Speaker 1: Zone Media. Welcome to Better Offline. I'm your host ed
Speaker 1: zitrun as Eva. Check out the episode notes for merchandise,
Speaker 1: my newsletter, various links, things that I just write for myself.
Speaker 1: I don't actually leave little notes, but maybe I should.
Speaker 1: But as promised in my last monologue, we've got a
Speaker 1: three part episode this week, and we're talking about AI boosters,
Speaker 1: the people who insist despite very little proof and all
Speaker 1: evidence of the contrary. The generative AI is the future,
Speaker 1: open AI and anthropic of perfectly healthy companies. And what
Speaker 1: we're witnessing isn't an inflation of a massive, dangerous bubble
Speaker 1: that might eat our entire economy, but rather the emergence
Speaker 1: of a brand new paradigm in tech that's just as
Speaker 1: meaningful as the smartphone and cloud computing revolutions. I even
Speaker 1: saying that out loud makes me feel a little crazy,
Speaker 1: but yeah, that's what they believe. These people exist. They're
Speaker 1: often wrong. In fact, they're mostly wrong, and they're also
Speaker 1: really really annoying. And so as a public service, I'm
Speaker 1: going to spend the next three episodes explaining how to
Speaker 1: talk with them without losing your sanity, will to live,
Speaker 1: or your ability to operate in society, and now what
Speaker 1: makes me qualified to do this well? In the last
Speaker 1: two years, I've written no less than half a million words,
Speaker 1: with many of them dedicated to breaking both existent and
Speaker 1: previous myths about the state of technology and the tech
Speaker 1: industry itself. Now let's start with something every critic is experienced,
Speaker 1: the massive double standard between those perceived as skeptics and
Speaker 1: those who are optimistic or optimists. To be skeptical of
Speaker 1: ais to commit yourself to a near constant amount of
Speaker 1: demands to prove yourself and endless nags of But what
Speaker 1: about with each one, no matter how small, presented as
Speaker 1: a fact that defeats any points you may have had,
Speaker 1: have or will have in the future. Conversely, being an
Speaker 1: optimist allows you to take things like AI twenty twenty seven,
Speaker 1: which I will fucking get to seriously, to the point
Speaker 1: that you can write an entire feature length fan fiction
Speaker 1: piece in the New York Times and nobody will bat
Speaker 1: an eye lead. In any case, things are beginning to
Speaker 1: fall UPALM two of the actual reporters, the real ones
Speaker 1: at the New York Times rather than column nests, suit
Speaker 1: kind of act like adult babies. Reported in late August
Speaker 1: that Meta is yet again restructuring its AI department for
Speaker 1: the fourth time, and that it's considering downsizing their overall
Speaker 1: AI division, which sure doesn't seem like something you'd do
Speaker 1: if you thought AI was the future. Meanwhile, the markets
Speaker 1: are thoroughly spooked by an MIT study covered by Fortune
Speaker 1: that found that ninety five percent of generative AI pilots
Speaker 1: and companies are failing in providing no ROI. And though
Speaker 1: MIT Nanda has now replaced the link to the study
Speaker 1: with some sort of Google form to request access, you
Speaker 1: can find the full pdf linked in the show notes.
Speaker 1: By the way, this is the kind of thing that
Speaker 1: is a PR firm wanting to try and set up interviews,
Speaker 1: not for me, thank you, just like to read the
Speaker 1: thing that you put out very rude. In any case,
Speaker 1: the report is actually grimmer than Fortune made its sound,
Speaker 1: saying that ninety five percent of organizations are getting zero
Speaker 1: return on GENERATIVAI. The report says that adoption is high,
Speaker 1: transformation is low, adding that few industries show the deep
Speaker 1: structural shifts associated with past general purpose technologies, such as
Speaker 1: new market leaders, disrupted business models, or measurable changes in
Speaker 1: customer behavior, and saying that out loud. That is right,
Speaker 1: what is being disrupted by AI? Search? Kind of that
Speaker 1: just means that search sucks anyway. Yet the most damning
Speaker 1: part was there was a part called the five myths
Speaker 1: about AI in the Enterprise, which is probably the most
Speaker 1: wilting takedown of this movement I've ever seen. And I'm
Speaker 1: going to quote an adverbatum okay number one. And they
Speaker 1: present these as the first statement is something that isn't
Speaker 1: going to happen, or they're questioning AI will replace most
Speaker 1: jobs in the next few years. What it actually says
Speaker 1: is research found limited layoffs from JENAI, and only in
Speaker 1: industries that are already affected significantly by AI. There is
Speaker 1: no consensus among executives as the hiring levels over the
Speaker 1: next three to five years. Another statement they challenge generative
Speaker 1: AI is transforming business. No, no, No, adoption is high, but
Speaker 1: transformation is rare. Only five percent of enterprises have AI
Speaker 1: tools integrated into workflows at scale, and seven of nine
Speaker 1: sectors show no real structural change. And I want to
Speaker 1: thank them for putting this there. I made this exact
Speaker 1: point in February in my newsletter. There's no AI revolution.
