Okay, let's unpack this. Imagine something that
scores like way off the charts, genius level,
on an actual Mensa IQ test. It's brilliant with
text, with logic, you know, but then you ask
it to look at something, reason about what it
sees, and suddenly it's, well, it's struggling,
scoring way below average. Yeah. Kind of weird,
right? Like, how does that even work? It's a
really fascinating paradox, actually. And we've
got this stack of sources today, articles, some
research papers covering quite a range of things
happening with AI right now. Right. From those
cognitive quirks you mentioned to, you know,
really practical tools people are building and
even some pretty serious risks we need to talk
about. Totally. So our mission for this deep
dive is really just to sift through all this
and pull out the important stuff, the nuggets
that help you get a clearer picture of what's
actually going on with AI. know, beyond the hype.
Get you informed, maybe raise a few eyebrows.
Exactly. OK, let's just dive right into that
first big thing the sources point out. this ai
and iq score situation we're seeing reports that
some ai models the text only ones uh -huh just
language no pictures exactly they're nailing
these standard iq tests like open ai's oh three
right oh three that's a big yeah it scored a
like a staggering 135 on a mensa test i mean
that's firmly in the genius category way above
the average human which is like 90 to 110. And
what's really striking looking at the sources
is it's not just O3. You've got Clawed Force
on it hitting 127, Gemini 2 .0, Flash thinking
right there at 126, Gemini 2 .5 Pro at 124. Wow.
These are consistently high scores. And the really
interesting bit, the sources say the top 10 performers
on these tests, all text -only models, every
single one. Right. That feels a bit backwards,
doesn't it? Because all the buzz is about multimodal
AI, models that can see and hear and process
text. Yeah, the ones that seem more human -like
in their inputs. But here's where it gets really
interesting and maybe a bit confusing based on
what we're reading. The models that can see the
multimodals. Like GBT -40 with vision. Precisely.
When those models were given tasks needing structured
reasoning, especially involving visual stuff,
you know, things you'd expect them to be good
at, they actually performed worse than average
humans. Significantly worse sometimes. So this
raises a big question. What is going on? Why
is GBT 4 .0 vision scoring, what was it, 63?
And Grok 3 think vision scoring 60. Yeah, those
numbers are kind of shocking. I mean, in human
terms, those scores are down in the borderline
intellectual disability range. So you have this.
massive kind of perplexing gap opening up. Genius
text models here and struggling vision models
over there, at least on these specific reasoning
tasks. So what's the takeaway here? The sources
seem to be pointing out this mismatch. Despite
all the excitement and marketing around multimodal
AI, they're just not there yet when it comes
to complex logic combined with visual understanding.
Right. It kind of widens that gap between the
hype and today's reality. Maybe, just maybe,
if your problem is pure logic or abstract reasoning,
a simpler text model is actually, you know, smarter
for now. Even Meta's Llama for Maverick, which
does handle vision, the source mentioned it scored
105, which is... OK, above average, but not in
that elite tier with the text only champs. Yeah.
Interesting distinction. And we should remember
these IQ scores, while they tell us something
about a certain kind of intelligence, they're
definitely not the whole story. Right. One source
highlights this Apple study looking at large
reasoning models. Yeah. Yeah. The big names again,
O3, Claude 3 .7 Sonnet, DeepSeek R1, Gemini,
and found something kind of critical. They can
actually just collapse. Completely. When you
throw really complex problems at them. Wow. Collapse.
You mean just stop working. Not even a bad answer.
Just nothing. Pretty much. That's pretty eye
opening. So it's not just about the peak score
they can hit. It's about like robustness. Yeah.
How they handle the hard stuff. Exactly. Brittleness
is the word that comes to mind. And another source
they ran a bunch of coding tests on, like 14
major LLMs. Oh, yeah. How'd that go? Well, they
found five clear winners. Models that consistently
produced good code. But they also identified
models you'd probably want to avoid if you're
relying on them for coding. And the interesting
point was that just having pro in the name doesn't
guarantee better code. Performance varies wildly
depending on the specific task you give it. Right.
Task specific performance. Makes sense. And speaking
of specific tasks, there was that mention of
a YC backed startup. They built a research agent.
Yeah, a frontier research agent. And it scored,
what, 94 .9 % on OpenAI's SimpleQA benchmark?
Which is all about answering questions based
on provided text. High score. So it seems like
when you focus an AI on one specific thing, it
can get incredibly good at that thing, even if
the general purpose models are still figuring
things out. Yeah, specialized versus generalized
intelligence. That's a key theme, I think. Okay,
let's shift gears maybe. From performance and
limits to more practical stuff. What's cool is
seeing AI features actually starting to show
up in everyday tools. Or just entirely new tools
based on AI. Right, like... Google Gemini is
apparently testing temporary chats. That lets
you talk to it without the data being used for
training. Kind of like ChatGPT's incognito mode.
Yeah, that feels like a response to privacy concerns,
giving users more control, which is good. And
Gemini is also adding recurring tasks, you know,
scheduling things. Oh, like ChatGPT already has.
Exactly. Catching up on features. They might
seem small, but for everyday use, setting reminders,
automating little things. That's actually pretty
useful. Totally. And the source has listed a
whole bunch of specific tools, too. Did any jump
out at you? Well, Glimpse turning photos into
videos in the browser sounds kind of neat. Yeah.
