I tried GLM 5.2 and it blew my mind. By
the end of this video, you should know
where GLM 5.2, an open-source model, can
be clawed, where it can safely replace
an expensive model, and where switching
models is a bit of a trap because you're
not replacing a model call. You're
actually replacing a whole work system.
And that's the thing I want to draw
through in this video. So, let me start
at the beginning here. GLM 5.2 did not
fake impress me. It actually impressed
me because it's not just cheap, and it's
very cheap to run on the cloud, it's
free if you set up your own servers, and
for a lot of normal work, it's
incredibly good. It's It's often better
than Claude. And when I say normal work,
I mean the fat middle of everyday AI
tasks, right? So, if you're setting up a
brochure site for a client, if you have
a PowerPoint outline, it's a pretty
standard deck. For a first pass copy,
routine synthesis, for coding tasks that
are tackling familiar problem types in
coding, these are tasks with familiar
shapes, with lots of examples, with
outputs that a human can check quickly.
The nerdier phrase for this is that this
is the middle of the distribution work
for AI. In other words, what you are
getting is what someone has tried with
models millions of times before, where
the answer pattern is pretty normal, and
the output is pretty easy to inspect.
How many different brochure sites have
you seen, right? In that world, GLM 5.2
is incredible. It's fast, it's cheap,
it's easy, and it's extremely high
quality. It's higher quality than
Claude. And a lot of those tasks, I
don't think it's honest to say it's just
good enough. I think it's It's more
accurate to say this is the best model
in the world at those center of
distribution kinds of tasks, especially
ones where front-end taste is important.
And so, this is not a video about GLM
5.2 being bad, even though it's not my
daily driver, and I'm going to explain
why. And so, GLM 5.2 is incredible, but
I'm still not using it every day. And in
fact, a lot of companies I know are
really struggling with the idea that
they want to transition to more of a
generic router where they can route to
the cheapest model available, but it's
not actually easy to do in practice. Why
is that, right? We're going to talk
about why that is, talk about where open
source is going, talk about what the
shape of work looks like in 2026, and
we're going to tie it back into GLM 5.2
and the way we actually need to build to
take advantage of models like this.
Because cheap AI, it's not a theory
anymore. Cheap incredible AI is here. In
fact, it's going to be here more and
more and more and more because the US
government is now slowing down frontier
model releases. 5.6 is the latest model
to be affected. It's apparently going to
be released customer by customer, which
is code for we don't know when we're
going to get it. For the first time,
there is no defined expected cadence for
future model releases that are frontier,
even though the labs are still doing a
phenomenal job training and
reinforcement learning their models. And
so we're going to have more and more of
this open source conversation. And a lot
of the open source conversation is
frankly about moving down the cost
curve, right? Because these frontier
model costs are expensive. If you're
running a company, they get really
expensive. There are stories going
around where the numbers are absolutely
eye-popping. Like one engineer spending
$80,000 in token costs in a week. That's
a lot. So if you have that kind of
pricing power, if people are spending
tens of thousands of dollars a week on
tokens, there's an a tremendous amount
of incentive to make these models work.
So why is it so hard? Why are why are we
not seeing a tremendous tipping point
away? Why are we still seeing Anthropic
growing their revenue like crazy, OpenAI
growing their revenue like crazy when
these incredible good models exist?
Well, there's a number of factors to
that, and I want to list them for you so
that you can actually understand the
perspective. This is based on talking
with engineers at companies as well as
with leaders. The first one is the
ergonomics of work. If you are just
trying to get something you've heard
about, seen about, you have a frontier
model at you have a frontier model at
home on your phone, you just want access
to that. There's a lot of employee
pressure around Claude and around OpenAI
in a way that there just isn't for open
source models. So, that's one piece. Uh,
and it's not small. Like, when people
are asking for it vocally saying this
will help my work, overburdened IT
departments tend to listen to that.
Number two, it is actually very, very
difficult to correctly figure out
whether your task load is center of
distribution or edge of distribution
weighted. If it's edge distribution
weighted, you actually do want the
frontier models. If it's center of
distribution, the open source models are
going to be really, really good because
they're common patterns. But, people
don't They're not used to measuring
their work that way. Individuals aren't,
teams aren't. if you're a company trying
to figure out what is your model
strategy, you kind of got to tackle
what is your distribution of tasks? And
almost no one has asked that question
properly yet. And people are trying to
figure out how to measure that. The
folks that have gone the farthest,
actually, are folks like Flo Crivello,
who is, uh, leading the Lindy team, and
who very publicly wrote up his journey
to a deep seek architecture away from
Claude. And, you know, he saved a lot,
etc., etc. But, he was also very honest
about the fact that the Lindy team had
to essentially rewrite their harness
from scratch around deep seek, and they
could not just take all of their systems
for working with Claude, all of their
prompts, all of the way they handle
memory, all of their tool calls, and
just automatically lift and shift. It
doesn't work that way. These models need
their own harnesses.
He was incentivized to do that because
he is literally serving AI as a service,
and if he can deliver a cheaper and more
effective service that hits his margin,
and it's it's tremendously impactful.
