August 22, 2025 • 28 mins
In a bonus third episode, Ed Zitron reports exclusively on how OpenAI’s new "router-based" ChatGPT-5 makes it impossible for the company to cache the static prompt for any model or tool it uses every single prompt, doubling token burn for mediocre gains.
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:02):
Ze Media.
Speaker 2 (00:05):
Hi, my name said Tron, and welcome to Better Offline.
This is also Jackass. So you've just had a cheery
two part chuckle first about how Generative Ai made tanker
markets in our economy. So I'm going to give you
(00:27):
a lighter one an episode about GPT five, which is
a model from open Ai, and why just under three
years of hype have led to the software equivalent of
the launch of Saint Anger, except every time lars are hit.
The snare drama cost them fifty five thousand dollars. Now,
if we look at the positive reviews, we see takes
ranging from Simon Willison's tempered remark that GPT five is
(00:48):
just good at stuff to semi anass this is completely
insane statement that GPT five is setting the stage for
ad monetization and the open Ai GPT chat GPT super app.
In a piece that makes several assertions about how the
router that underpins GPT five is somehow the secret way
that Openaye will inject Dad's which is just distinctly silly.
(01:10):
It's I'll get into this in the episode a little bit,
but just with everything you're going to hear, you're going
to realize that this is just someone just saying stuff.
Took four bylines to do that shit too. I'm also British.
I'm gonna say router. I might say router as well,
because I've been here a while. Make fun of my
voice if you really must. But with that out the way,
here's a quote from semi Analysis' coverage. Before the router,
(01:30):
there was no way for a query to be distinguished,
and after the router, the first low value query could
be routed to a GBT five mini model that can
answer with zero tool calls and no reasoning. This likely
means serving this user is approaching the cost of a
search query. This does not make any sense. This None
of this makes it like it's just a bunch of assumptions.
Why would this be the case. The article also makes
(01:53):
a lot of claims about the value of a question
and how chat GPT could I am serious a agent
agentically reach out to lawyers. I'm not going to edit
that out because egentically is not a fun word to say.
It is just complete nonsense, and in fact, I'm not
sure this piece reflects how GPT five even works at all. Again,
quoting it, the router serves multiple purposes on both the
(02:14):
cost and performance side. On the cost side, routing users
to many versions of each bubble allows open ai to
service uses at a lower cost or with lower costs.
Even to be fair on semi analysis, it's not as
if open ai gave them much help. Open AI's official
writings about the router aren't exactly filled with details, talking
and glowing terms about what it does, but not how
here's what they say. Chat GPT's real time router quickly
(02:38):
decides which model to use based on the conversation type, complexity,
tool needs, and your explicit intent. For example, if you
say think hard about this in the prompt. The router
is continuously trained on real signals, including when users switch models,
preference rates for responses, and measured corrected correctness improving over time.
Once usage limits are reached, a mini version of each
(02:59):
model handles remains inquiries. In the near future, we plan
to integrate these capabilities into a single model. And that
last bit really doesn't make sense, but in any case,
the lordchip GPT five has been very, very weird. At first.
Some people seemed really happy about it. Chief of them
software YouTube of Theo Brown, who is over four hundred
and sixty eight thousand subscribers. He's also known as theogg
(03:19):
who said.
Speaker 1 (03:20):
I didn't know it could get this good. This was
kind of the like oh fuck moment for me in
a lot of ways, and I've had to fight like
a slow spiral into insanity. It's a really really good model.
Speaker 2 (03:39):
He finished by saying, and.
Speaker 1 (03:41):
Keep an eye on your job because I don't know
what this means for us long term.
Speaker 2 (03:45):
Pretty crazy, right. Comments on the video included people saying
things like if open aye is helding you hostage, blink
twice and yes that is an adverbating quote. Another saying
this dude, is everything wrong in it today? Another saying
this video was sponsored by open Ai, Another other saying
ge GPT five failed every test project I gave it today.
It's a lie in my experience. Maybe they haven't ramped
up the GPUs now. From what I can tell, THEO
(04:08):
Brown played with GPT five in open ais offices and
did all the benchmarking there. Open Ai, by the way,
fucking how come on? You can't benchmark in their offices anyway.
Open AI's API based access to GPT five models. You
know the thing that you use if you want to
integrate GPT into your app, does not root them, by
the way, nor does open ai offer access to its
(04:29):
router or any associated models. Important detail. Just want you
to know that because we need to make sure very
clear now A weekly a Theo Brown would put out
another video called I was wrong about GPT five, which
he would open by saying.
Speaker 1 (04:41):
So first and foremost, I want to make sure it
is very very clear that the experience that you probably
are having with chat, GPT and GPT five right now
is not the experience that I had when I was
first testing it.
Speaker 2 (04:53):
Brown goes on to explain that he was not paid
by open Ai at all, that he was sincerely impressed
by the company and GA five, and that he'd actually
spent over twenty five thousand dollars in inference testing it
on his own company software, and indeed also that he
turned down a grand appearance fee. Sorry, I mean that's
a very British thing, one thousand dollars appearance fee, not
just like a really nice one. Brown claims he asked
(05:16):
open Ai to try it out, and after they declined
to let him test it early on his own, he
was invited to try it on camera with a small
group of other people open AI's offices where they'd film
his reactions. He said that the API was incredible, but
that it's become apparent that the models he was using
in the video were not the same as those released
of the public. Making a post on August thirteenth on
xd Everything app that GPT five was nowhere near as
(05:37):
good as in cursor as when it was as it
was when he was using it a few weeks ago,
complaining that things that worked while demoing it at open
ai no longer did, adding that there was something somebody
else on Twitter that said they'd had a similarly great
experience GPT five on launch that has since decayed. It
isn't completely clear what happened here, but I'm going to
guess that open ai showed THEO Brown and others in
(05:58):
their offices some sort of heavily molded version of the
model that burns significantly more compute to provide its outputs,
though I'm also very suspicious of how significance the difference
is here. Brown's videos attempt to show the difference between
the generations that you received from the model when it
was good and when it was bad. In this video,
which I'll include a link to in the episode notes.
But if I'm honest, they look pretty similar in that
(06:20):
they're kind of mediocre. I'm not saying that as a hater,
by the way. They just kind of look like shit.
