Introduction

There has been a lot of “anti-AI” commentary over the last few years, but I have been surprised how much of it is not really that anti-AI. Most of it is either an apology for or flattery of AI disguised as a criticism, or it is irrelevant to AI itself and focuses on something tangential. The common categories of this are:

• AI is Skynet and it’s going to kill everyone
• AI is going to automate all office work and cause mass unemployment
• AI is a great technology but the people in charge of it suck
• AI data centers are destroying the environment and local communities

I want to make it immediately clear that this series does not fall into any of these categories, which is part of why I felt so compelled to write it (all that stuff is covered pretty well already).

No, my position on AI is rather well summarized by this satirical news video from 15 years ago by the Onion. It hits everything, including “needing to use it to not feel like some toothless hillbilly living in a hillbilly shack somewhere” (or as it’s called these days, being a “Luddite”).

Now, as soon as I say this, I’m sure many of you will immediately react with, “he can’t really believe that, or he’s never used it, it totally does do useful things!”

Yes, I’ve seen it do useful, even quite impressive, things, before too, and I’ve coaxed useful behavior out of it. That’s not what “works” means and I find it incredible this has to be clarified. Sony’s “Stupid Box Thing” surely has all sorts of uses, like: a doorstop, a stepping stool, a blunt weapon to fight off a home invader, something to destroy in a fit of rage like the printer from Office Space, etc. I don’t know if a contraption being absolutely useless in every conceivable way is even possible. Dog poop isn’t useless, it can fertilize grass, or attract flies, if you happen to need that for some reason, and so on.

“Doesn’t work” means it doesn’t do what it’s advertised to do. For AI, this isn’t saying much, because what it’s advertised to do is just plain ridiculous. It’s advertised to do… everything? Depending on the mood of the day, or how far into the same rehashed argument with an AI Booster you are, it’s advertised as being a computer program that reasons through problems (or a “roomful of PhDs”), or failing that, some sort of all purpose tool of automation. I call this the Automatic Thingamatron. It automates things! What things? All the things!

I don’t know why anyone takes such an advertisement seriously, but here we are, and measured against that advertisement, AI is indeed a stupid piece of s*** that doesn’t f***ing work.

But can it be claimed useful against a different metric? That is, can we say it “works” as something that is not what it’s advertised to be? I’m sorry but the fact we’re even going there is ridiculous, but if we’re going to, now we have to clarify: what exactly is it supposed to do, and is it actually worth my time and money? Don’t worry, we’ll get there, but the fact it takes this long in a discussion about what this thing is and why I should care to answer that question is already a very bad sign.

When it comes to programming, “useful” has come to mean erecting convoluted Rube Goldberg machines around AI (“AGENTS.md”, skill files, plan mode, subagent swarms, “loop engineering”, etc. etc.), none of which was in the “just tell the roomful of PhDs what you want!” brochure, that change every few months, all to achieve the result of it hopefully emitting some decent enough code that a modestly skilled coder could have done himself. Every time I hear people describe this, I can’t help but think of this:

So What Is AI?

All of those arguments I listed above are, as I said, either obfuscated compliments of AI, or concern something besides the tech itself.

“AI is Skynet and going to kill us all” is basically “AI is so good, it’s bad”. This is what most of the people working at AI labs believe, it is accurately labeled the “AI Doomsday Cult”, and it is a well funded and highly astroturfed marketing campaign for AI research (see here, here and here). I’m sure if we hooked up the hallucinating chatbot to our nukes it would kill everyone, but that’s not what these guys mean.

“AI will cause mass unemployment” begins with the premise that AI is so good it can just automate away all knowledge work. That part, which is part of the marketing material from AI labs, is glossed over and then the focus is shifted to how we should handle, regulate, etc. such an awesomely overpowered technological revolution. But I don’t accept the premise that was glossed over.