For folks who are using AI internally
for coding or for back office
automation, that ROI is not as clear,
and the incentive to move is not as
clear, either. And so, what I have seen,
and I have seen anecdotes from this, not
just from Flo, but from other folks that
I know personally. I know entrepreneurs
who are wrestling with this today. The
ones who are actually making the jump to
open source and dealing with the
different system prob dealing with the
different tool called dealing with a
different memory architecture, etc. That
is tuned around the fact that these are
center of distribution models. Those
guys or those gals are focused on ROI
for a particular AI tool they have in
market. Just like Lindy, they see value
back in their pockets when they can cut
their token costs. And for everyone
else, because the incentive is not as
strong, you don't have the same
commitment to wade through the challenge
of building a harness. And that is not a
small thing. And one of the things I
want you to take away from this video is
that a model can be an incredible
[snorts] brain in a jar. And it it just
isn't useful to you without a harness.
And so this is why I pay a ton of
attention to harness innovations. And I
want to name a couple that are top of
mind as we look at GLM 5.2 in context.
First, I notice that GLM 5.2 was
released with its own Codex clone
harness. That's one piece that I pay
attention to. It looks like the open
source model makers are realizing they
need to deliver harnesses as well. And
so I would expect more innovation in
that direction. I notice that Codex is
starting to call out publicly that you
can use Codex the harness without using
any OpenAI model. That's notable because
there's a different path to value for
OpenAI there. Maybe OpenAI's models are
the default, but if they're calling out
that they are actually the harness for
all of work, it gives them a way to be
stickier long term. Three, the Anthropic
team is not just sitting there as all of
these developments happen. They launched
Claude Tag this week, and Claude Tag is
an incredibly sticky product. It is a
team level harness, and team level
harnesses are where the energy is going
because so much of the work we've got is
individually productive work in AI. It's
not team productive work. And we're
trying to figure out, how do we align
our efforts that are individually
productive into something that is team
productive? And Claude tag, which is
just tag Claude, anyone can tag Claude
and get work done in Slack, is one of
the first examples of a sticky viral
consumer team harness. Where like if
you're an ordinary knowledge worker at a
particular company, you can envision
using that as as a team harness. And you
don't have to know the word team
harness, it's just going to work. You
tag Claude and it works. But look at it
strategically from Anthropic's
perspective. Now they're not just
getting the engineers. Now they're
getting everybody who's a knowledge
worker in Slack and they're reading all
of the messy context that lives in Slack
that no one knows how to codify and that
is now getting fed into Claude
automatically and it can be something
that the Anthropic team learns from
within privacy policies long term for
Claude in the context of that company to
start to own the harness itself in a way
that no company can get away from. It's
an incredibly sticky experience because
you think about it. Let's say you you
know that GLM 5.2 is a lot cheaper,
which it is. It's like 98% cheaper or
something like that. If it's that much
cheaper than Claude and it's just about
as good on most tasks, it is rational to
build a routing system and assign most
tasks to GLM 5.2. Except that hey, are
you going to have Claude tag, right? Are
you going to go to tag in Claude on that
stuff? Is that convenience going to be
there? Are are you going to have to
restart the job of giving this AI
context from your company because Claude
magically acquired it in Slack and you
didn't have to think about it? We have
taught companies for decades that data
is alpha. Data is something you have an
edge with if you're serious. If data is
alpha, what do we think about giving all
of that data to a frontier model
provider as context? Even if they don't
release it into training data, even if
they if the privacy policy is really
good and they're behaving really
ethically, which I have no reason to
think they're not, you still are
effectively renting your own context
back to yourself because Claude is going
to be in your slack as a team level
harness and is going to be incredibly
close to all the work your team does and
it's going to be impossible to rip out.
No matter how cheap the GLM 5.2 class
models are,
how can you rip out the model that's
that close to context? And I think that
the GLM 5.2 team knows this. That's why
they released a harness, a Codex-like
interface with their AI. It's a first
stab at it. But we got to get much
farther there in tech, where the
companies that know they need harnesses
generally cannot afford to hire the AI
talent to build those harnesses unless
they're extraordinary companies because
that AI talent is so in demand right now
that it can charge anything it wants and
it usually goes to one of the
hyperscalers or another large company.
And so we're in the dynamic where the
only companies that can build their own
last-mile harnesses, their own auto
routers, are companies that can afford
that, that can afford the AI talent to
do that, which is very scarce. And so if
you actually think through this dynamic
with GLM 5.2 and how it's possible but
at the same time we can have an
incredible open-source model that we're
excited about and also that Anthropic
still has pricing power to charge a lot
for their tokens even though their
tokens are just marginally better, it's
actually not a story of intelligence.
It's a story of the last-mile in AI and
the fact that the talent to build the
last-mile in AI is incredibly scarce.