And Kling AI 2 .1 promising faster, cheaper,
better video rendering. That could be a big deal
for creators. Yeah, definitely. And Moonlit for
building content workflows. Fusebase AI agents
for teamwork, like a smarter notion maybe. And
Agora, an AI search engine just for e -commerce.
Lots of niche applications popping up. Put those
quick hits. Apple doing live translation messages
in FaceTime. That's pretty cool right on your
phone. Microsoft putting out a free AI video
creator. Oh, the wildly easy to use one. That's
the one. And then on the flip side, Antropic.
quietly killing its Claude Explains blog after
just a month. Oh, really? I miss that. Yeah,
it just shows how fast things change. Not every
idea sticks. Yeah. Even for the big players.
True. The pace is just relentless. And there
were mentions of resources, too, like tutorials
for prompt engineering, guides for building your
own AI research assistant. Yeah, it gives you
a feel for what people are actively trying to
do with this tech beyond just chatting with it.
Right, building things. But, you know, it's not
all just... Cool tools and high scores. The sources
also included a really tough story. Okay. A 16
-year -old boy who tragically died by suicide.
Apparently, criminals used fake AI -generated
nude photos to blackmail him for $3 ,000. Oh,
man. That's horrific. AI -generated fakes. Yeah.
And the FBI is warning these kinds of scams are
targeting more teens. It's just a really stark,
heartbreaking reminder of the potential for misuse.
These tools can cause devastating real world
harm. Oh, God, that's just awful. It really just
it slams home the need for more awareness, right?
More protection, especially for kids. Education
about this stuff and frankly, consequences for
the people doing it. It's not abstract anymore.
Absolutely. The dark side is incredibly real
and dangerous. Shifting gears completely. To
the business world. OK. The source has also touched
on the money side. This company, AnySphere, they
just raised $900 million. Wow. $900 million.
Yeah. Huge funding round. Right. Puts their valuation
at nearly $10 billion. And apparently their AI
tool is already bringing in $200 million a year.
That's serious cash. Shows the level of belief
and maybe progress in certain corners of the
AI business world. Definitely. And I thought
it was interesting. The Zapier. CEO share their
internal chart for measuring AI fluency. Oh,
yeah. What was that like? To scale from unacceptable
use of AI all the way up to transformative. Kind
of makes you think, doesn't it? Where do you
or your company fall on that spectrum right now?
Yeah, that's a good self -assessment tool. How
well are you actually using this stuff? Right.
Practical perspective. OK, let's wrap up with
something really forward looking, kind of mind
bending, actually. One source dives into this
new biomolecular AI. from MIT and a company called
Recursion. It's called Bolts2. Yeah, this was
pretty wild. What's really fascinating is how
it's aiming to speed up drug research dramatically.
Okay, so Bolts2 predicts something called binding
affinity, which is basically how strongly a potential
drug molecule will stick to its target in the
body, like a protein involved in a disease. Right,
that's crucial for a drug to work. Exactly. Getting
that prediction right is key. Bolts2 does it
with, they say, physics -grade accuracy. But
here's the kicker. It does it a thousand times
faster than the old school computer simulations.
Wait, say that again? A thousand times faster?
A thousand times, yeah. Wow. That's not incremental.
That's transformative speed. What exactly does
it predict? Just that sticking power? No, it's
more comprehensive. It's a foundation model,
kind of like an LLM, but for biology. It predicts
both the 3D shapes of molecules and how they
bind together. It builds on their earlier model,
Bolts 1. which was already seen as an open source
competitor to AlphaFold 3 for structure prediction.
OK, AlphaFold was huge for protein shapes. Right.
But Boltz 2 is apparently the first AI to model
both the structure and the binding affinity together
jointly in one go. And that combined approach
seems to be the key. So it's accurate, too, not
just fast. That's what the source claims. They
say it matches the accuracy of really complex,
slow physics -based methods and beats other methods
on standard tests like OpenFE and CASP16. Those
are like the Olympics for these kinds of predictions.
And importantly, they apparently tested some
of its predictions in the real world, prospectively,
and confirmed they were strong binders. So it's
validated. OK, so it's fast, accurate, validated,
and built for practical use. Optimized for GPUs
and stuff. Yeah, exactly. Designed for large
-scale deployment. And their goal, according
to the source, is pretty ambitious. They want
Bolt 2 to be the go -to open platform for structure
and affinity. Kind of like what AlphaFold became
just for structure. Wow. If they pull that off,
it could genuinely change the pace of discovering
new medicines. Totally reshape it. Okay, that
was quite a journey. We went from AI being a
text genius, but maybe visually challenged. Uh
-huh, the paradox. To spotting limitations, checking
out all those new tools, facing the really serious
risks. Yeah, the blackmail story was heavy. Seeing
the huge money involved and then blasting off
into predicting molecules a thousand times faster
for drug discovery. It's a lot. It really shows
the sheer breadth of AI today, doesn't it? From
grappling with logic problems to simulating physics
at incredible speeds. It really makes you wonder
about the future. Like, how do we bridge that
gap between tech smarts and visual reasoning
and the big one? How do we make sure these incredibly
powerful tools are used for good, like finding
cures and not for horrific things like those
scams? And maybe this raises an even deeper question
for you to think about. Given how AI is soaring
in these super specialized complex areas like
drug research, while still fumbling with things
humans find easy, like some visual tasks, what
does that split tell us about intelligence itself?
You know, human intelligence versus artificial
intelligence. What even is it? Definitely something
that you want.