> when technological improvements that increase the efficiency of a resource's use lead to a rise, rather than a fall, in total consumption of that resource.
[1] - https://en.wikipedia.org/wiki/Jevons_paradox
Las Vegas replaced the expensive incandescent lighting on the strip with cheaper to run LED equivalents. But the costs didn't come down because they were able to add more lights and larger displays.
I think the same will happen with tokens. As the cost of tokens comes down, these models will just consume more tokens.
People are a gas; they expand the fill the space they're in. If you give someone a big house, they'll fill it with crap. If you make food cheap, they'll eat too much, even when it harms their health.
Making things cheaper usually just makes making them more prevalent. It's why computers are not faster than 20 years ago. They're merely more capable -- developers quickly and aggressively fill up (and overflow) all that added capability until you're back in the same place you used to be.
I don't think I had any in my builds until 2014ish
These days, I feel the most performance uplift when I switch from Windows/macOS over to a Linux or BSD installation, if only because there is so little wasted effort in running a bajillion little background tasks for crap I didn't ask for. Windows 10 on my PC is highly debloated and optimized, very little in my startup services and so on, but CachyOS on the same hardware feels leagues better.
When the M1 was released, I couldn't believe how fast it was.
Compare the AMD Ryzen 7 9800X3D to the AMD A12-9800, you go from 4 cores to 8, and you double the power consumption. But it's not twice as fast. It's 10 times faster. Depending on the exact metric, it could be as little as twice as fast or as much as 1000 times faster, depending on the exact operations.
On top of that memory throughput between DDR4 and DDR6 is about 2.5 times faster as well.
Oh and this isn't 20 years apart, this is 7 years apart. 20 years will see almost an exponentially larger gap even still.
AI datacenters, absolutely. People? Nah we probably use 10 percent for 90 percent of the time.
But when I need to compile MAME, oh boy is it nice having a fast CPU.
If we're talking about video game graphics it's a bit of a mixed bag.
If we're talking about local LLM models then you need all the power you can get.
Etc, etc.
(...or over 40 years ago)
what you're saying reflects corporation tendencies
take away the propaganda, let people live, see how they react
You can't use propaganda to trick people into becoming experts at calculus, for instance.
Perhaps not an individual instantly. But how about settling for making a generation of parents fear that there will be no future for their kids unless they get fantastic grades, and that they have to use them to go into stem?
My guess is that advertising raises desire by a few percent in most cases. There would be some instances of a run away craze, probably with a massive rise followed by a collapse, but that most products would largely be unaffected. I assume some advertising strategies would backfire and reduce demand.
For me, I feel like most of the time it is been exposed to the existence of an item and my ability to see how it could be useful. I don't think random advertisements I come across impact me that much since most of the advertisements I see don't seem to correspond to anything I want or do or spend money on.
Humans use their imagination to figure out what choice of action leads to a future of less uncomfortability. Often times, these are choices between short term future and long term future. Advertising is a way of helping shape that argument as to what choice is to be taken, but the underlying drive is already there.
You could also look for cases where propaganda is used against consumerism and see how impactful that tends to be.
Obesity is nearly everywhere. In the old days, "prosperous" might have been a euphemism for "fat," as only the rich could afford to be fat. Now even people who are "food insecure" are often quite fat. Some of this of course is due to food quality, but ultimately there are no starving fat people.
You're right that obesity is not necessarily linked to overeating . How is this relevant to your original point? Isn't it a complete contradiction to what you were saying before?
The original post was hyperbolic to the point of nonsense and now it seems we're on a completely different topic.
This can be true, but I think some people are confused. The vast majority of the time, it is linked to overeating. There are some edge cases where there is something else going on metabolically, but most people do over eat.
It has been credibly hypothesized that fat people are indeed malnourished. What would you do if all your food sources were appallingly lacking in essential nutrients and the sorts of things your body craved in order to get fed? You would begin to eat more, and more and more, seeking to satisfy those cravings for nutrition. But if nutritional cravings aren't satisfied, you're still reaching for those foods, because they are faking, very accurately, the tastes and feels that you would get from something that's nutritious and good for you.