Which should, honestly, for a lot of you
watching, be a source for optimism. If
we have that scarce a talent, where
people are ending up locked into
contracts with a frontier model provider
because they don't know how to build a
harness for themselves, wow is there a
lot of opportunity in knowing how to
build an AI. Like it's an incredible
opportunity right now. It is not easy to
do this work. It's not easy to know this
is how you handle a tool call in GLM 5.2
and how you should do it differently
from Claude. So does figuring out how
memory will work for that system. So
does figuring out how the system prompt
needs to change because it's a center of
distribution model. It's a lot of
technical work. And if you know how to
do that work or know how to do parts of
that work to essentially refactor
agentic pipelines so they work with an
open-source model, you are going to be
incredibly in demand. Especially if you
compare that with the ability to route
tasks where you can take a task and
recognize on the fly that it's a
frontier model task and it should go to
a frontier model versus everything else
going to a cheaper open-source model.
That is going to be a huge investment
theme for companies in 2026, 2027 and
they're going to keep innovating. Claude
tag is a fantastic example of how of how
incentives in frontier close-source
models are giving us incredible
experiences. If you have pricing power,
you are heavily incentivized to make
sure that your experience is as
convenient and ergonomic as possible.
And so features like Claude Claude tag
are going to appear really, really fast,
really rapidly, really completely from
teams at Anthropic, also from OpenAI
because they're incentivized to keep
those those prices high and to go after
that business. And with open-source
models, you don't have the same margin
to work with, you don't have the same
cash flow to work with and you don't
have the same incentive to dig in and
deploy thousands of forward-deployed
engineers and really make these
harnesses sing. And so one of the really
interesting facts that we come to after
all of this can simultaneously be an
incredible model, a model that a lot of
entrepreneurs switch to when the ROI is
clear and they're technically savvy
enough to do it, and also not a model
that is easy for a given company that
you turn up in phone book to actually
use. It any given company is going to
have to think about how they use GLM 5.2
to use it usefully, and they're going to
have to think a lot less to sign up for
a frontier model contract that's going
to fit right into their existing
workflows. That last mile is literally a
trillion-dollar last mile in AI. And one
of the biggest open questions right now
is whether we will scale our talent fast
enough to enable businesses to tackle
that problem set without paying so much
that they can't afford it. I don't know
what the answer's going to be, but
that's a question we're going to have an
answer to. We will all collectively
answer together in the next 3 to 6
months. We are going to find out,
especially as the US government has this
effective pause in place on frontier
model releases,
and the open-source systems are going to
continue to be available, we're going to
find out whether companies can adjust to
the fact that intelligence is 98%
cheaper and takes a last mile to build.
Can they actually build that last mile?
Can they find teams to build that last
mile? If you are in an agency or in a
consulting space, this is a golden goose
moment. Like you have a chance here. You
can really go to town and basically
promise to save people a ton of money on
tokens as part of your ROI proposition,
as long as you can deliver that refactor
in a way that maintains quality, which
is not a trivial task. If it was easy,
we wouldn't be having this video. So,
where does this leave us? GLM 5.2 is an
incredible model. It is important not to
shame a model or diss a model because
it's good at center of distribution
task, because by definition that is most
of our work. Collectively as a species,
most of our knowledge work is center of
distribution, just by definition. And if
that's the case, a model that's really
good at that is worth taking really
seriously. And if we take it seriously,
that means we have to take the last mile
seriously. We have to take the idea that
we need a harness for that last mile
seriously. And that's a lot of what I
have been doing in public is starting to
articulate what it takes to build a
harness, whether it's open skills or
open brain or open engine, which I've
all talked about on this channel. How do
you start to take these pieces and put
them together in a way that is agent
agnostic, that is model agnostic, so you
can start to install those pieces and
actually take advantage of all the
intelligence on tap. Whether it's
Claude, whether it's it's Codex, whether
it's Hermes, whether it's whatever
whatever system you want, whether it's
your own iPhone 2, you should be able to
easily build to that last mile. And and
I know that there's a lot of custom work
for individual companies, and that's why
I keep saying this is a time for
builders. But if we don't start down
that path, we're essentially going to be
renting our company brain and company
context back from the frontier model
providers.
And they're going to have it. And
they're going to be able to use it to
continue to improve their systems and
make them more useful, and they'll be
incredibly convenient, incredibly sticky
products. And what are we going to do?
We're going to have to use them. So,
this is a very pivotal moment for
corporations. The firm has never faced a
moment where the firm's brain has been
on rent. And that is what we're on the
verge of with tools like Claude Tag,
which are incredibly useful. I'm not
saying they're not useful, they're very
useful. That's exactly the dangerous
thing. So, I would encourage you
if you are even if it's a tiny company,
let's say you're building your own
agency, you're an individual
entrepreneur, think seriously, just as
you would if you're a larger company
leader, think seriously about whether
you want to rent that context and
intelligence or not. Think seriously
about where you want to go with your
context long term. Ask yourself, do you
have an idea of the distribution of your
tasks? Do you have access to technical
talent that you can use to build out
that last mile? What are the task sets
that you would want to assign that would
save you a ton in tokens? A lot of
people don't sit down and get pencil and
paper and actually ask themselves those
kinds of questions. And I have a whole
sort of question set that's in more
detail that I've been going over with
leaders. I put that on the Substack. Uh
but this is a really serious thing. This
is a moment for open source. GLON 5.2
opened that door for all of us, and it's
going to be up to us to see how we take
advantage of it. Good luck with that.
Cheers. Bye.