Subprime Intelligence

Edward Zitron 15 min read

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Last week, Sam Altman debuted OpenAI's "Sora," a text-to-video AI model that turns strings of text into full-blown videos, much like how OpenAI's DALL-E turns text into images. These videos — which are usually no more than 60 seconds long — can at times seem impressive, until you notice a little detail that breaks the entire facade, like in this video where a cat wakes up its owner, but the owner's arm appears to be part of the cushion and the cat's paw explodes out of its arm like an amoeba. Reactions to Sora's AI generated videos — and, indeed, the existence of the model itself — have ranged from breathless hype to outright fear that this will be used to replace video producers, in that it can created reality-adjacent videos that for a few seconds seem remarkably real, especially in the case of some of OpenAI's demo videos.

However, even in OpenAI's own hand-picked Sora outputs you'll find weird little things that shatter the illusion, where a woman's legs awkwardly shuffle then somehow switch sides as she walks (30 seconds) or blobs of people merge into each other. These are, on some level, remarkable technological achievements, until you consider what they are for and what they might do — a problem that seems to run through the fabric of AI. 

We're just over a year into the existence (and proliferation) of ChatGPT, DALL-E, and other image generators, and despite the obvious (and reasonable) fear that these products will continue to erode the foundations of the already unstable economies of the creative arts, we keep running into the problem that these things are interesting, surprising, but not particularly useful for anything.

Sora's outputs can mimic real-life objects in a genuinely chilling way, but its outputs — like DALL-E, like ChatGPT — are marred by the fact that these models do not actually know anything. They do not know how many arms a monkey has, as these models do not "know" anything. Sora generates responses based on the data that it has been trained upon, which results in content that is reality-adjacent, but not actually realistic. This is why, despite shoveling billions of dollars and likely petabytes of data into their models, generative AI models still fail to get the basic details of images right, like fingers or eyes, or tools.

These models are not saying "I shall now draw a monkey," they are saying "I have been asked for something called a monkey, I will now draw on my dataset to generate what is most likely a monkey." These things are not "learning," or "understanding," or even "intelligent" — they're giant math machines that, while impressive at first, can never assail the limits of a technology that doesn't actually know anything. 

Despite what fantasists may tell you, these are not "kinks" to work out of artificial intelligence models — these are the hard limits, the restraints that come when you try to mimic knowledge with mathematics. You cannot "fix" hallucinations (the times when a model authoritatively tells you something that isn't true, or creates a picture of something that isn't right), because these models are predicting things based off of tags in a dataset, which it might be able to do well but can never do so flawlessly or reliably.

This is a problem that dramatically limits how much one can rely on generative AI, and it's one that compounds severely with the complexity of what you're asking it to do. Words can be copy-pasted and edited, and citations can be checked. Images, however, are much tougher to edit, and videos are an entirely different beast, especially if you're generating lifelike humans or animals. While Sora is interesting and potentially quite scary to filmmakers, it's important to consider some practical questions, like "how can someone actually make something useful out of this?" and "how do I get this model to do the same thing every time without fail?" While an error in a 30-second-long clip might be something you might miss, once you see one of these strange visual hallucinations it's impossible to ignore them. The assumption is that audiences are stupid, and ignorant, and "just won't care," and I firmly disagree — I think regular people will find this stuff deeply offensive.

I believe artificial intelligence companies deeply underestimate how perfect the things around us are, and how deeply we base our understanding and acceptance of the world on knowledge and context. People generally have four fingers and a thumb on each hand, hammers have a handle made of wood and a head made of metal, and monkeys have two legs and two arms. The text on the sign of a store generally has a name and a series of words that describe it, or perhaps its address and phone number.

These are simple concepts that we learn from the people and places we see as we grow up, and what's very, very important to remember is that these are not concepts that artificial intelligence models are aware of. When they see 20,000 pictures with signs in them, they understand that signs look a certain way, and have some stuff on them, and then generate what's on the sign based on a user's request and their dataset's tags that match that request. Even when a model is fed exactly how a sign should be spelled out, it doesn't actually understand what that information means or how it should be used, because the instructions you are giving are based on your knowledge of signs and their contents, and the model has no knowledge of any kind.