It's just kind of okay, like shit. They look like
regular fucking generated websites. They don't look special. The good
one is fine, and the bad one has weird gradients
on it. This whole thing sucks, though, and was a
clear set up by open Ai to overstate the abilities
(06:41):
of GPT five, one that fell apart with the lightest
brush with reality. I imagine their assumption was that Brown
would post the glossy video and then walk away, and
it gave THEO some credit for straight up stating he
was misled. This was a desperate move and one that
blew up in the face of open Ai. Along with
the rest of the GPT five launch. People hate the model,
customers are mad for taking models away like four to
(07:02):
H and have remained mad even with their return, and
the chat gpt subreddit is almost entirely people complaining about
how ineffective the new version is and how even GPT
four ROH is not the same They got game of
brain Baby. As I said in last week's monologue. I
believe open Ai has grown a fandom rather than any
kind of sustainable product market fit, and they're now suffering
fandom like hate with every minor change they make in
(07:24):
an attempt to push GPT five further, further aggravating people
that barely understand why they use the product to begin with. Yeah,
the center of the angle laid the reason for GPT
five's launch, the belief that this was somehow a cost
cutting measure, where OpenAI had added a router to chat
GPT as a means of sending certain requests to cheaper
models to save money. But when I hear router, I
(07:45):
hear latency, and I never or even a second believe
that this would somehow be cheaper to run. It didn't
make sense. I'm a curious little criator, so I went
and found out how chat GPT five actually works, and
unlike the following incredible products that you should buy, it's
actually kind of a big piece of shit. And we're back,
(08:13):
and from here on out, I will define two things.
GPT five referring to the model and its associated mini
and nano models, and Chat GPT five referring to the
current state of chat GPT, which features an auto fast
and thinking and thinking mini model selections. You also can
see legacy models, but that's not what we're talking about today,
and that's also only for a little bit. It's a
distinction I have to make, by the way, and make earlier,
(08:34):
because the two things are different, they work in different ways,
and chat GPT five structure induces a bunch of trade
offs and downsides that, as I'll discuss later, make this
whole thing even more wasteful. In discussions with a source
that an infrastructure provider familiar with the architecture, it appears
that chat GPT five is in fact potentially more expensive
to run than previous models, and due to the complex
(08:55):
and chaotic nature of said architecture, can at times spun
upwards of double The tokens per quid tokens, for those
who don't know, are basically chunks of texts that the
AI models do stuff with. I'm simplifying this. Do not
email me and correct some minor thing nobody cares. A
sentence like the quick brown fox jumps over the lazy
dog will be broken into lots of smaller four character chunks.
(09:17):
There are different kinds of tokens, and they're all priced differently.
An input token refers to the data you send to
the model when you ask a question. Output tokens are
used to measure the size of its response, with bigger
responses requiring more tokens. The more tokens you burn paquery,
the more expensive it is to run that query. The
fact that chat GPT five can, in certain circumstances burn
twice the number of tokens of query means that every
(09:38):
question costs more. Chat GPT is also significantly more convoluted,
plagued by latency issues, and is more compute intensive thanks
to open a ey's new, smarter, more efficient model routing system.
In simpler terms, every user prompt on chat GPT, whether
it's in auto, fast thinking or Thinking Mini, starts by
putting the users prompt before the static prompt. I don't
(09:59):
want to lose you here. This is important. A static
prompt is the invisible instructions given by open Ai to
chat GPT, in the models themselves and the tools associate
with them to tell them how to operate. Instructions like
you are chat GPT, you're a large language model, You're
a helpful chat bot. Do not threaten them with a knife,
and so on and so forth. These static prompts are
different with each model you use. A reasoning model will
(10:19):
have a different instructions set to a more chat focused one,
such as think harder about a particular problem before giving
an answer. Break down problems into component answers. When you
get a certain thing, like if someone asks you a
coding question, query a coding tool. That kind of thing,
a user prompt is exactly what it sounds like, the
thing that a user wants the AI model to do.
The new order in chat GPT five becomes an issue
(10:40):
when you use multiple different models in the same conversation.
Because the router, the thing that selects the right model
for the request, has to look at the user prompt.
It can't consider static instructions first because they may be
different based on what the user asked. In fact, the
order has to be flipped for the whole thing to work.
But simpler previous versions of chat GPT would take the
static prompt and then invisibly append the user prompt onto it.
(11:03):
This static prompt would typically be cashed massively, reducing the
amount of compute the model needs to perform a task.
Chat GPT cannot do this. Every time you use chat
GPT five. Every single thing you say or do can
cause it to do something different. Attach a vile might
need a different model. Ask it to look into something
and be detailed. Might trigger a reasoning model or a
different depth of reasoning. Ask a question in a weird way. Sorry,
(11:26):
the route is going to need to send you to
a different model entirely each time, coming up with new
instructions based on the subtle interpretation of what you asked in.
Every single thing that can happen when you ask chat
GPT to do something may triget the route to change model.
A request a new tool, and each time it does
so requires a completely fresh static prompt, regardless of whether
you select auto thinking Faster or any other option on
(11:47):
chat GPT. This in turn requires it to expend more
compute with queries consuming more tokens compared to previous versions.
It's like you started a job, and every time you
do a task, right an email, make a cup of copy,
attend a meeting, email someone with a threat your workplace
requires you to complete the entire mandatory onboarding training first.
One way that it is spreadsheet, not before you brush up
(12:08):
on your anti biberary legislation. First your prick. As a result,
Chat GPT may be smart, but it doesn't really seem
efficient in the GPT five version. Now to play Devil's advoca,
open Ai likely added the routing model as a means
of creating a more sophisticated output for a user, and
I imagine with the intention of cost saving. Then again,
(12:29):
this might just be the thing it had to ship.
After all, GPT five was meant to be the next
great leap in AI, and the pressure was on to
get it out the door by creating a system that
depends on an extern and or routing model, likely another LM.
In this case, open ai has removed the ability to
cash the hidden instructions that dictate the how the models
generate answers in chat GPT, creating massive infrastructural overhead. Worse still,
(12:51):
this happens with every single turn as in message on
Chat GPT five, regardless of the model you choose, creating
endless infrastructural baggage with no real way out that only
could pounds based on how complex the user's queries get
or how much they change. They could be simple, but
just going in different directions every time, could open ai
make a better router? Sure? Does it have a good
one today? No, every time you message CHATGBT as the
(13:13):
potential to change model or tooling based on its own whims,
each time requiring a fresh static prompt, and short of
totally reworking the architecture of chat GPT five, there's no
way to change this. And if it's an LLM choosing
which model, I don't know, maybe it hallucinates just a guess.
It doesn't even need to be the case where a
user asks chet GPT five to think, and based on
my test with GPT five, sometimes you can just ask
(13:36):
it a forward question and it will think about it.
