How To Argue With An AI Booster, Part Two

are easily disproven with the right information. And in that last episode we addressed the quips that say were in the early days of AI and that people doubted smartphones and the internet. Things they didn't do just like they did generative AI, which they should do in the cycle of grief. That's the denial stage. Now we're going to move on to bargaining. This is just that the dot com boom, even if of this collapses, the overcapacity will be practical for the market like the fiber boom was.

All right, folks, time for a little history. You know me, I'll love me some mystery. The fiber boom began after the Telecommunications Act of nineteen ninety six deregulated large parts of America's communications infrastructure, creating a massive boom, a five hundred billion dollars one to be precise, primarily funded with debt. Obviously, we're still using the infrastructure bought during that boom, and this fact is used as a defense of the insane

capex spending surrounding generative AI. High speed Internet is useful, right, sure, But the fiber optic boom period was also defined by a gluttony of overinvestment, ridiculous valuations, and genuine, outright fraud. In any case, this is not remotely the same thing, and anyone making this point needs to learn the very fucking basics of technology. Let's get going now. The fiber optic cable of this era is mostly owned by a

few companies. Forty two percent of Nvidia's revenue is from the Magnificent seven, and the companies buying these gps are for the most part not going to go bust once the AI bubble bursts. You can also already get the cheap fiber of this era too cheap aigpus already here. GPUs are depreciating assets, meaning that the good deals are

already happening. I found an in Vidia a one hundred for two or three thousand dollars multiple times on eBay, and you can get the h one hundreds which are more powerful for well, I think thirty grand and those things go forty five thousand retails, So not brilliant. Aigpus also do not have a variety of use cases and are limited by Kuda, in Vidia's programming libraries and APIs. Aigpus are integrated into applications using this language Kuda, and

this is specifically in Vidia's programming language. While there are other use cases scientific simulations, image and video processing, data science and analytics, medical imaging, and so on. Kuder is not a one size fits or digital panacea. While fiber optic cable was, and it was also put everywhere, it truly did set up the future. What are the these GPUs setting up exactly? Also, widespread access to cheaper GPUs has already happened, and what new use cases are there?

What are the new innovative things we can do? As a result of the AI bubble, there are now many, many, many, many, many different vendors to get access to GPUs. You can pay at an hourly rate. Who knows if it's probitable, but you can do it, and sometimes you can get them for as little as one dollars an hour, which is really not good. It definitely isn't making them money but putting the financial collapse aside. While they might be cheaper when the AI bubble bursts, does cheaper actually enable

people to do new stuff? Is costs the problem because I think the costs are going to go up. But even if they weren't going up, what are the things that you could do that a new What is the prohibitive cost? No one can actually answer this question because the answer isn't fun. GPUs are built to shove massive amounts of compute into one specific function, again and again and again, like generating the output of model, which remember,

mostly boils down to complex maths. Unlike CPUs, a GPU can't easily changed tasks or handle many little distinct operations, meaning that these things aren't going to be adopted for another mass market use case because there probably isn't one. In simpler terms, this was not an infrastructure built out. The GPU boom is a heavily centralized, capital expenditure funded asset bubble where a bunch of chips will sit in warehouses or kind of fallow data centers waiting for somebody

to make up a use case for them. And if an endearing one existed, we'd already have it, because we already have all the fucking GPUs. Now here's a really big boost e quip and I have been looking forward to. I get a lot of people asking you about this. I'm ed, you're so stupid. Why am I stupid? Exactly? Well, five really smart guys got together and wrote AI twenty twenty seven, which is a very real sounding extrapolation that shut the fuck up, shut up, shut up. AI twenty

twenty seven is fan fiction. If you were scared by this, and you're not a booster, you shouldn't feel bad. By the way this was written to scare you. By the way, if you don't know what it is I'm talking about, you should consider yourself lucky. It's essentially a piece of speculative fiction that describes where GENAI companies get fatter models that get exponentially better, and the US and China are in brailed in an AI arms race. It's really silly. It's so very silly, and I call it fan fiction

because it is. If we're thinking about this in purely intellectual terms. It's up there with my immortal and no, I'm not explaining that you can google that one for yourselves. It doesn't matter if all the people writing the fan fiction are scientists or that they have the right credentials. They themselves said that AI twenty twenty seven is a guess an extrapolation, which means guess with expert feedback, which means someone editing your fan fiction and involves experience that

open AI. There are people that worked on the shows they write fan fiction about. We're not even insulting fan fiction. By the way, go nuts, you're more You are one hundred times more ethically positive than these people. At least you admits fan fiction could knuckles get pregnant. I'm sure somebody's found out. I'm not going to go line by line and cut this any more than I'm going to go and do a lengthy takedown of someone's erotic Bancho

