if you've ever uh gotten something from generative
AI that looked OK on the surface, but just felt
off, like it needed a ton of work. You're definitely
not alone. We're talking about work slop. It's
this AI generated stuff that kind of pretends
to be productive work. But really, it just makes
the next person down the line waste hours fixing
it. It's like this invisible tax we're putting
on ourselves inside companies. And it's a huge
tax. I mean, one source we look at put a number
on it for a, say, 10 ,000 person company. We're
talking about nine million dollars a year lost.
beat. That's just pure waste. Productivity down
the drain, basically fueled by taking the easy
digital way out. Welcome to the Deep Dive. Today,
we're going to try and cut through some of that
noise. We're using the sources you sent over
to look at the hard reality of scaling AI, trying
to figure out where it's actually delivering
genius and where it's just making these expensive
messes. Yeah, we've got a plan. First up, we'll
dig deeper into this work slop thing, the actual
cost. financially, but also psychologically.
Old trust tax idea. Then second, we'll pivot.
Look at how fast AI capabilities are actually
jumping forward. I mean, from gaming to even
warnings about the job market. And finally, we'll
tackle the big one. This potential AI energy
apocalypse. Is this massive build -out of power
infrastructure really needed? Or is it maybe
speculative hype? Right. The goal here is really
separating that hype from the... The hard reality
when you actually try to deploy this stuff at
scale. Let's get into it. So the basic idea behind
work slop, it's really just pushing the cost
onto someone else, isn't it? Someone hits generate,
maybe glances at it. Feels good because they
send something. Reductive. Right. But then the
person who gets it might spend, what, two hours
cleaning it up? They end up eating the entire
cost of that quick solution. Exactly. It's the
hidden cost of their convenience. Passed straight
to their colleague. And, you know, this isn't
rare. The data we saw suggests 40 % of employees
got work slop just last month. and maybe 15 %
of all the stuff flying around the workplace,
emails, reports, code, whatever, is basically
slop now. Scale that up, right? The estimate
was about $186 per incident per month. That's
how you get to that $9 million figure pretty
fast in a big company. This may be less like
stacking Lego blocks badly and more like, hmm,
like building software on a shaky foundation.
The code looks okay, maybe compiles. But try
adding a new feature. Everything breaks. You
face huge delays, costly rewrites you have to
do. It's unstable underneath. Okay. That analogy
makes more sense for maybe our technical listeners.
But I still have to push a little on that 9 million
number. Is there a chance that's consultants
kind of hyping the problem to sell solutions?
Or is it a solid measured cost? That's a really
important question. And yeah, consultants are
always going to sell solutions, right? But this
specific calculation seems based on... documented
time sinks. Things like average hourly wage times
the hours people report spending just verifying
or rewriting this AI output. So you can quibble
with the exact dollar amount maybe, but the cost
is definitely real. And it's not just money.
It's the human side. This trust tax idea. You
keep sending out subpar AI stuff and people notice.
Over half, 53%, they get annoyed. Okay, fine,
but worse. 42 % start seeing the sender as less
trustworthy. And 37 % literally think they're
less intelligent. Ouch. Your credibility just
tanks. That's the real danger, systemically.
And that happens when companies just say use
AI everywhere, right, without clear rules. So
people become passengers just using it to dodge
work instead of being pilots who use it to genuinely
amplify their own skills, their creativity. I'll
admit, I still wrestle with this temptation myself
sometimes. You know, prompt drift, getting that
easy answer that looks good initially, but, you
know, deep down, it's going to need five more
rounds of checks and edits before it's actually
usable. Good point. Let's quickly define prompt
drift for everyone because it's key to work slop.
It's basically when the AI. This output quality
gets worse over a long chat or lots of edits.
Like the model loses the plot, loses context,
and just starts spitting out nonsense. So given
this trust problem and people acting like passengers,
what's the single most critical thing leaders
need to do? to shift people towards being pilots.
Leaders need to prioritize critical thinking
and verification over just speed. Okay, so that
work slop issue, that's one side of the coin.
It's misusing a powerful tool, basically. But
the irony is while we're struggling with that,
the tech itself is just leaping forward exponentially,
achieving things that are genuinely, you know,
genius level. Oh, the pace is wild. Look at connectivity.
ChatGPT connectors, which are just... Tools linking
language models to other software for automation.
They now work with over 500 apps. Automation
isn't some future thing. It's right there. Plug
and play, pretty much. And the models are getting
smarter, deeper. Google's Gemini AI, for instance,
it's showing how it can think through a search
query, not just matching keywords, but producing
these detailed summaries. Better reasoning. And
then there's this. This moment that really makes
you pause. GPT -5 codex. It apparently one -shotted
Minecraft. Just think about that. One prompt
in plain English. And it builds a whole 3D world.