For no apparent reason, open ai has created a product
with latency issues and an overwhelmingly convoluted routing system that's
already straining capacity, to the point that this announcement feels
like open ai is walking away from its API entirely. This,
as a reminder, is the thing that people use to
incorporate open AI's models into their apps while also running
(13:56):
set models on the infrastructure open Ai rants from Microsoft
and call even at some point as well as Oracle,
and this API thing is really weird by the way
because these are new models, but Open Eyes really not
talking about the models themselves that much. Unlike the GPT
four rower announcement, which mentions the API in the first paragraph,
the GPT five announcement has no reference to it and
(14:17):
only has a single reference to developers at all when
talking about coding. Some woman has already hinted that he
intends to deprecate any new API demand, though I imagine
it will let anyone who will pay for priority processing,
which is essentially open eyes way to require minimum commitments
and extra payments from API customers just so they never
feel the bite of any compute shortages and throttling, which
they absolutely will do to people that don't pay. Chat
(14:40):
GPT five feels like the ultimate comeuppance for a company
that has never been forced to build a product, choosing
instead to bolt increasingly complex tools onto the side of
models in the hopes that one will magically appear. Now,
each and every feature of Chat GPT burns more money
than it ever did before. Chat GPT five feels like
a product that was rushed to market by a desperate
company that had to get something out of the In
(15:00):
simpler terms, here, it's actually really funny. When I worked
this out, I chuckled. I chuckled vigorously. This is just
a case where open ai has given chat gpt middle manager.
But now I'm giving you the chance to open up
your hearts and do something better. Open up your wallets too,
and send money to a company that follows here, But
hold my advertisements and we're back. Like every great middle manager,
(15:38):
chat GPT five's rutter creates more work based on its
own interpretation of what's going on, and has a separate
large language model. I can't imagine it has a ton
of training data available if I had to guess, and
this is a guess by the way open ai has done,
and we'll do a lot of fine tuning and reinforcement
learning to make it work. Though, to give it a
little grace, this is a new thing that it's doing,
and it's doing sort of a huge scale. The problems start,
(16:01):
by the way, with the fact that chat GPT five
is taking the user's initial prompt and then deciding which
model to use, unlike previous models, which sent your prompt
directly to the model along with the static prompt which
was cashed and came first. An important feature in how
these models, limit tokenburn. Open ai starts with a router
model that makes takes what you ask and gives its
chat GPT and tags it based on what kind of
(16:22):
thing your question might need. The thing might be a tool,
such as whether it has to do a web search
to spit out the thing at the end, a reasoning model,
whether it needs to use a coding language, and so
on and so forth. Once chat GPT has bounced your
query across various models, burn and compute along the way,
it then pushes it towards the chat portion of the generation.
And each time you ask chat GPT a question or
(16:44):
to do something and you specialized static prompt is generated,
sometimes several make it impossible to cash them in advance.
In simpler terms, each time you message it, chat GPT
is to dump all cased information and instructions for what
you need to do and reload it with each prompt.
Now here's some examples of what chat GPT five has
to reload every single time you prompt him whether or
(17:04):
not to use a browser or search the internet, and
under what conditions to do so, because they will change
with each prompt. How to approach a particular problem based
on what the user asked, including any specific ways you
meant to answer, tone, brevity, and so on based on
their request, specifics around how it might use, say open
ais code interpreter, such as the usage rules for running
a Python script, or how you want the code's output,
(17:25):
which again will be different based on each prompt. And
you can even say, do it in the exactly the
same way, and because it's a large language model, it
may hallucinate something different every single goddamn time you prompt
chat GPT five it has to do this. Worse still,
a particular conversation can involve you using multiple different models
and tools, requiring you with each and every prompt, having
(17:47):
to inject a different static prompt for each component that
chat GPT five uses. And you can't catch the static
prompt before the user's intent because if you did that,
it might send an instruction to a model that doesn't
make sense, such as telling a reasoning model to give
a quick and simple line answer remini or nanomodel to
do some sort of deep reasoning, which would create a
crappy answer and burn tokens in the process. And this
(18:07):
is all thanks to the complicated way that open ai
insisted on building GPT five. Every single time you send
something to chat, GPT can trigger it to use a
different series of models audio vision, reasoning, each with their
own instructions, static prompts, all while pulling different tools, each
requiring their own instructions based on what you asked, and
reasoning models even have different depths of reasoning. Unlike four
(18:30):
to ZH, which is a multimodal model combining text, vision,
and voice, GPT five is a ratking of open AI's
models and tools that gets reborn every single time you
ask it to do anything prompt It can prompt cash
some things, but the core instructions not so much. But
let's get a little more granular, because I know I've
been quite repetitive, but this is detailed. So from what
(18:51):
I've been told, there are either one or two models
at work for the routing. I'm going to go with
what I think is most likely based on the discussions
I've had with people familiar with the architecture. I've heard
the term orchestrator thrown around potential to potentially suggesting the
router may be more omnipresent throughout the process, but I
was unable to confirm its existence. Reach out of you
here differently, I'll explain things as they were explained to me. Though.
(19:13):
When a user sensor prompt, it goes through the Splitter leg,
which decides to send the query on one of two paths.
One is called the fast path, where a query is straightforward,
such as a text only conversation that doesn't require any
analysis or extra tools or thinking, a path where the
query may require reasoning or more complex tools like codgeneration
or access to web browser for research. To be clear,
(19:33):
there are prompts where it may be split into multiple
paths that trigger multiple models or tools, each requiring their
own static instructions. From what I understand, the splitter model
is a completely separate large language model, though we don't
have a ton of details about it. I also, based
on conversations I've had, think there's a chance there could
be a separate model that sits above the splitter that
does much lighter classification of how a query might be routed.
(19:55):
So you ask it to do something, it might just
go Okay, this looks like it needs a tool and
going off. Why now? In any case, none of this
can be cashed because all of this exists before inference,
which is where, by the way, it's inference I've misstated
in the past. Is like it inferring, meaning inference is
everything that happens to get an output to you. So
all of the stuff that's happening. And by the way,
(20:17):
this is all a completely new cost that open ai
has created. No one does this like this, it's so
fucking stupid. But now we get to the chat leg.
Now the open ai has added layers of extraction, it
can begin cooking up the output, by which I mean
do inference. The chat leg is where the pieces that
the splitter model created are pulled together, each loaded into
their with their respective static prompts based on what the
(20:38):
user asked chat GPD five to do. Each piece of
the model a tool to generate Python and an image
generation tool a reasoning model. To generate an output has
to process an entirely new static prompt and again that's
every interaction. Remember, static prompts are effectively instruction. So the
splitter model has told each piece of the pie how
to act to create a particular output. As a result,
(20:58):
much of this can't be cashed, creating more and more
repetitious token bone response and mean to have to repeat
this stuff so that you really get him. The upshot
of the chat legs static prompt baggage is that you
can do a little more here, at least in theory,
because each component can be instructed separately, they can again,
in theory, be made to give more individualized, specialized outputs,
like creating an image with tags that is as I'll
(21:18):
give you an example of very shortly generated using a
specific reasoning model. I'm clutching it straws here. I don't
really know if this's better, but I'm trying to be reasonable.