So, it stands to reason: the more fake nutrition available to someone, the more they will try and eat, and get fatter. On the other hand, if we're eating real food, perhaps those cravings will be easier to satisfy without overindulging?
When I hit 300 lbs I might suspect there was a flaw in my reasoning.
- Obesity is not isolated to food deserts. The people in food deserts may be in a pitiable situation, but most of us are obese; not just the people in food deserts. If obesity were isolated to food deserts, or even concentrated in food deserts, I might feel differently.
- Even the people in food deserts have access to nutrition labels. One thing that makes a lot of sense is ensuring you get your vitamins, and then keeping the rest of your food cheap. In other words, the most cost-effective food regarding calorie-per-dollar might be zebra cakes, but obviously you don't actually to just buy Zebra cakes. Living on black beans and rice might not sound appealing, but it is not the path to obesity. When you live on black beans and rice, other foods are your garnish. Sparing, small servings, but present from time to time. Most breakfast cereal is also effectively just grass seed + vitamin powder, as well.
[edit]
There was a time in my life where I had very little money, and I lived on black beans and rice for quite a while. I biked and walked around the city and I was thinner than I've ever been since.
To describe a lifestyle of "surviving on black beans and rice" and riding your bike around, implies that you maintained a functioning household and kitchen with paid, active utilities; you were able to clean the room, vessels, appliances and utensils on a regular basis; you did pest control as necessary, and you were able to accomplish basic cooking tasks 2-3 times a day, every day.
Accomplishing all these tasks well enough to survive, feed yourself without hazard, and not become overrun with cockroaches is a sure sign of baseline mental capacity and cognitive function. Again, there are lots of poor people who were homeless, or grew up in dysfunctional households, or are on the brink of homelessness, and struggle with mental illness that militates against them "surviving on black beans and rice" day by day.
So I congratulate anyone who can accomplish these seemingly-trivial tasks. You deserve a pat on the back for sure!
I anticipate we will see something similar with intelligence. There is probably headroom to consume 100x as much intelligence in R&D. But that isn't most of the economy. Will run of the mill service jobs increase their use of intelligence by enough to offset the effect of cheaper prices? I think that's the real question.
True high-intelligence outputs (Maxwell's equations, the Fourier Transform, quantum theory) are fundamentally transformative in ways that mid-high-competence (starting a generic B2B SaaS, making another CRUD app) aren't.
Which is why we've assumed we're already pretty far down the path to AI, but we really aren't. Solving random Erdős problems isn't the same as opening up a completely new kind of math/science with game changing practical applications.
I don't think you can get to that level with more compute and more tokens. I think it's going to take new higher level knowledge representations and new kinds of training to get there.
And the token count and compute may turn out to be lower than what we're using now.
The lightbulb thing seems different though? Or at least it is a specific subset. Lights in Las Vegas are sort of an advertisement, right? In the sense that having the brightest or most interesting (or whatever) lights draw attention to your show, casino, hotel, whatever. It’s kind of a zero sum game in that the different shops are competing for the finite attention of a more-or-less set number of tourist. I think part of the Jevons paradox is that society generally finds more useful applications of the newly cheap thing. If the thing’s only purpose is to compete better in a competition with a set prize (all of the tourists’ money), that’s constrained in some way.
Intelligence is weird though. I guess we could eventually hit the point where, I dunno, maybe there’s some information theoretic bound where we process all of our signals as cleverly as possible and aren’t bound by intelligence anymore. Obviously we’re nowhere near that. It would be a very alien environment.
That sounds a race to the bottom for AI companies profits.
I do not think that LEDs are high profit margin items.
But we never scaled intelligence like this. The industrieal revolution created for the people at that time quite a huge issue / it was disruptive.
What will hapen to us though?