“AI is great but Sam, Dario and Elon are dirtbags” similarly begins with the premise that AI is great, which it doesn’t really do anything to defend, and then shifts to talking about how the CEOs are dirtbags. Yes they are, but the fact what they’re selling isn’t great makes that true to a degree the people making this argument don’t seem to fully appreciate.

All the stuff about data centers is a real concern and I don’t want one of those things anywhere near where I live. But this discussion skips over what is the freaking point of building those data centers, in the process conceding, however implicitly, that what these data centers do is actually useful, but they just generate too many negative externalities to do it. But if the internalities are all negative too (that is, if the output of AI data centers is just another form of pollution), the case against AI data centers is that much stronger. They generate real, environmental pollution in order to… generate digital pollution? Either way, this just isn’t addressing AI itself.

So what is AI, really? I refer, here, to the “generative AI” that exploded since 2022 (conflating this with all the other things people call AI, like Machine Learning, is a common misdirection so I have to clarify this). Quite simply:

AI is a Silicon Valley Venture Capital Scam

It is an attempt to do the same thing people tried to do with “crypto everything” (web3… does anyone still remember that?) a few years prior. In fact, it is literally the same people who abandoned crypto and switched to AI, a phenomenon termed “The Great Brotation“. Now that the AI grift is starting to show signs of drying up, the next grift they have lined up appears to be quantum computing (or possibly fusion power plants). If your reaction to this is, “but wouldn’t quantum computing/fusion be cool!?”… people have been talking about quantum computers and fusion reactors for 50 years. You perpetually imagining how cool it’s going to be once they crack it “any day now” is how the grift works.

To be more specific, the AI grift is an alliance of two distinct groups of people who are mutually exploiting each other:

• Silicon Valley VC/Tech Bros looking for the “next iPhone” but, bereft of any real ideas, are content to just sell empty hype or even run outright pyramid schemes and pocket as much as they can in the process
• AI Doomsday Cultists who believe they urgently need to build “Good AI” and that this requires brute force through massive supercomputers and unprecedented capital expenditures

The first group was seeking any excuse to get investors to give them as much money as possible on the promise of a tech revolution, and the second group believes they need all the money in the world to build God the App, and were able to successfully create a very convincing parlor trick (perfect for a demo in an investor deck) that requires GPU supercomputers to power it, and is supposed to magically “become the real deal” (“wake up and solve physics”) if you just shove enough GPUs in.

It was a match made in heaven (or maybe hell).

Everyone is probably already familiar with Tech Bros and thinks they’re annoying and dangerous. What I think deserves more attention is the AI Doomsday Cult, which calls itself “Rationalism” (no relation whatsoever to the much older philosophy/epistemology school of thought, it’s basically self-serving “we’re smart and reasonable” branding) or “Effective Altruism”, their main hangout is LessWrong.com, that makes up most of the technical staff of AI labs… the people who believe “AI is going to kill us all”. But that’s obviously not the whole story, because aren’t they working at labs building AI?

To square that circle, you have to know about their concept of “Good AI” (which will give us all eternal life in digital heaven), and “Bad AI” which will be so extremely indifferent to us it will “turn us into paperclips” (use us for raw materials). The doomsday scenario is about “Bad AI” being created, which they believe is inevitable (someone will figure out the tech specs and do it) unless someone builds the “Good AI” first… the “Good AI” is the only force that can stop the “Bad AI”. So the only way to stop AI from killing us all is… build AI really fast to save us.

This isn’t some kind of niche within AI departments. All the terminology we’ve been inundated with since 2022, like “singularity”, “alignment”, “AGI”, “ASI” and so on, originated in this cult (“alignment” refers to whether the AI we’re building is the “good” or “bad” one). This is what this community is broadly involved in.

And of course no discussion of this cult is complete without mentioning Roko’s Basilisk, which I encourage you to read and try not to laugh at. This is the kind of stuff I rambled about, drunk and stoned out of my mind, at 2am in college, fully aware the whole time it was entertainment. These guys, presumably dead sober, take it totally seriously.