Speaker 1: Links in the show notes as ever, enterprises are slow
Speaker 1: in adopting new tech is another commonly held thing, and
Speaker 1: they say enterprises are extremely eager to adopt AI, and
Speaker 1: ninety percent of seriously explored buying an AI solution. Another
Speaker 1: statement they challenge, the biggest thing holding back AI is
Speaker 1: model quality, legal data, and risk. And then they say,
Speaker 1: which is my favorite quot, what's really holding it back
Speaker 1: is that most AI tools don't learn and don't integrate
Speaker 1: well into workflows. I really do love that. I love
Speaker 1: that so much because it's like the thing holding AI
Speaker 1: back is that it sucks. And the final one, the
Speaker 1: final challenge statement they make is the best enterprises are
Speaker 1: building their own tools. Internal builds fail twice as often,
Speaker 1: and that's twice as often in a thing where ninety
Speaker 1: five percent anyway, These are brutal, dispassionate points that directly
Speaker 1: deal with the most common boosterisms. Generative AI isn't transforming anything.
Speaker 1: AI isn't replacing anyone. Enterprises are trying to adopt generative AI,
Speaker 1: but it doesn't fucking work. And the thing holding back
Speaker 1: AI is the fact it doesn't fucking work. This isn't
Speaker 1: a case where the enterprise is suddenly going to save
Speaker 1: these companies because the enterprise has already tried and it
Speaker 1: is not working. And what's hilarious as well is so
Speaker 1: many people say, well, there's just this enterprise opportunity they
Speaker 1: haven't got into yet. Actually they have, They've got into
Speaker 1: the enterprise. They're sticking their fingers and all the bits
Speaker 1: of the sas and it isn't working. And the enterprise
Speaker 1: loves money. The enterprise will do even if it's a
Speaker 1: genuinely evil product. They will push it, Like look at Salesforce,
Speaker 1: an entire company based on them. Anyway, an incorrect read
Speaker 1: of the study that's been going around is that the
Speaker 1: learning gap that makes these things less useful is because
Speaker 1: of human issues like human error, when the study actually
Speaker 1: says that the fundamental gap that defines the GENAI divide
Speaker 1: is that users tools that don't adapt model quality fills
Speaker 1: without context, and UX suffers when systems can't remember this
Speaker 1: isn't something you as a person learn your way out of.
Speaker 1: These products don't do what they're meant to do, and
Speaker 1: people are realizing it when they try and use them. Nevertheless,
Speaker 1: boosters will still find a way to twist this study
Speaker 1: to mean something else. They'll claim that AI is still early,
Speaker 1: that the opportunity is still there, that we didn't confirm
Speaker 1: that the Internet and smartphones with productivity boosting, or that
Speaker 1: we're in the early days of AI somehow three years
Speaker 1: and hundreds of billions of dollars and thousands of articles
Speaker 1: in Yeah, we're in the early days. We're in the
Speaker 1: early days when all the money in all the land
Speaker 1: has gone into this. And I'm tired. I'm tired of
Speaker 1: having the same arguments with these people, and frankly, I'm
Speaker 1: sure you are too. No matter how blindly obvious evidence
Speaker 1: is to the contrary, they will find ways to ignore it.
Speaker 1: They continually make these smug comments about people wishing things
Speaker 1: would be bad or suggesting you are stupid, and yes,
Speaker 1: that is their belief, by the way, for not believing
Speaker 1: that generative AI is disruptive today. And in this three
Speaker 1: part tern I'm going to give you the tools to
Speaker 1: fight back against the AI boosters in your life. I'm
Speaker 1: going to go into the generalities of the booster movement,
Speaker 1: the way they argue, the tropes they cling to, and
Speaker 1: the ways in which they use your own self doubt
Speaker 1: against you. They're your buddy, your boss, a man in
Speaker 1: a Ginghamshire Epic steakhouse who won't leave you the fuck alone.
Speaker 1: A editor, a writer, a founderer, or just a common
Speaker 1: or garden conn artist. Whoever the booster in your life is,
Speaker 1: I want you to have the words to fight them with.
Speaker 1: So an AI booster is not, in many cases, an
Speaker 1: actual fan of artificial intelligence. People like Simon Willison or
Speaker 1: Max Wolf who actually work with LMS on a daily
Speaker 1: basis don't see the need to repeatedly harass everybody or
Speaker 1: talk down to them about their unwillingness to pledge allegiance
Speaker 1: to the graveyard smash of generative AI. In fact, the
Speaker 1: closer I've found someone to actually building things with LLMS,
Speaker 1: the less likely they are to emphatically argue that I'm
Speaker 1: missing out on something by not doing so myself. No, no, no.
Speaker 1: The AI booster is symbolically aligned with generative AI. They're
Speaker 1: fans in the same way that somebody is a fan
Speaker 1: of a sports team. Their houses in blazons with every
Speaker 1: possible piece of tat and crap they can find. They're Sundays,
Speaker 1: living and dying by the successes of the team. Except
Speaker 1: even fans of the Dallas Cowboys of a tighter grasp
Speaker 1: on reality, but not Micah Parsons anyway. Kevin Rus and
Speaker 1: Casey Newton are two of the most notable boosters, and
Speaker 1: as I'll get to later in this series, neither of
Speaker 1: them have a consistent or comprehensive knowledge of AI, despite
Speaker 1: being at The New York Times. Though Casey Newton is
Speaker 1: a contractor, he is a contractor just for a podcast,
Speaker 1: which I can't insult due to my own contractual relationships. Nevertheless,
Speaker 1: they will insist that everybody is using AI for everything,
Speaker 1: which is the title of an article they put out,
Speaker 1: a statement that even a booster should realize is incorrect
Speaker 1: based on the actual abilities of the models. But that's
Speaker 1: because it isn't about what's happening. It's not about what's
Speaker 1: actually happening. It's about allegiance. AI symbolizes something to the
Speaker 1: AI booster, a way that they're better than other people.
