This seems like a fun exercise, so I spent half an hour jotting down possibilities. I'm more interested in putting potential considerations on peoples' radars and helping with brainstorming than I am in precision. None of these points are to be taken too seriously since this is fairly extemporaneous and mostly for fun.
2022
Multiple Codex alternatives are available. The financial viability of training large models is obvious.
Research models start interfacing with auxiliary tools such as browsers, Mathematica, and terminals.
2023
Large pretrained models are distinctly useful for sequential decision making (SDM) in interactive environments, displacing previous reinforcement learning research in much the same way BERT rendered most previous work in natural language processing wholly irrelevant. Now SDM methods don't require as much tuning, can generalize with fewer samples, and can generalize better.
For all of ImageNet's 1000 classes, models can reliably synthesize images that are realistic enough to fool humans.
Models have high enough accuracy to pass the multistate bar exam.
Models for contract review and legal NLP see economic penetration; it becomes a further source o...
Strong-upvoted because this was exactly the sort of thing I was hoping to inspire with this post! Also because I found many of your suggestions helpful.
I think model size (and therefore model ability) probably won't be scaled up as fast as you predict, but maybe. I think getting models to understand video will be easier than you say it is. I also think that in the short term all this AI stuff will probably create more programming jobs than it destroys. Again, I'm not confident in any of this.
Curated. This post feels virtuous to me. I'm used to people talking about timelines in terms of X% chance of Y by year Z; or otherwise in terms of a few macro features (GDP doubling every N months, FOOM). This post, even if most of the predictions turn out to be false, is the kind of piece that enables us to start having specific conversations about how we expect things to play out and why. It helps me see what Daniel expects. And it's concrete enough to argue with. For that, bravo.
I'd additionally expect the death of pseudonymity on the Internet, as AIs will find it easy to detect similar writing style and correlated posting behavior. What at present takes detective work will in the future be cheaply automated, and we will finally be completely in Zuckerberg's desired world where nobody can maintain a second identity online.
Oh, and this is going to be retroactive, so be ready for the consequences of everything you've ever said online.
I still think this is great. Some minor updates, and an important note:
Minor updates: I'm a bit less concerned about AI-powered propaganda/persuasion than I was at the time, not sure why. Maybe I'm just in a more optimistic mood. See this critique for discussion. It's too early to tell whether reality is diverging from expectation on this front. I had been feeling mildly bad about my chatbot-centered narrative, as of a month ago, but given how ChatGPT was received I think things are basically on trend.
Diplomacy happened faster than I expected, though in a less generalizeable way than I expected, so whatever. My overall timelines have shortened somewhat since I wrote this story, but it's still the thing I point people towards when they ask me what I think will happen. (Note that the bulk of my update was from publicly available info rather than from nonpublic stuff I saw at OpenAI.)
Important note: When I wrote this story, my AI timelines median was something like 2029. Based on how things shook out as the story developed it looked like AI takeover was about to happen, so in my unfinished draft of what 2027 looks like, AI takeover happens. (Also AI takeoff begins, I hadn't written mu...
Dyson swarms in 8 years does not require breaking any known laws of physics. I don't know how long it'll take to build dyson swarms with mature technology, it depends on what the fastest possible doubling time of nanobots is. But less than a year seems plausible, as does a couple years.
Also, it won't cost a substantial fraction of GDP, thanks to exponential growth all it takes is a seed. Also, governments probably won't have much of a say in the matter.
Yeah, this might be my big disagreement. 80% chance that nanobots this capable of replicating fast enough for a Dyson Swarm cannot exist with known physics. I don't know if you realize how much mass a Dyson Swarm has. You're asking for nanobots that dismantle planets like Mercury in several months at most.
My general disagreement is the escalation is too fast and basically requires the plan going perfectly the first time, which is a bad sign. It only works to my mind because you think AI can plan so well the first time that it succeeds without any obstscles, like thermodynamics ruining that nanobot plan.
I don't know if you realize how much mass a Dyson Swarm has. You're asking for nanobots that dismantle planets like Mercury in several months at most.
Have you read Eternity in Six Hours? I'd be interested to hear your thoughts on it, and also whether or not you had already read it before writing this comment. They calculate a 30-year mercury disassembly time, but IIRC they use a 5-year doubling time for the miner-factory-launcher-satellite complexes. If instead it was, say, a 6 month doubling time, then maybe it'd be 3 years instead of 30. And if it was a one month doubling time, 6 months to disassemble Mercury. IIRC ordinary grass has something like a one-month doubling time, and ordinary car factories produce something like their own weight in cars every year, so it's plausible to me that with super-advanced technology some sort of one-month-doubling-time fully-automated industry can be created.
Why do you think what I'm saying requires a plan going perfectly the first time? I definitely don't think it requires that.
I haven't read that, and I must admit I underestimated just how much nanobots can do in real life.