I'm trying to be normal. Every day, I try and
be normal. Previously, Open Eye's advantage was that a model
like four to oh was a kind of a jack
of all trades. But to get the benefits of chat
(21:39):
GPT five and that's in air quotes, it's engaged a
conductor model that can just make things more convoluted, even
in the case of simple requests. Let me give you
an example. You upload a chart of NFL player's stats
and ask chat GPT to decide which is the best
of the group and create an image to show the results.
In GPT four oh, chat GPT would use one more
(22:00):
and thus one static prompt to look at the image,
decide which tools to use, and then how to format
the response. You only needed one prompt, which was cased
because one model can look at the stats for all
the data and make the decisions and then use the
image generation tool to make the final image. In GPT five,
the chet GPT conductor model would see the stats, root
it to a vision model requiring its own static prompt,
(22:20):
then a separate text only reasoning model, one that has
no ability to use tools, but it might be cheaper
to get an answer from and also requires a static prompt,
and that would then decide which players are best and
then spit out an output, and then root it to
a completely separate model that can generate texts to query
the image tool again need a stag prompt for this
to then generate the image. On top of all this
(22:41):
onerous baggage lies another problem. The GPT five's various models
are just more complex. By splitting out the component elements
of what a model can do and allowing each model
to have different levels of reasoning, even the cheaper ones
like MIDI and nano open AI has created an endless
combination of different reasons to have to make a brand
new static prompt instruction automated by a router, a large
language model that chooses what large language model to choose
(23:04):
for a query. It is, if I'm honest, kind of funny.
Reasoning models work when simply described by breaking up a
prompt into component pieces, looking over them, and deciding what
the best course of action might be. Chat GPT's router
is effectively an abstraction higher breaking up the prompt into
component pieces, then choosing different models for each of those pieces,
which may in turn be broken up by a reasoning model.
(23:26):
While I wouldn't say this is a hat on a
hat situation, it is at this point unclear what exactly
the benefits of chat GPT five's new architecture are, less hallucinations,
better answers. Based on what I've been told, this was
a decision made to increase the model's performance, what I
can say is that this very likely increased open ayes
overhead at a time when it needs to do the
exact opposite. Even if chat GPT five pushes people towards
(23:49):
cheaper models, it does so while guaranteeing extra costs and
latency and whatever signals it may learn as people use.
This will have to create significant benefits massive one hundred
percent plus game for it to be anything close to worthwhile.
While open ai is rude to may be smart in
terms of nuance of how it might answer a query,
and even that I question it most decidedly, is not
(24:09):
more efficient and may have actually increased the burn rate
for a company that will lose as much as eight
billion dollars this year, and I think that number might
be low too. Yet what I'm left with in writing
this script is how wasteful all of this is. Open Ai,
a company that is already incinerated upwards of fifteen billion
dollars in the last two years, has chosen to create
a less efficient way of doing business as a means
(24:31):
of eking out and monest the best performance improvements. It
just sucks. In our own lives, we're continually pushed and
pressured and punished if we get into debt, judged by
our peers and our parents, if we spend our money recklessly,
and if we're too reckless, we find ourselves less likely
to receive anything from credit to housing. Companies like open
Ai live by a different set of standards. Some Mormon
(24:52):
intends to lose more than forty four billion dollars by
the end of twenty twenty eight on open Ai, and
graciously told CNBC, like Lord Farquad, he was willing to
run at a loss for a long time where he
was treated like he was this smart, reasonable decision maker
rather than someone that needs to rein in their horrendous
spending habits and be more mindful. The ultra rich are
(25:12):
rewarded far more for their errant spending habits than we
ever are for any thrifty inness or austerity measures we make,
and none of us are afforded the level of grace
that Clammy sam Altman has been and has been feels appropriate.
Chat GPT five is an engineering nightmare, a phenomenally silly
and desperate attempt to duce what remains of the dying
innovation and excitement within the walls of open Ai. It's
(25:34):
not November twenty twenty two anymore. And let's be honest,
there really hasn't been anything exciting or interesting out this
company since GPT four. There's nothing exciting happening at this company.
As many as seven hundred million people a week allegedly
used chat GPT, but nobody can really say why. An
open Ai, despite its massive popularity. Cannot seem to stop
losing billions of dollars, and it can't seem to explain
(25:56):
why that's necessary other than this shit's really expensive. Dude,
Can anyone actually articulate a reason why we need to
burn billions of dollars to do this? What are we doing?
Why are we doing it? Has everybody just agreed to
do this until it becomes a completely untenable Do we
all yearn for the abyss so much that we can't
find camaraderie and admitting we were wrong? Look at GPT five.
(26:17):
This is, if you believe the hype, the best funded,
best resourced company in the world, with the greatest mind
and its helm and the greatest minds within its wars.
And this is the best they've gone. A large language
model that chooses which large language model will answer your question.
G fucking wit, Sam Mortman sounds dandy, and how much
better is this? You say, Oh, you can't really say
(26:38):
fucking brilliant? Hey does it do anything new?
Speaker 3 (26:40):
No?
Speaker 2 (26:41):
Oh, what's that? It's actually our job to work out
for ourselves. Thanks man, I love it. I love this shit.
And if you're someone that is a hype merchant listening
to this and you've done really well getting to the
end of the third part. By the way, I respect you.
I want you to email me and explain why they
should be justified in burning billions of dollars if you
tell me, if you tell me Aws, I will eat
(27:03):
you alive. I mean that, does it? I mean that
completely literally, I will unhinge my jaw. I'll eat you
like Kirby and shit out of dance. I've said that
one before, but I'm going with him in any case.
This three parter has also really reminded me how ridiculous
this is, how nonsensical things have become, and how much
(27:23):
waste has been kind of justified, justified on this idea
that this will become something by people that don't really
know what it does today or might do in the future.
None of this is going to end well, and not
even the boosters seem to be having fun anymore. Everybody's
just flating around waiting for it to end. Even Sam
Ortman seems tired of it all. I know, I bloody
(27:43):
well I am. Thank you for listening to Better Offline.
Speaker 3 (27:54):
The editor and composer of the Better Offline theme song
is Metosowski. You can check out more of his music
and audio projects at Mattasowski dot com m A T
T O S O W s Ki dot com. You
can email me at easy at better offline dot com
or visit better offline dot com to find more podcast
links and of course, my newsletter. I also really recommend
(28:16):
you go to chat dot where's youreaed dot at to
visit the discord, and go to our slash.