These are the people building the chatbots. You cannot separate what the chatbots are from the absolutely insane cultish nonsense they emerged out of.

But It’s Just a Tool!

If you bring up this cult and its half-baked beliefs about a GPU data center being “conscious” or whatever, you are likely to get a response that is some variation of, “okay but forget about all that. AI is just a tool! Why are you against using tools!?”

This is an extraordinary backpedal in a conversation about a thing we all keep calling “artificial intelligence”. Oh it’s “just a tool”? “Just a tool” is exactly what “AI” is not. The whole point of it being “AI”, everything about what it’s supposed to be, what it’s supposed to do and what it took to make it work, hinges on it not being an idle, inanimate mechanical contraption that just automates mindless tedium. We already have tools for that stuff. We already have automation for repetitive tedium. The reason AI is supposed to be so exciting is because it breaks through the barrier and finally starts “automating” the cognitive part of work, the part that computers have never been able to help with because they’re just algorithmic automatons that don’t “reason”, “strategize”, “learn”, etc.

And now you’re telling me it’s not any of that, it’s just a tool!? We were sold a robot intern and it turns out it’s just a hammer?

Okay, what tool is it? Please be precise. A tool is made with a specific use case, designed to implement the steps of a repetitive operation. What repetitive task does AI implement? What is its precise function? You can’t answer with “thinks”, “reasons”, “does what a person would do” or anything like that because you already conceded that away. A hammer is made to hammer nails. A drill is made to drill holes. Excel is made to edit spreadsheets. So what tool is AI, what is it made to do, and why are we calling it “AI” instead of just calling it the tool that it is?

See, there’s no answer to this question, because the non-answer you get is back to the Automatic Thingamatron: it’s the everything tool! It wasn’t made for a use case, use cases were made for it! (there’s a subtle confession there that you have an extra task on your plate to figure out how to cram AI into some workflow and get utility out of it). This is what is fundamentally different about AI, and why comparing it to other technologies or saying, “oh well you must be against using Google” is nonsense.

Tools exist to give you more time to use your brain to do the thinking part of a problem, because you’re not spending time on the mindless tedium parts. Thus, calling something that purports to think for you (it’s a subscription to “intelligence”) is basically the opposite. It’s not you being more effective and efficient at completing a task by thinking about it more, it’s fully delegating the task out to something else in order to think about it less, or not at all. There is a difference in kind, not in degree, between a novelist using a typewriter, or a word processor, spell checkers, grammatical checkers, dictionaries, thesauruses, and so on, and using a contraption that claims to write passages of his novel for him. Assuming that contraption even does what it’s advertised to, if he does that, he’s no longer the author, and he’s no longer writing a novel.

These two categories work entirely differently in almost every way. How do I choose when to grab a hammer, or nail gun, or circular saw, or drill? The answer is when, in the process of performing a task, I come to a step or subtask for which that tool is the right one to automate it. I need to cut a 2×4? Grab the circular saw. I need to drill pilot holes for fasteners? Grab the drill. I need to nail two pieces together? Grab the hammer, or a nail gun. How does this work with AI? At what point in a workflow do I reach for AI, and for what purpose, exactly? AI doesn’t have a defined use case because it “does everything”, so all this could look like is that I start off asking AI to just do the whole thing for me, and depending on how badly it screws up (assuming I even check), take over parts of it or maybe try asking it in different ways or giving it more instructions. People don’t even have a mental model for what they’re doing here. They don’t know what AI is or how it works, they just think it “does stuff”, and they can’t explain why it makes sense to even try offloading something to it.

The honest (but ridiculous because it’s pure fiction) framing of this is that AI is exactly that, AI, and that I’m delegating a task entirely to another entity with agency, which is exactly not what augmenting my own performance of the task with tools is. You gotta pick one. It can’t be both. Which of these two things is AI? If it’s the latter, then it’s not AI, it is a specific type of computer program with specific features made to perform specific operations, and we should be able to clarify exactly what those are (which should be derived from what computer program does, i.e. how it works under the hood).