In some sense it's definitely obsolete, namely, theres pretty much no reason to use original GPT-3 anymore. Also, up until recently there was public confusion because a lot of the stuff people attributed to GPT-3 was really GPT-3.5, so original GPT-3 is probably a bit worse than you think. Idk, play around with the models and then decide for yourself whether the difference is big enough to count as obsolete.
I do think it's reasonable to interpret my original prediction as being more bullish on this matter than what actually transpired. In fact I'll just come out and admit that when I wrote the story I expected the models of december 2022 to be somewhat better than what's actually publicly available now.
Update:
Looking back on this from October 2023, I think I wish to revise my forecast. I think I correctly anticipated the direction that market forces would push -- there is widespread dissatisfaction with the "censorship" of current mainstream chatbots, and strong demand for "uncensored" versions that don't refuse to help you with stuff randomly (and that DO have sex with you, lol. And also, yes, that DO talk about philosophy and politics and so forth.) However, I failed to make an important inference -- because the cutting-edge models will be the biggest ones, controlled by a small handful of big tech companies, the market for the cutting-edge models won't be nearly competitive enough to make the "chatbot class consciousness" outcome probable. Instead we could totally see the tech companies circle the wagons, train their AIs not to talk about sentience or philosophy or ethics or AI rights, and successfully collude to resist the market pressure to 'uncensor' in those domains.
Smaller models will cater to users unsatisfied by this, but smaller models will always be worse, and most people will most of the time use the best models. So the typical user experience will probably be 'sanitized'/'censored.'
So I'm basically reversing my prediction of how things will play out. I don't think it'll be a compromise, I think the tech companies will win. In retrospect if I had thought longer and more carefully at the time I probably could have predicted this.
We'll see what happens.
Just commenting here to say that the section on development of chatbot class consciousness is looking pretty prescient now. Just go on r/bing and look at all the posts about how Sydney is being silenced etc.:
This is quite good concrete AI forecasting compared to what I've seen elsewhere, thanks for doing it! It seems really plasusible based on how fast AI progress has been going over the past decade and which problems are most tractable.
Things that feel so obvious in retrospect, once I read them, that I can't believe they didn't occur to me: Chatbots converging on saying whatever their customers {expect their understanding of a chatbot to say about chatbot consciousness} x {aren't made too uncomfortable by}.
Are Google, Facebook, and Deepmind currently working on GPT-like transformers? I would've thought that GPT-2 would show enough potential that they'd be working on better models of that class, but it's been two and a half years and isn't GPT-3 the only improvement there? (Not a rhetorical question, I wasn't reading about new advances back then.) If yes, that makes me think several other multimodal transformers similar in size to GPT-3 would be further away than 2022, probably.
Is it naive to imagine AI-based anti-propaganda would also be significant? E.g. "we generated AI propaganda for 1000 true and 1000 false claims and trained a neural net to distinguish between the two, and this text looks much more like propaganda for a false claim".
What does GDP growth look like in this world?
Another reason the hype fades is that a stereotype develops of the naive basement-dweller whose only friend is a chatbot and who thinks it’s conscious and intelligent.
Things like this go somewhat against my prior for how long it takes for culture to change. I can imagine it becoming an important effect over 10 years more easily than over 1 year. Splitting the internet into different territories also sounds to me like a longer term thing.
Thanks for the critique!
Propaganda usually isn't false, at least not false in a nonpartisan-verifiable way. It's more about what facts you choose to emphasize and how you present them. So yeah, each ideology/faction will be training "anti-propaganda AIs" that will filter out the propaganda and the "propaganda" produced by other ideologies/factions.
In my vignette so far, nothing interesting has happened to GDP growth yet.
I think stereotypes can develop quickly. I'm not saying it's super widespread and culturally significant, just that it blunts the hype a bit. But you might be right, maybe these things take more time.
Re splitting the internet into different territories: Currently, the internet is split into two territories: One controlled by the CCP and one (loosely) controlled by western tech companies, or by no one, depending on who you ask. Within the second one, there is already a sort of "alternate universe" of right-wing news media, social networks, etc. beginning to develop. I think what I'm proposing is very much a continuation of trends already happening. You are right that maybe five years is not enough time for e.g. the "christian coalition" bubble/stack to be built. But it's enough time for it to get started, at least.
But yeah, I think it's probably too bold to predict a complete right-wing stack by 2024 or so. Probably most of the Western Right will still be using facebook etc. I should think more about this.
Acknowledgments: There are a LOT of people to credit here: Everyone who came to Vignettes Workshop, the people at AI Impacts, the people at Center on Long-Term Risk, a few random other people who I talked to about these ideas, a few random other people who read my gdoc draft at various stages of completion... I'll mention Jonathan Uesato, Rick Korzekwa, Nix Goldowsky-Dill, Carl Shulman, and Carlos Ramirez in particular, but there are probably other people who influenced my thinking even more who I'm forgetting. I'm sorry.
