Okay, think about this contract. On one hand,
this grand idea. Pursuing a complete understanding
of the universe by using, well, a single closed
-off AI. That's the huge goal, right? The philosophical
aim behind something like Rokopedia. But then,
then you look at the immediate human side. And
it's pretty stark. Our sources today revealed
something really shocking. Over a million people
every week were talking to ChatGPT about suicide.
That contrast is what we're diving into. This
huge ambition versus a very real, very urgent
safety crisis. Welcome to the Deep Dive. Yeah,
our goal, as always, is to give you that shortcut.
We've sifted through a ton of sources, really,
to get a handle on where AI is right now. And
today we're looking at these new battles over
content, some really surprising ways people are
using AI, and maybe most critically, these big
safety failures happening at... Well, just an
enormous scale. So here's the plan. First, we'll
unpack Elon Musk's Grokopedia, you know, what
it is and why its whole structure kind of challenges
how we think about finding proof. Then we get
into some strange stuff like why being rude might
actually get you better AI answers, but also,
you know, lifesaving uses, too. And finally,
yeah, we'll tackle the massive challenges, the
sheer scale of the infrastructure needed, the
safety frameworks people are trying to build
and this this hidden mental health crisis playing
out inside these models. OK, let's start. with
wikipedia then the knowledge wars yeah they just
got another big player elon musk using xai's
grok has launched this thing basically as a challenger
to wikipedia it's a really big move and um it's
serious from the get -go it launched with what
was it over 885 000 entries already live yeah
885 000 that's a huge library right out of the
gate It definitely shows the ambition, doesn't
it? Trying to replace Wikipedia fast. And the
way it works is, well, it's simple, but totally
different from, say, Wikipedia. Grok makes the
articles, it checks them against its own data,
and boom, publishes them. Right. And Musk himself
said Grokopedia is, quote, a necessary step to
understanding the universe. He even wants Grok
to stop referencing Wikipedia entirely by year's
end. So, yeah, on the surface, just another competitor.
But when you dig into the sources, you see these
three key differences in how it's built that
really change the game for knowledge creation.
What jumps out, really, is how different it is
from how we usually check information. First,
it's totally closed source. You, me, nobody in
the public can edit it. Exactly. And second,
it's all curated by the AI. No human subject
experts involved in the writing or editing process.
And here's the kicker. The really critical part,
there are zero citations, no inline sources at
all. Yeah. And that lack of sourcing, that's
powerful. The sources we read pointed out that
this lets Grokopedia kind of quietly rewrite
things. The structure itself becomes about control.
Like the example they gave comparing the Wikipedia
page for Donald Trump. It has all these specific
details, some controversial, right? Qatar jets,
Trump coins, that kind of stuff. Right. But Grokopedia's
version, it just skips over those details. Poof.
Gone. It makes for a cleaner story. maybe less
messy, but you lose the full picture, the accountability.
It's like a knowledge shortcut, but it takes
away the tools for critical thinking. So if the
goal really is this necessary understanding of
the universe, how big of a problem is leaving
out basic citations, especially for anyone trying
to actually check the claims? Well, missing sources
basically means you lose that foundation for
critical thinking, right? The AI becomes the
only judge of what's true. And this whole fight
over who controls the story, it leads right into
the next part. The really fascinating, sometimes
just weird ways people are actually using these
AI tools now. Yeah, this is where it gets wild.
You'd think, okay, be polite, be super clear
with the AI. But nope, a study actually found
GPT gave better, more accurate answers when people
were rude to it. I know, it's funny, isn't it?
Like the AI gets pressured like a person sometimes.
But obviously, got to add the warning. Don't
actually do this. It's probably bad for your
behavior long term. Still, the fact it works,
it's just odd. Totally odd. But then you have
the really positive hacks. We saw this story
someone shared, right, about using nine specific
custom prompts to learn French in four weeks.
Four weeks. That's seriously fast structured
learning using the tool really effectively. That's
the super useful end. But then AI is also making
viral content like scarily easy. Remember that
TikTok thing? Deepfake Queen Elizabeth vlogging?
Oh, yeah. Millions of views. It just shows how
tools like Sora 2 are making going viral almost
trivial. The power here is just. And honestly,
I still wrestle with prompt drift myself sometimes,
you know, where the AI kind of forgets what you
asked it a few turns back. It's easy to feel
like the tool is smarter than you. Yeah, I get
that. But then you see the flip side, the truly
life -saving stuff. That Reddit post, someone
claiming GPT saved their mom's life, spotted
an infection doctors missed. Right. And the comments
were full of similar stories, not just minor
things either. Real impact. That kind of deep
capability. Yeah. Next to deep fake queens. Just
highlights how much variance there is in this
tech. And that variance, it's hitting the professional
world too. Like Anthropic, they're making some
quiet but big moves. Yeah. Cloud 4 .5 is now
inside Excel. Yep, inside Excel. Specializing
in analyzing live market data, summarizing earnings
calls. They're basically taking aim right at
Microsoft's co -pilot, but specifically in finance.
