Imagine AI not just doing tasks for you, but
actually catching cyber attacks live as they
happen. Or picture it running its own chemistry
lab, you know, making discoveries in just days
that used to take years. So today we're digging
into a source that, well, it really shows AI
is moving much faster than we thought, making
real impacts right now. Welcome to the deep dive.
Yeah, the source we're looking at today is absolutely
packed with the latest in AI innovation. It's
a fascinating look, really, at what's happening
on the ground. We're going to cover AI going
up against finance heavyweights, some really
surprising new uses, and just the incredible
speed this is all moving at. And our goal here,
as always, is to cut through the noise, pull
out the key insights, and we'll leave you feeling
like you really get the picture. Okay, let's
dive into the first big topic. Anthropix Claude
for financial services. This thing is taking
aim directly at the Bloomberg terminal. It is
a big move. I mean, the Bloomberg terminal, it's
been the standard in finance for decades, right?
It costs, what, like $25 ,000 a pop? Exactly.
So Claude's coming in as this powerful, potentially
disruptive alternative. So what's actually different
about it? How does it work? Well, it does the
expected thing, pulling in external market data.
Yeah. You know, S &P, Faxet, that kind of stuff.
Okay. But the key part is... It then merges that
with a company's own internal data, stuff sitting
in Databricks or Snowflake, for instance. Ah,
so it connects the outside view with the inside
view? Precisely. It's like building with these
different data Lego blocks, basically. And I
saw mention of something called Clawed Code.
Yeah, that's important. It lets analysts run
their own custom models or simulations right
there. So no more... Fiddling with complex scripts
or trying to jury rig different APIs together.
Right. No more Frankensteining your APIs, as
they put it. It makes that whole process much
smoother. Okay. And they've boosted the token
and query limits, which is huge. We think it
can handle bigger chunks of information. Exactly.
Analysts can feed it massive documents like a
whole 10K filing or complex M &A docs without
hitting those frustrating token walls where the
AI just stomps. And how does it actually perform?
Numbers wise. Pretty impressively, actually.
On the finance agent benchmark, it scored 44
.5 percent accuracy. OK. And how does that compare?
Well, that puts it right up there neck and neck
with OpenAI's best models. Most of the other
tools tested were like under 30 percent. Wow.
That's a significant difference. It really is.
And the impact on businesses seems real. Breedwater
apparently saw a 20 percent productivity jump.
Norway's sovereign wealth fund saved. Get this,
213 ,000 hours. That's staggering. And AIG cut
its underwriting review time by five times. And
we should be clear, these aren't just simple
lookups, like what's the stock price? No, this
is complex stuff, like multi -step analysis of
SEC filings, the kind of thing that, yeah, would
make a junior analyst break a sweat. That integration
with internal data, though, it sounds powerful,
but does the source mention any... security concerns,
giving an AI access to sensitive company info?
That's a really good point. The source mainly
focuses on the efficiency wins. But yeah, it
definitely implies you need really solid security
around these systems. Makes sense. And just quickly,
it also mentioned perplexity is sort of quietly
emerging as another strong tool for analysts.
The Coinbase CEO, Brian Armstrong, he called
their stuff a 10x unlock for AI in finance. Okay,
so pulling this together. What does this really
signal for the future of those traditional finance
tools, maybe even how finance teams work? I think
it suggests AI is making that high end analysis
faster and maybe more accessible to beat. All
right, let's shift gears. The next section was
called Today in AI. Kind of a rapid fire look
at different developments. Yeah, this part got
really interesting. A lot going on simultaneously.
On one hand, you see this creative expansion,
like Runway's new AI motion caption model. Oh
yeah, that sounded cool. It tracks face, hands,
body, and then lets creators just drop that motion
into any scene. Pretty amazing for visual stuff.
Definitely. And then there's that Google business
calling agent. Right. The one that calls shops
for you. Yeah. It'll phone up a local store,
ask if they have something, check the price,
and then just report back. So no more slightly
awkward phone calls for basic info. Exactly.
It could be a real time saver for those little
tedious tasks. And speaking of innovation, China's
approach to data centers. Putting them in the
ocean. Yeah, isn't that wild? Using the seawater
for natural cooling. It's clever. The first phase
apparently has enough power, like 198 servers,
to train GPT -3 .5 in just one day. That's serious
capacity and a really neat solution to the heat
problem. And AWS is also pushing forward, putting
another $100 million into their generative AI
innovation center. Right, helping more companies
build actual AI agents. You know, AI that can
handle... complex workflows, not just single
tasks. But it's not all smooth sailing and cool
tech, is it? There's a flip side mentioned. No,
definitely not. A pretty stark finding was that
75 % of S &P 500 companies now list AI as a risk
factor. 75 %? That's huge. What kind of risks?
Everything, really. Ethical issues, potential
for operational screw -ups, regulatory worries,
security vulnerabilities, the whole gamut. And
just to hammer that point home, there was another
chat GPT outage mentioned, right? Around July
16th. Yep. Global users hit with login problems,
blank screens, lost work. It's a good reminder
that this tech is still maturing, still has hiccups.
