Huge by itself isn't the same as huge winner.
GM was America's largest employer as recently as the 90s.
I'd call an 80 year run pretty damn good. having my company not just survive me and my children, but dominate an industry for that time seems like a good deal. It shows it wasn't my fault it failed.
>the business of cars lent itself to a dominant player.
I'd rather measure my business by impact, not stock numbers. That mentality is exactly why GM fell very slowly through the 70's (defying a while bunch of strategies Ford implemented to beat out the compeition. like knowledge retention ) and crashed by the 90's.
Money to keep operating is important too, but I don't think Ford lived a life of Picasso here.
It's just not a huge winner. Many industries don't work that way, there are no "huge winners" even if there are some companies that are huge. Oil & gas doesn't really have "huge winners". The huge companies are a result of huge amounts of capital being put to work.
What companies are you referring to?
American Automative filled for Bankruptcy multiple times.
American Government had to step in to back them up and bail them out.
And yea, it will vary. Amazon crashed hard on stocks through the 2000's. Google completely thrived. they are still considered on the same standing today as a trillionaire tech company
But yes, a lot of energy wasted in the growing phase.
Why exactly is energy wasted during this phase?
Are you expecting hardware to become obsolete much faster? But that only depends on TSMC's node cadence, which is still 2-3 years. Therefore, AI hardware will still be bound to TSMC's cadence.
They’re way more efficient at matmuls, but start throwing branching logic at them and they slow down a lot.
Literally a percentage of their cores will noop while others are executing a branch, since all cores are lockstep.
I think OP is suggesting AI algorithms and training methods will be improve resulting in enormous performance gains with existing hardware causing a similar surplus of infrastructure and crash in demand.
I'd be shocked if more than like 20% of the VC money going into it would come out the other side during such an event.
Like Intel and Samsung might make a handful of better chips or whatever, but neither of their business models really involve being TSMC. So if the bubble pop took out TSMC, there wouldn’t be a new TSMC for a while.
The energy build out, data centers are not wasted. You can swap out A100 GPUs for BH200 GPUs in the same datacenter. A100s will be 5 years old when Blackwell is out - which is just about right for how long datacenter chips are expected to last.
I do, however, think that the industry will move to newer hardware faster to try to squeeze as much efficiency as possible due to the energy bottleneck. Therefore, I expect TSMC's N2 nodes to have huge demand. In fact, TSMC themselves have said designs for N2 far outnumber N3 at the same stage of the node. This is most likely due to AI companies that want to increase efficiency due to the lack of electricity.
Even poor people can enjoy 8k gaming on a phone soon.
The question remains is when the bubble will crash. We could be in the 1995 equivalent of the dotcom boom and not 1999. If so, we have 4 more years of high growth and even after the crash, the market will still be much bigger in 2029 than in 2024. Cisco was still 4x bigger in 2001 than in 1995.
One thing that is slightly different from past bubbles is that the more compute you have, the smarter and more capable AI.
One gauge I use to determine if we are still at the beginning of the boom is this: Does Slack sell an LLM chatbot solution that is able to give me reliable answers to business/technical decisions made over the last 2 years in chat? We don't have this yet - most likely because it's probably still too expensive to do this much inference with such high context window. We still need a lot more compute and better models.
Because of the above, I'm in the camp that believe we are actually closer to the beginning of the bubble than at the end.
Another thing I would watch closely to see when the bubble might pop is if LLM scaling laws are quickly breaking down and that more compute no longer yields more intelligence in an economical way. If so, I think the bubble would pop. All eyes are on GPT5-class models for signs.
Well, this is taken on faith by OpenAI/etc, but obviously the curve has to flatten at some point, and appears to already be doing so. OpenAI are now experimenting with scaling inference-time compute (GPT-O1), but have said that it takes exponential increases in compute to produce linear gains in performance, so it remains to be seen if customers find this a worthwhile value.
If you run chain of thoughts on an 8B model, it becomes a lot smarter too.