Kazoui's story, because both are fictional. The entire premise of this nonsense is that at one point someone invents a self learning agent that teaches itself stuff, and it does a bunch of other stuff requiring a Brazilian compute points with different agents with different numbers after them. There is no proof that this is possible. Nobody has done it,

and nobody will do it. AA twenty twenty seven was written specifically to fool people that want to be fooled, with big chants and the right technical terms used to lull the credulus into a wet dream and a New York Times column where one of the writers folds their hands and looks worried. It was also written to scare people that are already scared. It makes big, scary proclamations with tons of links to stuff that looks really legitimate, but when you piece it all together, is literally just

fan fection, except really not that endearing. My personal favorite part is mid twenty twenty six China Wakes Up, which involves China's intelligence agents. He's trying to steal Open Brains agent no idea who this companicably referring to please email if you can work it out to I don't care at business dot org before the headline of AI take some jobs. After Open Brain releases a model. Oh God,

I'm so bored even fucking talking about this now. Sarah lyonce puts this well, arguing that AI twenty twenty seven and AI in general is no different from the spurious spectral evidence used to accuse someone of being a witch during the Salem witch trials, and I quote and the evidence is spectral. What is the real evidence in AI twenty twenty seven beyond trust us and vibes? People who wrote it site themselves in the piece, do not demand

I take this seriously. This is so clearly a marketing device to scare people into buying your product before this imaginary window closes. Don't call me stupid for not falling for your spectral evidence. My whole life, people have been saying artificial intelligence is around the corner, and it never arrives. I simply do not believe a chatbot will ever be more than a chat pot, and until you show me it doing that, I will not believe it anyway. AI

twenty twenty seven is fan fiction nothing more. Just because it's full of fancy words and has five different grifters on its byline doesn't mean a goddamn thing. Now now, now, now, now, folks, we've all been waiting for this moment, and here's the ultimate booster quip the cust of inference is coming down.

This proves that things are getting cheaper. And here's a bonus trick for you before I get to my ben Here we go, ask them to explain whether things have actually got cheaper, and if they say they have, ask them why there are no profitable AI companies. If they say they're in the growth stage, ask them why there are no profitable AI companies. Again, I'd say it's been several years and not got one. At this point they should try and kill you. But really, I'm about to

be petty. I'm about to be petty for a fucking reason though. In an interview on a podcast from earlier this year that I will not even quote because the journalist in question did not back me up and it pisses me off, Journalist Casey Newton said the following about my work.

these companies and understand how the technology works. Because the entire industry has been on this curve where they are trying to find micro innovations that reduce the cost of training the models and to reduce the cost of what they call inference, which is when you actually enter aquarium the chat GBT and if you plotted the curve of how the cost has been following over time, Deep Seek is on that curve. Right, So everything that Deep Seek did it was expected by the AI labs that someone

would be able to do. The novelty was just that a Chinese company did it. So to say that it like up ends expectations of how AI would be built is just purely false and the opinion of somebody who does not know what he's talking about.

If I was doing this, I personally would not have said these things in a sentence that began with me suggesting that I be in case and Newton in this example knew how the technology works. Now here's the case in Newton wib inference, which is when you actually enter a query into chat GPT. This statement is false. It's not what inference means. Inference and I've gotten this wrong in the past too. I'm being accountable. Is everything that happens when you put in a prompt to generate an output.

It's when an AI based on your infers meaning. To be more specific, in quoting Google machine learning, inference is the process of running data points into a machine learning model to calculate an output, such as a single numerical score. Except that's what these things are bad at. But nevertheless, Casey will try and weasel out of this one and

say this is what he meant. It wasn't. He also said, if he planted the curve of how the cost of inference has been falling over time, well that's wrong, Casey, that's wrong the man. The cost of inference has gone up over time. Now, Casey, like many people who talk about stuff without learning about it first is likely referring to the fact that the price of tokens for some models has gone down in some cases. But you know what, folks, let's establish and facts about inference. I'm doing the train.

