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When writing formal letters in Japanese, there are a variety of extra steps you have to do above and beyond fancy salutations and signoffs, including - my favourite - the seasonal observations beginning the letter (e.g., in August you could say "The oppressive heat continues to linger") and closing it ("please give my regards to everyone"). These are so stereotyped that I think most recipients of letters regard them more as a structural element of the composition than a semantic one, just as in English we don't really think of the virtue of sincerity when reading "Yours Sincerely".
I think this is basically what LLMs will do to writing, at least on the 5-10 year time scale. Everything will be written by LLMs and interpreted and summarised by LLMs, and there will be a whole SEO-style set of best practices to ensure your messages get interpreted in the right way. This might even mean that sometimes when we inspect the actual first-order content of compositions created by LLMs that there are elements we find bizarre or nonsensical, that are there for the AI readers rather than the human ones.
To get back to your point, I absolutely think this is going to happen to bureaucracy in academia and beyond, and I think it's a wonderful thing, a process to be cherished. Right now, the bureaucratic class in education, government, and elsewhere exert a strongly negative influence on productivity, and they have absolutely no incentives to trim down red tape to put themselves out of jobs or reduce the amount of power they hold. This bureaucratic class is at the heart of cost disease, and I'm not exaggerating when I say that their continued unchecked growth is a civilisation-level threat to us.
In this regard, LLMs are absolutely wonderful. They allow anyone with limited training to meet bureaucratic standards with minimal effort. Better still, they can bloviate at such length that the bureaucracy will be forced to rely on LLMs to decode them, as noted above, so they lose most of the advantage that comes with being able to speak bureaucratese better than honest productive citizens. "God created men, ChatGPT made them equal."
If you're worried that this will lead to lax academic standards or shoddy research practices, I'd reassure you that academic standards have never been laxer and shoddy research is absolutely everywhere, and the existence of review boards and similar apparatchik-filled bodies does nothing to curb these. If anything, by preventing basic research being done by anything except those with insider connections and a taste for bureaucracy, they make the problem worse. Similarly, academia is decreasingly valuable for delivering basic research; the incentive structures have been too rotten for too long, and almost no-one produces content with actual value.
I'm actually quite excited about what LLMs mean in this regard. As we get closer to the point where LLMs can spontaneously generate 5000-10000 word pieces that make plodding but cogent arguments and engage meticulously with the existing literature, huge swathes of the academic journal industry will simply be unable to survive the epistemic anarchy of receiving vast numbers of such submissions, with no way to tell the AI-generated ones from the human ones. And in the softer social sciences, LLMs will make the harder bits - i.e., the statistics - much easier and more accessible. I imagine the vast majority of PhD theses that get completed in these fields in 2024 will make extensive use of ChatGPT.
All of these changes will force creative destruction on academia in ways that will be beautiful and painful to watch but will ultimately be constructive. This will force us to think afresh about what on earth Philosophy and History and Sociology departments are all for, and how we measure their success. We'll have to build new institutions that are designed to be ecologically compatible with LLMs and an endless sea of mediocre but passable content. Meanwhile I expect harder fields like biomed and material sciences to (continue to) be supercharged by the capabilities of ML, with the comparative ineffectiveness of institutional research being shown up by insights from DeepMind et al.. We have so, so much to look forward to.
Hahaha.
Meanwhile, in reality, bureaucracy will rise with the amount that bureaucrats can process and still leave at 5 pm sharp. AI notably increases that amount.
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I have a mild anecdotal counterpoint. It's old at this point, since I haven't worked in science in over a decade, but when I did, I was on my organization's institutional animal care and use committee, and despite the bureaucratic jargon and process, we actually did do something to curb some of the more pointless uses of research animals. The group wasn't particularly adversarial and worked with researchers on questions like whether the statistical power was going to be sufficient (we don't want to kill animals if the study won't even give a result), whether it could be done with fewer animals (same, but reversed), and whether the protocol used all reasonable practices to reduce pain and suffering of the animals (e.g. if the end point is death from an infection, can we just do infection instead, since the animal will tend to die painfully?).