Speaker 1: That makes them superior because they're, unlike cynics and skeptics,
Speaker 1: able to see the incredible potential in the future of AI,
Speaker 1: but also how great it is today, though it never
Speaker 1: seemed to be able to explain why it is other
Speaker 1: than it replaced search for me and I use it
Speaker 1: to draw connections between articles I write, which is something
Speaker 1: I do for free without AI with my fucking brain.
Speaker 1: Boosterism is a kind of religion interested in finding symbolic
Speaker 1: proof that things are getting better in some indeterminate way,
Speaker 1: and that anyone that chooses to believe otherwise is ignorant
Speaker 1: or stupid or I actually don't know what it is
Speaker 1: that they're meant to be missing. Let me give you
Speaker 1: an example. Thomas Potastic. He wrote a piece called my
Speaker 1: ais skeptic friends are all nuts, and it was catnip
Speaker 1: for boosters. A software engineer using technical terms like interact
Speaker 1: with GIT and MCP, vague chants, and of course an
Speaker 1: extremely vague statement that says hallucinations aren't a problem, and
Speaker 1: I quote now, I'm sure there are still environments where
Speaker 1: hallucination matters. But hallucination is the first thing developers bring
Speaker 1: up when somebody suggests using llms, despite it being more
Speaker 1: or less a solved problem. Is it anyway? My favorite
Speaker 1: favorite part though, let me quote this. A lot of
Speaker 1: LLM skepticism probably isn't really about llms. It's projection. People
Speaker 1: say llms can't code, when what they really mean is
Speaker 1: llms can't write Rust which by the way, is a
Speaker 1: coding language. Fair enough, but people select languages in part
Speaker 1: based on how well lms work with them, So RUSS
Speaker 1: people should get on that. What nobody projects more than
Speaker 1: an AI booster. They thrive on the sense that they're
Speaker 1: oppressed and villainized after years of seemingly every other, every
Speaker 1: goddamn outlet on Earth claiming they're right, regardless of whether
Speaker 1: there's any proof. They sneer and jeer and cry constantly
Speaker 1: the people not showing adequate amounts of awe when an
Speaker 1: AI lab says we did something in private, we can't
Speaker 1: share it with you, but it's so cool, and constantly
Speaker 1: act as if they're victims as they spread outright misinformation,
Speaker 1: either through getting things wrong or never really caring enough
Speaker 1: to check if they're right. Also, none of the booster
Speaker 1: arguments actually survive a thorough response, as Nick Saresh proved
Speaker 1: with his hilarious and brutal takedown of Potastics. Peachs Siesh
Speaker 1: is a great guy. He's been on the show before,
Speaker 1: and I've linked to his piece in the show notes,
Speaker 1: and I'm going to bring him back on. He's written
Speaker 1: for my newsletter as well. Absolute legend now there are
Speaker 1: I believe some people who truly do love using llms,
Speaker 1: yet they are not the ones defending them. But Tastic's
Speaker 1: peace strips with condescension to the point that I feel
Speaker 1: like he's trying to convince himself how good lms are.
Speaker 1: Because boosters are eternal victims. He wrote them a piece
Speaker 1: that they could send around to skeptics saying hh see
Speaker 1: without being able to explain why it was such a
Speaker 1: brutal takedown, mostly because they can't express why other than well,
Speaker 1: this guy gets it. One cannot be a big, smart
Speaker 1: genius that understands the glory and power of AI while
Speaker 1: also acting like a scared little puppy every time somebody
Speaker 1: tells them it sucks. You know what, This is a
Speaker 1: great place to start. This is a great place to
Speaker 1: get into how to deal with AI boosters because AI
Speaker 1: boosters love being victims, and you should not play into it.
Speaker 1: When you speak to an AI booster, you may get
Speaker 1: the instinct to shake them vigorously or respond to their
Speaker 1: posts by saying to do something with your something, or
Speaker 1: that they're stupid. I understand the temptation, but you want
Speaker 1: to keep a level head here. Keep your head on
Speaker 1: the swivel. They thrive on this victimization. I'm sorry. If
Speaker 1: you're an AI booster and this makes you feel bad,
Speaker 1: Please reflect on your work and how many times you've
Speaker 1: referred to somebody who didn't understand AI in a manner
Speaker 1: that suggested that they were ignorant or tried to gaslight
Speaker 1: them by saying AI was powerful while providing no actionable
Speaker 1: ways of proving this or prove or just being able
Speaker 1: to point in it being powerful. You cannot and should
Speaker 1: not allow these people to act as if they're being
Speaker 1: victimized or other Throughout this series, I'm going to address
Speaker 1: a very specific thing. I'm going to use a term
Speaker 1: boost the quip. This refers to the things that they
Speaker 1: say and how often you hear them. These are lines
Speaker 1: that you'll hear them say again and again and again
Speaker 1: and again. They're common arguments, common cliches that demand response.
Speaker 1: And let's start with our first booster quip. You are
Speaker 1: just being a hater for attention. Contrarians just do it
Speaker 1: for clicks and headlines. First and foremost, there are boosters
Speaker 1: are pretty much every major think tank, government agency, and
Speaker 1: media out there. It's extremely lucrative being an AI booster.
