Imagine a scientific paper. Okay, now imagine
it has over 3 ,000 authors. Wow. Yeah, more authors
than some small towns have people. Just think
about that for a moment. Welcome everyone to
the Deep Dive. So today we're digging into this
really fascinating newsletter. We're going to
unpack what it's telling us about, you know,
the absolute cutting edge of AI. Our mission
basically is to explore how AI research itself
is changing, the incredible new tools it's building,
and get this, even how it's creating labs that
drive themselves. Self -driving labs. Yeah, should
be some real aha moments in here for you. All
right, let's dive right into that first kind
of startling fact then. The recent Google Gemini
2 .5 paper. Yeah. It listed, astonishingly, 3
,295 authors. I mean, just let that number sink
in. Yeah, 3 ,000. And that's not a typo. Seriously.
To put it in perspective, right, the first Gemini
paper that had, like, 1 ,250 authors and GPT
-4, you know, the open AI model. That's smaller.
Way smaller. Only 417. It seems like... OpenAI
and Anthropic are, I don't know, maybe a bit
more selective with credits? Could be. But Google's
list for Gemini, it jumped, what, 144 % in less
than two years? It's wild. And what's really
fascinating is that the paper apparently had
this hidden message. Oh, yeah. The first initials
of the, I think the first 43 authors, reportedly
spelled out something like, AI is a team sport.
Nice. Which isn't just, you know, a clever little
Easter egg. Yeah. It feels like Google was intentionally
highlighting this new reality. That AI development
is now just this massive collective effort. Exactly.
These papers are starting to look like phone
books. Seriously. If this keeps going, I mean,
just projecting it out, by 2040, we could be
looking at like 2 .6 million names on one paper.
You'd need AI just to read the author list. Right.
We'll need AI to summarize its own author list.
It's kind of funny, but also a bit... Mind bending
when you think about the scale. It really makes
you wonder, though, doesn't it? When you have
thousands of contributors, likely all over the
world, how do you even begin to coordinate something
like that? Yeah. How do you track who did what
specific piece? Right. It feels like we're genuinely
stepping into a new era for science collaboration.
More like particle physics or the human genome
project, maybe. Yeah. Big science. Yeah, absolutely.
And this huge growth in authors, it's not just
more bodies, right? It really signals that AI
isn't just a coding problem anymore, not by a
long shot. It's become this grand, like interdisciplinary
symphony. You've got linguists, ethicists, policy
people, neuroscientists. All sorts. All working
together to build something. Well, pretty unprecedented.
It's not a sprint by a few geniuses anymore.
It's like a huge expeditionary force. Yeah. So
stepping back, this huge shift in authorship.
What's it really telling us about AI development
right now? Well, I think it clearly shows that
AI research now demands vast, diverse and interdisciplinary
collaboration. It's just that complex. OK, that
makes sense. And here's where it gets really
interesting beyond just the giant papers. We're
seeing new AI capabilities pop up super fast.
Like what? Well, breakthroughs in video generation,
for one. There's a new real time model. It lets
you direct a full minute of AI video. A full
minute. Yeah. And you can actually adjust the
prompts while it's generating. No more of those
like eight second clips like VO3. That's a big
jump. And I saw something about FluxPro combined
with Seedence producing hyper realistic video.
Oh, yeah. The results from that are. Honestly,
pretty remarkable. Getting harder and harder
to tell what's real anymore. But maybe the biggest
thing gaining traction right now, this idea of
agent mode. It's OpenAI's latest thing. Agent
mode. Think of it like ChatGPT, but it's connected
to a virtual computer. It can actually do stuff.
So let me get this straight. An AI agent is basically,
it's a program that can think and act for you.
Yeah. By controlling a whole computer. Exactly.
So it can carry out complex tasks all by itself.
Autonomously. Precisely. It's like delegating
to a really, really smart intern who can actually
use your software, browse the web, write code,
the whole deal. Wow. And look, Mistral just upgraded
its assistant, LeChat. It's got a deep research
mode now, native multilingual reasoning, even
image editing built in. So it's becoming a serious
alternative. Yeah, definitely a compelling open
alternative to something like Microsoft Copilot.
And speaking of movement. We've seen some pretty
big talent shuffles too, right? Anthropic hiring
back key cloud code leaders. Yeah, that was kind
of an unreversed card on the usual talent poaching,
wasn't it? Pretty interesting move. Definitely.
And Perplexity AI, the search company, just got
a huge valuation boost, like $18 billion. Yep.
Positioning itself as a major rival to Google
search using AI. Things are moving incredibly
fast in that space. It really is. So these new
agent capabilities. How do they really change
how we're going to interact with AI, do you think?
Well, fundamentally, it shifts AI from just being
a thing you talk to to something that can actually
do things for you, a digital colleague almost.
Right. So in short, these agentic capabilities
mean AI can now take initiative and complete
complex actions on your behalf. Exactly. It's
a big step. Okay. So beyond those big headlines
and these new agents, there's also just this
flood of new AI tools, right? Designed to automate
stuff, boost creativity. Oh, yeah. It's like
a Cambrian explosion of tools right now. Take
Uncursor, for instance. It lets you build and
deploy live apps. in literally seconds in seconds
yeah imagine what that unlocks for people who
aren't coders the speed of innovation there that's
huge or fast3d .io you type text or feed it an
image and boom eight seconds later you have a
3d model eight seconds That could totally transform
design workflows. Artists could iterate like
crazy. Totally. And then there's stuff like force
equals, which claims to turn just an idea into
like a full execution ready plan. Interesting.