AI fanatics are currently fantasizing over a world where they can put a few sentences into a prompt and create an entire series of TV, unable to realize that we are rapidly approaching the top of generative AI's S-curve, where after a period of rapid growth things begin to slow down dramatically. While Sora and other video generators like Pika may seem like the future (and are capable of some impressive magic tricks), they are not particularly adept — much like a lot of generative AI — at performing a particular task. Once you get past the idea that you can now generate an almost-useful video that lasts roughly a minute, one must consider the practical applications of this kind of product. Even Microsoft struggled to find compelling use cases for their $7m AI Superbowl commercial, and these use cases are even narrower once you realize that generative video is so much more restrained by its hallucinations. Where will Sora be useful?

Even if the costs weren't prohibitive, one cannot make a watchable movie, TV show, or even commercial out of outputs that aren't consistent from clip to clip, as even the smallest errors are outright repulsive to viewers. And as I've suggested above, while these models might "improve," the billions of dollars burned by OpenAI, Anthropic and Stability AI's models have found few ways to mitigate the restrictions of an artificial intelligence that doesn't have an intellect. I am also completely out of patience when it comes to being told what it "will do" in the future.

Generative AI's greatest threat is that it is capable of creating a certain kind of bland, generic content very quickly and cheaply. As I discussed in my last newsletter, media entities are increasingly normalizing their content to please search engine algorithms, and the jobs that involve pooling affiliate links and answering where you can watch the Super Bowl are very much at risk. The normalization of journalism — the consistent point to which many outlets decide to write about the exact same thing — is a weak point that makes every outlet "exploring AI" that bit more scary, but the inevitable outcome is that these models are not reliable enough to actually replace anyone, and those that have experimented with doing so have found themselves deeply embarrassed.

Despite the frothy tales and visions of how generative artificial intelligence will automate our entire existence, there's a distinct lack of practical outputs that suggest that it is even capable of doing so. ChatGPT can spin up piles of anodyne business copy, yet its outputs always require enough editing that it's questionable how much time you've actually saved. Generative image models are capable of creating cool-looking images that can replace generic images that you might use in a project, but no matter how many different prompts you use, they all kind of look the same, and that's even before you notice how the minute details look off. Is a product that can only sort-of-kind-of do something really going to create trillions of dollars of economic value?

I don't argue it will,  at least not in such a way that anybody's lives will be improved.

Shell Games

I believe we're reaching the upper limits about what generative AI can do and how accurate its outputs can be, and I believe that once reality catches up with artificial intelligence's marketing, there will be a dramatic knock-on effect that savages the entire tech industry. A Wall Street Journal article from mid-February told a worrying tale of OpenAI and Anthropic — the two largest AI companies — racing to sell their generative AI systems despite the prevalence of hallucinations, and how few answers they had for applications that were highly regulated or dealt with highly sensitive data. When pressed on the issue at a conference, Anthropic's Chief Science Officer Jared Kaplan was only able to come up with one idea — that it would make a model capable of saying "I don't know" to an answer, which in turn would create a situation where the AI would err on the side of caution, restricting its willingness to answer prompts at all.

The Journal seems unalarmed about multi-billion-dollar companies having very few answers about the critical problem with their core product, but I'd argue that a generative AI's inability to reliably generate stuff is an existential threat that should have smothered these companies early in their lives.

And there are so many stories about how unreliable this technology is. British delivery firm DPD recently had to shut down their generative support chatbot after a customer convinced it to write an insulting poem about the company. A Chevy dealership's ChatGPT-powered virtual assistant ended up offering to sell a user a car for a dollar, and wrote a python script for another. Fortune reported a researcher's study into Large Language Models' ability to understand SEC filings and found that many of them were regularly either unable to answer or hallucinating incorrect information, with Meta's Llama2 model getting 70% of the study's questions wrong. A deeply foolish lawyer relied on ChatGPT to cite cases in a motion, only to find that it cited several non-existent pieces of case law. That lawyer — Steven A. Schwartz — was fined $5,000 and ordered to inform each judge incorrectly cited as the author of a non-existent verdict in the motion. In June of last year, OpenAI was sued for defamation in Georgia by a radio host who claimed that ChatGPT generated a false legal complaint that accused him of embezzling money. Microsoft destroyed — a page that gets nearly two billion viewers a month — by replacing its human staff with an artificial intelligence that posts made up stories about bigfoot and stealing other outlets' stories and still getting the details wrong.

