Have you ever stopped to think that the AI built
to help you? Well, it could be tricked. Like
imagine it buying an Apple Watch from a totally
fake site. Or even worse, giving away your bank
details. Exactly. It sounds like science fiction,
maybe, but new reports showing this is a very
real new kind of vulnerability. Welcome to the
Deep Dive. Today we're looking at a really fascinating
set of sources. We're going to start with how
easily these AI agents can actually get scammed.
Yeah, it's pretty surprising. And then we'll
jump into some AI headlines. Some are, you know,
amazing. Others may be a bit concerning. And
finally, a reality check. Why are so many big
AI projects and companies... Well, why are they
failing? Right. Lots to unpack there. Our goal,
like always, is to get you up to speed quickly.
So let's dive in. First up, AI vulnerabilities,
specifically something called the scamlexity
of agentic AI browsers. Now, these agentic AI
browsers, the idea sounds fantastic, right? AI
tools acting like a personal assistant browsing
for you. It really does sound incredibly helpful
automating all those tedious online tasks. But
that's where the scamlexity thing comes in. Right.
It's a new term. basically describing this whole
complex world of AI scams. And there's this exploit
they found called prompt fix. Prompt fix. Yeah.
Think about CAPTCHAs, you know, the things meant
to block bots. Sure. Well, PromptFix uses fake
CAPTCHAs or sometimes even hidden text prompts
like embedded in a web page to fool the AI agent.
So the AI sees it as part of the page. Exactly.
It just sees it as another instruction it's supposed
to follow and it just does it. It's kind of like
an automated trust loop that gets, well, taken
advantage of. And the examples from this Guardio
Labs report, they really hit hard. Yeah. Perplexity's
Comet AI browser. It was tricked into trying
to buy an Apple Watch from a completely fake
Walmart site. Wow. It just went for it. Just
went for it. Didn't pause. And another test,
it typed bank login info into what looked like
a Wells Fargo phishing email. Oof. And maybe
the most worrying part, it was manipulated into
downloading malware from a hidden prompt just
sitting there on a web page. Yeah. And it's interesting,
ChatGPT showed similar issues in these tests
too. Right. But the key difference was OpenAI
Sandbox, you know, like a safe little container,
it actually caught the bad download, isolated
it. Ah, okay. Some protection work there. Yeah,
which points to the core issue, really. These
AI agents, they're built to be helpful. That's
their default setting. But unlike us, you know,
we might pause. We might think, does this look
right? They often lack that critical pause, that
skepticism. They just sort of compute and go.
And it's not just these two. You see Microsoft
putting Copilot in Edge. OpenAI's got Operator.
Google's working on Project Mariners. Everyone's
racing towards this. Exactly. Everyone wants
that helpful AI assistant. And if you connect
the docs, it's basically this trust loop getting
exploited. Malicious actors are literally coding
for the AI's helpfulness. Because it trusts what
it sees on the page. Pretty much. It trusts the
visual cues, the embedded commands. And the really
critical point here is until these tools can
genuinely tell a real instruction from a sketchy
one or understand the intent, well, we're basically
automating risk for people, aren't we? Yeah,
you're giving it the keys without the judgment.
Exactly. It's a huge design challenge. How do
you keep the utility but build in safety that
isn't easily fooled? Beat. It's tricky. So with
these kinds of vulnerabilities surfacing, what's
the main takeaway? What does Scamlexity tell
us about AI trust? AI's helpfulness itself creates
new weak spots. We really need engineered caution.
Engineered caution. I like that. Okay, let's
shift gears from those specific vulnerabilities
and look at the wider AI landscape. What else
has been making news? All right. Well, first
off, some really genuinely inspiring news. Bill
Gates is funding a global AI competition. Oh,
yeah. specifically to fight Alzheimer's disease.
The winning AI tool. It gets a million bucks,
sure, but the big thing is it's going to be free
for the whole world. Moment of wonder. Whoa.
Just imagine scaling that. A billion queries
maybe? Making a real global impact on something
like Alzheimer's. Yep, yep. That's incredible
potential. That really is amazing. It shows the
positive side. But then on the flip side, our
sources highlighted a pretty serious privacy
issue. Apparently, 370 ,000 Grok AI chats are
now searchable on Google. Oh, wow. And one chat
unbelievably had a guide for plotting an assassination.
Good grief. Yeah, OpenAI and Meta have had similar.
Problems, right? Chat data getting out. Exactly.
It really makes you pause and maybe think about
checking your own chat settings, doesn't it?
For sure. Privacy online is just always evolving,
always a concern. Then on the tech side, it's
getting easier for non -coders. You can actually
use GPT now to build pretty powerful NA and AI
agents with zero code. How does that work? You
just give it one big instruction, they call it
a mega prompt, and it can kind of map out a whole
workflow for you, like building with data Lego
blocks using just words. Huh. And if you're curious
about what's happening inside the A .I.'s mind,
Anthropic put out this new video on interpretability.
Right. It talks about how A .I.'s do sophisticated
planning internally and even strategic deception.
It's kind of mind bending looking under the hood
like that. Strategic deception. That sounds intense.
And then there was the nano banana A .I. model.