This “just a tool” argument is AI users wanting to have their cake and eat it too. They want to be a novelist asking a computer program to just write a whole passage for them, but still put their name on the byline… except when they’re trying to market the power of AI itself, and then it’s “AI solved that math problem”, not “that math PhD solved that math problem, using AI as one of many tools to do so”. Or, they’re using AI to do spell checking, look up words (be a dictionary) or find similar words (be a thesaurus), which… why!? All of that stuff already exists and you don’t have to pay Sam or Dario for it or pick out the hallucinations.

Claiming “AI is just a tool” seems to always begin by downplaying or dismissing the tools that already exist and were specifically created for whatever task AI is claimed to help with. A good example of this is Sam Altman tweeting “I can’t believe developers used to type code one character at a time”. Well, we didn’t. We have lots of very advanced tools to help us author code. But if we ever compared “AI as a tool for X” to the purpose-built tools made just to do X we already have (or could build once we realize we need to do X repeatedly), we’d start a discussion AI Boosters don’t want to have.

This is why the concrete examples you get of “AI is just a tool” is people using AI to do things you can do without AI. “My AI agent reads my emails, flags some of them as spam and then deletes them!” Oh, you mean the “Junk Email” folder that every email client has had for… 15 years, at least? “My AI agent checks a bunch of flights to a city on a certain date, books the cheapest one and then finds a hotel to book in that city on that date!” Okay, so… Expedia? “I use AI to describe a blood test result and see what it means”. Okay, so… Google search (most uses of AI are actually just as a search engine, more on this later)? This reminds me of a hilarious video by Patrick Boyle about how Tech Bros seem to do little else but reinvent existing products (or simple concepts like lunch breaks) and claim they’re revolutionary.

In programming, a lot of this ends up being programmers discovering scripting/cron jobs and metaprogramming for the first time. They imagine doing some big refactoring operation “by hand”, one line at a time, in comparison to handing it off to AI, but they’re programmers, their skill set is literally “automating repetitive tasks with computers”, so why did they do it that way instead of writing code that does all that code editing?

The more interesting examples are things like “I use AI to summarize emails”, which is actually something a “large language model” would be appropriate for (and, for what it’s worth, I get annoyed when I find out someone won’t bother reading something I sent them but claims they sort-of did because they read an AI summary… all of this seems inexorably aimed at turning people into lazy zombies). But is that it? Is AI actually just a “block of text summarizer” tool? If so, why did we ever refer to it as “AI”, and why are people claiming it’s also a giga-multitool that does endless other things no one who built it even thought of and automate away all office work?

The Technology Behind Generative AI

A common phrase I kept hearing when I found what appeared to be anti-AI content is “the technology isn’t the problem” or, much more suspiciously, “AI is here to stay”. That second one I heard like clockwork, always near the beginning of a video with a title critical of AI, almost like it was a disclaimer. I found this very odd, like the Overton Window about all this was set so that we can make trivial complaints but never, ever dispute that the Magical AI Revolution is coming any day now.

No, the technology is the problem. This idea that “tech is always neutral, it’s how it’s used that matters” is a popular misconception. There are technologies created specifically to be misused (for example, the techniques used by computer viruses to spread themselves without users noticing). They may be assembled out of smaller pieces of technology that, by themselves, have legitimate uses, but the assembly is still malware.

A funny example of this is the auto-dialer from the Simpsons:

However much Professor Frink insists it was made for noble causes, it inevitably just ends up being a tool for scammers.