I'm pulling the big horn on the invisible train. I'm cooking now. Inference is a thing that costs money, is entirely different to the price of tokens, and conflating the two is journalistic malpractice. The cost of inference would be the price of running the GPU and the associated architecture. Of course, we do not at this point have any real insight into token prices are set by the people who sell access to the tokens, such as open ai

and Anthropic. For example, open ai dropped the price of its O three models token costs almost immediately after the launch of Claude Opus four. Do you think it did that because the price of serving the models got cheaper. If you do, I don't know how you possibly put your trousers on every morning without cutting yourself in half. Now, the cost of inference conversation comes from articles that say that we now have models that are cheaper that can

now hit higher benchmark scores. Though the article I'm referring to, which will be in the show notes, is from November twenty twenty four, and the comparison it makes is between GPT three, which is from November twenty twenty one, and LAMA three point two to three b September twenty twenty four. Now, the suggestion is in any case, that the cost of

inference is going down ten x year over year. The problem is, however, that these are raw token costs, not actual expressions of evaluations of token burn in a practical setting. And to really I realized that it was a bit technical. These are just what it costs to do something. It doesn't actually tell you how how many tokens will be burned at what volume they will be burned, because that would change things. And well, wouldn't you know it, the

cost of inference actually went up as a result. In an excellent blog from Killer Code, and I did not get the chance to find out the pronunciation of this second name, so I'm just going to call her. It is ewasyz sz Ka. I am so sorry. I would rather spell it out, miss than actually mispronounce it. I hate when people say z tron wrong. Great blog anyway, let me quote, application inference costs increase for two reasons. The frontier models cost per token stayed constant, and the

token consumption per application grew a lot. Token consumption per application grew a lot because models allowed for longer context windows and bigger suggestions from the models. The combination of a steady price per token and more token consumption caused that inference cost to grow about ten times over the

past two years. To explain that in really simple terms, while the costs of old models may have decreased, new models, which you need to do most things, cost about the same, and the reasoning that these new models use do actually burn way way more tokens. When these new models reason, they break the user's input down and break it into component parts, then run inference on each of those parts.

When you plug an L and M into an AI coding environment, it will naturally burn an absolute shit ton of tokens, in part because of the large amount of information you have to load into the prompt and the context window, or the amount of information you can load in at once, and in part because generatingcode is inference intensive and also breaking down all those coding tasks. At each of those tasks requiring a coding tool and taking a bunch of inference themselves. It's really bad. In fact,

the inference costs are so severe. The Killer Code says that a combination of a steady price for token and more token consumption caused app inference costs to grow about ten x over the last two years. I'm repeating myself. I realized, But I really need you to get one thing, which is that the cost of inference went up. But

I'm not done. I refuse to let this point go because people love to say the cost of inference is going down when the cost of inference has increased, and they do so to a national audience, all while suggesting I'm wrong somehow and acting superior. I don't like being made to feel this way. I don't think it's nice to do this to people. And if you're gonna do it, if you have the temerity to call someone out directly, at least be fucking right. I'm not wrong, You're wrong.

In fact, software developer influencer Theo Brown recently put out a video called I was wrong about AI costs They keep going up, which he breaks down as follows, reasoning models are significantly increasing the amount of output tokens being generated. These tokens are also more expensive. In one example, Brown finds that Grockfor's reasoning mode uses six hundred and three tokens to generate two words. This was a problem across every single reasoning model, as even cheap reasoning models would

do the same thing. As a result, tasks are taking longer and burning more tokens. Another writer called Ethan Deing noted a few months ago that reasoning models burn so many tokens that there is no flat subscrips price that works in this new world. As the number of tokens they consume to an absolutely nuclear the price drops have

also for the most part stopped. You cannot at this point fairly evaluate whether a model is cheaper just based on its cost per tokens, because reasoning models inherently burn and are built to inherently burn more tokens to create

an output. Reasoning models are also the only way that model developers have been able to improve the efficacy of new models, using something called test time compute to burn extra tokens to complete a task, and in basically anything you're using today, there's going to be some sort of reasoning model, especially if you're coding, the cost of inference has gone up. Statements otherwise are purely false and are the opinion of somebody who does not know what he's

talking about. But you ask, could the costs of inference go down? Maybe it sure isn't trending that way, nor has it gone down yet. I also predict that there's going to be some sort of sudden realization in the media that inference is going up, which is kind of