GPT-o1 isn't GPT5 though. I think OpenAI will have a chain of thoughts model for GPT5-class models as well. They're separate from normal models.
GPT-5 and Claude-4 will be interesting, assuming these are both pure transformer models (not COT), as they will be a measure how much benefit remains to be had from training set scaling. I'd expect gains will be more against narrow benchmarks, than in the overall feel of intelligence (LLM arena score?) one gets from the model.
Training costs are killing them, and it's obviously not sustainable to keep spending more on research and training than the revenue generated. Training costs are expected to keep growing fast, while revenue per token in/out is plummeting - they need massive inference volume to turn this into a profitable business, and need to pray that this doesn't turn into a commodity business where they are not the low cost producer.
We've seen this before, with for example Amazon where they made a deliberate effort to avoid profitability by spending as much as possible on infrastructure until the revenue became some much they couldn't spend it.
Being in a position where you are highly cash-flow positive and it's strategic investment that is the cost seems like a good position.
Amazon were reinvesting and building scale, breadth and efficiency that has become an effective moat. How do you compete with Amazon Prime free delivery without your own delivery fleet, and how do you build that without the scale of operations?
OpenAI don't appear to have any moat, don't own their own datacenters, and the datacenters they are using are running on expensive NVIDIA chips. Compare to Google with their own datacenters and TPUs, Amazon with own datacenters and chips (Graviton), Meta with own datacenters (providing value to their core business) and chips - and giving away the product for free despite spending billions on it ... If this turns into the commodity business that it appears it may (all frontier models converging in performance), then OpenAI would seem to be in trouble.
Of course OpenAI could stop training at any time, but to extent that there is further performance to be had from further scaling and training, then they will be left behind by the likes of Meta who have a thriving core business to fund continued investment and are not dependent on revenue directly from AI.
Glean.com does it for the enterprise I work at: It consumes all of our knowledge sources including Slack, Google docs, wiki, source code and provides answers to complex specific questions in a way that’s downright magical.
I was converted into a believer when I described an issue to it, pointers to a source file in online git repo and it pointed me to another repository that my team did not own that controlled DNS configs that we were not aware about. These configs were the reason our code did not behave as we expected.
AI code assistants have been a net neutral for me (they get enough idioms in C++ slightly incorrect that I have to spend a lot of time just reading the generated code thoroughly), but being able to say "tell me what the timeline for feature X is" and have it comb through a bunch of internal docs / tickets / git commit messages, etc, and give me a coherent answer with links is amazing.
They control the OS so they can combine and feed all your digital information to an LLM in a seamless way. However, in the very long term, I think their advantage will go away because at some point, LLMs could get so good that you don't need an OS like iOS anymore. An LLM could simply become standalone - and function without a traditional OS.
Therefore, I think the advantage for iOS, Android, Windows will increase in the next few years, but less powerful after that.
They recently hired Jony Ive on a project - it could be this.
I think it'll be a long term goal - maybe in 3-4 years, a device similar to the Rabbit R1 would be viable. It's far too early right now.
And the point stands: you would not write a new OS, even to incorporate it into your LLM. You'd clone a BSD (or similar) and start there.
Underneath, it can be Linux, BSD, Unix, or nothing at all, whatever. It doesn't matter. That's not important.
OS was just a convenient phrase to describe this idea.
No need to get to the technicals such as whether it's UNIX or Linux talking to the hardware.
Just from a pure user experience standpoint, OpenAI would become iOS.
You're just burning money bothering to rewrite the rest of the stack when off the shelf will save you years.
I don't think AI is ultimately even an application, it's a feature we will use in applications.
well, that's not a false statement. As much as I might dislike it, the raise of the web and web applications have made the OS themselves irrelevant for a significant number for tasks.
But I think there will be a long period when people want both the traditional UI with buttons and sliders, and the AI that can do what you ask. (Analogy with phone keyboards where you can either speech-to-text, or slide to type, or type individual letters, or mix all three.)
this is part of why I stay in python when doing ai-assisted programming; there's so much training information out there for python and I _generally_ don't care about if its slightly off-idiom, its still probably fine.