Related:
- Parkinson's law: "Work expands to fill the available time." https://en.wikipedia.org/w/index.php?title=Parkinson%27s_Law
- Lewis–Mogridge position: "Traffic expands to meet the available road space." https://en.wikipedia.org/wiki/Lewis%E2%80%93Mogridge_positio...
And I pretty much just plain agree, this is exactly what will happen.
I don't think there's anything wrong with it (in isolation) either, though I do already find myself pointing out that we're misusing LLMs at work sometimes (most notably, a recent mini project could have been a jinja template - and it did become one thanks to me pushing back on this). Abundance is one thing, waste and misuse is another.
I love how in our day "reading everything" means "the computer reads it for me".
I expect soon the computer will be able to go on bicycle rides, and spend time with my wife.
"So's your mom."
Sure you can speed things up with parallel work under subagents, but as with parallelizing traditional computational tasks, there are diminishing gains.
I keep hearing people saying just change the way you work to trust long-running agents and multi-task more, because they’re too slow to work with interactively for many use cases. I think that’s painful in a world where we expect humans to still heavily guide and interact with agents for their day-to-day work.
This just demonstrates how much we already take for granted the LLMs that we have now. If you compare it to what we had before (hand the task off to a junior dev and wait for them to complete the work) then it doesn't seem slow at all.
What's especially bewildering to me is that translated back to raw bandwidth, even 15000 tok/sec is just like what, 75 KB/s? Extremely meager amounts of data, moving mountains.
It's already kinda funny seeing LLMs throw out effort estimates in wall time terms. It's always some "hours, days, weeks" tier thing, when in reality, it's gone and done in minutes.
Robots right now generally move at glacial speeds. You might have seen robots doing flips in semi controlled environments but watch how slowly they open doors etc. processing time is a major bottleneck.
You can do backflips with pretty much just visual sensors for your environment, a good IMU for your spatial orientation, and some feedback on the position of a small number of really beefy joints and the force exerted on them. Folding laundry and opening doors is much more difficult, and trying to compensate with mostly vision requires going slow enough that things have time to move over appreciable distances before you take the next adjustment
You can do all the same with robots. Current advantage of human that on top of imperfect sensor data we have hyper-efficient brain connecting all dots and making calculations and approximations, and this gap looks very closeable today.
Still slow compared to humans, but Chinese robots will be as successful as Chinese EVs, phones and solar panels.
The most recent video which actually impressed me was a demonstration from Gemini Robotics 2, where a robot was shown autonomously removing the bag from a trash can and folding the loops closed in real time.
I don't follow robotics advances closely so it's possible I'm just ignorant, do you know any autonomous robotics demonstrations of useful activities that you would suggest checking out?
That's why 'stackoverflow programmers' will have a hard time competing with LLMs but engineers are still needed for their intelligence.
Well that's just my 2 cents.
Btw. an advanced search engine is probably the worst comparision i have read so far.
A LLM is a latent space which is capable of a lot of things a search engine can't do. It can apply different type of patterns and flows onto data, it can combine these etc.
My 'advanced search engine' was just able to create a working PR for exactly what i wanted it to solve (fixing a bug) by analysing the bug, finding a valid solution then commiting the solution itself.
But you forget all development today is just searching for a template, copy pasting, changing some small things. And a smart-ish search engine can do all that.
It's not different. The delusion humans have is that intelligence is special and magical. It's not. It's just nature's prediction machine. A very fancy version to be sure. But not qualitatively different .
All statements that "oh but it'll never be able to do that" will prove false.
Artificial intelligence is artificial. It can still be called intelligent, just not the same kind as biological since it's not remotely biological. It's human-like but also alien. To have something artificial be human you'd need something like replicants from Blade Runner which are synthetic biological robots.
I remember in one of the Hugging Face incident threads here, simply acknowledging that the agents were operating autonomously was super controversial. Thousands of years old concept [1], still inherently human for a lot of people.