Speaker 1: You're showered with panel invites access to executives, and you're
Speaker 1: able to get headlines by saying how scared you are
Speaker 1: of the computer. And it's really easy to do. So
Speaker 1: being a booster is easy. And I must be clear
Speaker 1: when I say booster, it doesn't always have to mean
Speaker 1: over It could just mean the things you choose not
Speaker 1: to do. It could mean the things you choose not
Speaker 1: to criticize them for, or the things you just write
Speaker 1: down that they say. But really we're talking about the
Speaker 1: worst of them. But if you hear that sentence and
Speaker 1: you don't think you're a booster, you can be a booster,
Speaker 1: by the way, if you just choose not to criticize them.
Speaker 1: But we're talking about the real assholes. Being a critic
Speaker 1: requires you to constantly have to explain yourself in a
Speaker 1: way that boosters never have to. Now, if a booster
Speaker 1: says this to you, if they say you're just being
Speaker 1: a hater for attention, you're just doing this for clicks,
Speaker 1: ask them to explain first of all what they mean
Speaker 1: by clicks or attention, and how they think you are
Speaker 1: monetizing it, how this differs in its success from say,
Speaker 1: anybody who interviews and quotes Sam Altman or Dario Wario,
Speaker 1: Ama Day or whomever from Anthropic on Hard Fork. Ask
Speaker 1: them what the difference is, and ask them why do
Speaker 1: they believe your intentions as a critic are somehow malevolent
Speaker 1: as opposed to those literally reporting what the rich and
Speaker 1: powerful want them to. There's no answer here, because this
Speaker 1: is not a coherent point of view. Boosters are more successful,
Speaker 1: get more perks, and are in general treated better than
Speaker 1: any critic and pretty much every major out there. Fundamentally,
Speaker 1: these people exist in the land of the vague, and
Speaker 1: they don't like it when you force them to get specific.
Speaker 1: They will drag you toward what's just on the horizon,
Speaker 1: but never quite define what the thing that dazzles you
Speaker 1: so much will be or when it will arrive. Really,
Speaker 1: their argument comes down to one thought. You must get
Speaker 1: on board now because at some point it will be
Speaker 1: so good you'll feel so stupid for not believing something
Speaker 1: that it sucks wouldn't be really good. And if this
Speaker 1: line sounds familiar, it's because you've heard it a million
Speaker 1: times before, most notably with cryptocurrency, NFTs, Mataverse, Clubhouse, tons
Speaker 1: of movements. Actually, they will make you define what would
Speaker 1: impress you, which is not your job. In the same way,
Speaker 1: finding the use case for them isn't your job. In fact,
Speaker 1: you're the customer, you're the consumer. You are the person
Speaker 1: AI needs to prove itself too, not the other way around.
Speaker 1: But let's go to another booster quip. When they go,
Speaker 1: you just don't get it, here's a great place to start. Say,
Speaker 1: that's a really weird thing to say. It is peculiar
Speaker 1: to suggest that somebody that doesn't get how to use
Speaker 1: a product is weird, and that we as the consumer,
Speaker 1: as the customer, must justify ourselves to our own purchases. No, no,
Speaker 1: no no. If I don't get it, it's the booster's
Speaker 1: job to tell me. Why make them justify their attitude.
Speaker 1: Just like any product, we buy software to serve a need.
Speaker 1: This is meant to be artificial intelligence. Why is it
Speaker 1: so fucking stupid that I have to work out why
Speaker 1: it's useful? The answer is, of course, that it has
Speaker 1: no intellect. It is not intelligent, and large language models
Speaker 1: are being pushed up mounted by a cadre of people
Speaker 1: who are either easily impressed or invested, either emotionally or
Speaker 1: financially in its success. Due to the company. They keep
Speaker 1: all their intentions for the world. And if a booster
Speaker 1: suggests you just don't get it, ask them to explain
Speaker 1: the following What am I missing, What specifically is it
Speaker 1: that is so life changing about this product based on
Speaker 1: your own experience, not on any anecdotes from other people,
Speaker 1: because they will say, well, I heard of a guy
Speaker 1: who are ten billion lines of coat, and then the
Speaker 1: baby looked at me and I cried. They don't have
Speaker 1: real things themselves, So cut off the exits brought up
Speaker 1: the doors. And then also ask them what use cases
Speaker 1: are truly transformative about AI. Don't let them say well,
Speaker 1: I heard in an industry. Actually make them prove themselves.
Speaker 1: The use cases will likely be the AI is replaced.
Speaker 1: Search for them that they use it for brainstorming or journaling,
Speaker 1: proof reading an article, or looking through a big pile
Speaker 1: of their notes or some other corpus of information and
Speaker 1: summarizing it or pulling out insights. Who gives a shit?
Speaker 1: Sorry not to be too a cerbic, but really, who
Speaker 1: fucking cares? That shit's so boring? Hundreds of billions of
Speaker 1: dollars of wasted investment on this and this is what
Speaker 1: We've got three years in fucking Humpty dumpty. Could never
Speaker 1: have it this good anyway. Our next booster quip is
Speaker 1: one of my faves. Ahem, AI is powerful and getting
Speaker 1: exponentially more powerful. Now, if a booster ever refers to
Speaker 1: AI being powerful and getting more powerful, ask them the following,
Speaker 1: what does powerful mean? In the event that they mentioned benchmarks,
Speaker 1: ask them how those benchmarks apply to real world scenarios.