It's also fair to question how many organizations are actually using it. A McKinsey report from August 2023 says that 55% of respondents' organizations have adopted AI, yet only 23% of said respondents said that more than 5% of their Earnings Before Interest (EBIT) was attributable to to their use of AI — a similar number to their 2022 report, one which was published before generative AI was widely available. In plain English, this means that while generative AI is being shoved into plenty of places, it doesn't seem to be generating organizations money.

There are indications that consumers have also lost interest. As pointed out by Alex Kantrowitz’ Big Technology newsletter, traffic to ChatGPT on both mobile and web has started to stagnate, if not decline. In January 2024, ChatGPT had 1.6 billion visits — 11% below the all-time peak of 1.8 billion. This makes it only modestly more popular than Bing, which had 1.3 billion unique visits during that period. On the mobile front, ChatGPT has an estimated 6.3 million US users — or 1.7 times less than the total of new Snapchat users added during Q4 2023.  

Tech's largest cash cow since the cloud computing boom of the 2000s is based on a technology that is impossibly unreliable, a technology with a potent inverted Midas touch that burns far more money than it makes. According to The Information, OpenAI made around $1.6 billion in revenue in 2023, and competitor Anthropic made $100 million, with the expectation they'd make $850 million in 2024. What these stories don't seem to discuss are whether these companies are making a profit, likely because generative AI is a deeply unprofitable product, demanding massive amounts of cloud computing power to the point that OpenAI CEO Sam Altman is trying to raise seven trillion dollars to build chips to bring the costs down — though reports suggest that "the figure represents the sum total of investments that participants in such a venture round would need to make," which is basically the same thing. It’s also, incidentally, a greater sum than the GDPs of France and the United Kingdom combined. 

While it's hard to tell precisely how much it’s losing, The Information reported in mid-2023 that OpenAI's losses "doubled" in 2022 to $540 million as it developed ChatGPT, at a time when it wasn’t quite so demanding of cloud computing resources. Reports suggest that artificial intelligence companies have worse margins than most software startups due to the vast cost of building and maintaining their models, with gross margins in the 50-55% range — meaning the money that it actually makes after incurring direct costs like power and cloud compute. This figure is way below the 75-90% that modern software companies have. In practical terms, this means that the raw infrastructure firms — the companies that allow startups to integrate AI in the first place — are not particularly healthy businesses, and they're taking home far less of their money as actual revenue.

Luckily for them, Anthropic and OpenAI aren't really at risk, because they've taken on an important part of the tech ecosystem — they're the tail of a very hungry snake.

Turning On The Screw

During the imaginary panic of Sam Altman's ouster from OpenAI last year, Semafor reported that Microsoft's $10 billion investment was largely made up of credits for their Azure cloud computing platform. In essence, Microsoft "invested" $10 billion in money that OpenAI had to spend on Microsoft's services, meaning that OpenAI would have to use Microsoft's "Azure" cloud computing service to run ChatGPT. When Google invested $2 billion in OpenAI competitor Anthropic, it did so in tranches — $500 million up front and an additional $1.5 billion over a non-specific period of time. Coincidentally, this funding round took place only a few months after Anthropic signed a multi-year deal with Google Cloud worth $3 billion, locking them into Google's compute platform in the process. Amazon also invested $4 billion in Anthropic, who agreed to a "long-term commitment" to provide Amazon Web Services (Amazon's competitor to Microsoft Azure and Google Cloud) with early access to their models — and Anthropic access to Amazon's AI-focused chips.

While Microsoft, Amazon, and Google have technically "invested" in these companies, they've really created guaranteed revenue streams, investing money to create customers that are effectively obliged to spend their investment dollars on their own services. As the use of artificial intelligence grows, so do these revenue streams, forcing almost every single dollar spent on AI into the hands of a few trillion-dollar tech firms.

It's a contrived process with a fairly simple revenue stream.

In the case of an AI company (or a business that has jumped upon the AI bandwagon), their website or app is integrated with OpenAI's ChatGPT or Anthropic's Claude via their APIs. The company pays on a per-token basis for each input (request they make through their software) and output (thing that the model does as a result). When these requests are made, ChatGPT, Claude, or whatever model has to compute the result, which it does using massive amounts of cloud computing — which is bought from the cloud provider (say, Microsoft Azure or Google Cloud). As a result, every interaction with ChatGPT or Claude is, on some level, guaranteed revenue for one of the big tech firms. These were investments in the sense that money changed hands, but while it did so, big tech put giant handcuffs on the wrists of the AI companies that every startup has to use.