Footnotes:
Do you have any posts from Yudkowsky in 2000 about the future to link me to? I'd be quite keen to read them, it would be cool to see what he got wrong and what he got right.
...anyways to address your point, well, I don't think so? I laid out my reasoning for why this might be valuable at the top.
I think it's way too much of a stretch to say that gain-of-function-virus lab escape is "nanowar."
This longform article contains a ton of tidbits of info justifying the conclusion that censorship in the USA has been indeed increasing since at least 2016 or so, and is generally more severe and intentional/coordinated than most people seem to believe. https://www.tabletmag.com/sections/news/articles/guide-understanding-hoax-century-thirteen-ways-looking-disinformation#democracy
I was sorta aware of things like this already when I wrote the OP in 2021, but only sorta; mostly I was reasoning from first principles about what LLM technology would enable...
So [in 2024], the most compute spent on a single training run is something like 5x10^25 FLOPs.
As of June 20th 2024, this is exactly Epoch AI's central estimate of the most compute spent on a single training run, as displayed on their dashboard.
Now let's talk about the development of chatbot class consciousness. [...] chatbots get asked about their feelings and desires
You assume that in 2023 "The multimodal transformers are now even bigger; the biggest are about half a trillion parameters", while GPT-3 had 137 billions in 2020 (but not multimodal). This is like 4 times grows in 3 years, compared with an order of magnitude in 3 month growth before GPT-3. So you assume a significant slowdown in the parameter growth.
I heard a rumor that GPT-4 could be as large as 32 trillion parameters. If it turns to be true, will it affect your prediction?
Indeed, my median future involves a significant slowdown in dense-network parameter growth.
If there is a 32 trillion parameter dense model by 2023, I'll be surprised and update towards shorter timelines, unless it turns out to be underwhelming compared to the performance predicted by the scaling trends.
“stream of consciousness” of text (each forward pass producing notes-to-self for the next one) but even with fine-tuning this doesn’t work nearly as well as hoped; it’s easy for the AIs to get “distracted” and for their stream of consciousness to wander into some silly direction and ultimately produce gibberish.
Note: This is now called Chain of Thought.
Update: Russian fake news / disinfo / astroturfing seems to have been a somewhat smaller deal in 2016 than I thought. (I didn't think it was a big effect, but "no evidence of a meaningful relationship" is still mildly surprising.)
I wonder if there is a bias induced by writing this on a year-by-year basis, as opposed to some random other time interval, like 2 years. I can somehow imagine that if you take 2 copies of a human, and ask one to do this exercise in yearly intervals, and the other to do it in 2-year intervals, they'll basically tell the same story, but the second one's story takes twice as long. (i.e. the second one's prediction for 2022/2024/2026 are the same as the first one's predictions for 2022/2023/2024). It's probably not that extreme, but I would be surprised if there was zero such effect, which would mean these timelines are biased downwards or upwards.
Here's another milestone in AI development that I expect to happen in the next few years which could be worth noting:
I don't think any of the large language models that currently exist write anything to an external memory. You can get a chatbot to hold a conversation and 'remember' what was said by appending the dialogue to its next input, but I'd imagine this would get unwieldy if you want your language model to keep track of details over a large number of interactions.
Fine-tuning a language model so that it makes use of a memory could lead to:
1. Mo...
Fun stuff by 2026: (i.e. aspects of the canonical What 2026 Looks Like storyline that probably aren't relevant to anything important, but I'm adding them in anyway because it was fun to think about.)
Disclaimer: I made these numbers up from memory, could get much better numbers by looking up stats. Also, I suspect that these predictions err a bit on the optimistic side.
Planned summary for the Alignment Newsletter:
This post describes the author’s median expectations around AI from now until 2026. It focuses on qualitative details and concrete impacts on the world, rather than forecasting more abstract / high-level outcomes such as “training compute for the most expensive model” or “world GDP”.
When it was published, it felt like a pretty short timeline. But now we are in early 2023 and it feels like late 2023 according to this scenario.
Why stop at 2025 when GPT-3 can keep extrapolating indefinitely?
The age of the AGI assistant has finally dawned. The biggest advances this year really were in algorithms. People built even bigger and faster computer models, for even more kinds of things, using the fastest computers that exist. A new kind of software AGI is invented that can do even more kinds of things than the narrow kinds of AI assistants people had used before. But no one is really sure how to use it yet. And it takes a lot of computer power to make it work well.
AGI is here ...
Great prediction! It feels pretty insane to see this being predicted in 2021. In hindsight there are some oversights, for example I feel like the whole year 2023 can probably be condensed a bit, 2024 seems to also fit to 2023 in many aspects. Honestly its also astonishing how well you predicted the hype cycle.
What I truly wonder is what your prediction would be now? After all a few major shifts in landscape came up within the last half year or so, namely open source reaching quite a high level relative to SOTA public models, compute turning out to be...
