IF the right questions are asked, and IF steered into and corrected at a few crucial points. IF not it goes off in the wrong direction really quick and that's a problem that's still mostly unsolved in the last 2 years.
And that can be catastrophic in high risk environments, like legal, medical or high risk software products where being wrong in the wrong place can mean bankruptcy or even cost a life.
I help run a few marketing websites where I let the CEO's run crazy with Claude cowork, they are making PR's like a madman, but they are not allowed to touch any of the API's & platforms where there is real user data & sensitive information.
IDK "not any of it" seems a bit strong, especially thinking towards 2028. For a lot of knowledge professions, there is a surprising amount of tasks that are just dumb work compared to the rest.
Out of curiosity, why would you love to be wrong about that? What possible outcome could you see being a net positive for society if the vast majority of knowledge workers (and ultimately, as robotics progress, most workers in general) are replaced by AI?
It is not hard for me to imagine a world where if my bosses didn't need me, they would prefer me to be dead than to pay me some kind of permanent income to me. They would prefer to keep that power to themselves
These are already the sort of people who will happily lay you off into a recession, leave you without a way to pay your rent or for food if it improves their bottom line. They do not care if you starve. Or at least they care less than they do about their quarterly bonus
So no, I don't trust these fucks to continue playing nice if they view my value as going to zero
I get that you might have a 'UBI/alternative general welfare is impossible' up your sleeve, but you've written this like it's somehow unfathomable that not forcing everybody to work just to survive would be a good thing. Of course it would be good! It's just a matter of dealing with the (huge) side effect of lost income.
Somewhat related to that -- I was just this weekend watching a YouTube essay about PTSD in knights back in the medieval times, and the main point made in the video is that the psychological impacts incurred by the knights after battle were not just from seeing fucked up shit... the most apparent and serious cases of "PTSD" occurred when a knight was injured enough on the battle field resulting in them no longer able to be soldiers. Their entire purpose in the world got stripped away resulting in serious psychological stress. I think that same issue would apply to many people today (lawyers, engineers, investment bankers, etc) who would no longer be able to practice their craft. (This is the video for reference, was a good watch https://www.youtube.com/watch?v=849dmdc-Qf8)
I understand the counter argument to this is going to be some anti-capitalist rhetoric like "Well people shouldn't live to be workers and that's fucked up that they have live that way!" but IMO, some people like what they do and don't want to be made useless. (Not implying that is what you were insinuating, but just in a broad sense I that genera of argument doesn't make sense to me)
Believe it or not, some people actually do enjoy their jobs and work they do.
> I get that you might have a 'UBI/alternative general welfare is impossible' up your sleeve, but you've written this like it's somehow unfathomable that not forcing everybody to work just to survive would be a good thing.
UBI absolutely is unfathomable here (US). The USG won't even give people health care. People go bankrupt to afford life saving care on a regular basis. Or just die... Even if those cases are a minority, just the fact that it happens says a lot. So I do think it is unfathomable that UBI would be implemented here. I don't think that's unreasonable to say.
Make sure to use a deterministic pipeline or harness to go step by step so agents aren't checking their own work and I sometimes get alpha from having a codex check the work of a clod but I am seeing pretty good output across multiple domains when I have three independent quality gates and a loop which only spits it out to a human if it doesn't converge at a reasonable cost.
But it depends on the skill:
- For landing pages & simple saas solutions: marketeers & founders have more skill, since they understand the user best. The real skill is not the basic coding, but understanding the market.
- For security risks/architecture: senior devs can spot things in seconds
Im not a doctor or lawyer, but im sure there are cases where AI is really good in a similar way and cases where they miss the most crucial aspects.
I mean thats what is wanted by some companies.
The problem, especially for things like legal is that it requires someone more skilled to read through and understand that the argument is bollocks, or the law/precedent they are banking on is in fact the right one.
We have a tool that auto-writes letters to our management companies when they break SLAs. We have a slider that goes from polite to we are going to extract your first born.
Thats simple ish to do for LLMs, and low risk.
Drafting contracts is also something we could probably do, as its mostly boilerplate. However the consequence for mis-drafting a contract is multi-million dollars.
For example, my sister is a translator and she says that checking AI translations is actually harder in many ways than doing a translation in the first place, but the agencies pay less for checking than actual translation.
Which also happens with humans – does it do so at a lower rate? On its own, it kind of sounds like similar anti-self-driving-car arguments.
I agree that you can create a set of domain specific rules, reinforcement layer validation tools, like self driving, that vastly improves the accuracy of au & llm's. Making humans less and less needed. But where LLM's comes from the magic of generic knowledge, this will be the opposite, narrowing it down.
It's not like self driving cars where better than a human 80% of the time isn't good enough and they aren't really usable until its 95%, 99% etc.
There’s also the fact that they can’t possibly keep improving frontier models at the same rate (I.e. training investment) when investment starts slowing down. The amount of cash being burned is completely unsustainable and you’re already seeing some pullback.
Every new model might not be a leap like it used to be, but give it enough time and improvements add up.
The further we get into this, the more AI feels like 3-D printing. Significantly bigger and will be more widely used for sure. But nowhere near the “new industrial revolution” that all these companies are making it out to be
Ultimately they are clearly here to stay but I think they are going to be incredibly important in some industries and minimally present in others (a glorified chatbot/summarizing tool for instance). Whatever form it takes it’s definitely not going to be a model where individuals have subscriptions they pay for monthly.
exactly my point to compare it with pre-iPhone mobile market: wide (and growing fast!) adoption, clear potential (WAP websites, J2ME games), many players in the game, some real market fit discovered already (Blackberry), influx of capitial and tinkerers alike, but still a lot of unknowns where it will ultimately land.
Even if no single improvement was revolutionary (even first iPhone was just a fancy phone without App Store), overall mobile made billion dollar industries possible, for better or worse, and changed the way we live. Counts as industrial revolution, comparable to the Internet itself in my eyes.
Context is still a large limiting factor, and we have band aids around that area already. And the further along we go the further distributed LLMs get in terms of additional pieces.
As for the original article and sentiment I'm sure AI will be a boon for law. It's going to be much easier for the general consumer / person / small business to represent themselves which feels like a win. The downside is I feel like we're tracking towards a digital hell of "virtual lawyers" that will be at the whim of any org. Consumer laws really need to change now to help avoid this dystopian path we're on.
But it might be that the optimization target itself has a ceiling. If you're training toward human approval ratings from a broad population, you converge toward what median preference selects for. The plateau is baked into what you're measuring against.
In my opinion, the main thing we need to do is have training happen continuously. And probably more real world data (from sensors).
Not necessarily. In many (most?) areas of tech the rate of advancement follows a logarithmic curve. That is to say, the first 90% is achieved quickly but the last 10% takes significantly more time.
I just wish people would take a step back and think about the timescales here. Language Models are Unsupervised Multitask Learners was in 2019. Here we are seven years later and LOOK AROUND. The landscape is unrecognizable. It's worth thinking about who, in those seven years, had an accurate estimate of the future and whose estimate fundamentally failed. And just as it is valuable to note where propaganda about progress speeds past where we are, we should remember that it is costless to announce that at some unspecified future time all of this will settle down and things will go back to the way they were.
People can understand all this and still disagree with you.
The point is that if the study can't validate the claims being made then we can't actually extrapolate from that claim. What you're predicting may or may come true, but the study (which is the topic at hand) isn't useful for supporting the assertion.
With that kind of logic ... anything is possible.
That isn’t even remotely what this study is looking at.
so extrapolating from that, in another two years it will continue to bamboozle