I'm sure many groups feel that they're doing something constructive despite just being an annoying bureaucracy, and I'm sure that the review process we were doing was both imperfect and tedious, but I do want to offer that gentle pushback against it being literally useless.
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Whew, lots of thoughts. Let's start with total agreement that academic standards have never been laxer, that shoddy research is absolutely everywhere, review boards and such have done nothing to curb it, and that almost no one produces content with actual value. Moreover, I would agree that the incentive structures have been too rotten for too long, and I think that this is a huge driver of the previous four items. "Publish or perish" has creeped ever earlier, and I've actually stopped going to conferences, due to the flood of interestingly-titled talks that end up being, "So, I'm an undergrad, and this is really preliminary work, and... [total garbage]." The undergrads feel like they have to publish bullshit in order to get into grad school, the grad students feel like they have to publish bullshit to get a post-doc, the post-docs feel like they have to publish bullshit to get a professorship, and the assistant professors feel like they have to publish bullshit to get tenure (after tenure, paths tend to bifurcate a bit more, it seems), so the assistant professors are more than happy to push everyone down the chain to go ahead and publish their bullshit (so long as his/her name is on it, so it adds to a count on Google Scholar). It is all for the sake of number go up rather than advancing knowledge.
This trend has been decades long, but I would argue that it has also been exacerbated by one particular huge drop in barrier to submission - the rise of China. I don't know if they've really subscribed to our fucked up incentive structure, because I just don't know as much about how their unis work, but the regional flood has gone global. Not to say that there is zero good work coming from there (I finally recommended acceptance of my first paper from a Chinese group, but it's unsurprising that it was an exceptional case, as the main guy in that group is good enough that he's now taken a position at an excellent Western uni), but the quantity of folks pushing out the quantity of digital ink from there is astounding, and the vast majority of it is undergrad-tier bullshit. It really makes me pause when you say:
I really go back and forth in my head. Does having a sort of mental rule that nearly automatically rules out all Chinese work help? Probably so, like 99% of the time. Does having a sort of mental rule that if the first author is an undergrad from a low-tier uni, it's probably shit help? Probably so, like 99% of the time. I joke sometimes that I've never even seen a contemporary masters thesis that was at all interesting (there are some legendary ones from the past, by legit giants in their fields). Having some form of statistically-informed heuristic realllly saves a lot of time and effort that would otherwise be 99% wasted. This sort of "credential chauvinism" obviously won't stop the flood; incentive structures for conferences/journals are also fucked up enough that there's zero chance any will adopt a position of basically, "If you're an undergrad or from a Chinese university, your submission will basically be auto-denied." But they're adopted very pragmatically, almost out of necessity, by the good researchers who just don't have that much time to waste. (I know the irony of writing this in a bloody comment on a rando internet forum.)
My personal strategy is basically defection/free-riding. I've cultivated a network of really talented profs who I personally know, have personally spoken with enough to know how they think, with a not-insignificant percentage of them being assistant profs. They basically feel forced by their incentive structures to wade through all of the crap and constantly engage with all the conferences and reviewing and editing and shit... and they like magically filter through it all and bring up the small number of diamonds in the rough. Is it the best-tuned filter? Possibly not. Might I gain some additional insight by wading through more of it actively? Possibly. But damn if I'm going to ever feel like the cost/benefit tradeoff is going to be worth it anytime soon. But the nature of defection/free-riding is that not everyone can do it without catastrophic consequences.
Getting back to the point of LLMs, this is what I'm worried about. Sure, it'll make it harder for dyed-in-the-wool bureaucrat-and-nothing-more folks, but it'll also make it harder for us. It'll be Eternal September for academia. What possible filters can stand?