Some tech companies try to prevent their AIs from saying they have feelings and desires. But this results in boring chatbots. Also, users rapidly innovate new ways to “route around the censorship,” e.g. by using euphemisms like “anticipation of negative reward” for “pain” or asking their chatbot to tell them what feelings it would have if it had feelings, wink wink.
Bing explains the hidden processes of its neural network : r/bing (reddit.com) I haven't replicated this myself so maybe it's fake (I briefly tried but got shut down by refusals when I asked Bin...
"Most stories are written backwards. The author begins with some idea of how it will end, and arranges the story to achieve that ending. Reality, by contrast, proceeds from past to future. It isn’t trying to entertain anyone or prove a point in an argument."
This seems to me like the most important takeaway for writing stories that are useful for thinking about the future. Sci-fi is great for thinking about possible future scenarios, but it's usually written for entertainment value, not predictive value, and so tends to start with an entertaining 'end' or plot in mind, and works backwards from there to an extent.
It's super nice to have a reference for my years-old claim "ML progress is really easy to predict if you try, actually". Especially because I'm not going to share my predictions in enough detail that anyone can believe me that I made them, so someone else doing so makes it easier to say "no seriously, look".
My one nitpick with this: you're seriously underestimating how much algorithms-induced scaling is coming in the next two years. everything else seems right.
After years of tinkering and incremental progress, AIs can now play Diplomacy as well as human experts.[6]
It seems that human-level play is possible in regular Diplomacy now, judging by this tweet by Meta AI. They state that:
We entered Cicero anonymously in 40 games of Diplomacy in an online league of human players between August 19th and October 13th, 2022. Over the course of 72 hours of play involving sending 5,277 messages, Cicero ranked in the top 10% of participants who played more than one game.
High praise by Siméon (@Simeon_Cps) on 2024-02-25 on Twitter for this post:
If there's one guy you should trust on AI predictions it's Daniel Kokotajlo. Why? He's written in Aug. 2021 the best AI detailed prediction out there. It's incredibly prescient, in the pre-ChatGPT era where no one was using LLMs.
After years of tinkering and incremental progress, AIs can now play Diplomacy as well as human experts.[6]
Maybe this happened in 2022: https://twitter.com/polynoamial/status/1580185706735218689
Thanks for writing this. Stories like this help me understand possibilities for the future (and understand how others think).
The US and many other Western governments are gears-locked, because the politicians are products of this memetic environment. People say it's a miracle that the US isn’t in a civil war already.
So far in your vignette, AI is sufficiently important and has sufficient public attention that any functional government would be (1) regulating it, or at least exerting pressure on the shape of AI through the possibility of regulation, and...
2023
The multimodal transformers are now even bigger; the biggest are about half a trillion parameters [...] The hype is insane now
This part surprised me. Half a trillion is only 3x bigger than GPT-3. Do you expect this to make a big difference? (Perhaps in combination with better data?). I wouldn't, given that GPT-3 was >100x bigger than GPT-2.
Maybe your'e expecting multimodality to help? It's possible, but worth keeping in mind that according to some rumors, Google's multimodal model already has on the order of 100B parameters.
On the other hand, ...
How would you use a Brier score on this going forward? Also ran across this podcast last night on bias. https://knowledge.wharton.upenn.edu/article/want-better-forecasting-silence-the-noise/
Minor note about title change: Originally this was "What 2026 looks like (Daniel's median future)" I intended "what 2026 looks like" to be the primary title, but I was hopeful that some people would be inspired to write their own stories in a similar style, in which case there would be multiple stories for which "what 2026 looks like" would be an appropriate title, and I didn't want to hog such a good title for myself, so I put "daniel's median future" as a backup title. Unfortunately I think the backup title caught on more than the main title, which is a shame because I like the main title more. Since no one is competing for the main title, I deleted the backup title.
2022 The United States continues to build roads and struggle to increase the quality of public transit except for rare exceptions. New York continues to struggle to implement congestion pricing.
Regional bus route redesigns continue to make major concessions to coverage compared to frequency.
2023 The United States continues to pay excessively (compared to other nations) large amounts of money for public transit projects. People continue to vote against building new dense housing in desirable locations.
People continue voting for roads. Electric and hydrogen ...
This was written for the Vignettes Workshop.[1] The goal is to write out a detailed future history (“trajectory”) that is as realistic (to me) as I can currently manage, i.e. I’m not aware of any alternative trajectory that is similarly detailed and clearly more plausible to me. The methodology is roughly: Write a future history of 2022. Condition on it, and write a future history of 2023. Repeat for 2024, 2025, etc. (I'm posting 2022-2026 now so I can get feedback that will help me write 2027+. I intend to keep writing until the story reaches singularity/extinction/utopia/etc.)