Speaker 1: If they bring up swe bench the standard benchmark for coding,
Speaker 1: ask them if they can code, and if they cannot,
Speaker 1: ask them for another example. I mean, they will tell
Speaker 1: you that they've spoken with coders. I've talked with a
Speaker 1: lot of coders. I have a great one with Colt
Speaker 1: Vogie coming up, another software engineer talking about MS. It's
Speaker 1: so funny. It's so funny when you actually lay this
Speaker 1: stuff out, how weak their arguments are. But in the
Speaker 1: event they mentioned reasoning, ask them to define them. Once
Speaker 1: they've defined reasoning, ask them to explain in plain English,
Speaker 1: what reasoning allows you to do on a use case level,
Speaker 1: not just how it works. They will likely bring up
Speaker 1: the gold medal performance the open AI's model got on
Speaker 1: the meth OLYMPIAD. Ask them why open ai hasn't released
Speaker 1: that model. Then ask them what the actual practical use
Speaker 1: case is that this success has opened up. They will
Speaker 1: say it's an innovation. You've got to be patient, and
Speaker 1: then pairper spray them. No, don't, don't pepper spray anyone anyway.
Speaker 1: You should also then ask them what use cases have
Speaker 1: arrived as a result of models becoming more powerful. If
Speaker 1: they say vague things like oh in coding and oh
Speaker 1: in medicine, ask them to get specific, and then ask
Speaker 1: them what new products have arrived as a result. If
Speaker 1: they say coding LMS, they will likely add that this
Speaker 1: is replacing coders. Ask them where that has happened and
Speaker 1: ask them to show you proof. Blinks and it will
Speaker 1: not be sufficient to say that a CEO mentioned that
Speaker 1: they did something with AI and efficiency. Get numbers. They
Speaker 1: just won't. They won't do this. They will turn into
Speaker 1: a pillar of salt. And we got two more fucking
Speaker 1: parts of this. I mean you're gonna have a ball
Speaker 1: with this. Look. The core of the AI booster's argument
Speaker 1: is that they need to make you feel bad. They
Speaker 1: like the gaslight, and you should. You need to refuse
Speaker 1: to let them. You need to push back heavily. If
Speaker 1: there's ever a point where you feel like they are
Speaker 1: trying to make you feel stupid, ask them why they're
Speaker 1: doing so. And to be clear, anyone with a compelling
Speaker 1: argument doesn't have to make you feel bad to convince you.
Speaker 1: The iPhone didn't need a spurious hype cycle of proof
Speaker 1: of people saying you must look at this, it is
Speaker 1: so important. When it didn't really work. It worked immediately.
Speaker 1: Now I said in my news that it didn't need
Speaker 1: a fucking marketing campaign. Yes there was marketing dollars behind
Speaker 1: the iPhone. Eric newcomer, come at me with better WORKMATEE
Speaker 1: respond to the rest of this, But we all kind
Speaker 1: of got it with the iPhone. In fact, the moment
Speaker 1: Steve job announced their piece of shit that he was,
Speaker 1: he was. He said, here's the phone, here's an IPO,
Speaker 1: here's here's ema. You could do all these on one device,
Speaker 1: and everyone, oh, yeah, that is good. That is really
Speaker 1: obvious that that was good. It was impressive because it
Speaker 1: was impressive. And boosters will suggest you are intentional or
Speaker 1: in not liking AI because you're a hater or a
Speaker 1: cynic or a luddie. They'll suggest that you're ignorant for
Speaker 1: not being amazed by chat GPT. Let me tell you something,
Speaker 1: You don't have to be impressed by anything. By defam
Speaker 1: and any product, especially software designed to make you feel
Speaker 1: stupid for not getting it is poorly designed. Chat GPT
Speaker 1: is the ultimate form of silicon value sociopathy. You must
Speaker 1: do the work to find the use cases and thank
Speaker 1: them for giving you the chance to do so. AI
Speaker 1: is not even good reliable software. It resembles the death
Speaker 1: of the art of technology. Inconsistent and unreliable by definition,
Speaker 1: inefficient by design, financially ruin us, and adds to the
Speaker 1: cognitive load of the user by requiring them to be
Speaker 1: ever vigilant of the shit asked outputs that can come
Speaker 1: out of them. So here's a really easy way to
Speaker 1: deal with this. If a booster ever suggests you are
Speaker 1: stupid or ignorant, ask them why it's necessary to demean
Speaker 1: you to get their point across, even if you are
Speaker 1: unable to argue on a technical level, make them explain
Speaker 1: why the software itself can't convince you, and be vigilant.
Speaker 1: Boosters seldom live in reality and will do everything they
Speaker 1: can to pull you off course. And I should add
Speaker 1: that there is a fair criticism here. I do insult people,
Speaker 1: I do demean them. I call them babies, and I
Speaker 1: do funny voices, and I do that because I don't
Speaker 1: respect them. I'm not. I'm I am here to tell
Speaker 1: you how I feel. I will convince you through the
Speaker 1: large amounts of work I do in the research I have.
Speaker 1: I don't if you disagree with me, you disagree with me, Eh,
Speaker 1: it's fine. And yeah, these people do sound kind of silly.
