throwaway0123_5

Beyond scientific knowledge, being there would (much more quickly) yield quite a lot of (higher quality) knowledge about "how to navigate and do things in the Grand Canyon."

If I'm going to hire a guide, I'm going to hire the guy who spent a week hiking it instead of the guy who spent a week reading about it or watching videos about it.

> There's so much natural wonder in the world in and around your home.

Sure, no doubt. But I don't really think "video of my local state park + imagining the parts that aren't conveyed over video" is particularly close to the real thing either.

> only worth so much.

Worth... quite a lot imo.

First, in the slightly objective sense of "What is this place like in real life, under X conditions." But, more subjectively, watching a video of a glacier and imagining the wind/rain/cold doesn't even approach 5% of the intensity and awe of climbing up a mountain yourself to see a seemingly endless expanse of ice, struggling to stand steady because of the wind, shivering because of the cold and rain. And finally, in the financial sense, a lot of people routinely spend many thousands of dollars and days-weeks of their time to experience natural wonders in real life.

I've been to a few natural wonders (including the Grand Canyon) that I saw in advance on video. At least for me it isn't close at all. Even if audiovisual elements could be near-perfectly reproduced by video (imo not even close with modern tech, no screen is capturing the brilliance of sunlight), you aren't capturing the temperature, the feel of wind or rain, the smell of the plants around you, etc.

Some parts are pretty annoying to read... the paragraph beginning in "Our mission is straightforward:" has many lines with just 2-3 words, massive font, and tons of unused whitespace to the right. Changing the page/browser zoom doesn't seem to help much either.

What percentage of people work at a startup though? Not just new/small business, which could include restaurants, local services, etc., but tech/science startups that would meaningfully benefit from AI.

I'd bet you could 10x the number and still be in low single digit percentages of the US workforce. And it seems pretty likely that AI-enabled startups will also employ less people per-startup.

If AI causes a white-collar jobs apocalypse, I don't think startups are picking up the slack, although it'll plausibly cushion the blow somewhat for top-performing tech workers.

Agreed, a LOT of UBI advocates gloss over the "B" in UBI. If AI increases human productivity overall, the only morally acceptable outcomes (imo) are that everyone's standard of living increases (or at least is the same without having to work) and wealth inequality decreases (if AI is doing ~all the work, there really isn't any sensible justification for some people having significantly more wealth than others). Frankly anything else seems like a recipe for massive social instability.

> others are in denial about whose living standards are actually going to be uplifted.

I don't know if it is fair to say they're in denial. For my part, I don't expect life to get much better for regular people (especially short term), but that doesn't mean we shouldn't work to try to make it happen.

> suddenly then there came to be a lot of talk of 'prompt engineering' as a skill.

I would've thought pretty much the exact opposite. "Prompt engineering" was somewhat important in 2023/2024 when the models were much weaker, it doesn't seem at all necessary anymore (unless just "clearly stating your requirements" counts as prompt engineering). Most of the discussion I've seen seems consistent with this?

I agree somewhat, but for the most part the signal is that you can succeed in some kind of knowledge work. If AI replaces ~all knowledge work, that signal isn't actually going to be valuable I think. While I don't find it certain that AI will do that, it seems increasingly plausible with every model release. In that world, I don't think very many people are pursuing degrees.

> The net effect of this appears to be that we'll see far fewer, but far more elite math Phd's, potentially discouraging many young people from the field.

It seems plausible that the value of education will go down for the vast majority of fields and as a result less people will be getting degrees of all types.

Not a good outcome I think for humanity to be less educated, even if people are provided for when they can't get jobs... things like mathematical and scientific literacy, as well as history knowledge (which even STEM majors often receive via undergraduate degree breadth requirements), etc. I would expect strongly result in more informed and harder to deceive citizens.

I agree with the general premise with respect to current SOTA, but for making nice plots that I would've never otherwise made or learned how to make (to the same standard), easily 100x.

The outcome that you're describing of AIs facilitating education for disadvantaged people is great, but isn't AI simultaneously significantly devaluing education (excluding of course education pursued for intrinsic reasons, which I'm sure many people will always pursue) and removing the need for educated people? Or at least intended to be doing so, even if it isn't accomplishing it yet?

Your examples seem to be of humans educating themselves so as to contribute to the world intellectually, but the promise of AI (and what most frontier lab employees seem to be saying) is that it will soon eclipse human intellect in almost all ways. If such a world is what we end up with, I don't see humans meaningfully contributing to "groundbreaking research" and a golden age for humanity seems to be mostly contingent on whether or not we take political actions to ensure that AI benefits everyone.

> If AI obviates the need for human labor, then obviously those who control AIs will become the elite while the rest are left to rot.

Absent political intervention, I think we agree here.

> Therefore, if we ensure everyone controls AIs, the power differences will not become so staggering as to be irreversible.

This part isn't clear to me though, but I'm open to being convinced (and frankly, would like to be convinced?). Right now most people (indirectly, via money) trade their labor for access to essentials like food/housing. If we can't do that, and everyone has access to roughly equivalent AI capabilities, how do I monetize my own access to SOTA AI? It only seems possible if you already have a lot of physical capital that the AI can manage as a business.

I guess if the endgame is instead very good non-AGI AI that doesn't entirely obviate human labor, your scenario makes a lot more sense to me. But not in the case of total replacement. In that scenario it seems like ownership over physical capital (land, data centers, energy, factories, robots, etc.) would become the only remaining source of power.

On a side note, somewhat optimistically, I think "absent political intervention" is carrying a lot of weight. Unemployment during the Great Depression peaked at <25% (iirc) and incited a lot of political change that advantaged much of the working class. AGI would be capable of inducing much higher unemployment and it would start (is starting?) with the relatively more political powerful white-collar segment of the working class.