Speaker 2 (28:20):
Better Offline to check out I'll Reddit. Thank you so
much for listening. Better Offline is a production of cool
Zone Media. For more from cool Zone Media, visit our
website cool Zonemedia dot com, or check us out on
the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts.
Ze Media.
Speaker 2 (00:05):
Hi, my name said Tron, and welcome to Better Offline.
This is also Jackass. So you've just had a cheery
two part chuckle first about how Generative Ai made tanker
markets in our economy. So I'm going to give you
(00:27):
a lighter one an episode about GPT five, which is
a model from open Ai, and why just under three
years of hype have led to the software equivalent of
the launch of Saint Anger, except every time lars are hit.
The snare drama cost them fifty five thousand dollars. Now,
if we look at the positive reviews, we see takes
ranging from Simon Willison's tempered remark that GPT five is
(00:48):
just good at stuff to semi anass this is completely
insane statement that GPT five is setting the stage for
ad monetization and the open Ai GPT chat GPT super app.
In a piece that makes several assertions about how the
router that underpins GPT five is somehow the secret way
that Openaye will inject Dad's which is just distinctly silly.
(01:10):
It's I'll get into this in the episode a little bit,
but just with everything you're going to hear, you're going
to realize that this is just someone just saying stuff.
Took four bylines to do that shit too. I'm also British.
I'm gonna say router. I might say router as well,
because I've been here a while. Make fun of my
voice if you really must. But with that out the way,
here's a quote from semi Analysis' coverage. Before the router,
(01:30):
there was no way for a query to be distinguished,
and after the router, the first low value query could
be routed to a GBT five mini model that can
answer with zero tool calls and no reasoning. This likely
means serving this user is approaching the cost of a
search query. This does not make any sense. This None
of this makes it like it's just a bunch of assumptions.
Why would this be the case. The article also makes
(01:53):
a lot of claims about the value of a question
and how chat GPT could I am serious a agent
agentically reach out to lawyers. I'm not going to edit
that out because egentically is not a fun word to say.
It is just complete nonsense, and in fact, I'm not
sure this piece reflects how GPT five even works at all. Again,
quoting it, the router serves multiple purposes on both the
(02:14):
cost and performance side. On the cost side, routing users
to many versions of each bubble allows open ai to
service uses at a lower cost or with lower costs.
Even to be fair on semi analysis, it's not as
if open ai gave them much help. Open AI's official
writings about the router aren't exactly filled with details, talking
and glowing terms about what it does, but not how
here's what they say. Chat GPT's real time router quickly
(02:38):
decides which model to use based on the conversation type, complexity,
tool needs, and your explicit intent. For example, if you
say think hard about this in the prompt. The router
is continuously trained on real signals, including when users switch models,
preference rates for responses, and measured corrected correctness improving over time.
Once usage limits are reached, a mini version of each
(02:59):
model handles remains inquiries. In the near future, we plan
to integrate these capabilities into a single model. And that
last bit really doesn't make sense, but in any case,
the lordchip GPT five has been very, very weird. At first.
Some people seemed really happy about it. Chief of them
software YouTube of Theo Brown, who is over four hundred
and sixty eight thousand subscribers. He's also known as theogg
(03:19):
who said.
Speaker 1 (03:20):
I didn't know it could get this good. This was
kind of the like oh fuck moment for me in
a lot of ways, and I've had to fight like
a slow spiral into insanity. It's a really really good model.
Speaker 2 (03:39):
He finished by saying, and.
Speaker 1 (03:41):
Keep an eye on your job because I don't know
what this means for us long term.
Speaker 2 (03:45):
Pretty crazy, right. Comments on the video included people saying
things like if open aye is helding you hostage, blink
twice and yes that is an adverbating quote. Another saying
this dude, is everything wrong in it today? Another saying
this video was sponsored by open Ai, Another other saying
ge GPT five failed every test project I gave it today.
It's a lie in my experience. Maybe they haven't ramped
up the GPUs now. From what I can tell, THEO
(04:08):
Brown played with GPT five in open ais offices and
did all the benchmarking there. Open Ai, by the way,
fucking how come on? You can't benchmark in their offices anyway.
Open AI's API based access to GPT five models. You
know the thing that you use if you want to
integrate GPT into your app, does not root them, by
the way, nor does open ai offer access to its
(04:29):
router or any associated models. Important detail. Just want you
to know that because we need to make sure very
clear now A weekly a Theo Brown would put out
another video called I was wrong about GPT five, which
he would open by saying.
Speaker 1 (04:41):
So first and foremost, I want to make sure it
is very very clear that the experience that you probably
are having with chat, GPT and GPT five right now
is not the experience that I had when I was
first testing it.
Speaker 2 (04:53):
Brown goes on to explain that he was not paid
by open Ai at all, that he was sincerely impressed
by the company and GA five, and that he'd actually
spent over twenty five thousand dollars in inference testing it
on his own company software, and indeed also that he
turned down a grand appearance fee. Sorry, I mean that's
a very British thing, one thousand dollars appearance fee, not
just like a really nice one. Brown claims he asked
(05:16):
open Ai to try it out, and after they declined
to let him test it early on his own, he
was invited to try it on camera with a small
group of other people open AI's offices where they'd film
his reactions. He said that the API was incredible, but
that it's become apparent that the models he was using
in the video were not the same as those released
of the public. Making a post on August thirteenth on
xd Everything app that GPT five was nowhere near as
(05:37):
good as in cursor as when it was as it
was when he was using it a few weeks ago,
complaining that things that worked while demoing it at open
ai no longer did, adding that there was something somebody
else on Twitter that said they'd had a similarly great
experience GPT five on launch that has since decayed. It
isn't completely clear what happened here, but I'm going to
guess that open ai showed THEO Brown and others in
(05:58):
their offices some sort of heavily molded version of the
model that burns significantly more compute to provide its outputs,
though I'm also very suspicious of how significance the difference
is here. Brown's videos attempt to show the difference between
the generations that you received from the model when it
was good and when it was bad. In this video,
which I'll include a link to in the episode notes.
But if I'm honest, they look pretty similar in that
(06:20):
they're kind of mediocre. I'm not saying that as a hater,
by the way. They just kind of look like shit.
