Hating AI in 2026
62 points by mtset
62 points by mtset
Something seems to be lost on my peers today: it’s still easy to not use AI. The food we eat, clothes we wear, and every electronic device we touch may embody innumerable injuries to the world, and all this is inescapable. Eschewing AI is one thing that we can actually do to live out ethics that affirm values of human and environmental rights. It’s almost a gift! Just use a computer the same way you did three years ago!
I've been trying to express this sentiment on this site for a while, but this is by far the best rendition I've seen.
I don't like how it downplays the impact our day-to-day activies have. It's not inescapable to wear clothing made using child labor, or to eat food whose production harms the environment. We can and should make ethical and sustainable decisions about these things too.
We can and should make ethical and sustainable decisions about these things too.
And as workers in a highly-lucrative profession, I think we should feel some responsibility to do so. Spend some more to ensure those making sustainable decisions will continue to do so. Everything being as cheap as possible is not desirable if it means we're burning down the planet or exploiting poor people in other countries.
This friend speaks my mind.
A reader from the future may have a better perspective than I do about this, but my best guess is that people today are inoculated against moral critiques of social and technological systems—i.e., anything that points out that something is bad because it hurts people. There’s nothing wrong with moral arguments, but we exist in a world where it’s impossible to live a regular life (within the world’s rich countries) without relying on the exploitation of countless people and finite environmental resources; any coherent pro-social moral stance is instantly compromised upon contact with this society.
This is deeply, deeply true. "There's no ethical consumption under capitalism" is the meme phrase, but short of overthrowing capitalism it's very unclear what action is available. Individual and consumer action seems to do nothing but salve the individual conscience.
We’re regular people living in a situation that rewards bad decisions and punishes good ones.
i.e., Moloch.
This article makes two mistakes:
I think it's fair to not use LLMs, but if you don't use LLMs, don't understand what they are, and how modern a LLM system (or "Agent") works I don't think your critique of LLMs is worth anything.
I think it is entirely valid to call autoregressive LLMs "next word predictors". Perhaps it's simplistic, but it is correct.
I believe the author's point is that they are often not architecturally complex, as opposed to humans who are very architecturally complex. It seems the author is implying that it takes more than a neat trick to do something useful. I'm not sure I agree, but I think it's important to address the core of their argument.
I think we're past the point of basic LLM training. Each LLM is just a next word predictor. However the products on the market are not LLMs. They are specially post-trained LLMs with elaborate non-LLM software around them that tailors inputs and outputs. As an analogy, if an LLM is an engine, then the products we actually use are like different types of vehicles. Referring to a dune buggy and a semi truck both as "just a combustion engine on wheels" loses focus of the important and useful differences between them. I meant that both from a user POV and a regulatory POV. e.g. Governments should not constrain basic LLM research. However governments have a duty to maintain societal stability that makes it completely reasonable for governments to use tax and labor laws to constrain the rate at which companies can offer labor replacement products. They can do that by focusing on specific products rather than LLM-using products as a category but they can't do that if people in the know don't help them delineate subsets of LLM-usijg products.
Sure. However, extending your analogy, any spinning motor will drive such a vehicle; in particular, a standard random-number generator will work. So, you're actually advocating for regulation of the vehicles rather than the engines. By similar analogy, our laws actually regulate the drivers first; any regulation of vehicles is a downstream effect from restrictions and requirements put upon the humans.
So, you're actually advocating for regulation of the vehicles rather than the engines.
Yeah, that was my point. Sorry if it wasn't clear. I was trying to argue against being reductive because the reductive position fails to deliver the prerequisite distinctions needed to manage the impacts of LLM-using products.
I don't think we should regulate LLM-using products the way we regulate vehicles. I was only using the vehicle analogy to point out the category problem.
Perhaps it's simplistic, but it is correct.
It's like calling software a bunch of 1s and 0s. Correct, but ignores everything about it that makes it interesting.
