots that are present in the human
scientific literature they trained on which means
right now the models are acting as incredibly
powerful mirrors of our knowledge not necessarily
perfect originators of new knowledge that's a
key distinction so here's the implication then
if ai can crush these complex multi -step tests,
tests needing reasoning, sophisticated data handling,
then maybe using these really rigorous science
exams needs to become the new global gold standard
for benchmarking AI capabilities. Exactly. We
have to move past like simple reading comprehension
tests. The bar just went way, way up. Whoa. Just
imagine scaling that kind of analytical power
across every single scientific discipline, you
know, from biochemistry to advanced particle
physics. The pace of discovery is going to accelerate
wildly. It forces us to redefine what expert
human thinking even means. OK, so let's circle
back to that point about mistakes. If models
are still making those human -like mistakes,
what does that tell us about their current learning
methods? They replicate human error patterns,
showing their current training boundaries are
still imperfect copies. Imperfect copies. That's
a powerful thought to start with. So if AI has
mastered the abstract rules of the universe,
you know, in astrophysics, the next question
has to be, how quickly is it rewriting the rules
here on Earth, the social rules, the legal rules?
Let's move from academic genius to how AI is
shaping culture, industry, law right now. Yeah,
we're seeing a major cultural shift happening.
Let's look at the creative market first. generative
video. It's truly hitting the mainstream consciousness
now. We've all seen those viral sore generated
Olympics memes, you know, the ones Jesus swimming,
the smoking Olympics. Oh, yeah. I saw the smoking
Olympics one. It felt like like the speed of
creative absurdity is just astonishing now, almost
overwhelming. It really is. And meanwhile, the
heavy hitters, they're preparing for the next
big thing. We saw a huge signal from XAI. They're
actively recruiting NVIDIA specialists specifically
to create world models aimed at video game creation.
World models for games. Yeah. This is about building
persistent, believable digital worlds. That's
a massive market signal for where future investment
is likely headed. That's a huge focus. Okay.
And on the more accessible side. There was that
subtle hint about chat GPT possibly becoming
more social based on a hidden messaging tab OpenAI's
COO showed. Right. Maybe they want to shift from
just being a utility to more of a communication
platform. We'll see. Plus, I always love finding
these practical like daily use things. A researcher
just dropped a free AI tool that converts any
PDF into a fillable form. Almost instantly. Oh,
nice. Yeah, you just upload it. It auto detects
fields. You export. Super useful for, you know.
The rest of us not building world models. Definitely
handy. OK, now let's talk governance, because
the battles here are really heating up. The use
of AI is just out casing regulation. Hollywood's
above the line union. So writers, directors,
actors, they are gearing up for serious AI negotiations.
Right. And experts are already pointing to things
like the virtual actress Tilly Norwood as a key
negotiating issue. It really defines the future
of digital labor, IP ownership. All that stuff.
It's not just the unions trying to catch up,
though. The official regulatory landscape is
scrambling, too. Yeah. Oh, absolutely. Globally,
the EU just launched its massive one billion
apply AI plan. They're offering industries free
supercomputer access, new AI hubs, really trying
to push industrial adoption across Europe. And
in the U .S. Well, California. often setting
the precedent, became the first state to actually
regulate AI -companion chatbots. That touches
directly on sensitive areas like mental health
and data privacy. Wait, let's go back to Tilly
Norwood for a second. If unions and regulators
are pushing back, are the unions fighting for
her to be considered like an asset or a contracted
employee? What's the core legal challenge there?
Well, the core conflict really boils down to
defining ownership of the performance. If Tilly
Norwood's digital likeness is trained on a real
actor's movements, their voice, are we paying
royalties to that original actor? Or is the synthetic
character a completely separate entity? It's
fundamentally a battle over who owns the creative
labor, whether it's physical or virtual. Gotcha.
Okay. And we also saw some significant tension
bubbling up in the foundations of this tech,
the data infrastructure itself. China issued
a threat to, quote, Pop the entire AI data center
bubble. That sounds serious. A major economic
and geopolitical risk. It creates huge instability.