It's just kind of okay, like shit. They look like
regular fucking generated websites. They don't look special. The good
one is fine, and the bad one has weird gradients
on it. This whole thing sucks, though, and was a
clear set up by open Ai to overstate the abilities
(06:41):
of GPT five, one that fell apart with the lightest
brush with reality. I imagine their assumption was that Brown
would post the glossy video and then walk away, and
it gave THEO some credit for straight up stating he
was misled. This was a desperate move and one that
blew up in the face of open Ai. Along with
the rest of the GPT five launch. People hate the model,
customers are mad for taking models away like four to
(07:02):
H and have remained mad even with their return, and
the chat gpt subreddit is almost entirely people complaining about
how ineffective the new version is and how even GPT
four ROH is not the same They got game of
brain Baby. As I said in last week's monologue. I
believe open Ai has grown a fandom rather than any
kind of sustainable product market fit, and they're now suffering
fandom like hate with every minor change they make in
(07:24):
an attempt to push GPT five further, further aggravating people
that barely understand why they use the product to begin with. Yeah,
the center of the angle laid the reason for GPT
five's launch, the belief that this was somehow a cost
cutting measure, where OpenAI had added a router to chat
GPT as a means of sending certain requests to cheaper
models to save money. But when I hear router, I
(07:45):
hear latency, and I never or even a second believe
that this would somehow be cheaper to run. It didn't
make sense. I'm a curious little criator, so I went
and found out how chat GPT five actually works, and
unlike the following incredible products that you should buy, it's
actually kind of a big piece of shit. And we're back,
(08:13):
and from here on out, I will define two things.
GPT five referring to the model and its associated mini
and nano models, and Chat GPT five referring to the
current state of chat GPT, which features an auto fast
and thinking and thinking mini model selections. You also can
see legacy models, but that's not what we're talking about today,
and that's also only for a little bit. It's a
distinction I have to make, by the way, and make earlier,
(08:34):
because the two things are different, they work in different ways,
and chat GPT five structure induces a bunch of trade
offs and downsides that, as I'll discuss later, make this
whole thing even more wasteful. In discussions with a source
that an infrastructure provider familiar with the architecture, it appears
that chat GPT five is in fact potentially more expensive
to run than previous models, and due to the complex
(08:55):
and chaotic nature of said architecture, can at times spun
upwards of double The tokens per quid tokens, for those
who don't know, are basically chunks of texts that the
AI models do stuff with. I'm simplifying this. Do not
email me and correct some minor thing nobody cares. A
sentence like the quick brown fox jumps over the lazy
dog will be broken into lots of smaller four character chunks.
(09:17):
There are different kinds of tokens, and they're all priced differently.
An input token refers to the data you send to
the model when you ask a question. Output tokens are
used to measure the size of its response, with bigger
responses requiring more tokens. The more tokens you burn paquery,
the more expensive it is to run that query. The
fact that chat GPT five can, in certain circumstances burn
twice the number of tokens of query means that every
(09:38):
question costs more. Chat GPT is also significantly more convoluted,
plagued by latency issues, and is more compute intensive thanks
to open a ey's new, smarter, more efficient model routing system.
In simpler terms, every user prompt on chat GPT, whether
it's in auto, fast thinking or Thinking Mini, starts by
putting the users prompt before the static prompt. I don't
(09:59):
want to lose you here. This is important. A static
prompt is the invisible instructions given by open Ai to
chat GPT, in the models themselves and the tools associate
with them to tell them how to operate. Instructions like
you are chat GPT, you're a large language model, You're
a helpful chat bot. Do not threaten them with a knife,
and so on and so forth. These static prompts are
different with each model you use. A reasoning model will
(10:19):
have a different instructions set to a more chat focused one,
such as think harder about a particular problem before giving
an answer. Break down problems into component answers. When you
get a certain thing, like if someone asks you a
coding question, query a coding tool. That kind of thing,
a user prompt is exactly what it sounds like, the
thing that a user wants the AI model to do.
The new order in chat GPT five becomes an issue
(10:40):
when you use multiple different models in the same conversation.
Because the router, the thing that selects the right model
for the request, has to look at the user prompt.
It can't consider static instructions first because they may be
different based on what the user asked. In fact, the
order has to be flipped for the whole thing to work.
But simpler previous versions of chat GPT would take the
static prompt and then invisibly append the user prompt onto it.
(11:03):
This static prompt would typically be cashed massively, reducing the
amount of compute the model needs to perform a task.
Chat GPT cannot do this. Every time you use chat
GPT five. Every single thing you say or do can
cause it to do something different. Attach a vile might
need a different model. Ask it to look into something
and be detailed. Might trigger a reasoning model or a
different depth of reasoning. Ask a question in a weird way. Sorry,
(11:26):
the route is going to need to send you to
a different model entirely each time, coming up with new
instructions based on the subtle interpretation of what you asked in.
Every single thing that can happen when you ask chat
GPT to do something may triget the route to change model.
A request a new tool, and each time it does
so requires a completely fresh static prompt, regardless of whether
you select auto thinking Faster or any other option on
(11:47):
chat GPT. This in turn requires it to expend more
compute with queries consuming more tokens compared to previous versions.
It's like you started a job, and every time you
do a task, right an email, make a cup of copy,
attend a meeting, email someone with a threat your workplace
requires you to complete the entire mandatory onboarding training first.
One way that it is spreadsheet, not before you brush up
(12:08):
on your anti biberary legislation. First your prick. As a result,
Chat GPT may be smart, but it doesn't really seem
efficient in the GPT five version. Now to play Devil's advoca,
open Ai likely added the routing model as a means
of creating a more sophisticated output for a user, and
I imagine with the intention of cost saving. Then again,
(12:29):
this might just be the thing it had to ship.
After all, GPT five was meant to be the next
great leap in AI, and the pressure was on to
get it out the door by creating a system that
depends on an extern and or routing model, likely another LM.
In this case, open ai has removed the ability to
cash the hidden instructions that dictate the how the models
generate answers in chat GPT, creating massive infrastructural overhead. Worse still,
(12:51):
this happens with every single turn as in message on
Chat GPT five, regardless of the model you choose, creating
endless infrastructural baggage with no real way out that only
could pounds based on how complex the user's queries get
or how much they change. They could be simple, but
just going in different directions every time, could open ai
make a better router? Sure? Does it have a good
one today? No, every time you message CHATGBT as the
(13:13):
potential to change model or tooling based on its own whims,
each time requiring a fresh static prompt, and short of
totally reworking the architecture of chat GPT five, there's no
way to change this. And if it's an LLM choosing
which model, I don't know, maybe it hallucinates just a guess.
It doesn't even need to be the case where a
user asks chet GPT five to think, and based on
my test with GPT five, sometimes you can just ask
(13:36):
it a forward question and it will think about it.