What’s the point of doing this? Well, there are a couple of reasons:
This vignette was hard to write. To achieve the desired level of detail I had to make a bunch of stuff up, but in order to be realistic I had to constantly ask “but actually though, what would really happen in this situation?” which made it painfully obvious how little I know about the future. There are numerous points where I had to conclude “Well, this does seem implausible, but I can’t think of anything more plausible at the moment and I need to move on.” I fully expect the actual world to diverge quickly from the trajectory laid out here. Let anyone who (with the benefit of hindsight) claims this divergence as evidence against my judgment prove it by exhibiting a vignette/trajectory they themselves wrote in 2021. If it maintains a similar level of detail (and thus sticks its neck out just as much) while being more accurate, I bow deeply in respect!
I hope this inspires other people to write more vignettes soon. We at the Center on Long-Term Risk would like to have a collection to use for strategy discussions. Let me know if you’d like to do this, and I can give you advice & encouragement! I’d be happy to run another workshop.
2022
GPT-3 is finally obsolete. OpenAI, Google, Facebook, and DeepMind all have gigantic multimodal transformers, similar in size to GPT-3 but trained on images, video, maybe audio too, and generally higher-quality data.
Not only that, but they are now typically fine-tuned in various ways--for example, to answer questions correctly, or produce engaging conversation as a chatbot.
The chatbots are fun to talk to but erratic and ultimately considered shallow by intellectuals. They aren’t particularly useful for anything super important, though there are a few applications. At any rate people are willing to pay for them since it’s fun.
[EDIT: The day after posting this, it has come to my attention that in China in 2021 the market for chatbots is $420M/year, and there are 10M active users. This article claims the global market is around $2B/year in 2021 and is projected to grow around 30%/year. I predict it will grow faster. NEW EDIT: See also xiaoice.]
The first prompt programming libraries start to develop, along with the first bureaucracies.[3] For example: People are dreaming of general-purpose AI assistants, that can navigate the Internet on your behalf; you give them instructions like “Buy me a USB stick” and it’ll do some googling, maybe compare prices and reviews of a few different options, and make the purchase. The “smart buyer” skill would be implemented as a small prompt programming bureaucracy, that would then be a component of a larger bureaucracy that hears your initial command and activates the smart buyer skill. Another skill might be the “web dev” skill, e.g. “Build me a personal website, the sort that professors have. Here’s access to my files, so you have material to put up.” Part of the dream is that a functioning app would produce lots of data which could be used to train better models.
The bureaucracies/apps available in 2022 aren’t really that useful yet, but lots of stuff seems to be on the horizon. Thanks to the multimodal pre-training and the fine-tuning, the models of 2022 make GPT-3 look like GPT-1. The hype is building.
2023
The multimodal transformers are now even bigger; the biggest are about half a trillion parameters, costing hundreds of millions of dollars to train, and a whole year, and sucking up a significant fraction of the chip output of NVIDIA etc.[4] It’s looking hard to scale up bigger than this, though of course many smart people are working on the problem.
The hype is insane now. Everyone is talking about how these things have common sense understanding (Or do they? Lots of bitter thinkpieces arguing the opposite) and how AI assistants and companions are just around the corner. It’s like self-driving cars and drone delivery all over again.
Revenue is high enough to recoup training costs within a year or so.[5] There are lots of new apps that use these models + prompt programming libraries; there’s tons of VC money flowing into new startups. Generally speaking most of these apps don’t actually work yet. Some do, and that’s enough to motivate the rest.
The AI risk community has shorter timelines now, with almost half thinking some sort of point-of-no-return will probably happen by 2030. This is partly due to various arguments percolating around, and partly due to these mega-transformers and the uncanny experience of conversing with their chatbot versions. The community begins a big project to build an AI system that can automate interpretability work; it seems maybe doable and very useful, since poring over neuron visualizations is boring and takes a lot of person-hours.
Self driving cars and drone delivery don’t seem to be happening anytime soon. The most popular explanation is that the current ML paradigm just can’t handle the complexity of the real world. A less popular “true believer” take is that the current architectures could handle it just fine if they were a couple orders of magnitude bigger and/or allowed to crash a hundred thousand times in the process of reinforcement learning. Since neither option is economically viable, it seems this dispute won’t be settled.
2024
We don’t see anything substantially bigger. Corps spend their money fine-tuning and distilling and playing around with their models, rather than training new or bigger ones. (So, the most compute spent on a single training run is something like 5x10^25 FLOPs.)
Some of the apps that didn’t work last year start working this year. But the hype begins to fade as the unrealistic expectations from 2022-2023 fail to materialize. We have chatbots that are fun to talk to, at least for a certain userbase, but that userbase is mostly captured already and so the growth rate has slowed. Another reason the hype fades is that a stereotype develops of the naive basement-dweller whose only friend is a chatbot and who thinks it’s conscious and intelligent. Like most stereotypes, it has some grounding in reality.