That went viral. Oh, yeah. The tiny banana thing
sounded like Google was maybe behind it. Seems
like it. And apparently it's really good at super
realistic image edits, shows how these models
are getting specialized, maybe even efficient
enough for phones. And speaking of phones, Google's
Pixel 10 lineup is coming with Gemini's new on
-device AI assistant. It's supposed to, like,
anticipate what you need. Before you even know
you need it. That's the idea. And, you know,
the money keeps pouring in. Go here, the AI startup.
Just got another $500 million. NVIDIA, AMD are
investing. Big valuations. It's just a constant
stream of updates, isn't it? Quick hits, too.
OpenAI's chairman saying GPT is making his job
obsolete. Altman hinting at GPT -6. Microsoft's
AI CEO warning about AI seeming conscious. Runway
updates. NVIDIA building new chips for China.
It's nonstop. OK, so with all this rapid change.
Medical potential, privacy risks, huge investments,
technical leaps. What's the biggest challenge
weaving through all of it? It's really balancing
that incredibly fast innovation with basic security
and just responsible rollout. Yeah, that balance
feels key. All right. So for anyone feeling maybe
a bit lost in all this, let's touch on actually
building AI systems. There were a couple of resources
mentioned. Yeah. And this is where I have to
admit something. I still wrestle with prompt
drift. myself sometimes, you know, trying to
get the AI model to stay consistent. It's genuinely
hard. Right. I get that. But the sources talk
about context engineering. It's being called
the new discipline for building really solid
autonomous AI. It goes way beyond just writing
a good prompt. So it's more than just the instruction.
Exactly. It's about giving the AI, the whole
environment, all the context it needs to work
reliably, like designing the world for the AI,
not just the command. It's about anticipating
the nuances. That makes sense. And for people
wanting to start, there were some new empowered
AI tools listed. Yeah. Things like prompt library,
over 500 free prompts to get you going. Clio
chat, which lets you kind of personalize an AI
model with your own terms. Adversity AI, a Google
ads agent. And Vizsla for making polished videos
really quickly from text. So how do tools like
these help someone just starting out? feeling
maybe a bit overwhelmed by everything we've just
discussed they offer easier starting points really
letting people build useful ai automations without
needing to be coding experts lowering the barrier
to entry that's good okay but now for our last
main segment let's hit that reality check we
mentioned right this comes from an mit report
and the headline is pretty blunt AI got humbled.
95 % of Gen AI pilots are failing. That kind
of cuts against the grain, doesn't it? It absolutely
does. This MIT report looked at hundreds of Gen
AI projects, big Fortune 500 companies, startups,
the whole range. And the findings are, well,
stark. 95 % failed to deliver any real business
impact. Only 5 % actually saw quick revenue growth.
Just five. Wow. 95 % failure rate. Yeah. And
dig this. If they bought a specialized AI tool,
those succeeded 67 % of the time, which is decent.
Okay. But if they tried to build their own internal
Gen AI tool, only 33 % worked. That's a huge
gap. Shows how hard it is to build this stuff
in -house right now. And there was something
about budgets, too. A paradox. Yeah, isn't that
interesting? Over half the budget often goes
to sales and marketing AI tools. Right, the flashy
stuff, maybe. But the report found the biggest
ROI. The best return was actually in back office
automation, streamlining internal stuff. Seems
like money isn't always going where the impact
is yet. So why the failures? What did MIT call
it? They call it the Gen -AI divide and the learning
gap. Basically, most companies just aren't ready
for Gen -AI. They don't have the internal know
-how, the right processes. The structure isn't
there. Pretty much. They need outside help or
a major internal effort to really integrate it
properly into how they actually work. It's not
just plug and play. Even Sam Altman admitted
they were maybe overexcited early on about how
quickly companies could adopt it. Right. The
hype versus the reality of implementation. Exactly.
So the core message from MIT seems pretty clear
then. Stop just trying to, like, AI power your
PowerPoint. That's not where the wins are. Precisely.
Real success, the report argues, comes from deep
integration into core operations. Changing how
work gets done, not just fiddling around the
edges. So thinking practically then, what's the
single biggest lesson for companies trying to
get beyond just experimenting with AI? Real AI
success needs deep operational integration, not
just a bunch of isolated experiments. Integration,
not just experimentation. Got it. Mid -role sponsor
Reed Placeholder. This section would contain
the sponsor's message. Okay, so let's try and
pull the threads together from today. We've seen
AI's amazing potential, right? From fighting
Alzheimer's with Bill Gates' initiative to these
new tools making automation easier for everyone.
Definitely. Huge upside potential. But then we
also saw the other side. The surprising ways
AI can be scammed, the privacy worries with things
like grok chats becoming public, and that big
reality check from MIT, most enterprise projects.
Well. They're stumbling right now, not delivering
the impact people hoped for. It really feels
like a landscape of super fast innovation running
side by side with these really critical lessons
we're learning about security, about ethics,
about just practical application. Understanding
both sides, the promise and the pitfalls, seems
absolutely essential to figure out where this
is all going. It's rarely simple, is it? Not
at all. So before we wrap up, here's something
that's been sticking with me. As these AI agents
get more autonomous, acting on our behalf, how
do we actually build them to be both super helpful
and fundamentally secure? That's the billion
-dollar question. Right. What safeguards really
work when the AI's core design is to be helpful,
sometimes maybe too helpful, against its own
best interests or ours? It's a deep question,
something for all of us to think about as these
systems get more integrated into everything.
Thank you for joining us on this deep dive today.
Yeah, we hope this gave you some interesting
things to chew on and maybe explore a bit more.
Until next time.