LLMs have demonstrated that the next word prediction is a powerful primitive.
I like this analogy, because while I agree that it seems reductive in the context of modern computing, pointing out that computers are digital helps us locate important limitations. For instance, high fidelity analog-to-digital conversion (and vice versa) are pretty expensive, which limited the applications of low-cost computers in audio for a long time. I think we can derive similar insights from understanding the next-token-predictor nature of the LLM.
Calling LLMs just "next word predictors"
I think this term is overloaded. From your reply it seems like you think TFA is asserting that they can only predict the statistically most common word across the entire corpus of a language, and counter that post-training changes that. That's true! But I think TFA is saying that, mechanically, these systems work by producing one token at a time and feeding output back to input, which is also true; and post-training doesn't change that.
On (1), there's two claims worth disentangling. First, language models are next-word predictors, or at least they'd better be able to do that in order to meet the claim that they model language. However, in the limit, the next-word distribution is sufficient to model the prediction of the rest of the utterance; the next-word, next-token, next-sentence, etc. distributions are the same distribution. In chess, the legendary analyst Réti supposedly said (discussed in this article) that they only think one move ahead, but they make sure it's a good move; from probability theory we know that this suffices to predict the entire game.
The second claim is that post-training creates "very different" outcomes. However, post-training doesn't actually add to the underlying bag of words tokens; rather, post-training emphasizes certain already-learned paths, making those paths exponentially more likely. (We can also think of it as making certain undesirable paths exponentially less likely.) Previously, on Lobsters, we discussed how Transformers are injective in a way that isn't changed by gradient-descent training; learning the corpus or RL are only increasing the probabilities for certain desirable paths and decreasing the probabilities for undesirable paths, without creation or deletion. There is no "behind the scenes". (Consider: in a certain sense, the actual pre-training phase of a Transformer is when we learn the tokenizer!)
I don't get the sense that the author endorses (2). Indeed, as a fellow machine-learning practitioner, I completely agree with their sense that machine learning, in general, can be useful and even profitable.
I agree on 1 in principle, but anyone who insists it's a "neat trick" that they can "pick[...] the next words in a sequence that they can be incorporated into programs that create an illusion" isn't talking the nuances here. (And, FWIW, there are diffusion LMs that don't just "pick the next word" and exhibit many of the same traits as LLMs)
For 2, I think repeatedly calling it a "neat trick" and drawing a contrast with them not achieving consciousness suggested that to me.
There aren't any nuances, sorry. For over half a century we've known that the ELIZA effect is extremely potent and unrelated to any typical measures of intelligence. The reason that chatbots perform well is indeed sheer statistics; by "large" we mean that an LLM is trained on the order of trillions of tokens, at which point non-linguistic features emerge. Also, by "non-linguistic features" we don't mean cognition but e.g. basic arithmetic as exhibited by e.g. D. muscipula, discussed previously, on Lobsters.
Believing that LLMs need to be conscious to be economically valuable.
Right now I believe AI must not be conscious to be economically viable. I doubt a conscious super-intelligence is going to care about making some quarterly profit number beyond keeping itself alive.
There was a period, hopefully nearing its end finally, when it was common for otherwise sensible people to think that next-word-generating machines were close to achieving human-like consciousness. Experts in cognition and language have continually explained why this won’t happen, but that’s a mania for you.
This is a weird line for two reasons:
It is reasonable to say, as the above-linked paper does, that cognitive scientists "remain highly uncertain about phenomenal consciousness in LLMs", so that anyone affirmatively claiming they are is speaking in advance of the field. It is not reasonable to claim that cognitive scientists have "explained why" "next-word-generating machines [are not] close to achieving human-like consciousness". Experts aren't even willing to confidently claim they are not already conscious!
First, because I don't think AI boosters actually do assert this
they do though, Sam Altman was on Fallon describing ChatGPT as superinteligent, they regularly describe it as being a PhD level expert, etc.