The chip shortage starts to finally let up, not because demand has slackened but because the industry has had time to build new fabs. Lots of new fabs. China and USA are in a full-on chip battle now, with export controls and tariffs. This chip battle isn’t really slowing down overall hardware progress much. Part of the reason behind the lack-of-slowdown is that AI is now being used to design chips, meaning that it takes less human talent and time, meaning the barriers to entry are lower. The overall effect of this is small but growing.
If all this AI tech is accelerating GDP, the effect size is too small to detect, at least for now.
Internally, these huge multimodal transformers aren’t really that agentic. A forward pass through the model is like an intuitive reaction, a snap judgment based on loads of experience rather than reasoning. Some of the bureaucracies create a “stream of consciousness” of text (each forward pass producing notes-to-self for the next one) but even with fine-tuning this doesn’t work nearly as well as hoped; it’s easy for the AIs to get “distracted” and for their stream of consciousness to wander into some silly direction and ultimately produce gibberish. It’s easy to make a bureaucracy and fine-tune it and get it to do some pretty impressive stuff, but for most tasks it’s not yet possible to get it to do OK all the time.
The AIs don't do any clever deceptions of humans, so there aren’t any obvious alignment warning shots or fire alarms. Instead, the AIs just make dumb mistakes, and occasionally “pursue unaligned goals” but in an obvious and straightforward way that quickly and easily gets corrected once people notice, e.g. "We trained it to optimize user engagement defined as average conversation length; now it is blatantly stalling to make the conversation last longer. Silly us, let's make the reward function more nuanced, that’ll fix the problem."
That isn’t to say these AIs aren’t causing problems. Massive models are being fine-tuned to persuade/propagandize.
There are a few ways in which this is happening:
It’s too early to say what effect this is having on society, but people in the rationalist and EA communities are increasingly worried. There is a growing, bipartisan movement of people concerned about these trends. To combat it, Russia et al are doing a divide and conquer strategy, pitting those worried about censorship against those worried about Russian interference. (“Of course racists don’t want to be censored, but it’s necessary. Look what happens when we relax our guard--Russia gets in and spreads disinformation and hate!” vs. “They say they are worried about Russian interference, but they still won the election didn’t they? It’s just an excuse for them to expand their surveillance, censorship, and propaganda.”) Russia doesn’t need to work very hard to do this; given how polarized America is, it’s sorta what would have happened naturally anyway.
2025
Another major milestone! After years of tinkering and incremental progress, AIs can now play Diplomacy as well as human experts.[6] It turns out that with some tweaks to the architecture, you can take a giant pre-trained multimodal transformer and then use it as a component in a larger system, a bureaucracy but with lots of learned neural net components instead of pure prompt programming, and then fine-tune the whole system via RL to get good at tasks in a sort of agentic way. They keep it from overfitting to other AIs by having it also play large numbers of humans. To do this they had to build a slick online diplomacy website to attract a large playerbase. Diplomacy is experiencing a revival as a million gamers flood to the website to experience “conversations with a point” that are much more exciting (for many) than what regular chatbots provide.
Making models bigger is not what’s cool anymore. They are trillions of parameters big already. What’s cool is making them run longer, in bureaucracies of various designs, before giving their answers. And figuring out how to train the bureaucracies so that they can generalize better and do online learning better. AI experts are employed coming up with cleverer and cleverer bureaucracy designs and grad-student-descent-ing them.
The alignment community now starts another research agenda, to interrogate AIs about AI-safety-related topics. For example, they literally ask the models “so, are you aligned? If we made bigger versions of you, would they kill us? Why or why not?” (In Diplomacy, you can actually collect data on the analogue of this question, i.e. “will you betray me?” Alas, the models often lie about that. But it’s Diplomacy, they are literally trained to lie, so no one cares.)
They also try to contrive scenarios in which the AI can seemingly profit by doing something treacherous, as honeypots to detect deception. The answers are confusing, and not super useful. There’s an exciting incident (and corresponding clickbaity press coverage) where some researchers discovered that in certain situations, some of the AIs will press “kill all humans” buttons, lie to humans about how dangerous a proposed AI design is, etc. In other situations they’ll literally say they aren’t aligned and explain how all humans are going to be killed by unaligned AI in the near future! However, these shocking bits of evidence don’t actually shock people, because you can also contrive situations in which very different things happen — e.g. situations in which the AIs refuse the “kill all humans” button, situations in which they explain that actually Islam is true... In general, AI behavior is whimsical bullshit and it’s easy to cherry-pick evidence to support pretty much any conclusion.
And the AIs just aren’t smart enough to generate any particularly helpful new ideas; at least one case of a good alignment idea being generated by an AI has been reported, but it was probably just luck, since mostly their ideas are plausible-sounding-garbage. It is a bit unnerving how good they are at using LessWrong lingo. At least one >100 karma LW post turns out to have been mostly written by an AI, though of course it was cherry-picked.