already started. The Information had a piece on it in late August where they note that into it paide twenty million dollars to as your last year, primarily to access open AI's models, and it's on track to spend thirty million this year, which outpaces the company's revenue growth in the same period, raising questions about how sustainable the spending is and how much of the cost it can pass along to customers. Christopher Mims and The Wall Street Journal

also had a piece about the costs going up. Do not be mad at Chris. Chris and I chatted before he submitted that piece, like he literally on Blue Sky called me out if fucking rocks. By the way, big up to Chris Mims because it's nice to see the mainstream media actually engaging with these things, even though it's dangerous to the bubble. But you know what, the truth must win out, and the problem here is that the

architecture underlying large language models is inherently unreliable. I imagine open AI's introduction of the router to chat GPT five as an attempt to moderate both the costs of the model chosen and reduce the amount of exposure to reasoning models for simple queries. Though Sam Moltman was boasting on August tenth about the significant increase in both free and paid users exposure to reasoning models, they don't teach you this

in business school. Still, A study written up by VentureBeat found that open weight models burn between one point five to four times more tokens, in part due to a lack of token efficiency and in part thanks to you guessed it reasoning models. I quote the finding's challenge of prevailing assumption in the AI industry that open source models

offer a clear economic advantages over proprietary alternatives. While open source models typically cost less per token to run, the study suggests that this advantage could be and I quote the study easily offset if they require more tokens to reason about a given problem, and models keep getting bigger

and more expensive too. So why did this happen? Well, it's because model developers hit a wall of diminishing returns and the only way to make models do more was to make them burn more tokens to generate a more accurate response, which is a very simple way of describing reasoning a thing that opening I launched in September twenty twenty four, and others followed. As a result, all the gains from powerful new models come from burning more and

more tokens. The cost per million token number is no longer an accurate measure of the actual cost of generative a because it's much much, much much harder to tell how many tokens of reasoning model may burn, and it varies as the boint the O Boying, I'm keeping that all right. You get the real cuts as the O Brown noted from model to model. In any case, there really is no changing this path. These companies are out of ideas now another another one of my favorite ultimate

booster gripts. This is a classic and I still get this on social media. I'm I have people yapping in my ear saying open air and Anthropic are just like Uber because Uber bent twenty five billion dollars over the course of fifteen or so years and look look edward, they're now profitable. Why are you calling me Airport? Shut up? This proves the open Ai, a totally different company with

different economics, will be totally fine. So I've heard this argument maybe fifty times in the last year, to the point that I had to talk about it in my piece how does open Ai Survive, which I also turned into a podcast around July twenty twenty four. Go back and link a link to it in the piece. Yaddy yaddy, yadda. Nevertheless, people make a few points by Uber and AI that I think are fundamentally incorrect, and I'm going to break

them down for you now. They claim that AI is making itself too big to fail and betting itself everywhere and becoming essential, and none of these things are the case. I've heard this argument a lot, by the way, and it's one that's both ahistorical and alarmingly ignorant of the very basics of society. But ed the government, no no, no, no, no, no, you've heard, you've heard. OpenAI got a two hundred million dollar Defense contract with an estimated completion date of July

twenty twenty six. And just to be clear, that's up to two hundred million dollars, and that they're selling chat GBT Enterprise to the US government for a dollar a year, along with Anthropic doing the same thing, and even Google's doing it, except they're doing forty cents for a year. Now, you're probably hearing this and thinking, ah shit, this means the government's paid them. They're never going away. And I cannot be clear enough that you believing this is the

very intention of these deals. They are built specifically to make you feel like these things are never going away. This is also an attempt to get in with the government at a rate that makes train these models a no brainer. At which point I ask, and the government is going to have cheap access to AI software does not mean that the government relies on m every member of the government having access to chat GPT, something that is not even necessarily the case, does not make this

software useful, let alone essential. And if open ai burns a bunch of money making it work for them, it still won't be essential because large language models are not actually that useful for doing stuff now let's talk Uber. Uber was and is useful, which eventually made it essential.