Speaker 1: I'll get you a Casey Newton thing in a couple
Speaker 1: of episodes. I think that really it's impossible to call
Speaker 1: them otherwise. Look, if you say that none of these
Speaker 1: companies make money, they'll say it's in the early days.
Speaker 1: If you say AI companies burn billions, they'll say the
Speaker 1: cost of inference is coming down. If you say the
Speaker 1: industry is massively overbuilding, they'll say that this is actually
Speaker 1: just let the dot com boom and that the infrastructre
Speaker 1: will be picked up at used in the future. If
Speaker 1: you say there are no real use cases, they'll say
Speaker 1: chat GBT as seven hundred billion weekly users. Every single time.
Speaker 1: There's the same goddamn arguments, which is why from here
Speaker 1: on out, I'm going to give you the responses to
Speaker 1: all of them. And there's an article versus you could
Speaker 1: print up and stuff it in them. No no, no, no,
Speaker 1: no no violence. Everyone be nice, but you're going to
Speaker 1: be able to use these arguments going forward. And let's
Speaker 1: start with my favorite booster, quip AI will. Any time
Speaker 1: an AI booster says AI will tell them to stop,
Speaker 1: tell them to stop and explain what AI can do now.
Speaker 1: And if they insist, ask them both when they expect
Speaker 1: things to happen in the way they're talking about, and
Speaker 1: if they say very soon, ask them to be more specific.
Speaker 1: Get them to agree to a date and then call
Speaker 1: them on that day or show up at their house. Hell,
Speaker 1: you could be waiting inside when they get there, assuming
Speaker 1: you have a key legally. Now here's another one, another
Speaker 1: boost the whip. They will say agents will automate large
Speaker 1: parts of our economy, and I will say, fucking stop.
Speaker 1: There's that will bullshit again. There's that will will will will.
Speaker 1: Agents don't work. They don't work at all. The term
Speaker 1: agent means, to quote Maxwolf, a workflow where the LLM
Speaker 1: can make its own decisions, such as in the case
Speaker 1: of web search, where the LLM is told you can
Speaker 1: search the web if you need to, then can output.
Speaker 1: I should search the web and search the web. Yet
Speaker 1: agent has now become a mythical creature that means totally
Speaker 1: autonomous AI that can do an entire job. If anyone
Speaker 1: tells you agents are dot dot dot, you should ask
Speaker 1: them to point to one. If they say coding, please
Speaker 1: demand that they explain how autonomous these things are. If
Speaker 1: they say they can reflect her entire code bases, ask
Speaker 1: them what that means, and also laugh at them because
Speaker 1: they will not know. Ask them to explain how self force.
Speaker 1: His own research shows that agents only have a fifty
Speaker 1: eight percent success rate on single step tasks that you
Speaker 1: ask it to do one thing, and thirty five percent
Speaker 1: on multi step tasks. And what's crazy as well? And
Speaker 1: I won't have this in the episode now, it's just
Speaker 1: gonna have to look it up. There is a story
Speaker 1: that just came out about open Aiyes projections, they reduce
Speaker 1: the amount of money they're making over the next few
Speaker 1: years in agents down twenty six billion dollars less they
Speaker 1: just remove that part. Do you think agents exist now?
Speaker 1: Agents are not autonomous. They do not replace jobs. They
Speaker 1: cannot replace code as they are not going to do so,
Speaker 1: because probabilistic models are a horrible means of taking precise actions.
Speaker 1: And almost anyone who brings up agents as a booster
Speaker 1: is either misinformed or in the business of misinformation. Now
Speaker 1: here's another booster grip for you. What I mean is
Speaker 1: that AI is like the early days of the Internet
Speaker 1: any cases. I think they're referring to AI as being
Speaker 1: early as a reference to those early days, and they
Speaker 1: never really refer to what that means, because the early
Speaker 1: days of the Internet can refer to just about anything.
Speaker 1: Are we talking about dial up DSL? Are we talking
Speaker 1: about the pre platform days when people accessed it the
Speaker 1: Internet via compy server? Al well, yes, yes, I remember
Speaker 1: the article from Newsweek. I already explained it in my
Speaker 1: newsletter reality Check. I'm going to quote myself about this
Speaker 1: fucking article where the guy said the Internet wouldn't take off.
Speaker 1: In any case, one guy said, saying that the Internet
Speaker 1: won't be big doesn't mean a fucking thing about generat
Speaker 1: iv AI, And you're a simpleton if you think it does.
Speaker 1: One guy being wrong in some way is not a
Speaker 1: response to by work, and I will crush you like
Speaker 1: a bug. Again, I'm using ad hominin attacks. Who cares
Speaker 1: these fucking people are rude as hell. Now, if your
Speaker 1: argument is that the early Internet required expensive Sun microsystem
Speaker 1: service to run, Jim Cavello of Goldman Sachs addressed that
Speaker 1: in June twenty twenty four by saying that the costs
Speaker 1: were that the cost piled in comparison, adding that we
Speaker 1: also didn't need to expand our fucking power grid to
Speaker 1: build the early web. Well, sir, there's another booster whip, sir. Actually,
Speaker 1: people said smartphones wouldn't be big. This is a straight
Speaker 1: up lie. By the way, I've heard this a good amount.