To be clear, I'm not trying to be judgemental with this, I more consider it to be a communications breakdown, and find that to be frustrating instead. I'm not really sure how to meaningfully help it either, cause no matter how one slices it, you will in the end ask these people do desecrate these terminologies in favor of some more twisted-seeming ones. Same the other way around, the humanist understanding of these terms is basically non-workable.
[0] as opposed to personification, which is what people are actually doing almost always: https://en.wikipedia.org/wiki/Personification
Right now all three of those are at abnormally high levels. Competition will come for all three.
textiles had jevons paradox, and many more textile workers were employed even when textile machines were being created, until we saturated the demand for cheap clothing in the world and then textile workers were kaput (same for farming, and horses)
software is currently undergoing jevons paradox, but it's very unknown how high the ceiling of demand for software is. web dev might be doomed, but software in general i think is probably limitless
Intelligence is also probably unbounded (atm software and intelligence are very closely tied together). its very possible token spend rides up the curve forever.
Entirely feasible that by 2031, Fable 5 (or greater) intelligence level models will run cool on smart phones, if not sooner.
Maybe it'll take 10 years or 20 years. <5 years is not long enough for manufacturing to catch up.
Not much of a comment on the phone stuff but I'd caution against suggesting technology will never be good enough to do X. Maybe it'll be horrendously wasteful but it might happen.
(Actually talking to it, it was about as coherent as you'd expect, i.e. 3/10)
The floor for "actually usable model" keeps dropping though. (Seems to be about 27B right now?)
For a lot of tasks, even small models have saturated them a while ago, and then going cheaper and faster is just pure gains.
For coding I also prefer to do it interactive/realtime, micro-prompting, surgical edits, which the small models can handle just fine.
And then at the top, the real question is consistency. Not "can they do it" but "reliably enough that you don't need to constantly double check everything." (In my experience, not quite there yet, although it's getting way better.)
Pretty much already happening
Balance sheets
> Are all these third party Chinese model providers subsidizing the true cost?
It could be argued the subidies are even heavier than American model providers as the price war among Chinese providers is so fierce. https://www.scmp.com/tech/big-tech/article/3358868/after-tri...
Third party inference providers for Chinese models are in the US and there are so many players it would be shocking they are all heavily subsidizing it.
If you’re trying to get me to argue that every seller is operating at a loss, I don’t know. But overall the market absolutely is, again because balance sheets
Plans are definitely subsidized. Inference (token consumption) has never been proven to be operating at a loss and now that you can run many of the large SOTA models from China on sparks or other clustered servers you can figure out some of the math.
Not trying to argue but just saying “balance sheets” makes zero sense.
Yes lots of capex spend, hard to say if anyone has spent too much. At the same time demand is increasing for compute.
My bet is the assumption was one company would ultimately monopolize the space and then they could jack up rates. Alas, the opposite seems to be happening.
Surprisingly, some of the small models would not only give worse results, but also took longer than Mistral, because they were thinking so much.
That is an important detail which I was previously overlooking.
Even if its not intelligence, a LLM found a bug due to one error message, fixed it, created a PR and it solved it.
If an LLM is only able to do all of this after training on it and never achieving AGI, we already at the point were it is cheaper to teach one LLM one problem than teaching humans to do so.
Just like real life!
If we're going cynical, may as well go full throttle.
all the US economy is tied to video cards being used in lieu of gold. cost dropping 100x means the economy bottom falls out.
I wonder if it might drive the point even further if the graph scales were linear? Or maybe the progress has been so great that this would make the graphs unreadable?
With coding agents, cheap intelligence doesn't just mean producing the same software for less, but rather trying five implementations, letting agents run longer, touching larger scopes, and supervising fewer intermediate steps.
That changes which infrastructure matters. When attempts are expensive, you optimize for success, while when attempts are cheap, you start optimizing for how cheaply you can inspect, reject and recover from failure.
Git was an enormous enabler of cheap human experimentation, and I suspect we'll end up building analogous primitives around autonomous work.
ppl are doing all sorts of gymnastics to tell claude to slow its roll with verbosity.