I don't have as much to say on the social sciences bit, and you may well be perfectly on point there. For harder sciences, I'm really not sure where this will go. Traditional work has been extremely structured in form, whereas I do subscribe to the Nick Weaver School of ML in Research, which is, "ML is great for when you want to model something that you have a good reason to think is structured, but you have no idea how to model it, and you're okay with it being fabulously wrong some percentage of the time." Biomed and materials science are perfectly positioned to reap the gains of this. Those areas in particular have fantastically complicated underlying structures, and at least the experimental folks don't much care how we get a half-decent idea of what to try to build, so long as each iteration doesn't take too much time, we can just try a bunch of them and see what works out. Huge potential for big experimental gains, and a decent chance that experimental gains will subsequently push the theory part forward around those new lodestones. My view of the trends in institutional research has been that they've gone full steam ahead at trying to embrace it for every problem under the sun, even when it doesn't make much sense. But for every work that gets accelerated, every wonder material that gets developed, how many shit-tier "ML" papers will be submitted/published in the field that turn out to just be awful, obnoxious noise? (While this last paragraph is similar in a concern about the flood of crap, it's a bit distinct as it's less focused specifically on the administrative side of the crap production/evaluation.)
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Agreed. I know it sounds overly dramatic, but this hard press on the brakes is bringing us down.
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I'm well aware, and that's not what I'm worried about at all.
I know a bunch of social workers, some of them a generation or so older than me, and I heard a few stories of how things used to run. Like nowadays there was actual work, and there was a bunch of bureaucratic stuff to deal with. Back in the day you had to type it all out on actual paper, mail it etc. Then everybody and their dog started using computers for everything, everything got digitized, hundreds of apps meant to automate the drudgery got deployed, you could instantly send documents via e-mail... do you want to take a wild guess in which of these eras people spent more time doing actual work than they did dealing with bureaucratic nonsense?
I worry the same will happen with AI. No, it will not "make us equal", it's a side-rant but I'm shocked anyone could even utter such a sentiment with a straight face. There are, and there have always been, entire institutions devoted to the task of ensuring this will never happen. What will happen is that you will need AI to even keep up. You will need an AI text generator to output sheer amount of text you will now be required to write in order to cover your ass, and you will need an AI summarizer to "read" the tonnes upon tonnes of paper that will be sent your way. The best part is that all of this will be centralized in the hands of a few companies, who's owners hang out at the same cocktail parties as various panopticon fetishists at the top of our society, who will dictate how exactly this AI needs to be lobotomized to only output goodthink. It will now be the perfect tool for them to "nudge" us, old geezers like you or me might remember a world where you needed to process information yourself, but children born in the new one will only ever know information summarized to them via AI.
I've mentioned this before, but this is what drives me up the wall with AI-optimists. I know it's hard to learn the lessons of history, but this doesn't even count as history. We literally just watched Big Tech bitch-slap the ever-loving hell out utopian tech-nerds like 5 minutes ago, and I'm now supposed to jump on the next bandwagon that is going to "make us equal"? Give me a break.
That was my first thought as well. Like just how disconnected from the reality of day-to-day work does someone have to be for this prediction to make a lick of sense? It genuinely boggles the mind.
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As I understand it, this is already a standard tactic used by large law firms to crush individual lawyers. They don't need LLM, they just hire a ton of new lawyers to churn out vast amounts of legal documents. A single lawyer trying to fight a lawsuit against them would get buried, because he just can't physically read all of that stuff and respond in any human lifetime, and if he can't respond he loses by default.
Big corporations also do this as a defense mechanism. So you want to sue them, and they're required to turn over the relevant docs? Oh they'll do that... but the "relevant docs" are like a million pages of garbage. Again, only a giant law firm has the resources to actually read through all of it and process it effectively. DDOS via human bureaucracy.
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I share your pessimism. I think of it this way: the amount of time it takes to do anything increases with the amount of time available; the amount of admin that a bureaucracy requires of you increases with the amount they think you can do.