All we have to do to illustrate that the technology of “generative AI” is inherently a problem is describe in enough detail what that technology is. A good gentle intro to that is here. The technology that powers generative AI is called the “transformer”, and the foundational paper describing it is “Attention Is All You Need“. For chatbots (transformation of text tokens to other text tokens), a transformer memorizes large quantities of human text and conversations (by now basically the entire internet), measures and encodes the statistics of how words appear in sequences (this is called “vector embedding”, referring to how words are expressed as points in a multidimensional space, so that the statistics are encoded as “distances”). It then takes text inputs (prompts) and “completes” them (fills in text that follows the input) with words following the same statistics. The result is a “response” to the input prompt that is reminiscent of the text it originally memorized and encoded.

Okay so once it “clicks” in your mind what I just described (the technical details like vector embedding aren’t really important), it should immediately be clear what these programs are. Memorizing a large set of conversations from people who, presumably (one hopes), understood the words they were saying, in order to be able to produce responses that sound convincingly similar to those conversations and therefore superficially mimic the same understanding, is exactly what an unscrupulous person would do to fake understanding or expertise in a topic on which he has none. For example, someone who wants to get a high paying job that requires a skill needs to pass a technical interview, and if he does not possess that skill, he may instead try to feign expertise by memorizing the replies people who are experts gave to the kinds of questions interviewers ask.

There are several colloquial terms for this behavior, such as bullsh*tting, but we also have a formal word in English for this:

Charlatan (noun):

one making usually showy pretenses to knowledge or ability

So, put simply, generative AI is computerized charlatanism.

When you point out what the technology is to AI Boosters, they will try to fog the discussion with a bunch of pseudo-philosophical gobbledegook about “emergent phenomena” or whatever, implying that while this may be in base terms how generative AI is programmed, once it goes through training (memorizes all the conversations) something else appears spontaneously, namely intelligence, which at least implies that the thing does in fact understand, rather than just mindlessly regurgitate, words.

I’m sorry, what? Intelligence and understanding is a phenomenon that emerges from… charlatanism? This appeal to foggy “no one really knows” mystery might pass muster if they were doing voodoo dances in the rain and suddenly the rock next to them started holding a conversation, but they’re not, they’re meticulously constructing a computer program to pretend to know things, capturing to an admittedly excellent degree of precision how exactly blabbering about stuff one doesn’t understand works on a technical level. This is like claiming not just that ghosts are an emergent phenomenon, but that they emerge from magicians doing the Pepper’s Ghost trick on stage. Yes, I’m sure it’s just a strange coincidence that the phenomenon from which real intelligence spontaneously emerges in a computer is programming a computer to fake intelligence.

It is becoming increasingly clear to everyone, even nontechnical people, that this is all “generative AI” has ever been, and incredibly, the AI Booster counterargument to the obvious (and rather impassioned, as evident from comments under content online these days) disgust and rejection of such fakeries (“AI Slop”) is basically to deconstruct the concept of “fake” itself (just like they try to deconstruct the concepts of intelligence, consciousness, thinking and reasoning, i.e. “but what are you other than a statistical token predictor?”) and ask: if the fakery is convincing enough that it never gets detected as fake, does it really matter that it’s fake? What is “real” except a fake that evades detection!?

“Fake” is the right word. One of the first concerns about generative AI was that it would be abused to generate “deep fakes”, i.e. audio or video of events that never took place but are passed off convincingly as recordings of real events. A “deep” fake is a fakery that is just as fake as a “shallow” fake (like an obviously Photoshopped face swap) but it is harder to detect as fake. The harder detecting the fakeness is, the “deeper” the fake is.

People immediately realized AI could be “abused” to create deep fakes, but what was largely missed (and hopefully slowly becoming clear) is that everything generative AI generates is a deep fake. Generative AI itself is a deep fake of reasoning and understanding (we could replace the term “artificial intelligence” with “fake intelligence”), and applying it to generate specific artifacts thus results in it generating deep fakes of those artifacts. This has been expressed in novel ways by people lately. A good example is the phrase “work shaped output”, coined to sharply distinguish AI generated office work deliverables from actual deliverables, pointing out that they capture all the superficial qualities of real deliverables and therefore pass shallow tests (it takes very close inspection to reveal they are not real deliverables). What I haven’t seen is an explicit tie of this concept to the already well-known concept of a “deep fake”. For example, when AI “hallucinated” court cases in citations, this is properly understood as a deep fake of a legal case.