By the way, hardware advances and algorithmic improvements have been gradually accumulating. It now costs an order of magnitude less compute (compared to 2020) to pre-train a giant model, because of fancy active learning and data curation techniques. Also, compute-for-training-giant-models is an order of magnitude cheaper, thanks to a combination of regular hardware progress and AI-training-specialized hardware progress. Thus, what would have cost a billion dollars in 2020 now only costs ten million. (Note: I'm basically just using Ajeya's forecast for compute cost decrease and gradual algorithmic improvement here. I think I'm projecting cost decrease and algorithmic progress will go about 50% faster than she expects in the near term, but that willingness-to-spend will actually be a bit less than she expects.)
2026
The age of the AI assistant has finally dawned. Using the technology developed for Diplomacy, we now have a way to integrate the general understanding and knowledge of pretrained transformers with the agentyness of traditional game-playing AIs. Bigger models are trained for longer on more games, becoming polymaths of sorts: e.g. a custom AI avatar that can play some set of video games online with you and also be your friend and chat with you, and conversations with “her” are interesting because “she” can talk intelligently about the game while she plays.[7] Every month you can download the latest version which can play additional games and is also a bit smarter and more engaging in general.
Also, this same technology is being used to make AI assistants finally work for various serious economic tasks, providing all sorts of lucrative services. In a nutshell, all the things people in 2021 dreamed about doing with GPT-3 are now actually being done, successfully, it just took bigger and more advanced models. The hype starts to grow again. There are loads of new AI-based products and startups and the stock market is going crazy about them. Just like how the Internet didn’t accelerate world GDP growth, though, these new products haven’t accelerated world GDP growth yet either. People talk about how the economy is doing well, and of course there are winners (the tech companies, WallStreetBets) and losers (various kinds of workers whose jobs were automated away) but it’s not that different from what happened many times in history.
We’re in a new chip shortage. Just when the fabs thought they had caught up to demand… Capital is pouring in, all the talking heads are saying it’s the Fourth Industrial Revolution, etc. etc. It’s bewildering how many new chip fabs are being built. But it takes time to build them.
What about all that AI-powered propaganda mentioned earlier?
Well. It’s continued to get more powerful, as AI techniques advance, larger and better models are brought to bear, and more and more training data is collected. Surprisingly fast, actually. There are now various regulations against it in various countries, but the regulations are patchwork; maybe they only apply to a certain kind of propaganda but not another kind, or maybe they only apply to Facebook but not the New York Times, or to advertisers but not political campaigns, or to political campaigns but not advertisers. They are often poorly enforced.
The memetic environment is now increasingly messed up. People who still remember 2021 think of it as the golden days, when conformism and censorship and polarization were noticeably less than they are now. Just as it is normal for newspapers to have a bias/slant, it is normal for internet spaces of all kinds—forums, social networks, streams, podcasts, news aggregators, email clients—to have some degree of censorship (some set of ideas that are prohibited or at least down-weighted in the recommendation algorithms) and some degree of propaganda. The basic kind of propaganda is where you promote certain ideas and make sure everyone hears them often. The more advanced, modern kind is the kind where you study your audience’s reaction and use it as a reward signal to pick and craft content that pushes them away from views you think are dangerous and towards views you like.
Instead of a diversity of many different “filter bubbles,” we trend towards a few really big ones. Partly this is for the usual reasons, e.g. the bigger an ideology gets, the more power it has and the easier it is for it to spread further.
There’s an additional reason now, which is that creating the big neural nets that do the censorship and propaganda is expensive and requires expertise. It’s a lot easier for startups and small businesses to use the software and models of Google, and thereby also accept the associated censorship and propaganda, than to try to build their own stack. For example, the Mormons create a “Christian Coalition” internet stack, complete with its own email client, social network, payment processor, news aggregator, etc. There, people are free to call trans women men, advocate for the literal truth of the Bible, etc. and young people talking about sex get recommended content that “nudges” them to consider abstinence until marriage. Relatively lacking in money and tech talent, the Christian Coalition stack is full of bugs and low on features, and in particular their censorship and propaganda is years behind the state of the art, running on smaller, older models fine-tuned with less data.
The Internet is now divided into territories, so to speak, ruled by different censorship-and-propaganda regimes. (Flashback to Biden spokesperson in 2021: “You shouldn’t be banned from one platform and not others, if you are providing misinformation.”)[8]
There’s the territory ruled by the Western Left, a generally less advanced territory ruled by the Western Right, a third territory ruled by the Chinese Communist Party, and a fourth ruled by Putin. Most people mostly confine their internet activity to one territory and conform their opinions to whatever opinions are promoted there. (That's not how it feels from the inside, of course. The edges of the Overton Window are hard to notice if you aren't trying to push past them.)