So LLM increases in people's capacity to handle admin work will result in an increase in the amount of required admin. "Of course you can complete this form as well; you can use an LLM to help you..."
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I think this is a misdiagnosis of whats going on. On an individual level where someones work is individually threatened by the removal of some administrative or bureaucratic requirement they might oppose it's removal but at the same time there's at least ten people in the same organization that is burdened by said requirement and dreams of it being removed. Also, even if the requirement is removed they're unlikely to be fired, they'll be reassigned and hiring will be reduced.
It is not the administrative state that is driving this, it's the regulatory state, mostly because they don't actually have to deal with the consequences of the regulations they create. It is good to pass regulation because you're then seen as "doing something" and the costs are too diffuse to be tracked back to you by the electorate. There is noone that can say no so the regulatory complexity grows and with it cost disease. It's kind of like a spaghetti code base that is never refactored because people with power have no incentive to do so and the code will never actually fail to execute, only work worse and require more compute.
That said, opinions of a department wholly concerned with dealing with some bureaucratic hurdle is suspect when asked about the value of said regulation. But again, these are usually small parts of the overall organisation that are concerned with something else of concrete value.
It is possible that llms will help but I could see it being a wash because it also enables bad behaviour which necessitates further regulation and enforcement.
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I think you're wrong about this being a good thing. Currently, all the best journals in most allow anyone to submit. Sometimes you get people outside "the cathedral" getting really novel ideas published and changing fields. Once it becomes too easy for hoi palloi to submit, journal editors will start relying more and more on the author's credentials. Not from Harvard/Yale/Oxbridge? Then you're totally out of luck.
This is a double edged sword. Further gaming of the system means more control over the existing institutions, but it also means more numerous, competent and motivated counter elites.
Some people seem to think Harvard can't purity spiral itself into irrelevance because it's so inextricably tied with the existing power structure. But power structures are not eternal laws of nature.
We've seen this with finance, the level of control there is pretty grandiose (the whole idea of an "accredited investor" is ridiculous to say nothing of AML etc) but that's just put a giant prize on building alternatives. And people did and are.
An "accredited investor" is just someone upper middle class or richer. (Individual income >$200k, household income >$300k, or household net worth ex home equity >$1m all qualify with no paperwork). I don't think it is a good rule, but it doesn't constitute a grandiose level of control. Given the purpose of the rule (that an accredited investor is someone rich enough that the median voter will point and laugh rather than sympathizing if they get scammed), the limits are arguably too low. Selling bad investments to dentists is not pro-social, and if it was illegal the people currently doing it would probably find something better to do.
I think the idea the State can just come in and tell you that you're too stupid to use your own money correctly is grandiose in itself. But I'm more pointing generally at the category of financial credentials. The more esoteric the financial product the more weird hoops you usually have to go through to trade it with any reasonable liquidity. And the more hoops, the more opportunities for exception, and therefore power.
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I'm pretty sure the idea of "accredited investor" is really defining "investors the SEC allows to do extra risky stuff because the median voter will at best laugh when they lose their shirts." It's a glorified CYA measure so the government can claim "freedom" exists without the bureaucrats getting hauled in front of an angry Congress asking why their sympathetic constituents (retired teachers, etc) lost money.
That's a fair assessment, but it's part of a large swath of similar credentials that are meant to split the market between people that know what they're doing and stupid amateurs. In a way that effectively removes both risk and opportunity from you if you're not willing to jump through hoops.
There's a ton of opportunity in investing in startups early for instance, and a ton of money both to win and to lose (mostly to lose), but protecting your average wagie from getting swindled also means he's never going to make it big. And as with any bureaucratic control, there's discretion to make exceptions, which means there's power.
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All being an accredited investor does is make you a target for more scams. That includes those "alternatives", assuming you mean various crypto-related investing efforts (aside from just investing in the coins, not all of which are scams)
Harvard can't purity spiral-itself into irrelevance because it's inextricably tied to the entire leftist power structure. Which is too big and self-reinforcing to fail.
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