Textures, water, physics, the works. Whoa. I
mean, imagine scaling that ability. Solving incredibly
complex real -world problems with that kind of
power. That's huge potential. Kind of terrifying,
too. Yeah, but that power surge has a flip side
as it filters into daily life. You see meta using
AI and Facebook dating now. trying to optimize
human connection. And in the really dark end,
those hyper -realistic AI -generated murder videos
made with VO that showed up on YouTube only got
taken down because a journalist flagged them.
The tech moves faster than our ability to manage
it or even moderate it sometimes. And that speed
crashes right into the economy. Which brings
us to that stark warning from the Wharton professor
you mentioned. Their research suggests this kind
of exponential leap like the Minecraft thing
shows is possible could make a lot of human jobs,
especially white -collar ones, obsolete and fast.
The timelines seem to be shrinking way faster
than people thought. Right, so we need to ask,
does this sheer speed of deployment building
a whole game world instantly mean job losses
are coming much, much sooner than most predictions?
Yeah, seems like it. The exponential leap demands
we start preparing now for big job shifts. Okay,
we've covered the software side. The slop and
the genius. Let's switch gears to the hardware.
The physical infrastructure. Specifically, the
massive energy needed to actually run these things.
Oh, it's staggering. One modern AI data rack.
It pulls the same power as maybe 80 to 100 average
homes. Compare that to a traditional server rack,
maybe three homes worth of power. So this need
for huge computation is driving these gigantic
energy requests to utilities everywhere. We're
talking gigawatts. But here's the tension, right?
This predicted AI energy apocalypse. Maybe it's
not all based on solid confirmed need. Maybe
there's a lot of speculative hype driving overbuilding.
The signs of potential inflation are definitely
there. Since this whole AI boom kicked off, proposals
for new natural gas power plants are up 70%,
mostly earmarked for future data centers. In
the U .S. Southeast, the demand utilities are
forecasting is four times higher than what independent
researchers think the actual need will be. Four
times. Four times. That's a massive difference.
How does that even happen? Why are utilities
seemingly accepting forecasts that might be wildly
inflated? What's the mechanism? Well, it seems
like a mix of things. System incentives, maybe
some regulatory capture. Developers put in these
huge requests often before they even have the
money lined up or firm customers. Speculative
requests. Utilities often accept them, perhaps
to lock down the rights to build generations,
secure land, get permits. They call it nameplate
capacity. It's like a land grab for future power
needs based on potential, not guaranteed use.
And apparently even utilities admit privately
the real usage might end up being, you know,
three to five times smaller than what's on paper
right now. OK, so if that high bubble bursts
or even just shrinks significantly, who pays
for all that potentially unnecessary gas infrastructure
they build? It's going to be the consumers, right?
Through higher electricity bills, the costs get
socialized. Exactly. Stranded assets paid for
by ratepayers and the environmental cost. Also
huge. One example cited was a metadata center
project down in Louisiana. If all that speculative
demand actually happens, it could lock in over
100 million tons of extra carbon emissions just
from that one project's power needs over its
life. So this rush to build feels like it creates
a major policy clash. It lines up nicely with
political agendas favoring fossil fuels, but
it runs directly against stated goals like getting
to a carbon -free grid by, say, 2035. It feels
like we're planning a fossil fuel future based
on maybe a ghost demand. Yeah, it's a real paradox.
So if utilities know they might be over forecasting
demand by potentially 4X, what's the single biggest
systemic risk here for energy policy for all
of us consumers? Consumers risk paying for unnecessary
fossil fuel plants based on inflated AI hype.
Okay, let's try to pull the threads together
from this deep dive. What we're seeing is this
fundamental AI paradox, right? On one hand, it's
creating huge new inefficiencies inside companies
that work slop, costing millions. But on the
other hand, it's achieving these incredible genius
level things that could totally reshape the job
market. Yeah, the connection seems to be this
deep misalignment everywhere. Misalignment between
the tools capability and how it's actually being
applied. That's the whole passengers versus pilots
thing causing the slop. And then this huge misalignment
between the real energy needs right now versus
this potentially overblown. speculative infrastructure
build out based on hype. It feels like, based
on the sources, we're building this massive energy
future based on the promise of AI, what it might
consume someday. But maybe not on its proven,
confirmed necessity today. So here's a provocative
thought for you to chew on this week. Thinking
about that work slot, what existing human institution,
maybe a big government agency, a traditional
industry, maybe even a university, what would
be the first one to really start crumbling under
the weight of all that AI -generated inefficiency?
That is a fascinating and slightly unsettling
thought exercise. So your call to action this
week, just be mindful. Pay attention to the AI
-generated content you encounter. Ask yourself,
is this saving time? Is it quality? Or is it
slop? Are you seeing genius? Or just more digital
noise? Thanks for joining us for this deep dive
into AI's hidden costs and its undeniable power.
Otiro music fades in.