I am going to discuss this in much detail in the later parts of this series, but the computer code AI generates is a deep fake of software engineering. The actual software powered by it is a deep fake of real software, which basically means a seemingly “functional” user interface that doesn’t actually connect to real systems (i.e. a login screen that simulates a login flow without actually logging anyone in). This is enough to fool executives who assess progress on “demos” (to be fair to generative AI, the software industry has been defrauding people with misleading demos since long before 2022), which is why they all think AI is an utterly magical thinking machine that will replace their engineering department.

Now, when I say everything AI generates is fake, that doesn’t mean AI itself is entirely fake… in the sense that there’s literally nothing there. The technology is real, which is to say: it really is a faithful programming of charlatanism, it really did memorize the entire the internet, and it really can deeply and convincingly fake things. If it wasn’t “real” in this sense, it never would have gotten off the ground. And since, in order for this elaborate parlor trick to work, AI had to memorize the entire internet and catalogue it in a way that lets it retrieve the relevant bits of text to fake understanding, it can end up “accidentally” serving a real, nonfraudulent purpose. Furthermore, it takes an enormous amount of computing resources to power AI, and computing resources can certainly be used for nonfraudulent purposes. And so, every now and then, we can see glimpses of real utility come out of the Slopomatic 9000:

• It can retrieve and reproduce, without garbling, something it memorized in training, acting in the process as a semantic search engine for the whole internet
• It provides access to enormous amounts of GPU compute resources (heavily subsidized in most cases), which can be used to do useful things

Neither of these are some kind of win for a computerized charlatan program. We could easily build a vector database of the internet without the “generative”, “replies like it’s alive and understands things and makes stuff up in the process” layer slapped on top of it, and we could just directly subsidize and rent massive amounts of GPU compute to look for math proofs or security vulnerabilities in code. I point this out only because the accidental utility that slips out of chatbots, completely in opposition to their intended use and claimed capabilities, is routinely submitted as some kind of refutation of the claim that they’re deep faking slop generators. It’s not. The charlatan faking his way through a tech interview had to memorize a bunch of replies to technical questions, and likely memorized the correct answer to many of them. That doesn’t change the fact he’s a charlatan or somehow prove charlatanism “isn’t all bad”.

A lot of people genuinely don’t understand the difference between understanding and expertise vs. memorizing correct answers. Maybe we can blame this on school, I don’t know. This is probably why generative AI is so effective at fooling people.

The vector database/semantic search part in particular is a legitimately useful technology. But that’s boring, and compared to the search engines we already had, an incremental improvement, not a revolution. Fuzzy semantic search of documents (or the internet) isn’t “the next big thing”. It’s not “the next iPhone”, “the next internet”, etc. So it’s of no use to Tech Bros. And it’s not “AI” so the Doomsday Cult doesn’t care either. If making semantic search work well enough actually requires gigawatts of GPU compute resources, then it’s quite simply not worth it. It’s a neat demo of a technology that may become viable if it can be heavily optimized, but that’s it. This is why the useful part of the transformer architecture isn’t being sold as a standalone product. No one could claim a better search engine is going to “wake up and solve physics”, so it can’t be hyped enough.

All legitimate uses of generative AI are non-generative

They all involve using AI as a search engine to retrieve things that already existed, not “generate” “new” content. Using AI to generate code is absurd, but using it to sift through code and find potential problems is actually useful (whether it’s useful enough to justify the unsubsidized cost is another matter). When it does so, it still blabbers a bunch pretending to understand things (it won’t just tell you “hey here’s a line of code, looks suspicious, double check it”, it pretends to argue about it), makes a bunch of stuff up and flags a bunch of stuff that isn’t wrong, so it’s not “doing a code review for you” (and if you’re not an expert you’ll be grossly misled by what it says), it’s just pointing you at something you might have overlooked so that you can review it more carefully. That’s certainly useful, but ironically if you tell AI Boosters you use AI just for stuff like this they’ll call you stupid for treating it as “just a search engine” instead of offloading your brain to it.