For no apparent reason, open ai has created a product
with latency issues and an overwhelmingly convoluted routing system that's
already straining capacity, to the point that this announcement feels
like open ai is walking away from its API entirely. This,
as a reminder, is the thing that people use to
incorporate open AI's models into their apps while also running
(13:56):
set models on the infrastructure open Ai rants from Microsoft
and call even at some point as well as Oracle,
and this API thing is really weird by the way
because these are new models, but Open Eyes really not
talking about the models themselves that much. Unlike the GPT
four rower announcement, which mentions the API in the first paragraph,
the GPT five announcement has no reference to it and
(14:17):
only has a single reference to developers at all when
talking about coding. Some woman has already hinted that he
intends to deprecate any new API demand, though I imagine
it will let anyone who will pay for priority processing,
which is essentially open eyes way to require minimum commitments
and extra payments from API customers just so they never
feel the bite of any compute shortages and throttling, which
they absolutely will do to people that don't pay. Chat
(14:40):
GPT five feels like the ultimate comeuppance for a company
that has never been forced to build a product, choosing
instead to bolt increasingly complex tools onto the side of
models in the hopes that one will magically appear. Now,
each and every feature of Chat GPT burns more money
than it ever did before. Chat GPT five feels like
a product that was rushed to market by a desperate
company that had to get something out of the In
(15:00):
simpler terms, here, it's actually really funny. When I worked
this out, I chuckled. I chuckled vigorously. This is just
a case where open ai has given chat gpt middle manager.
But now I'm giving you the chance to open up
your hearts and do something better. Open up your wallets too,
and send money to a company that follows here, But
hold my advertisements and we're back. Like every great middle manager,
(15:38):
chat GPT five's rutter creates more work based on its
own interpretation of what's going on, and has a separate
large language model. I can't imagine it has a ton
of training data available if I had to guess, and
this is a guess by the way open ai has done,
and we'll do a lot of fine tuning and reinforcement
learning to make it work. Though, to give it a
little grace, this is a new thing that it's doing,
and it's doing sort of a huge scale. The problems start,
(16:01):
by the way, with the fact that chat GPT five
is taking the user's initial prompt and then deciding which
model to use, unlike previous models, which sent your prompt
directly to the model along with the static prompt which
was cashed and came first. An important feature in how
these models, limit tokenburn. Open ai starts with a router
model that makes takes what you ask and gives its
chat GPT and tags it based on what kind of
(16:22):
thing your question might need. The thing might be a tool,
such as whether it has to do a web search
to spit out the thing at the end, a reasoning model,
whether it needs to use a coding language, and so
on and so forth. Once chat GPT has bounced your
query across various models, burn and compute along the way,
it then pushes it towards the chat portion of the generation.
And each time you ask chat GPT a question or
(16:44):
to do something and you specialized static prompt is generated,
sometimes several make it impossible to cash them in advance.
In simpler terms, each time you message it, chat GPT
is to dump all cased information and instructions for what
you need to do and reload it with each prompt.
Now here's some examples of what chat GPT five has
to reload every single time you prompt him whether or
(17:04):
not to use a browser or search the internet, and
under what conditions to do so, because they will change
with each prompt. How to approach a particular problem based
on what the user asked, including any specific ways you
meant to answer, tone, brevity, and so on based on
their request, specifics around how it might use, say open
ais code interpreter, such as the usage rules for running
a Python script, or how you want the code's output,
(17:25):
which again will be different based on each prompt. And
you can even say, do it in the exactly the
same way, and because it's a large language model, it
may hallucinate something different every single goddamn time you prompt
chat GPT five it has to do this. Worse still,
a particular conversation can involve you using multiple different models
and tools, requiring you with each and every prompt, having
(17:47):
to inject a different static prompt for each component that
chat GPT five uses. And you can't catch the static
prompt before the user's intent because if you did that,
it might send an instruction to a model that doesn't
make sense, such as telling a reasoning model to give
a quick and simple line answer remini or nanomodel to
do some sort of deep reasoning, which would create a
crappy answer and burn tokens in the process. And this
(18:07):
is all thanks to the complicated way that open ai
insisted on building GPT five. Every single time you send
something to chat, GPT can trigger it to use a
different series of models audio vision, reasoning, each with their
own instructions, static prompts, all while pulling different tools, each
requiring their own instructions based on what you asked, and
reasoning models even have different depths of reasoning. Unlike four
(18:30):
to ZH, which is a multimodal model combining text, vision,
and voice, GPT five is a ratking of open AI's
models and tools that gets reborn every single time you
ask it to do anything prompt It can prompt cash
some things, but the core instructions not so much. But
let's get a little more granular, because I know I've
been quite repetitive, but this is detailed. So from what
(18:51):
I've been told, there are either one or two models
at work for the routing. I'm going to go with
what I think is most likely based on the discussions
I've had with people familiar with the architecture. I've heard
the term orchestrator thrown around potential to potentially suggesting the
router may be more omnipresent throughout the process, but I
was unable to confirm its existence. Reach out of you
here differently, I'll explain things as they were explained to me. Though.
(19:13):
When a user sensor prompt, it goes through the Splitter leg,
which decides to send the query on one of two paths.
One is called the fast path, where a query is straightforward,
such as a text only conversation that doesn't require any
analysis or extra tools or thinking, a path where the
query may require reasoning or more complex tools like codgeneration
or access to web browser for research. To be clear,
(19:33):
there are prompts where it may be split into multiple
paths that trigger multiple models or tools, each requiring their
own static instructions. From what I understand, the splitter model
is a completely separate large language model, though we don't
have a ton of details about it. I also, based
on conversations I've had, think there's a chance there could
be a separate model that sits above the splitter that
does much lighter classification of how a query might be routed.
(19:55):
So you ask it to do something, it might just
go Okay, this looks like it needs a tool and
going off. Why now? In any case, none of this
can be cashed because all of this exists before inference,
which is where, by the way, it's inference I've misstated
in the past. Is like it inferring, meaning inference is
everything that happens to get an output to you. So
all of the stuff that's happening. And by the way,
(20:17):
this is all a completely new cost that open ai
has created. No one does this like this, it's so
fucking stupid. But now we get to the chat leg.
Now the open ai has added layers of extraction, it
can begin cooking up the output, by which I mean
do inference. The chat leg is where the pieces that
the splitter model created are pulled together, each loaded into
their with their respective static prompts based on what the
(20:38):
user asked chat GPD five to do. Each piece of
the model a tool to generate Python and an image
generation tool a reasoning model. To generate an output has
to process an entirely new static prompt and again that's
every interaction. Remember, static prompts are effectively instruction. So the
splitter model has told each piece of the pie how
to act to create a particular output. As a result,
(20:58):
much of this can't be cashed, creating more and more
repetitious token bone response and mean to have to repeat
this stuff so that you really get him. The upshot
of the chat legs static prompt baggage is that you
can do a little more here, at least in theory,
because each component can be instructed separately, they can again,
in theory, be made to give more individualized, specialized outputs,
like creating an image with tags that is as I'll
(21:18):
give you an example of very shortly generated using a
specific reasoning model. I'm clutching it straws here. I don't
really know if this's better, but I'm trying to be reasonable.