Speaker 1: I've heard at least five people say, yeah, well, people
Speaker 1: didn't didn't think the iPhone would be big. The iPhone
Speaker 1: wasn't going to be big. Actually, huhh. This is a lie.
Speaker 1: It's a lie. It's a lie. You're lying. Also, as
Speaker 1: Jim Cavello from gobmin Sex noted, there were hundreds of
Speaker 1: presentations in the early two thousands that included road maps
Speaker 1: that accurately fit how smartphones rolled out, and that no
Speaker 1: such roadmap exists for generative AI. The iPhone was also
Speaker 1: an immediate success as a thing that people paid for
Speaker 1: with Apple selling four million units in the space of
Speaker 1: six months, and this was on an exclusive contract for
Speaker 1: several years, I think with Singular Wireless now called AT
Speaker 1: and T hell in two thousand and six. Since the
Speaker 1: year before the iPhone launched, there were smartphones, and there
Speaker 1: was an estimated seven point seven million worldwide smartphone shipments,
Speaker 1: mostly from BlackBerry, Windows Mobile and Palm. Though, to be
Speaker 1: generous to the GENERATIVEAI boosters, I'm going to disregard those
Speaker 1: because they actually he'll prove my point more and I
Speaker 1: just want to be clear that the early days of
Speaker 1: the Internet are not a sensible comparison to GENERATIVEAI. The
Speaker 1: original Attentional is all you Need paper, the one that
Speaker 1: kicked off the transformer based large language model ERA, was
Speaker 1: published in June twenty seventeen. Chat GPT launched in November
Speaker 1: twenty twenty two. It's not early. We're not early anymore.
Speaker 1: We haven't been for a while. But nevertheless, if we're
Speaker 1: saying early days here, we should actually define what that means.
Speaker 1: As I mentioned previously, people paid for the iPhone immediately
Speaker 1: despite it being a device that was completely and utterly
Speaker 1: new on one specific carrier. While there was a small
Speaker 1: group of consumers that might have used similar devices like
Speaker 1: the compact ipack. It's kind of a cool device. The
Speaker 1: iPhone was a completely new kind of computing sold it
Speaker 1: at premium, requiring you to have a contract with that
Speaker 1: specific carrier. Conversely, chat GPT's annualized revenue in December twenty
Speaker 1: twenty three was one point six billion dollars, so about
Speaker 1: one hundred and thirty three million in that month for
Speaker 1: a product that had by that time raised over ten
Speaker 1: billion dollars. And while we don't know what OpenAI lost
Speaker 1: in twenty twenty three, reports suggested it burned over five
Speaker 1: billion dollars in twenty twenty four. Big Tech is spent
Speaker 1: over five hundred billion dollars in capital expenditures in the
Speaker 1: last eighteen months, and all told, between investments of cloud
Speaker 1: credits and infrastructure, will likely sink over six hundred billion
Speaker 1: dollars by the year's end. The early days of the
Speaker 1: Internet would define not by its lack of investment or attention,
Speaker 1: but by its obscurity. Even in two thousand, around the
Speaker 1: time of the dot com bubble, only fifty two percent
Speaker 1: of US adults used the Internet, and it would take
Speaker 1: another nineteen years for ninety percent of US adults to
Speaker 1: do so. These early days were also defined by its
Speaker 1: early functionality. The Internet would become so much more because
Speaker 1: of the things that hyperconnectivity allowed us to do, and
Speaker 1: both faster inte net connections and the ability to host
Speaker 1: software and the cloud would change everything. We could define
Speaker 1: what better would mean and make reasonable predictions about what
Speaker 1: people could do on a better Internet. Yet, even in
Speaker 1: those early days, it was obvious why you were using
Speaker 1: the Internet and how it might grow from them. One
Speaker 1: did not have to struggle to explain why buying a
Speaker 1: book online might be useful or quicker than a shop,
Speaker 1: or why a website might make a quicker reference than
Speaker 1: having to go to a library, or why downloading a
Speaker 1: game or a song might be a good idea. While
Speaker 1: habits might have needed adjusting, it was blatantly obvious what
Speaker 1: the value of the early Internet was. It's also unclear
Speaker 1: when the early days of the Internet ended. Only forty
Speaker 1: four percent of US adults had access to broadband internet
Speaker 1: in two thousand and six. Were those the early days
Speaker 1: of the Internet. The answer is no, and that this
Speaker 1: point is brought up by people with a poor grasp
Speaker 1: of history and a flimsy attachment to reality. The early
Speaker 1: days of the Internet were very, very, very different to
Speaker 1: any associated tech boom since, and we need to stop
Speaker 1: making the comparison. The Internet also grew in a vastly
Speaker 1: different information ecosystem. Generative AI has had the benefit of
Speaker 1: mass media driven by the Internet, along with social media
Speaker 1: and social pressure to adopt AI for multiple years. And
Speaker 1: now our last boost to quip for the episode, actually
Speaker 1: that I meant something else. What I mean is that
Speaker 1: we're in the early days of AI. All of the
Speaker 1: other things you said were very misleading. You misread my statements. Somehow,
Speaker 1: we are not in the early days of generat if AI,
Speaker 1: and anyone using this argument is either ignorant or intentionally deceptive.