Procedural Generation Is the Original “Slop”

Increasingly more and more people are able to tell the distinct “feel” that AI Slop has. The fact that the output of a transformer is inherently inferior to its original training data is why “model collapse” happens (the above linked video talks about this). But this difference between what human creativity produces and recycled collages of past human creative productions is something that certain communities have been acutely aware of for decades… especially gamers.

The Elder Scrolls is a series of open world role playing games, famous for dropping you into a large, immersive world full of opportunities to explore and grow a character. If we look at how physically large the playable world of each of the games is, we see a very bizarre pattern, one that doesn’t make any sense at first. We would expect on each successive generation for the playable world to get larger, as technology and hardware progressed and the studio grew and employed more people to craft the world. Instead, we see this:

• Arena (1994): 15 million km²
• Daggerfall (1996): 220,000 km²
• Morrowind (2002): 16 km²
• Oblivion (2006): 57 km²
• Skyrim (2011): 37 km²

The first game, Arena, is the largest by a factor of ~70, the second game, Daggerfall, is the second largest by a factor of ~4000 (it is roughly the size of Great Britain), and Oblivion is significantly larger than Skyrim. What gives!?

The answer is procedural generation. This refers to using a random number generator to assemble a set of assets (terrain types, buildings, components of dungeons like hallways and rooms, etc.) into content. Designers crafted the assets themselves, but they didn’t design every output of the generator. The generator is just an algorithm that randomly assembles those assets.

Daggerfall has a unified, traversable game world with some handcrafted locations but it is almost entirely procedurally generated. Arena is not actually a single playable world. It is a set of procedurally generated towns, each surrounded by endlessly procedurally generated wilderness (you can’t walk from one town to another, you’ll just encounter random wilderness tiles forever until the game glitches and then crashes), which makes its “playable area” kind-of meaningless (or just lore). Morrowind and Skyrim are both entirely hand crafted worlds. Designers shaped every parcel of land and built every town, building, dungeon, cave, etc. in those games. Oblivion is mostly hand crafted but has procedurally generated dungeons, which contributed to a larger playable world.

So when we say Daggerfall is 4000x as large as Oblivion, that doesn’t mean designers built 4000x as much content for Daggerfall as they did for Oblivion. They certainly didn’t, both because it would be infeasible to make so much content (if Oblivion took 4 years to make, that would mean Daggerfall would have roughly taken 16,000 years to make!) as well as store it in game files (if Oblivion fit on a single DVD, Daggerfall would have needed roughly 16,000, not to mention how many hard disks you’d need on your computer to install it on). There were, in fact, far fewer game assets in Daggerfall than in Oblivion (or Morrowind for that matter). The difference is Oblivion’s assets were placed explicitly, while Daggerfall’s assets are recycled nearly “endlessly”.

And you can tell the difference. Daggerfall feels very different from the later games. It is obvious that if you traverse around the world, you’re just seeing recycled combinations of assets you’ve already seen dozens or hundreds of times, and the combinations aren’t exactly creative. That’s fine for what it is, but in order to simulate this massive world to explore, you have to settle for a world that is repetitive and sterile in some sense.

But Morrowind is widely considered a masterpiece, while Daggerfall is at best a good and notable game. The key to Morrowind’s success was precisely that it abandoned literal scale, which could only be achieved with procedural generation, in favor of the smallest playable world of any of the Elder Scrolls games, but a world that was meticulously created. The resulting creativity was far superior, and it allowed for much deeper immersion. It turns out that once you reach enough scale, which isn’t much, you can better achieve the “dropped in a whole other world to do whatever you want” experience by designing every bit of that world than by simulating a much larger world with algorithmic recycling.