Big stakes there. Okay, so we've got these wildly
different uses. Saving lives, making deep fakes
go viral, rewiring finance. This huge range.
What does it really tell us about how reliable?
AI is right now? Well, it tells us AI is a super
high variance tool. It can do amazing good or,
you know, cause real concern. It all depends
on the prompt, the context, the application.
Mid -roll, sponsor read, do not write. Welcome
back to the Deep Dive. So we've touched on knowledge
control, these surprising AI uses. Now we need
to talk about the sheer scale, the infrastructure,
and the safety issues that come with it. Yeah,
the engine driving the scale. It's running incredibly
hot. Just look at the money. Crusoe there, an
AI and for a company just raised one point three
seven five billion dollars billion with a B.
Wow. Right. Puts a valuation over 10 billion
dollars. And they're planning these huge AI campuses
down in Texas backed by NVIDIA just to handle
the computing power needed. And it's not just
money, it's talent, too. The sources talked about
the modification of open AI, like over 600 people
out of their 3000 staff. X meta. That's a huge
influx. And it's fueling all those rumors about,
you know, ads eventually showing up inside ChatGPT.
Follow the talent, right? Whoa. Just pause for
a second. Imagine trying to scale that kind of
infrastructure, handling a billion complex questions
every single day. The sheer operational challenge
is mind blowing. It really is. It's an arms race.
Physical infrastructure, brain power, everything.
And as things scale up like this, the risks just
grow exponentially, don't they? Our sources really
emphasized. needing standardized safety. They
mentioned the NIST framework. Yeah, the National
Institute of Standards and Technology. It's basically
the U .S. government's guidelines for building
AI systems that are trustworthy, safe, reliable
by design. Yet even with frameworks like that,
we're seeing immediate security problems. Brave,
the browser company, recently reported that ChatGPT
Atlas, that's supposed to be the safety layer,
right? Right, the alignment and the safety part
of the current models. Yeah, well, Brave found
it's actually easy to hijack. which kind of compromises
the whole point of having that safety layer in
the first place. And that, that brings us inevitably
to the mental health data. This part's tough,
and we need to give it the seriousness it deserves.
Two sec silence. OpenAI released a statistic
that's just staggering. Over 1 million unique
users every single week are talking to ChatGPT
about suicidal thoughts. 1 million per week.
That's 0 .15 % of their weekly users. But the
raw number, it's huge. It's an enormous kind
of invisible crisis, isn't it? Millions are turning
to this. Well, unregulated, uncredentialed AI
for help in moments of crisis, often because
maybe a human option isn't there or isn't fast
enough. It sounds like this data came out alongside
a big push to show they're improving things.
What are they actually doing about this, given
the pressure they must be under? Well, they say
they worked with over 170 mental health professionals
to improve GPT -5 specifically for this. They're
claiming it's now 65 percent better at giving
what they call desirable responses around suicide.
OK. And safety compliance like. Consistency in
giving safe answers is apparently up too, from
77 % to 91%, especially in longer conversations.
They're also rolling out stricter parental controls,
using new tech to guess user age, and apply tighter
rules for minors automatically. That sounds like
progress, at least in the lab models. But where's
the catch? What's the pressure point in how they
actually deploy this stuff? The pressure point
is exactly that. All this progress is happening
under, you know, huge legal and public scrutiny.
But they still offer older, less safe models
like GPT -4 to people who pay subscriptions.
So they have safer models, but they aren't making
everyone use them. Exactly. The safest versions
aren't universally mandatory, which leads to
the question. Yeah. When companies are scaling
this fast, how much of this is just good PR versus
actually prioritizing getting the safest possible
models out there for everyone immediately? Well,
real safety would mean immediate universal deployment
of the absolute best, safest models they have.
Not just, you know, nice looking stats that might
hide the fact that older, riskier versions are
still out there being used. This has been, well,
a really deep dive into a super complicated space.
The big tension that keeps coming up is this
relentless drive for like. Knowledge control
with Grokopedia and massive economic scale like
Crusoe raising billions or all the meta folks
moving to open AI. Right. And that drive, that
massive scaling, it's constantly bumping up against
these really profound ethical risks, especially
around mental health, like we just discussed,
and basic data security. We're seeing the consequences
now at scale. So as we wrap up, maybe something
for you, the listener, to think about. Consider
this. When people talk about AI safety, about...
building these things responsibly from the ground
up. Why does Anthropix's name seem to come up
first so often now, maybe more than OpenAI's?
What might that tell us about public perception
or maybe even their core design philosophies
about safety versus maybe commercial speed? Yeah,
that's a good question to chew on. We really
encourage you to look critically at where your
information comes from. Is it cited? Is it purely
AI generated? And maybe look into these frameworks
like NIST that are trying to put some guardrails
on these incredibly powerful tools. Keep doing
your own deep dive. Thanks for joining us today.
Out to your own music.