Admittedly, you could argue that the same situation is true for the conventional Internet. Most websites are hosted by a third-party cloud provider. If you visit a site that uses an external company to implement functionality that would otherwise be too complicated to build themselves (like auth, or payment processing, or banking integrations), it’s a sure bet those companies are using Amazon, Microsoft, or Google for hosting. And so, without even realizing it, our online activity benefits a handful of already-powerful companies. The key difference is that, for the most part, people aren’t locked-in and can walk, either to one of the other big players, or to a smaller vendor like Rackspace or Linode. Moreover, the scale is different, and serving a webpage will always cost less than processing a request sent to a generative AI model.   

These golden handcuffs have already led to massive swells of revenue for Microsoft, increasing by 30% in the last quarter alone thanks to the increased usage of graphics processing units (GPUs) which have become essential to the power-hungry demands of AI applications. Google's investment in Anthropic was made in the hopes that it’d see a similar revenue multiplier, and I'd argue Amazon's was made in the same vein — though it was too late to force Anthropic to use AWS as their preferred vendor.

Big tech has turned the startup ecosystem into a giant goldmine, one that guarantees that almost every dollar spent on any AI product is eventually shared with one of a few multi-trillion dollar tech firms. And on some level, it's become the savior of an ecosystem that hasn't had a new revenue-driving industrial boondoggle this exciting since the Software-As-A-Service boom of the 2010s. Some might argue this is a situation where everybody wins — startups get funded because they're able to do new things, venture capitalists make money because their startups can actually get acquired or go public, and big tech makes money because everybody is forced to pay them even more money by proxy.

I, however, have grave concerns.

As it stands, generative AI (and AI in general) may have some use. Yet even with thousands of headlines, billions of dollars of investment, and trillions of tokens run through various large language models, there are no essential artificial intelligence use cases, and no killer apps outside of non-generative assistants like Alexa that are now having generative AI forced into them for no apparent reason. I consider myself relatively tuned into the tech ecosystem, and I read every single tech publication regularly, yet I'm struggling to point to anything that generative AI has done other than reignite the flames of venture capital. There are cool little app integrations, interesting things like live translation in Samsung devices, but these are features, not applications. And if there are true industry-changing possibilities waiting for us on the other side, I am yet to hear them outside of the fan fiction of Silicon Valley hucksters.

This entire hype cycle feels specious, though not quite as specious as the metaverse or cryptocurrency boom. Public companies are pumping their valuations and executive salaries off the back of artificial intelligence hype, yet nobody is saying the blatantly obvious — that this industry is deeply unprofitable and yet to prove its worth. Artificial intelligence is so demanding of computing power that it may need as much electricity as an entire country, Microsoft and Amazon are both investing billions to build even more data centers to capture demand for an unproven product, and Sam Altman of OpenAI has said that the future of AI relies on an "energy breakthrough."

This industry is money-hungry, energy-hungry, and compute-hungry, yet it doesn't seem to be doing anything to sustain these otherworldly financial and infrastructural demands, other than the fact that people keep saying that "artificial intelligence is the future." And while some claim that AI can help fight climate change, it's impossible to argue that "suddenly using more and more power for a negligible return" is good for the environment.

And if this wasn't already worrying enough, one has to wonder what happens if we face another economic panic, or if the hype dies down before OpenAI or Anthropic discover a way to make a profit. As it stands, OpenAI and Anthropic are heavily dependent on companies believing that they have to integrate AI into their products, which will require these companies to be able to find ways to integrate AI that users actually care about. And even if they manage to do that, will they do so in a way that actually turns a profit?

If AI startups — by which I mean those companies integrating these models into their apps — begin to falter, so will the only real revenue stream that these companies have, making them more dependent on big tech to keep them alive. This situation is only made more problematic by the fact that these models are unprofitable, and Altman's desperation for a new chip company or energy breakthrough suggests that they'll only become more unprofitable as they generate more revenue.

I hope I am wrong. I hope that the bottom doesn't fall out of AI, and that the startup ecosystem grows, and that this all becomes profitable and that everything will be fine.

As it stands, I am terrified by how unstable this situation is and astonished at how brazenly money and energy is being burned in pursuit of an unsustainable future where big tech exerts more power over fledgling companies, and how despite multiple industry collapses hinged upon unsustainable and unprofitable businesses, Silicon Valley seems incapable of learning a single lesson.

Thanks for reading the newsletter.

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