This week we talk about neural networks, AGI, and scaling laws.

We also discuss training data, user acquisition, and energy consumption.

Recommended Book: Through the Grapevine by Taylor N. Carlson

Transcript

Depending on whose numbers you use, and which industries and types of investment those numbers include, the global AI industry—that is, the industry focused on producing and selling artificial intelligence-based tools—is valued at something like a fifth to a quarter of a trillion dollars, as of halfway through 2024, and is expected to grow to several times that over the next handful of years, that estimate ranging from two or three times, to upward of ten or twenty-times the current value—again, depending on what numbers you track and how you extrapolate outward from those numbers.

That existing valuation, and that projected (or in some cases, hoped-for growth) is predicated in part on the explosive success of this industry, already.

It went from around $10 billion in global annual revenue in 2018 to nearly $100 billion in global revenue in 2024, and the big players in this space—among them OpenAI, which kicked off the most recent AI-related race, the one focusing on large-language models, or LLMs, when it released its ChatGPT tool at the tail-end of 2022—have been attracting customers at a remarkable rate, OpenAI hitting a million users in just five days, and pulling in more than 100 million monthly users by early 2023; a rate of customer acquisition that broke all sorts of records.

This industry’s compound annual growth rate is approaching 40%, and is expected to maintain a rate of something like 37% through 2030, which basically means it has a highly desirable rate of return on investment, especially compared to other potential investment targets.

And the market itself, separate from the income derived from that market, is expected to grow astonishingly fast due to the wide variety of applications that’re being found for AI tools; that market expanded by something like 50% year over year for the past five years, and is anticipated to continue growing by about 25% for at least the next several years, as more entities incorporate these tools into their setups, and as more, and more powerful tools are developed.

All of which paints a pretty flowery picture for AI-based tools, which justifies, in the minds of some analysts, at least, the high valuations many AI companies are receiving: just like many other types of tech companies, like social networks, crypto startups, and until recently at least, metaverse-oriented entities, AI companies are valued primarily based on their future potential outcomes, not what they’re doing today.

So while many such companies are already showing impressive numbers, their numbers five and ten years from now could be even higher, perhaps ridiculously so, if some predictions about their utility and use come to fruition, and that’s a big part of why their valuations are so astronomical compared to their current performance metrics.

The idea, then, is that basically every company on the planet, not to mention governments and militaries and other agencies and organizations will be able to amp-up their offerings, and deploy entirely new ones, saving all kinds of money while producing more of whatever it is they produce, by using these AI tools. And that could mean this becomes the industry to replace all other industries, or bare-minimum upon which all other industries become reliant; a bit like power companies, or increasingly, those that build and operate data centers.

There’s a burgeoning counter-narrative to this narrative, though, that suggests we might soon run into a wall with all of this, and that, consequently, some of these expectations, and thus, these future-facing valuations, might not be as solid as many players in this space hope or expect.

And that’s what I’d like to talk about today: AI scaling walls—what they are, and what they might mean for this industry, and all those other industries and entities that it touches.

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In the world of artificial intelligence, artificial general intelligence, or AGI, is considered by many to be the ultimate end-goal of all the investment and application in and of these systems that we’re doing today.

The specifics of what AGI means varies based on who you talk to, but the idea is that an artificial general intelligence would be “generally” smart and capable in the same, or in a similar way, to human beings: not just great at doing math and not just great at folding proteins, or folding clothes, but pretty solid at most things, and trainable to be decent, or better than decent at potentially everything.

If you could develop such a model, that would allow you, in theory, to push humans out of the loop for just about every job: an AI bot could work the cash register at the grocery store, could drive all the taxis, and could do all of our astronomy research, to name just a few of the great many jobs these systems could take on, subbing in for human beings who would almost always be more expensive, but who—this AI being a generalist and pretty good at everything—wouldn’t necessarily do any better than these snazzy new AI systems.

So AGI is a big deal because of what it would represent in terms of us suddenly having a potentially equivalent intelligence, an equivalent non-human intelligence, to deal with and theorize over, but it would also be a big deal because it could more or less put everyone out of work, which would no doubt be immensely disruptive, but it would also be really, really great for the pocketbooks of all the companies that are currently burdened with all those paychecks they have to sign each month.

The general theory of neural network-based AI systems, which basically means software that is based in some way on the neural networks that biological entities, like mice and fruit flies and humans have in our brains and throughout our bodies, is that these networks should continue to scale as the number of factors that go into making them scale: and usually those factors include the size of the model—which in the case of most of these systems means the number of parameters it includes—the size of the dataset it trains on—which is the amount of data, written, visual, audio, and otherwise, that it’s fed as it’s being trained—and the amount of time and resources invested in its training—which is a variable sort of thing, as there are faster and slower methods for training, and there are more efficient ways to train that use less energy—but in general, more time and more resources will equal a more girthy, capable AI system.

So scale those things up and you’ll tend to get a bigger, up-scaled AI on the other side, which will tend to be more capable in a variety of ways; this is similar, in a way, to biological neural networks gaining more neurons, more connections between those neurons, and more life experience training those neurons and connections to help us understand the world, and be more capable of operating within it.

That’s been the theory for a long while, but the results from recent training sessions seem to be pouring cold water on that assumption, at least a bit, and at least in some circles.

One existing scaling concern in this space is that we, as a civilization, will simply run out of novel data to train these things on within a couple of years.

The pace at which modern models are being trained is extraordinary, and this is a big part of why the larger players, here, don’t even seriously talk about compensating the people and entities that created the writings and TV shows and music they scrape from the web and other archives of such things to train their systems: they are using basically all of it, and even the smallest payout would represent a significant portion of their total resources and future revenues; this might not be fair or even legal, then, but that’s a necessary sacrifice to build these models, according to the logic of this industry at the moment.

The concern that is emerging, here, is that because they’ve already basically scooped up all of the stuff we’ve ever made as a species, we’re on the verge of running out of new stuff, and that means future models won’t have more music and writing and whatnot to use—it’ll have to rely on more of the same, or, and this could be even worse, it’ll have to rely on the increasing volume of AI-generated content for future iterations, which could result in what’s sometimes called a “Habsburg AI,” referring to the consequences of inbreeding over the course of generations: and future models using AI-generated content as their source materials may produce distorted end-products that are less and less useful (and even intelligible) to humans, which in turn will make them less useful overall, despite technically being more powerful.

Another concern is related to the issue of physical infrastructure.

In short, global data centers, which run the internet, but also AI systems, are already using something like 1.5-2% of all the energy produced, globally, and AI, which use an estimated 33% more power to generate a paragraph of writing or an image, than task-specific software would consume to do the same, is expected to double that figure by 2025, due in part to the energetic costs of training new models, and in part to the cost of delivering results, like those produced by the ChatGPTs of the world, and those increasingly generated in lieu of traditional search results, like by Google’s AI offerings that’re often plastered at the top of their search results pages, these days.

There’s a chance that AI could also be used to reduce overall energy consumption in a variety of ways, and to increase the efficiency of ene