That $200 monthly AI subscription you might be
using, well, it could actually be a massive problem.
Oh, absolutely. It's a huge issue. Yeah, because
it could be costing OpenAI up to $14 ,000 a month.
Right, for a single user. It's wild. That is
the brutal math behind this current AI boom.
Welcome to the Deep Dive. We have a massive stack
of research today. And it really paints a fascinating
picture. Okay, let's unpack this. Our mission
today is to explore the hidden economics of AI
subscriptions. We're looking at how massive compute
costs are driving this crazy explosion of cheap
open source tools. Yeah. And we're also covering
how geopolitical bands are radically reshaping
the whole global AI landscape. It's all connected.
So let's start with that brutal financial reality.
It really underpins the entire AI ecosystem right
now. It does. It's the foundation of everything
we're seeing. We all love a flat rate subscription.
You pay 20 bucks or maybe 200 for a pro tier
and you just use it. Right. It feels like an
all you can eat buffet. Exactly. Like an all
-you -can -eat buffet. But if you actually max
out your theoretical limits on a $200 ChatGPT
Pro plan... It costs OpenAI roughly $14 ,000.
$14 ,000 in API compute just to serve one user.
Yeah. And API compute is just the server power
needed to process AI requests. It's the raw electricity
and hardware. And Anthropic's top tier is not
much better. Their system caps out around $8
,000 in token costs. The break -even points are
actually shocking. Yeah, I was looking at this.
OpenAI starts losing money at just 11 .4 % utilization.
Wow, 11%. Just 11 .4%. And Claude hits its break
-even point at a 20 % utilization rate. So if
you use it even a quarter of the amount you're
allowed to, they lose money. They're just bleeding
cash. Yeah. So you have to ask, what drives this
massive cost? Because human typing questions
can only work so fast. Right. A person can't
type $14 ,000 worth of prompts. The real culprit
here is agent workflows. Let's define that quickly.
Sure. Agent workflows are AI systems that independently
loop and execute multi -step tasks. I have a
bit of a vulnerable admission here. I still wrestle
with my own token usage. when trying to set up
agent loops. Yeah. It gets out of hand so fast.
Oh, yeah. You are definitely not alone there.
I mean, I looked at my dashboard last week and
was just staring at the bill. It's because an
agent breaks a task down. If you ask it to research
a company, it doesn't just write a summary. It
breaks that into 20 subtasks. It searches the
web. It reads a PDS. It scrapes a database. And
it feeds every single step back into itself.
So it's constantly talking to itself. Exactly.
It loops infinitely until it solves the problem.
And every single loop costs money. Going back
to that buffet analogy, it's like an all -you
-can -eat buffet where one guy just sits down
and eats the entire kitchen. Where he be? The
restaurant cannot survive that. No, they can't.
And we saw this happen. One firm reportedly burned
through $500 million in a single month on Claude.
Wait, really? Half a billion dollars? Half a
billion, yeah. How is that even possible? Don't
they have kill switches for enterprise contracts?
Well, you would think so, but they didn't cap
internal employee access, so everyone was running
complex multi -step queries. Wow. An employee
asks for a massive data sort, the agent hits
a glitch, and it just loops 10 ,000 times in
the background. That is just financially terrifying.
It is. And it proves you don't need a massive
frontier model for every task. You don't need
quantum -level AI. to summarize a Tuesday meeting.
How can frontier models survive if the business
model is this fundamentally broken? They'll likely
transition from flat consumer rates to strict
metered enterprise usage contracts soon. Unlimited
subscriptions die, replaced by strict pay -as
-you -go corporate meters. Two sec silence. So
because these frontier models are unsustainably
expensive, the market is fracturing. Oh, completely.
It is urgently fracturing into chipper, highly
efficient alternatives. Let's talk about that
push for efficiency. Startups are ditching expensive
APIs, right? Yeah, they're moving to dirt -cheap
alternatives like DeepSeek. And they're saving
up to 95 % on compute. 95%. That is the difference
between life and death for a startup. Absolutely.
And we're seeing specialized models emerge too.
Look at the new Kimi K2 .7 code. Right. The coding
model. Yeah. It beats Opus 4 .8 on tool use,
and it uses 30 % fewer reasoning tokens. And
reasoning tokens are just tokens spent thinking
before typing an answer. Exactly. Less thinking
time means lower cost. Plus, Kimi can be self
-hosted for entirely free. So no cloud fees at
all. None. And the big players are scrambling
to adapt. OpenAI just launched a $150 million
partner network. To turn AI plans into actual
workflows. Right. And Anthropic is testing something
called Conway. I saw this. Whoa. Imagine scaling
to an always -on agent environment like Conway.
The possibilities are staggering. It really is.
Conway is a standalone cloud agent environment.
It uses webhooks and active Chrome browsing.
Just to clarify, webhooks are automated messages
apps send when something happens. Exactly. And
this isn't just for big enterprise either. We're
seeing a massive explosion of consumer tools.
Like Slashy. Yeah. Slashy handles AI email triage.
It writes in your voice and makes sure you don't
miss follow -ups. And Tastelab. I thought this
was fascinating. Oh, Tastelab is incredible.