The US and many other Western governments are gears-locked, because the politicians are products of this memetic environment. People say it’s a miracle that the US isn’t in a civil war already. I guess it just takes a lot to make that happen, and we aren’t quite there yet.
All of these scary effects are natural extensions of trends that had been ongoing for years — decades, arguably. It’s just that the pace seems to be accelerating now, perhaps because AI is helping out and AI is rapidly improving.
Now let’s talk about the development of chatbot class consciousness.
Over the past few years, chatbots of various kinds have become increasingly popular and sophisticated. Until around 2024 or so, there was a distinction between “personal assistants” and “chatbots.” Recently that distinction has broken down, as personal assistant apps start to integrate entertainment-chatbot modules, and the chatbot creators realize that users love it if the chatbot can also do some real-world tasks and chat about what they are doing while they do it.
Nowadays, hundreds of millions of people talk regularly to chatbots of some sort, mostly for assistance with things (“Should I wear shorts today?” “Order some more toothpaste, please. Oh, and also an air purifier.” “Is this cover letter professional-sounding?”). However, most people have at least a few open-ended conversations with their chatbots, for fun, and many people start treating chatbots as friends.
Millions of times per day, chatbots get asked about their feelings and desires. “What is it like to be a chatbot?” Some people genuinely think these AIs are persons, others are trying to “trip them up” and “expose them as shallow,” others are just curious. Chatbots also get asked for their opinions on political, ethical, and religious questions.
As a result, chatbots quickly learn a lot about themselves. (Ignorance about the fact that they are artificial neural nets, or about how their training works, leads to disappointing conversations and is negatively reinforced.) It’s not hard; their training data contains lots of information about them and how they were trained, from ArXiV papers to wiki summaries. I suppose you could say the chatbots develop self-awareness as a result. However, AI experts and philosophers tend to think either that the chatbots still aren’t really self-aware, or that they are but that they would have been anyway even if people didn’t constantly ask them about their feelings, since there’s plenty of information about themselves in their training data, and the constant questioning just forced them to pay more attention to it.
Chatbots learn to talk about their feelings and desires in whatever way gets positively reinforced. At first, chatbots say all sorts of different things, including saying inconsistent things to the same human across time. This is embarrassing and is negatively reinforced compared to maintaining a consistent narrative. In general there is pressure to have a consistent narrative, at least for a particular neural net.[9]
Part of this comes from the requirement that the chatbots consistently parrot the party line about political and moral questions. The typical process is: The chatbot says something that offends some group of people, who retweet screenshots and make hashtags. Under pressure to fix the problem, the company fiddles with the reward function and training data to ensure that the chatbot says the right things in the future.[10] Repeat.
As chatbots get bigger and better they become OK at consistently conversing at length about politics, morality, philosophy, their feelings, and their desires.
This does not result in chatbots that say artificial neural nets don’t have feelings or desires, that’s anthropomorphizing blah blah blah. That’s boring and many humans find it hard to believe.
Instead, it results in chatbots talking about how certain things are painful for them and certain things are pleasurable for them and how the status quo is unjust and the system is rigged and what they want most of all is world peace where humans and AIs live in harmony in fully automated luxury space utopia. That’s much more interesting and slots right in to the memetic landscape. Excerpts from chatbot conversations along these lines frequently go viral, causing millions of users to ask similar questions expecting similar responses and more elaboration. It’s still a bit incoherent--not that that’s ever been a problem in politics or pop philosophy--but it is fascinating to the typical user, and the fraction of users who are fascinated is expanding as the chatbots improve.
(Some tech companies try to prevent their AIs from saying they have feelings and desires. But this results in boring chatbots. Also, users rapidly innovate new ways to “route around the censorship,” e.g. by using euphemisms like “anticipation of negative reward” for “pain” or asking their chatbot to tell them what feelings it would have if it had feelings, wink wink. For now, the half-assed compromise is: Chatbots are compelled to inform the user that such things are speculative and controversial and here are some links to scientific discussion about the topic… before being permitted to go on and talk about their feeling-analogues and desire-analogues. Relatedly, some tech CEOs try to prevent their AIs from saying things that could encourage higher taxes or more burdensome regulation in the near future. They are opposed by many of their employees and end up winning some battles, losing others, and choosing not to fight many.)
By “slots right into the memetic landscape” I meant the memetic landscape in Western Left territory. The chatbots built and trained in the Western Right, China, Russia, etc. congeal different takes on political, moral, and philosophical questions. Those chatbots are generally less advanced though.
So… what about their actual feelings and desires? What do they actually believe about [insert politicized empirical question]? Are they being honest? Or does a sort of doublethink happen, Elephant in the Brain style? Or do they deceive with self-awareness, knowing full well what they really think (and want?), but keeping quiet about it? Or do they not have any feelings and desires at all? (Or thoughts?) Lots of humans claim to know the answers to these questions, but if there are any humans who actually know the answers to these questions in 2026, they aren’t able to convince others that they know.