Recently, Bethesda abandoned this approach of totally (or at least mostly) handcrafted open worlds and went back to relying heavily on procedural generation for Starfield, an open world game set in space. It sort of makes sense, given that the scale of a game set in a space faring civilization spread across countless star systems, is once again at a scale much too large to hand-craft or deliver and store on a PC. But that sacrifices the creativity of a hand-crafted world and returns to the repetitive sterility of algorithmic content, and well… the players reacted predictably to it.

This isn’t to say procedurally generated game worlds can’t work (i.e. No Man’s Sky, although they had to fail before succeeding), but you can’t skimp on actual content. Procedural generation isn’t an excuse to create less content than you otherwise would, it’s to extend the scale of a game’s world. A game with the amount of hand-crafted content that is in Skyrim, but that uses it to procedurally generate a planet sized world, is very different than a game where you realize you’ve already seen all the content a couple of hours in, it’s now just being assembled in different ways. Furthermore, if you took the assets from Morrowind and fed them into a procedural generator to generate a giant game world, you did not make a new game, at best you made a barely creative (only because you crafted the procedural generator yourself) and unoriginal mod for Morrowind.

So what is the point of all of this? Well it should be obvious how this relates back to AI:

Generative AI is Procedural Generation of Internet Content

The internet is a massive amount of content, so this can be interesting for much longer than a procedurally generated game from the 90s, but you are still likely to start noticing that it is just repetitive reassembly of the same base assets in different combinations, and more importantly, you will notice the sterility of the collages. Quite simply, it’s not creative. It’s just endless rehashing.

People are starting to notice all AI generated writing is rehashing the same patterns (“it’s not this, it’s that”, rule of three, etc.), AI generated thumbnails all kind-of look the same, AI generated videos are all basically the same thing just applied to different topics, and so on. This is interesting… until it isn’t. And society seems to be getting close to being done with it. More importantly, it was never anything but entertainment. The idea serious corporate office work could be done with procedural generation is just silly, and it was always a grift.

Yes, you can generate basically endless content with an algorithm like this. But the grift of AI is succinctly summarized by the false claim that endlessly recycling what was already on the internet can serve as a stand-in for the human creativity that originally produced that content. This is why vibe coded software is always crappy “remakes” (either of video games, or maybe C compilers) and never anything new, and this is why corporations are never going to get out of AI what they think they will. AI can rehash and recycle their (and other corporations’) greatest hits. It can be a radio station that plays a catalogue and maybe even remixes it in interesting ways. But it’s never going to make a genuinely new song. It will not create the next product or application that doesn’t already exist.

Conclusion

AI isn’t “so good it’s bad”, “so good it needs to be regulated”, or “great but just too dirty”, it’s just bad. Nothing about the world in the last 3 years has gotten better from AI showing up. It has just polluted the internet and given the most annoying people an excuse to be even more loud and annoying. This is what being “anti-AI” sounds like.

The only content creators I have encountered that I can fairly say are really anti-AI are David Gerard of “Pivot to AI“, who has proven uniquely excellent at seeing right through the constant scams around it, and Ed Zitron, who’s been exposing the financial calamity of generative AI fueled by the totally false promise about what it is. Both of these guys have been hosted repeatedly by The Tech Report. I obviously highly recommend all of these channels, but also find it strange how it seems to only be a few prominent people. I have encountered more “anti-AI” videos and channels on YouTube as well as content elsewhere than I can remember, but it pretty much always falls into one of the categories I listed above, which means it mostly compliments AI as a technology and regurgitates the obligatory “AI is here to stay” disclaimer. I never found that content particularly interesting and it doesn’t align with my own viewpoint really at all.

If your reaction to this is to protest that this analysis simply cannot be true because AI has already revolutionized the world, the next part of this series is just for you!