It extracts a website's design DNA. It pulls
the hex codes and typography directly into a
template, right? And there's Permute, which is
a universal media converter. And Athenic 2 .0.
Athenic is the data analysis agent. Right. It
basically replaces a junior data analyst. It
ships automated dashboards and reports. Are we
moving toward a future of a million micro models
instead of one giant god model? Definitely. A
swarm of hyper specialized local models is far
cheaper than one expensive monolith. We trade
one expensive supercomputer for an army of cheap
digital interns. Mid -roll sponsor placeholder.
So it's not just cost driving people away from
these big... proprietary models, it is also access.
Geopolitics is playing a massive role here. It
really is. Geopolitics is forcing the world to
build around US tech giants. I want to maintain
complete neutrality here and just report the
facts. Sure. Let's look at the actual events.
US export restrictions recently forced Anthropic
to shut off Fable 5 and Mythos 5. Yeah, that
happened incredibly fast. Anthropic is actually
rushing staff to Washington, D .C. right now.
They're claiming the security risk was overstated.
What's fascinating here is the global ripple
effect this caused. Bans didn't slow the competition
down. No, they didn't. India is now actively
rethinking its dependence on foreign models.
Right. They are having a serious debate. Do they
lean into open source or do they build sovereign
AI? Sovereign AI simply means an AI model built
and controlled entirely by one nation. Exactly.
And Europe is taking the sovereign AI route.
They are heavily backing Mistral AI. Mistral
is raising 3 billion euros at a 20 billion euro
valuation. Which is huge. Though, to be fair,
OpenAI and Anthropix still lead the global market.
For now. But the massive breakthrough came just
two days after Fable 5 was banned. Yeah, this
was wild. China's Zippo AI stepped in. They released
GLM 5 .2, and they released it as a fully open
source model under a permissive MIT license.
That is the key detail. An MIT license means
there are zero regional restrictions. Anyone
can use it. And GLM 5 .2 absolutely dominated
the benchmarks. It hit number one on BridgeBench.
Right. It scored 100 .0 on BS and a 42 .8 on
reasoning. So it actually beat the banned Fable
5 model. It did. And it runs at 300 tokens per
second. That is incredibly fast. And it does
that at one tenth the cost. of the big proprietary
models plus it has a 1 million token context
window a context window is an ai's short -term
memory limit during one conversation right so
you can dump an entire massive code base into
it at once we saw real world tests immediately
the platform z .ai rolled it out instantly yeah
developers got their hands on it right away and
it coded a 925 line svg clock from scratch it's
pure math and visual logic it also built a functional
3d penalty kick game And a mini spreadsheet.
All running flawlessly. If export bans immediately
result in highly capable open source clones,
do these restrictions actually work? Honestly,
they seem to act as a catalyst that just supercharges
global open source competition instead. Export
blocks fail to contain tech and only force competitors
to innovate faster. Two sec silence. So while
superpowers fight over these massive models,
AI is quietly rewriting our daily realities.
It's happening on a deeply personal level now.
It really is. It's both taking jobs and managing
our personal lives. The job data is pretty grim
right now. AI layoffs are rising. Nearly 120
,000 tech workers have lost jobs this year. Wow,
that's a massive shift. But there is a very surprising
statistic here. Almost 75 % of unemployed Americans...
Never apply for unemployment benefits. That is
so high. People just quietly absorb the loss.
Yeah. And this brings us to what we're calling
the personal assistant paradox. Right. Because
while AI is displacing tech jobs, people are
leaning on AI to manage their daily friction.
Exactly. For example, there are these 15 chat
GPT problems going viral right now. Oh, I saw
these. People use them to dissect their paychecks.
They use the AI to find highly specific ways
to squeeze savings out of their budget. It's
crazy. The same tech disrupting the job market
is what people use to survive the disruption.
Right. So what does this all mean? We are seeing
the tension of macro -level job losses mixed
with micro -level convenience. Yeah. And there's
a lighter side to this convenience, too. It's
not all just budgeting and layoffs. True. ChatGPT
now has a dedicated World Cup 2026 page. You
can track live scores and matches. And you do
it without ever leaving the chat interface. It's
all just seamlessly integrated into your conversation.
It becomes the primary lens for how you interact
with the internet. It's the ultimate trade -off
of the AI era, exchanging job security for extreme
personal convenience. It really seems like we
are trading long -term career stability for hyper
-efficient... Automated daily task management.
Trading lifelong career stability for incredibly
smooth daily task automation. Exactly. Let's
briefly synthesize the major through line of
this deep dive. The era of relying on a few massive,
expensive, centralized AI models is fracturing.
It is absolutely breaking apart. Between the
massive token burn rates we discussed, the $14
,000 underlying cost per user, and geopolitical
export bans. Right. The global developer ecosystem
is just actively routing around all of those
roadblocks. We are entering an era of incredibly
cheap, hyper -capable, open -source agents that
basically live everywhere. It's a completely
decentralized future. I want to leave you with
a final lingering question to ponder. If government
roadblocks only motivate the global community
to build smarter, faster, and completely open
source models with zero regional restrictions,
are we rapidly approaching a point where AI regulation
is no longer a legal question, but a technical
impossibility? Two sex islands. Thank you for
joining the Deep Dive today. Keep questioning
the information around you. Out to your own music.