yes experimenting with it actually, will update here! in fact we've generated most of the tourist spots in sf, should be reflected in the repo soon too
Yeah, I think this is fair criticism, and I definitely felt the pointedness in the tone as well.
On the overly-literal / narrow thing, I think thats the culture around evaluating predictions overall. Like, all those posts around christmas where people make predictions and evaluate how last year went. The rigor is the norm.
You made a poor characterization of the content of the post and were rebutted with direct quotes. It happens, thats okay! Its not an attack on your character. There's no need to feel defensive!
Right, but you know the thing. The market can stay irrational longer than you can stay solvent. Predicting the medium future is hardest.
Markets don't just pre-plan their behaviour three years earlier and act it out lock-step. Other circumstances can change. By now, the world's financial press has covered some of the scariest aspects of this, and the situation has evolved.
There are other moves (the OpenAI/Blackrock AI debt securitization idea for one) that could delay it even further.
Zitron, I get the impression, has moved on to talking about the horsemen of the bubble apocalypse — talking about banner events that would need to happen for his predictions to be true. This is safer ground for a forecaster, because every forecast affects the future.
His shorter term predictions have not all failed by any means: he described Oracle's woes before the ratings agency downgraded them specifically because of OpenAI.
But most of his predictions will be irrelevant if the insane securitization plan happens. Because it will stop being about an AI bubble then; the worst risk will be the collapse of the entire US economy. It will need a different kind of analyst.
Me, I don't really care either way. I'm not on the cloud AI hype train, I don't work for a YC company, I'm not an American taxpayer so I will not be directly on the hook, and as Americans like to point out, the UK economy is behind on the whole AI thing so (unlike Ireland, which the USA will 100% leave to fail) we are ironically insulated. Maybe a couple of small British investment banks will fail and a pension fund or two will default.
For the most part we'll just watch the flames.
I do enjoy watching a Brit — albeit an ex-pat — upset a bunch of po-faced AI evangelists. It’s like “Itanic” all over again.
I think he is directionally correct. My own impression is that the bubble will burst at the worst time, and so my assessment is that, given the way this is entangled with the functioning of the USA as an economic power and with the future of the US political hard right, it will therefore darkly but poetically burst sometime around Labor Day 2028, which is the worst possible time.
I agree he is directionally correct, or at least is a vessel to raise important points and valid criticisms.
But predictions need to be specific and falsifable. If not, its just rag-chewing over a beer (luv that shit, but i aint predicting on taco tuesday). If they arent falsifable, then its not a prediction.
I think a lot of the HN comments generally can be described as one camp which cares about and enforces the rigor of predictions and trying to direct limited ear-time to voices which tend to get predictions right, vs the other camp that puts more weight towards directional accuracy.
Why would one ever predict anything at all if it was always falsifiable? Do you mean like eventually falsifiable? Like, I don't know about you, but I tend to go ahead and do any falsifying of something first if possible, before I resort to predicting.
Can you give an example of what a good/proper prediction might be, even in a hypothetical universe, in this schema? Does one "predict" when they play blackjack? Or is there a different concept for that kind of thing?
Falsifiable means able to be correct or incorrect. So if i said that my stepmom was kinda whelp, that wouldnt be falsifable, because like what does that even mean. But if I said my stepmom is going to be on her third marriage by the end of the decade, that is, because in 2030 someone can yell at me either way.
This is a spectrum of course: a prediction that OAI will collapse is probably right as _eventually_ all companies come to an end, but under that interpretation, the prediction is useless. It's more signal / useful / falsifable to say OAI is going to collapse around/at <year> due to <thesis>.
Anyway thats my two cents. Its fine to outline forces and trends, but when you make predictions there are useful (better, falsifiable) and useless.
You can have a prediction be specific and falsifable while inside the system, its just then riskier (and less useful).
For example, that french dude that bet on a prediction market what the temperature would be, then broke into the weather station at the airport with a hair dryer. The prediction was still specific and falsifable!
I honestly don’t understand why you care so much about the “rigor of predictions”. Short-term predictions are almost always at least somewhat inaccurate, no matter who’s doing the predicting. Directional accuracy strikes me as far more important.
Take a look at the thread below yours, but basically if the prediction has to be wishy-washy or just interpreted as 'direction', theres way more noise and way less signal. Why not just state the underlying trend/forces instead? Why lean into the online culture of predictions and do it badly.
Bad (form) prediction: OAI is gonna be wobbly in a bit
Good (form) prediction (could be totally wrong): OAI as we know it today is going to collapse due to running out of money around/at 20xx.
And sure we can be pedantic about detail, but the litmus test is: is the prediction useful if you had a crystal ball and you could know if it was true/false a priori?
> OAI as we know it today is going to collapse due to running out of money around/at 20xx.
Now what happens if that prediction is published in a popular, widely-consumed way?
Pundits and analysts who are widely read, talking about a prediction that OpenAI will run out of money by a given date, will change when OpenAI runs out of money.
This is why directional accuracy is more valuable.
Who cares about predictions to this extent? They’re practically always incorrect or flawed in some way, and most people understand and accept that. No need to be so neurotic about it.