Uber used lobbyist Bradley Tusk to steam roll local governments into allowing Uber to operate in their cities, but Tasks did not have to convince local governments that Uber was useful or have to train people how to use Uber. Uber's too big to fail moment was that local cabs kind of fucking sucked just about everywhere. You ever try and take a yellow cab from downtown Manhattan to Hoboken, New Jersey, or Brooklyn or Queen's Do you ever try and pay with a credit card? How about trying to

get a cab outside a major metropolitan area. Do you remember how bad it was? It was really awful. I don't think people realize or remember how bad it was. And I'm not saying that Uber is good. I'm not glorifying Uber in any way. But the experience that Uber replaced was very, very bad. As a result, Uber did become too big to fail because people now rely on

it because the old system sucked. Uber used its masses of venture capital to keep prices low to get people used to it too, but the fundamental experience was better than calling a cab company and hoping they showed up. I also want to be clear that this is not me condoning Uber take public transport, if you can to

be clear. Uber has created a new kind of horrifying, extractive labor practice which deprives people of benefits and dignity, paying off academics to help the media gloss over the horrors of their platform, and also now having to increase prices so that they reached profitability by doing that. That isn't something that's going to happen with genitive AI. Just the costs are too high, They're way too high. But anyway, what is essential about generative AI? What exactly, and be specific,

is the essential experience of generative AI? What are we if chat, GPT disappeared tomorrow, what actually disappears? And on an enterprise or governmental level, what exactly are these tools doing for governments that would make removing them so painful? What use cases, what outcomes? If your answer here is to say, well, they're putting it in and they're choosing, they're choosing which people to cut out of benefits, and please, goddamn, this is what they want you to do. They want

you to be scared so they can feel powerful. They're not doing that. You notice that we get all these horrible stories by the way of internal government things, shoving stuff into olms. You know what, we don't get another thing we don't get, oh and then have It's just they're doing this scary, bad thing that they shouldn't be. This shouldn't be putting people's private information into anyway. I'm rambling.

Uber's essentral nature is that millions of people use it in place of regular taxis, and it effectively replaced de krepit of exploitative systems like the yellow cab Medallions in New York with its own tech enabled exploitation system that nevertheless worked far better for the user. Okay, I also want to do a side note just to acknowledge that the disruption from Uber brought something to the medallion system

that was genuinely horrendous. The consequences were horrifying for the owners of the medallions, some of who had paid more than a million dollars for the privilege of driving a New York cab and were burdened under mountains of debt. That our system is so fucking evil. I think it's horrifying, and I think the payday loan people involved should all

be in fucking prison, worst scum of the world. The people who are taking advantage of people come to this country to drive a fucking cab that they have to take out massive loans to buy. That is evil. Uber is also just to be clear, but that also is That's the point I'm trying to make. Should feel sorry

for the victims of that system. That system was a kind of corruption unto itself anyway, getting back to the thing, because I don't know, I feel I actually feel a lot for the people who are the victims of the medallion system. It's fucking rough, and every time I think of it, I feel very sad inside. But let's get back to the episode. I don't want to think about it any longer. There really are no essential use cases

for Chat, GPT, or really any Genai system. You cannot point to one use case that is anywhere near as necessary as cabs in cities, And indeed the biggest use cases, things like brainstorming and search, are either easily replaced by any other commoditized The lam will already exist in the case of Google Search. Now let's do another boost quip data centers are important economic growth vehicles and now helping drive innovation and jobs throughout America. Having data centers promotes innovation,

making open AI and AI data centers essential. And the answer to there is no no. Sorry, this is a really simple one. These data centers are not in and of themselves driving much economic growth other than the costs of building them, which I went into last episode. As I've discussed again and again, there's maybe forty billion dollars in revenue and no profit coming out of AI companies.

There isn't any economic growth. They're not holding up anything other than the massive, massive infrastructure built to make them make no money and lose billions. There's no great loss associated with the death of large language models or the death of this era. Taking away Ober would be genuinely catastrophic with some people's ability to get places and people's jobs, even if they are horrifyingly underpaid. But here's another booster, quipped.