I'm trying to be normal. Every day, I try and
be normal. Previously, Open Eye's advantage was that a model
like four to oh was a kind of a jack
of all trades. But to get the benefits of chat
(21:39):
GPT five and that's in air quotes, it's engaged a
conductor model that can just make things more convoluted, even
in the case of simple requests. Let me give you
an example. You upload a chart of NFL player's stats
and ask chat GPT to decide which is the best
of the group and create an image to show the results.
In GPT four oh, chat GPT would use one more
(22:00):
and thus one static prompt to look at the image,
decide which tools to use, and then how to format
the response. You only needed one prompt, which was cased
because one model can look at the stats for all
the data and make the decisions and then use the
image generation tool to make the final image. In GPT five,
the chet GPT conductor model would see the stats, root
it to a vision model requiring its own static prompt,
(22:20):
then a separate text only reasoning model, one that has
no ability to use tools, but it might be cheaper
to get an answer from and also requires a static prompt,
and that would then decide which players are best and
then spit out an output, and then root it to
a completely separate model that can generate texts to query
the image tool again need a stag prompt for this
to then generate the image. On top of all this
(22:41):
onerous baggage lies another problem. The GPT five's various models
are just more complex. By splitting out the component elements
of what a model can do and allowing each model
to have different levels of reasoning, even the cheaper ones
like MIDI and nano open AI has created an endless
combination of different reasons to have to make a brand
new static prompt instruction automated by a router, a large
language model that chooses what large language model to choose
(23:04):
for a query. It is, if I'm honest, kind of funny.
Reasoning models work when simply described by breaking up a
prompt into component pieces, looking over them, and deciding what
the best course of action might be. Chat GPT's router
is effectively an abstraction higher breaking up the prompt into
component pieces, then choosing different models for each of those pieces,
which may in turn be broken up by a reasoning model.
(23:26):
While I wouldn't say this is a hat on a
hat situation, it is at this point unclear what exactly
the benefits of chat GPT five's new architecture are, less hallucinations,
better answers. Based on what I've been told, this was
a decision made to increase the model's performance, what I
can say is that this very likely increased open ayes
overhead at a time when it needs to do the
exact opposite. Even if chat GPT five pushes people towards
(23:49):
cheaper models, it does so while guaranteeing extra costs and
latency and whatever signals it may learn as people use.
This will have to create significant benefits massive one hundred
percent plus game for it to be anything close to worthwhile.
While open ai is rude to may be smart in
terms of nuance of how it might answer a query,
and even that I question it most decidedly, is not
(24:09):
more efficient and may have actually increased the burn rate
for a company that will lose as much as eight
billion dollars this year, and I think that number might
be low too. Yet what I'm left with in writing
this script is how wasteful all of this is. Open Ai,
a company that is already incinerated upwards of fifteen billion
dollars in the last two years, has chosen to create
a less efficient way of doing business as a means
(24:31):
of eking out and monest the best performance improvements. It
just sucks. In our own lives, we're continually pushed and
pressured and punished if we get into debt, judged by
our peers and our parents, if we spend our money recklessly,
and if we're too reckless, we find ourselves less likely
to receive anything from credit to housing. Companies like open
Ai live by a different set of standards. Some Mormon
(24:52):
intends to lose more than forty four billion dollars by
the end of twenty twenty eight on open Ai, and
graciously told CNBC, like Lord Farquad, he was willing to
run at a loss for a long time where he
was treated like he was this smart, reasonable decision maker
rather than someone that needs to rein in their horrendous
spending habits and be more mindful. The ultra rich are
(25:12):
rewarded far more for their errant spending habits than we
ever are for any thrifty inness or austerity measures we make,
and none of us are afforded the level of grace
that Clammy sam Altman has been and has been feels appropriate.
Chat GPT five is an engineering nightmare, a phenomenally silly
and desperate attempt to duce what remains of the dying
innovation and excitement within the walls of open Ai. It's
(25:34):
not November twenty twenty two anymore. And let's be honest,
there really hasn't been anything exciting or interesting out this
company since GPT four. There's nothing exciting happening at this company.
As many as seven hundred million people a week allegedly
used chat GPT, but nobody can really say why. An
open Ai, despite its massive popularity. Cannot seem to stop
losing billions of dollars, and it can't seem to explain
(25:56):
why that's necessary other than this shit's really expensive. Dude,
Can anyone actually articulate a reason why we need to
burn billions of dollars to do this? What are we doing?
Why are we doing it? Has everybody just agreed to
do this until it becomes a completely untenable Do we
all yearn for the abyss so much that we can't
find camaraderie and admitting we were wrong? Look at GPT five.
(26:17):
This is, if you believe the hype, the best funded,
best resourced company in the world, with the greatest mind
and its helm and the greatest minds within its wars.
And this is the best they've gone. A large language
model that chooses which large language model will answer your question.
G fucking wit, Sam Mortman sounds dandy, and how much
better is this? You say, Oh, you can't really say
(26:38):
fucking brilliant? Hey does it do anything new?
Speaker 3 (26:40):
No?
Speaker 2 (26:41):
Oh, what's that? It's actually our job to work out
for ourselves. Thanks man, I love it. I love this shit.
And if you're someone that is a hype merchant listening
to this and you've done really well getting to the
end of the third part. By the way, I respect you.
I want you to email me and explain why they
should be justified in burning billions of dollars if you
tell me, if you tell me Aws, I will eat
(27:03):
you alive. I mean that, does it? I mean that
completely literally, I will unhinge my jaw. I'll eat you
like Kirby and shit out of dance. I've said that
one before, but I'm going with him in any case.
This three parter has also really reminded me how ridiculous
this is, how nonsensical things have become, and how much
(27:23):
waste has been kind of justified, justified on this idea
that this will become something by people that don't really
know what it does today or might do in the future.
None of this is going to end well, and not
even the boosters seem to be having fun anymore. Everybody's
just flating around waiting for it to end. Even Sam
Ortman seems tired of it all. I know, I bloody
(27:43):
well I am. Thank you for listening to Better Offline.
Speaker 3 (27:54):
The editor and composer of the Better Offline theme song
is Metosowski. You can check out more of his music
and audio projects at Mattasowski dot com m A T
T O S O W s Ki dot com. You
can email me at easy at better offline dot com
or visit better offline dot com to find more podcast
links and of course, my newsletter. I also really recommend
(28:16):
you go to chat dot where's youreaed dot at to
visit the discord, and go to our slash.
Speaker 2 (28:20):
Better Offline to check out I'll Reddit. Thank you so
much for listening. Better Offline is a production of cool
Zone Media. For more from cool Zone Media, visit our
website cool Zonemedia dot com, or check us out on
the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts.
Host
Ed Zitron
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