That’s the crux of it!! You don’t have to agree but people do care, and I think (ignoring the obvious partisans) that’s the two dominant camps of commenters on this thread.
One pony’s trash is another pony’s treasure, so I guess here one persons pedanticism is another persons hobby.
This is like a grade school-tier insult. The entire article is about predictions, of course I’m talking about them. I just think the obsession with the accuracy of short-term predictions is absurd and pedantic.
For datacenters specifically I've never understood what specifically consumes the water. Arent the water-cooling loops closed, so the water just cycles around and around and around?
At the datacenter side, it depends on the method of cooling. You can chill the air or the chips directly (or both), doesn't matter, you still need to cool, and that still needs water. The question is, where is the water being used?
- If they use either evaporative cooling or a liquid-cooled heat exchanger, that uses tons of water consistently. This requires less energy (it's mostly passive) so you use more water.
- If they use closed-loop water cooling and/or heat pumps/electric chillers, that uses much less water - at the DC. But it does require more energy to circulate the water, run fans, etc. If you are using more energy, where is the energy coming from? It's coming from power plants, which require... you guessed it... more water (e.g. thermoelectric, hydroelectric, geothermal, concentrated solar). They need water in order to generate the power, and lots of it. Coal, natural gas, nuclear, and concentrated solar, all use steam to generate energy. Nuclear also uses water to cool the reactor. And water is used extensively to extract coal, oil, and natural gas. Geothermal uses water in the ground.
You can't not use a ton of water in one fashion or another. It just depends what method, and on what end the water is used. And the crazy thing is, most new datacenters are being built in places with extremely little water. Guess how that's gonna work out as the planet gets hotter?
I don't know why I got downvoted to hell for stating facts every datacenter architect knows. HN be HN'in.
The system which runs coolant over the chips can be closed but the part which uses an evaporative system to cool that is still open loop and vents water into the air, no?
Evaporated water is condensed, and in the process transfers its heat into another place that removes it. Another simple example is a pot of boiling water with a lid on it.
That's the problem, removing heat at a sufficient rate. Of course it can be done, but the most efficient way (in terms of cost) is just open loop evaporation.
I'm not a datacenter engineer, but I used to work in the ski industry. Snowmaking systems use vast quantities of compressed air. It works better if that air is cool. Blowing hot compressed air out of a snow cannon means the air temperature (wet bulb to be specific) needs to be colder to make snow.
Anyways, most air compression stations use water to cool the air, and then evaporative coolers to cool the water. The water is reused, but a ton (not sure of the percentage) is lost into the air. It's more or less a tower with a big fan on top, and water percolates down from the top, being cooled by the air as it goes. The water is then collected and pumped through the system again (but of course has to be always topped up to counteract what was lost to evaporation).
Anyways, long story short is it's most cost effective to just spray water into the air to cool water, as long as water is free/cheap.
Oh like one of those scenic cone towers like on a nuclear power plant?
Iiuc youre saying: its more cost-efficient to waste water using evaporative cooling so thats what we'll get, not that a closed loop with a heat exchanger is technically infeasible?
Exactly. A car is a closed loop system. Coolant (which is water with some chemicals) cools the engine, then the hot coolant flows through a radiator which transfers that heat to the air, and then it just keeps cycling through. If there's no leaks then no coolant is lost.
The above method could be scaled up to data center levels, but is less efficient in terms of energy usage and cost. Cheaper/easier to just spray water into the air and get "free" cooling that way, as long as you have a source of free/cheap water to replenish what is lost to evaporation.
I'm not a nuclear engineer either, but I assume this is what is happening inside of the big iconic nuclear stacks. That's just cooling water being evaporated off to keep it cool.
Closed loop is of course feasible, and closed loop with an evaporation step is also feasible, but open loop evaporative is the most efficient in terms of total energy usage (cost) and most cost effective in general, so that's what we mostly get right now.
You are adding an extra step though. Use a closed loop coolant and remove the heat from that coolant with the heat exchanger. Why would evaporation in between be more efficient?
I consider 'acceptable speed' to be around 150t/s. Why? Well, this is generally what it takes to keep me engaged with the output, rather than immediately switching to other tasks and checking back later. When I check back later, I have more catch-up to do at once, and I haven't been following the process. So I have to recall it, familiarize myself with the new progress, and sort of get back into focus with it, which is a lot of mental work (even if it happens quickly in real-time). I prefer not to have to do this because of how much work it is, so I prefer to watch the agent in real-time and try to follow its reasoning. That also lets me interrupt it quickly when I see it about to make a mistake, or see an important detail I left out.
Interesting, for anything more than side chats/projects I usually am watching the output generate and thinking about the problem. I have the same issue with switching back, takes a while to recall and page everything back into my context, so I try not to alt-tab away.
Worth distinguishing knowledge/task benchmarks from IF / agentic. It doesn't seem out of the question that you can have a small model thats generally good at instruction following and long-horizon agentic, as usually in those cases any requisite knowledge is in the context.
Most of the benchmark improvements afaict are in agentic and instruction following benchmarks.