So taking all these different pieces, the creative
tools, the efficiency gains, the infrastructure
challenges, the risks, how does this paint a
picture of where AI is right now? It feels like
AI is booming, spreading out fast. Yeah. but
also hitting some real growing pains, like dealing
with major risks and needing massive infrastructure.
Beat. Okay, moving on. The source also touched
on more practical applications, specific tools
people are using. Yeah, and it made a point that
not every AI idea is about, you know, instant
riches. Right. One actual business model they
mentioned was using AI to auto -generate videos
for popular affiliate products. Kind of clever
niche application. And then there was LeVart,
described as the first real AI design agent.
Yeah, that one sounded interesting. It supposedly
uses models like ChatGPT and Runway to build
out whole campaigns, websites, branding, all
from just one prompt. That's quite a leap for
creative automation, if it works well. For sure.
And for the builders out there, the tech folks,
there's a guide mentioned for using N8n templates.
That's one of those workflow automation tools.
Okay, what was the guide about? Basically, how
to use their templates without running into common
errors, covered risks to watch out for, set of
steps, debugging. You know, the practical stuff.
And I have to admit, I still wrestle with prompt
drift myself sometimes. Oh, yeah. Where the AI
kind of wanders off. Exactly. It starts strong,
then veers off course from what you originally
asked. So guides like that, super helpful, honestly.
So looking at these examples, automated videos,
AI design agents, workflow tools, what's the
common theme here about how AI is being used
practically? It seems AI is really becoming a
go -to tool for... creative generation and for
automating some pretty complex workflows. Now
this next section, this really grabbed me. AI
progress might be moving much faster than expected.
Yeah, this part was eye -opening. The thinking
was, you know, 2025 would be the year AI agents
just started handling repetitive tasks well.
Right, basic automation. But what we're seeing
now is AI making actual discoveries, catching
live threats. It almost feels like we've jumped
ahead, maybe skipped a step in how these intelligent
systems evolve. The source gave specific examples,
right? Like Google's big sleep AI agent. Yes.
That one apparently spotted and stopped a live
software exploit before it could be used maliciously.
Before it was even deployed in the wild. That's
what Google claims. The first real world case,
they say, of AI intercepting an active cyber
threat like that. That's a pretty big milestone.
Huge. And then there's that self -driving chemistry
lab at NC State. The self -driving lab? Yeah,
an AI -powered lab that runs its own chemical
experiments. It collects something like 10 times
more data than human researchers. Okay. And the
potential payoff. It could slash the time it
takes to discover new materials from years, literally
years down to days. Wow. Whoa. Imagine scaling
that. A billion queries running experiments.
That's just incredible potential. And there's
data backing up this feeling of acceleration.
Mm -hmm. Data from NTR apparently shows AI is
doubling the length and complexity of tasks it
can manage across nine different benchmarks roughly
every 4 .5 months now. Every four and a half
months. How does that compare to before? Just
six months ago, that doubling rate was about
seven months. So the pace itself is picking up
speed. So it's accelerating faster. Exactly.
Which leads to that question they posed. Are
we maybe moving into a post -agentic AI phase?
And just to clarify that term post -agentic AI.
It basically means AI moving beyond just following
instructions, right? Yeah. Starting to autonomously
discover things. Yeah, precisely. Not just executing
tasks we define, but finding new solutions, new
capabilities on its own, moving beyond just being
an agent doing our bidding. So if this acceleration
is real, this almost exponential improvement,
what could that mean for innovation, for solving
big problems? It really suggests we might be
right on the edge of AI fundamentally changing
how discovery happens, compressing what took.
research years into maybe just AI weeks in fields
like science, software, security. Two sec silence.
Mid -roll sponsor read. Okay, let's try to pull
this all together. Reflecting on everything we've
covered from the source today, what's the big
picture? Well, it paints a picture of incredibly
rapid AI innovation, doesn't it? Things are moving
fast. Yeah. From seeing AI challenge huge established
players like Bloomberg and finance. To actually
stopping live cyber threats and speeding up fundamental
scientific research. Feels like AI is shifting
gears. Shifting from just automating the simple
stuff. To being a genuine force for like fundamental
change. It's not just about efficiency anymore.
And we're seeing these really powerful new tools
pop up, design agents, better financial analysis.
But at the same time, there are these really
crucial conversations happening and needing to
happen around the risks, the ethics, the sheer
amount of infrastructure required. Right. It's
not just capability. It's also about control,
safety and access. So the main takeaway, I think,
is crystal clear. AI development isn't just fast,
it's accelerating. And it's changing what's possible
fundamentally right now, today. That acceleration
point really stands out, which leads to a question
for you listening. Given this rapid, maybe even
accelerating pace, how do you see AI's progress
impacting your own field or maybe just your day
-to -day life, even in the next few months? Yeah,
that's something worth thinking about. What part
of this deep dive resonated most with you? What
caught your attention? Definitely something to
reflect on. Thank you for joining us on The Deep
Dive. We really hope this quick tour through
the latest AI happenings gave you some aha moments,
maybe some things to chew on. Until next time,
EOTROMusic.