And symbol, which can take a long PDF or an article
and just turn it into a video tutorial for you.
So it's all about making complex things easier,
faster, radically supercharging what one person
can do. Pretty much. And then you have the quick
hits. Seen some viral AI art coming from Perplexity's
Ambassador. And an interesting little note that
Anthropic might be quietly tightening the usage
limits on Claude for some people. Something to
keep an eye on. Yeah, definitely. And Adobe's
new tool, turning silly noises into realistic
audio effects. That just sounds like pure fun.
Right, a creative playground. And OpenAI is apparently
working on a native checkout system. So the AI
could potentially... complete purchases for you.
Whoa. Okay, that's interesting implications there.
And Microsoft Copilot can now apparently see
whatever's on your screen in real time. Real
time screen awareness. Yeah, that adds a whole
new layer of context for the AI. It's amazing,
really. All these tools, all these rapid developments,
they're incredible. But honestly. Yeah, I still
wrestle with just the sheer volume of it all.
Yeah. Yeah. It's a lot to keep up with. Oh, tell
me about it. You're definitely not alone there.
It feels like drinking from a fire hose most
days. Right. But what's fascinating, I think,
is that underneath all this rapid fire stuff,
there's actually a pretty clear pattern emerging,
isn't there? Yeah, I think so. For all their
differences, what's the common thread you see
weaving through these diverse new AI tools? Well,
they're not just automating tasks, you know.
They're fundamentally lowering the barrier to
creating complex things or getting complex things
done. Lowering the barrier. Yeah. Okay. So the
common thread is that they significantly simplify
complex processes and dramatically accelerate
creative output. Yeah. I think that sums it up
nicely. They empower the user in a big way. Okay.
So if we kind of connect that idea of empowerment
and automation to the really big picture, we're
starting to see AI not just assist with discovery,
but actually drive it. Exactly. Which brings
us to this work at North Carolina State University.
Researchers there have built a fully autonomous
AI -powered chemistry lab. Fully autonomous.
Like no humans involved day to day. Pretty much.
This lab runs continuously 24 -7. It collects
something like 10 times more data than traditional
lab methods. And it's finding promising materials
for things like clean energy, advanced electronics.
Finding them in days, not the years it usually
takes. It's a total game changer for material
science. So it's not just running one experiment
and stopping. Nope. It captures a new data point
literally every half second. It analyzes that
data, learns, and then adjusts the next step
of the experiment mid -run. On the fly. On the
fly. And it never stops testing. It's this constant,
intelligent loop of discovery. Design, test,
learn, repeat, nonstop. And you mentioned it's
efficient, too. Yeah. Big time. Uses fewer chemicals,
cuts down lab waste drastically. So it's actually
enabling sustainable science acceleration. That's
a huge win -win. Whoa. Okay. Just pause there.
Imagine scaling that up. A self -driving lab
like this, discovering materials for, I don't
know, a billion new applications we haven't even
dreamed of yet. Right. It's like Netflix for
chemical reactions. Just streaming experiments
constantly. No pauses. Things that normally take
researchers weeks or months, done in hours or
days. My mind kind of explodes thinking about
what this could mean for medicine, for battery
technology, for, well, everything. What makes
this really stand out, though, is, well, we have
systems like DeepMind's AlphaFold, right? Right.
Predicting protein structures or NVIDIA's drug
discovery pipelines. Sure, powerful tools. But
those are largely computation, right? They don't
typically operate physical experiments in real
time in a lab. Exactly. This NC State lab, it
fully automates the entire loop design, test,
learn, repeat, all within the physical world.
It's a closed loop system actually doing the
chemistry. It's the integration that's key. Totally.
So now that this kind of autonomous lab is clearly
possible. It kind of begs the question, right?
For you, for us, for everyone listening, where's
the open AI of chemistry or the clod for clean
energy materials? What's the next step in really
leveraging this kind of autonomy for scientific
breakthroughs? It's a huge question. Yeah. But
it's undeniable how this kind of self -driving
lab revolutionizes the pace of discovery. Yeah.
How would you summarize that impact? I'd say
it enables... continuous rapid experimentation,
basically compressing years of traditional research
work into mere days. Speed and scale. Years into
days. Okay. Sponsor. All right. So let's try
to wrap our heads around all this. What does
it all mean for us, for you listening? We've
seen AI research itself scale dramatically, needing
thousands of collaborators now. Yeah. The scale
is next. We've seen this explosion of new AI
tools and these aging capabilities really empowering
users to do much more, much faster. Democratizing
complexity almost. And we've explored the emergence
of these fully autonomous. AI systems like that
chemistry lab that can accelerate scientific
discovery at just an unprecedented rate. Yeah.
When you put it all together, the sheer scale
of the research effort, the rise of agents that
can act for us and now labs that literally think
and experiment for themselves. Right. It's becoming
really clear that AI isn't just, you know, assisting
us anymore. It's evolving into an active, intelligent
partner. A partner in discovery and creation,
even in basic science. So here's maybe a final
thought to leave you with. If AI research papers
now need thousands of authors and labs can design,
test, and learn entirely on their own, what happens
to the role of human curiosity? Human creativity.
Where do we fit in a world increasingly driven
by this kind of autonomous intelligence? That's
the big question, isn't it? A really profound
shift to think about. It really is. Well, we
hope this deep dive gave you some valuable insights
today, maybe sparked some of your own curiosity
about where all this is heading. Yeah, and if
something particularly struck you or made you
think, definitely let us know. We're curious,
too. Absolutely. Thanks for joining us for this
deep dive into the latest in AI. Until next time,
Aotiro Music.