Speaker 1: According to Pew, as of mid twenty twenty five, thirty
Speaker 1: four percent of US adults have used chat GPT, with
Speaker 1: seventy nine percent saying they'd at least heard of it
Speaker 1: and a little about it. Even Furthermore, chat GPT has
Speaker 1: always had a free version. On top of that, A
Speaker 1: study from May twenty twenty three found that over ten
Speaker 1: nine hundred news headlines mentioned chat GPT between November twenty
Speaker 1: twenty two and March twenty twenty three, and a brand
Speaker 1: Watch report found that in the first five months of
Speaker 1: its release, chat GPT received over nine point two million
Speaker 1: mentions on social media. Nearly eighty percent of people have
Speaker 1: heard of chat GPT, in over a quarter of Americans
Speaker 1: have used them. If we're defining the early days based
Speaker 1: on consumer exposure, that ship has sailed. If we're defining
Speaker 1: the early days by the passage of time, it's been
Speaker 1: eight years since Attention Is All You Need and three
Speaker 1: since CHADGBT came out. While three years might not seem
Speaker 1: like a lot of time, the whole foundation of an
Speaker 1: early day's argument is that in the early days, things
Speaker 1: do not receive the venture funding, research, attention, infrastructural support,
Speaker 1: or business interest necessary to make them big. In twenty
Speaker 1: twenty four, nearly thirty three percent of all global venture
Speaker 1: funding went to artificial intelligence, and according to the Information
Speaker 1: AI startups have raised over forty billion dollars in twenty
Speaker 1: twenty five alone, with Statista adding that AI absorbed seventy
Speaker 1: one percent of VC funding in the first quarter of
Speaker 1: twenty twenty five. These numbers also failed to account the
Speaker 1: massive infrastructure costs that companies like open Ai and Anthropic
Speaker 1: don't have to pay for the limitations of the early
Speaker 1: Internet were twofold, the fiber optic cable boom that led
Speaker 1: to the fiber optic bubble bursting when telecommunications companies massively
Speaker 1: overinvested in infrastructure, which I will get too shortly. There was
Speaker 1: also the lack of scalable cloud infrastructure to allow distinct
Speaker 1: apps to be run online, a problem solved by Amazon
Speaker 1: Web Services, among others. In Generative AI's case, Microsoft, Google
Speaker 1: and Amazon have built the fiber optic cables for large
Speaker 1: language models. Open AI and Anthropic have everything they need.
Speaker 1: They have even if they say otherwise, plenty of compute,
Speaker 1: access to the literal greatest minds in the field, the
Speaker 1: constant attention of the media and global governments, and effectively
Speaker 1: no regulations or restrictions stopping them from training their models
Speaker 1: and the works of millions of people or destroying our environment.
Speaker 1: They've already had this support too. Open ai was allowed
Speaker 1: to burn half a billion dollars on a single training
Speaker 1: run for GPT four point five and five, and they
Speaker 1: did multiple runs. If anything, the massive amounts of capital
Speaker 1: have allowed us to massively condense the time in which
Speaker 1: a bubble goes from possible to bursting and washing out
Speaker 1: a bunch of people. Because the tech industry is such
Speaker 1: a powerful follow up work culture that only one or
Speaker 1: two unique ideas can exist at one time, and I
Speaker 1: think those ideas are currently open AI and anthropic. The
Speaker 1: early Day's argument hinges on obscurity and limited resources, something
Speaker 1: that general does not get to whine about. Companies that
Speaker 1: make effectively no revenue can raise five hundred million dollars
Speaker 1: to do the same AI coding bullshit that everybody else does.
Speaker 1: In simpler terms, these companies are flush with cash, have
Speaker 1: all the attention and investment they could possibly need. After all,
Speaker 1: attention is all you need, and are still unable to
Speaker 1: create a product with a defined, meaningful mass market use case,
Speaker 1: let alone one that doesn't burn money. In fact, I
Speaker 1: believe that, thanks to effectively infinite resources, we've speed run
Speaker 1: the entire large language model era and we're nearing the end.
Speaker 1: These companies got what they wanted, and I think I
Speaker 1: want to die in Minecraft, obviously, but I must press on.
Speaker 1: I must. Just saying all these things out loud, you
Speaker 1: really get a sense for how illogical these people are
Speaker 1: and how much bullshit is going around to try and
Speaker 1: push back against skeptics. But it's not gonna work. It's
Speaker 1: not gonna work. I hope you're not tired of me
Speaker 1: talking about boost equips and doing silly voices. Is You've
Speaker 1: got two more of these fucking episodes coming, and I
Speaker 1: love recording them. I really do say recording. Jesus Christ,
Speaker 1: my friends were in hell, but we're together in hell
Speaker 1: and that's fun. And there's a lot more bullshit to
Speaker 1: break down. Speak to you tomorrow. Thank you for listening
Speaker 1: to Better Offline. The editor and composer of the Better
Speaker 1: Offline theme song is Matasowski. You can check out more
Speaker 1: of his music and audio projects at Matasowski dot com,
Speaker 1: M A T t O. S O w Ski dot com.
Speaker 1: You can email me at easy at Better offline dot com,
Speaker 1: or visit Better offline dot com to find more podcast
Speaker 1: links and of course, my newsletter. I also really recommend
Speaker 1: you go to chat dot Where's youreed dot at to
Speaker 1: visit the discord, and go to our slash Better Offline
Speaker 1: to check out our reddit. Thank you so much for listening.
Speaker 1: Better Offline is a production of cool Zone Media. For
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