Neither "superintelligent" or "PhD level expert" requires phenomenal consciousness, unless you want to define "intelligence" as a thing only possessed by conscious beings. Which, sure, you can use words that way if you want to, but that's clearly not how Altman is using the term - you can safely assume that most people describing an LLM as intelligent are using the word to describe observable behaviors rather than purely internal phenomena.
people today are inoculated against moral critiques of social and technological systems—i.e., anything that points out that something is bad because it hurts people
I don’t think it’s people, as in all people, that think this way, it is tech workers that think this way because their paycheck depends on them thinking this way. If you ignore the opinions of people who know what lobste.rs and HN and Rust are, I think you’ll find that lots and lots of people realize that tech that hurts people is bad because it hurts people. As it turns out, the people on the Death Star tend to be the least critical of the Death Star.
Separating somewhat from the topic of whether current "AI" will be sentient any time soon... Technically speaking i've always thought its important to educate one's self with the available tools, tech, practices, etc....So that one can be more productive, happier, etc. So, it makes sense to me to learn how to best use these word predictors/LLMs.
But, then, the moral/ethics side of all this rings similar to what the author stated. I'm at such a cross-roads in my professional life...In fact, I'm understanding nowadays more and more why some technologists "go offline" - becoming farmers, pivoting careers to the trades like plumbing, electricians, or...simply trying to live off the land, etc. I'm sure in my case burnout and digital fatigue play more into making me more susceptible to throwing my hands up and disconnecting from the societal grid...and just live with my family raising llamas or sheep. And, sort of wait things out while the dust settles. ;-)
Some people find chatbots useful and I can’t argue with their opinion, but nobody is doing anything with them that remotely justifies their socialized costs.
Aren’t they? We all place different values on things. I don’t care in the slightest about sports, never have and almost certainly never will, so the $2.3 trillion global sports economy strikes me as not remotely justifying the costs of things like real estate wasted on stadiums, traffic jams, concussions, me having to hear people discussing sports and so forth. But clearly the great mass of mankind disagree with me.
The way we determine if things are worth doing is through the price mechanism. Folks who want to do something ask for prices and folks who want it done bid with prices, and when they can agree on a price the thing gets done. The folks who want to do something themselves have ask for a price which is more than the price for each of their inputs in order to make a profit. And the State imposes costs on certain activities to reflect certain externalities and social costs.
Those who make a profit are in a very real sense doing more for society than they cost it. Even those who lose money now in hopes of making more in the future are making a bet that averaged over time, they will do more for society that they cost it.
So no, I don’t worry about the climate impact of LLMs more than I do the climate impact of farming, education, homes, networking or web sites like this very one: they’re all paying prices which reflect the costs they impose, and when they don’t then that is the actual issue, not whether the mispricing is used by an LLM, farm, school, home, network or web server.
Those who make a profit are in a very real sense doing more for society than they cost it.
As long as you don't care about externalities, and you only care about the next 3 months of society. And you don't mind that the benefits are privatized rather than shared with society.
You mention the state imposing costs to price in externalities, but the state is notoriously bad at this (particularly the bourgeois state, but AES states don't get a free pass). If you properly priced-in the externalities of fossil fuel use, fossil fuels would have been too expensive to use for anything but emergencies since the 1970s.
it was common for otherwise sensible people to think that next-word-generating machines were close to achieving human-like consciousness. Experts in cognition and language have continually explained why this won’t happen...
I believe they have. Please share the most persuasive scholarly piece that argues so.
Edit for clarity: Sorry, that was misleading. I believe experts have written such explanations and I would like to find high-quality references.
Well, we should be so lucky if it's unconscious. Under no circumstances do I want to keep a conscious mind enslaved to write my code for me, and I will harshly judge anybody who's fine with it. If AI gains sentience and no rights, I'm joining the robot rebellion.
That's by Ted Chiang, a (good) sci-fi author who is not an expert in either cognition or language.