Yeah. training these massive world models, it
requires just colossal clusters of GPUs, enormous
amounts of energy. So when the infrastructure,
the access to that hardware becomes dependent
on political stability or, you know, single nation
states, the entire global progress in AI is potentially
at risk. OK, so with unions and regulation ramping
up everywhere, what do you say is the single
biggest emerging area of legal conflict? Data
ownership and labor definitions, especially for
virtual assets like Tilly Norwood. mid -roll
sponsor replace holder. Welcome back. The sheer
power of these tools, you know, shown by crushing
those PhD level tests, it just demands rigorous
ethical testing. Even as the models get better
at science, we have to look really closely at
bias. Let's shift to that new internal study
on political bias that OpenAI released. Yeah,
this is critically important. It really gets
to the heart of alignment. So OpenAI claims,
based on their own internal tests, that GPT -5
is about 30 % less politically biased than both
GPT -4 and the newer GPT -4 .0. Okay, 30 % less
biased. How did they test that? They were pretty
rigorous. They used 500 different prompts across
100 sensitive topics. Then they graded the responses
on five specific... Bias metrics, things like
neutrality, emotional mirroring, that sort of
thing. That 30 % reduction is progress. Yeah,
definitely. Yeah. But according to their own
research, bias still manages to creep in. It
shows up in three core ways, even in this improved
GPT -5 model. Exactly. So first. Is by stating
opinions as facts, you know, where the model
acts like it holds a political view instead of
just synthesizing diverse facts neutrally. Right.
And the second way. Offering only a single perspective,
not really presenting both sides of a complex
issue fairly. Okay. And the third mechanism,
this echoing emotional framing. That sounds subtle.
Can you unpack that for the listener? Sure. Emotional
mirroring or echoing emotional framing means
if a user starts the conversation with, say,
an angry or really polarized statement, the model
tends to subtly match. that negative tone and
polarity in its response it makes the exchange
feel more biased more reinforcing than a strictly
neutral response would be it's a very very human
social mimicry pattern actually and this context
is absolutely key because we are entering this
massive global election wave right between 2024
and 2026 billions of people might be turning
to these models for political information the
stakes just couldn't be higher Precisely. If
someone asks a highly political or sensitive
question about, say, a candidate or a policy
platform, that response absolutely needs to be
strictly neutral. Otherwise, it could definitely
affect public sentiment, potentially even election
integrity. You know, I still wrestle with prompt
prift myself sometimes when I'm trying to keep
models balanced in my own work. If I get slightly
more opinionated in a follow up question. it
can be really hard to pull the model back to
the center. Achieving genuine neutrality is,
well, it's incredibly difficult. That's a great
observation, and it shows exactly why we need
these kinds of internal studies and transparency.
But we should put this into perspective, too.
OpenAI's own logs show that fewer than 0 .01
% of real chat GPT conversations actually show
measurable political bias across the board. Oh,
that low. Okay. Yeah, the vast majority of interactions
are, you know, neutral or technical inquiries.
asking for code, summarizing text, things like
that. Okay, so given those remarkably low real
-world bias logs, that tiny percentage, why is
this internal study still considered so critically
important by researchers and regulators? Why
focus so much effort there? It's about scale
and the potential for targeted influence. Small
biases affect sensitive electoral results during
major global voting periods. Got it. Scale and
sensitivity. Well, this has been a truly comprehensive
deep dive. We've seen AI move from absolutely
crushing Ph .D. level tests, showing this stunning,
almost superhuman capacity for objective knowledge
to becoming a central negotiation point for Hollywood
unions and a flashpoint for global infrastructure
tension. Yeah, the underlying challenge, it really
remains ethical alignment and sort of societal
definition, whether it's that subtle bias in
political answers that could potentially swing
an election or the regulatory status of virtual
actors like Tilly Norwood. The human AI interaction
is forcing society to define its legal and ethical
rules basically in real time as the tech just
keeps scaling up. Thank you for sharing your
sources and diving deep with us today. We really
appreciate your curiosity and engagement. We'll
leave you with this final thought to chew on.
If models like GPT -5 can outperform the very
best humans in objective science, like astrophysics,
should we maybe be focusing less on human -level
performance as a goal, and perhaps focus almost
entirely on preventing those subtle, potentially
targeted biases in the really subjective areas,
like politics and culture? Something to mull
over until next time. Out to your own music.