Uber burned a lot of money twenty five billion dollars or more to get where it is today. Ooh, mister Zichron, mister Zitchron, You're dead. And my response is the open AI and anthropic are both separately burned more than four times as much money since the beginning of twenty twenty four as Uber did in its entire existence. So the classic and wrong argument about open ai and companies like open ai is that Uber burned a bunch of money,

is now cash flow positive or profitable. I want to be clear that Uber's costs are nothing like large language models, and making this comparison is ridiculous and desperate. But let's talk about raw losses, shall we, and where people are

making this assumption. So Uber lost twenty four point nine billion dollars in the space of four years from twenty nineteen to twenty twenty two, in part because of the billions it was spending on sales and marketing in R and D four point six billion dollars and four point

eight billion dollars respectively in twenty nineteen alone. It also massively subsidized the cost of rights, which is why prices had to increase, and spent heavily on driver recruitment, burning cash to get scale, you know, the classic Silicon Valley way. This is absolutely nothing like how large language models are growing. And I'm tired of defending this point, but defended I shall open AI and Anthropic burn money primarily through compute

costs and specialized talent. These costs are increasing, especially with the rush to hire every single AI scientists at the most expensive price possible. There are also essential immovable costs that neither open AI or Anthropic have to shoulder. The construction of the data centers necessary to train and run inference for their models, and of course the GPU is

inside them, which I will get to in a little bit. Yes, Uber raised thirty three point five billion dollars through multiple rounds of posting IPO dam though it raised about twenty five billion dollars in actual funding. Yes, Uber burned an absolutely as ton of money. Yes, Uber a scale, but Uber has not burned money as a means of making its product functional or useful. Uber worked immediately. I mean was twenty twelve. I think I used it for the

first time. Maybe earlier. No, no, it would have been twenty ten. It worked immediately. You used it, You're like, wow, this, I can just put in my address. I don't have to say my address three times because I have a British accent and nobody can fucking understand me. Sometimes you can,

though you're special. Yeah, it was really obvious that it worked, and also the costs associate with Uber and its capital expenditures from twenty nineteen through twenty twenty four were around two point two billion dollars, by the way, on miniscule compared to the actual real costs of open ai and Anthropic. Both open Ai and Anthropic around five billion dollars each in twenty twenty four, but their infrastructure was entirely paid

for by either Microsoft, Google, or Amazon. And by which I mean the building of it and the expansion they're in what we don't know how much of this infrastructure is specifically for open ai or Anthropic. As the largest model developers, it's fair to assume that a large chunk at least thirty percent of Amazon and Microsoft's capital expenditures have been to support these loads. Great sentence to cut

and listen to again. I also leave out Google, as it's unclear whether it's expanded its infrastructure for Anthropic, but we know Amazon has done so. As a result, the true cost of open ai and Anthropic is at least ten times what uberburned. Amazon spent eighty three billion dollars in capital expenditures in twenty twenty four and expects one hundred and five billion dollars are the fuckers in twenty

twenty five. Microsoft spent fifty five point six billion dollars in twenty twenty four and expects to spend eighty billion dollars this year. I'm actually confident most of that is open Ai, but based on my conservative calculations, the true cost of open ai is at least eighty two billion dollars, and that only includes capex twenty twenty four onwards. Based

on thirty percent of Microsoft's capex. It's not everything has been invested yet in twenty twenty five, and open Ai might not be all of the capex, and also the forty one point four billion dollars of funding that open ai has received so far. The true cost of Anthropic is around seventy seven point one billion dollars, and that's not including the thirteen billion they just raised, but it does include all their previous funding and thirty percent of

Amazon's capex in the beginning of twenty twenty four. Now these are in exact comparisons, but the classic argument is that Uber burned lots of money and worked out okay, when in fact the combined couple expenditures from twenty twenty four onwards that are necessary to make open ai and Anthropic worker each on their own four times what Uber burned in over a decade. I also believe these numbers

are conservative. There's a good chance that open ai and Anthropic dominate the capex of Amazon, Google, and Microsoft in part because of what the fuck else are they buying all these GPUs for as their own AI services don't appear to be making much money at all anyway. To put it real simple, AI has burned way more in

the last two years than Uber burned in ten. Uber didn't burn money in the same way, didn't burn much in the way of capital expenditures, didn't require massive amounts of infrastructure, and isn't remotely the same in any way, shape or form other than that it burned a lot of money. And that burning wasn't because it was trying to build the core product. It was trying to scale. It's all so stupid, And you know what, I'm not

even done. Our next and final AI booster episode will breeze through the dumbest of the dumb arguments, and I'll say why I'm finally drawing a line under these arguments for real, because it needs to be said. We need to say something. I hope you've enjoyed this, see you tomorrow, godspeed. Thank you for listening to Better Offline. The editor and composer of the Better Offline theme song is Matasowski. You can check out more of his music and audio projects at Matasowski dot com, M A T T O S

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