> for ushering in the technofeudalism that will put us all in the permanent underclass.

Why is unlimited access to SOTA AI less likely to put us here? If AI obviates the need for human labor, how does having GPT-5 Sol help me get food or shelter any more than GPT-3.5 would?

> Arxiv is full of pre-prints that anyone can upload.

You now (at least for some categories) have to receive endorsement from someone who has multiple recent papers on arxiv in the same (or adjacent) category.

Agreed. I know nothing about nuclear physics. I still doubt you could pick a random person off the street and have them convincingly pose as a nuclear physicist to explain a "nuclear physics" concept to me. I doubt you could do it with a random PhD from a non-physics field. An LLM could probably convince me even if 90% of the content of the explanation is subtly or blatantly incorrect.

I get 0% (accurately) on my latest paper. Not super surprised, as I intentionally avoid some LLM-isms that I used to use because I don't want reviewers to have even the slightest indication that text is LLM-generated (even if in principle I'm not opposed to polishing or even wholesale generating academic text if it can convey the original research well, especially for non-native speakers).

I don't think the problem is as bad as a naive reading of this article suggests. I'm highly skeptical that anywhere near 65% of recent CS papers that I've read (mostly systems papers) are substantially AI-written. I threw some recent papers I've read into the system and they come back as 0-7%.

> As a software developer, I am used to finding and fixing the underlying problem instead of relying on the quick fixes these doctors were offering me.

I'm skeptical that avoidance of "relying on the quick fixes" generalizes to software developers as a whole :)

> I mean, you can read them even without the colors

I'm not colorblind and I was depending on the textual context implying Sol was better than Terra. I had to zoom in quite far to actually differentiate between the colors.

If they insist on terrible colors would it be so hard to differentiate by marker shape or line dashing too?

I was wondering the same thing. From textual context it is clear enough that Sol should be above Terra, but I had to zoom in really far to actually differentiate between the colors and I'm not colorblind. I saw a light mode version of the plot on twitter that was better but still not great.

OpenAI's plot design has been consistently awful and inaccessible, it seems like they're optimizing for something other than readability because I find it hard to believe they aren't putting in any effort for such major announcements. If the colors have to be awful they should at least differentiate with marker shapes or line dashes.

At least it isn't as bad as the stacked bar chart where the 50-something bar was higher than the 60-something bar.

I agree with the general sentiment of this comment, but national labs do hire foreigners/non-citizens, albeit possibly not from all countries with eligibility for all roles.

The funniest one I've noticed lately is a bunch of Capital One ads saying "We built a multi-agentic system for finding a car to buy!"

I'm not saying I 100% wouldn't use AI to help me in product searches, but isn't one of the main selling points of AI that it is general-purpose? Why can't I just boot up ChatGPT and ask it what cars have XYZ things I need? Certainly being informed that Capital One's system is "multi-agentic" doesn't tell me much about what is being offered.

Presumably ~100% of the employees want to feel secure in their jobs, so I don't think this would happen unless the benefit to the 51% is extreme.

> There are some people that believe that writing is an act of creative expression.

I think "some people" might be underselling it, as evidenced by the borderline innumerable fiction books in existence?

> and as such, it's a quite selfish activity

"quite" seems a bit harsh, surely "writing because you enjoy it" is pretty far down the list of all "selfish" activities? I'd imagine many authors also write because they think others will enjoy their works.

> I have yet to see a "error" that modern frontier models make that I could not imagine a human making

I mostly agree if "a human" is just any person we pluck of the street. What I still see with some regularity is the models (right now, primarily Opus 4.6 through Claude Code) making mistakes that humans:

- working in the same field/area as me (nothing particularly exotic, subfield of CS, not theory)

- with even a fraction of the declarative knowledge about the field as the LLM

- with even a fraction of frontier LLM abilities suggested by their perf in mathematical/informatics Olympiads

would never make. Basically, errors I'd never expect to see from a human coworker (or myself). I don't yet consider myself an expert in my subfield, and I'll almost certainly never be a top expert in it. Often the errors seem to present to me as just "really atrocious intuition." If the LLM ran with some of them they would cause huge problems.

In many regards the models are clearly superhuman already.

> because other individuals, organizations and nation states are not going to stop, and not going to leverage their AI if they get ahead of us.

I don't think that it is likely AT ALL, but it is probably only necessary for China and the US to agree to stop, not all organizations and nation states. It is at least possible given leadership in both countries that see AI as an existential threat.

The hardware needed to run and train SOTA AI can only be made by a very small handful of companies in a small handful of countries that either the US or China have significant influence over. Making AI R&D illegal would stop 99% of it overnight, most of the researchers are in it for money rather than some ideological commitment and there are plenty of other well-paid jobs they could take. Doing local inference in secret with existing models and GPUs would be possible, but training new SOTA models probably wouldn't be.

The account is 47 minutes old and with the writing style plus the hefty dose of em dashes, I think they are an LLM.

> Job loss is likely to have statistics more comparable to the Black Plague.

Maybe this is overly optimistic, but if AI starts to have negative impacts on average people comparable to the plague, it seems like there's a lot more that people can do. In medieval Europe, nobody knew what was causing the plague and nobody knew how to stop it.

On the other hand, if AI quickly replaces half of all jobs, it will be very obvious what and who caused the job loss and associated decrease in living standards. Everybody will have someone they care about affected. AI job loss would quickly eclipse all other political concerns. And at the end of the day, AI can be unplugged (barring robot armies or Elon's space-based data centers I suppose).

> LLM's are better at keeping consistency at details (but not at big picture stuff, interestingly.)

I think it makes sense? Unlike small details which are certain to be explicitly part of the training data, "big picture stuff" feels like it would mostly be captured only indirectly.