What it's really good at is taking my description of something and pointing me in the right direction to do my own research.
(two things that helped me with getting decent code were to describe the problem and desired solution, followed by a "Does that make sense?". This seems to get it to restate the problem itself and produce better solutions. The other thing was to copy the output into a fresh session, ask for a description of what the code does and what improvements could be made)
When you need something fast for whatever reason sure, but later when you want to tweak or add something, you'll have to finally sit down and learn basically the whole thing or at least a major part of it to do so anyway. Imo it's better to do that from the start but sometimes that's not always ideal.
I'm guessing this is because the only training material was blogs from uber-nerdy CS researchers on a language where "mistakes" are basically impossible to write, and not a bunch of people flailing on forums asking about hello world-ish stuff and segfaulting examples.
At the end of the day, it depends where you are in the hierarchy. Having it write code for me on a hobby project in react that's bad but works is one thing. I'm having a lot of fun with that. Having it write bad code for me professionally is another thing though. Either way, there's no going back to before ChatGPT, just like there's no going back to before Stack Overflow or Google. Or the Internet.
I suppose one reason Slack doesn't have a solution yet is because they're having a hard time getting it to work for large companies.
I think you may be confused about the length of the context window. These tools don't pull all of your Slack history into the context window. They use a RAG approach to index all of your content into a vector DB, then when you make a query only the relevant document snippets are pulled into the context window. It's similar for example to how Cursor implements repository-wide AI queries.
The point I'm trying to make is that increase context window will require more compute. Hence, we could still just be in the beginning of the compute/AI boom.
Not only is it an extremely poor source of information, it has ruined the company's Slack culture as people are no longer willing to (for lack of a better term) shitpost knowing that their goofy sarcasm will now be presented to Glean users as fact.
There are a few other companies in this space (and it's not something that complex to DIY either); the issue is data quality. If your Google Docs and wikis contain obsolete information (because nobody updated them), it's just going to be shit in, shit out. Curating the input data is the challenging part.
Has your barber/hairdresser recommended you buy NVDA?
But I see your point. And yes, I think it has trickled down to the mainstream.
(By that metric, I guess Bitcoin crashed a few years ago.)
I was thinking re the data in the tweet, that there were a lot of mentions of "soft landing" before the dot com crash, before the 2006 property crash and now, it is quite likely there was an easy money policy preceding them and then government policy mostly focuses on consumer price inflation and unemployment, so they relax when those are both low and then hit the brakes when inflation goes up and then it moderates and things look good similar to now. But that ignores that easy money can also inflate asset prices, eg dot com stocks, houses in 06, or money losing AI companies like now. And then at some point that ends and the speculative asset prices go down rather than up, leaving people thinking oh dear we've borrowed to put loads of money into that dotcom/house/ai thing and now it's not worth much and we still have the debts...
At least that's my guess.
> 2006 property crash
There's a confirmation bias there, though. Economists, particularly pop economists, have predicted all 20 of the last two recessions; if you just say "the world is going to end" every year, then occasionally it kinda will, and certain people will think you're a visionary.
Note that the presence of such a feature isn't the same as whether it's secure enough for normal use.
In particular, anything anyone said in the last 2 years in chat could poison the LLM into exfiltrating your data or giving false results chosen by the attacker, because of the fundamental problems of LLMs.
https://promptarmor.substack.com/p/data-exfiltration-from-sl...
So, your problem there is 'reliable'. LLMs, fairly fundamentally, cannot do 'reliable'. If you're looking for reliable, you likely are looking at a different tech entirely.
This is akin to an analytic statement, eg, "all bachelors are married". The truth is completely within the definition of the statement. Compare this to a synthetic statement such as "it is raining outside". In this case the truth is contingent on facts outside of the statement itself.
When LLMs are faced with an analytic statement they are more reliable. When they are faced with a synthetic statement they are prone to confabulate and are unreliable.
Hrm. I've found LLM summaries to be of... dubious reliability. When someone posts an article on this here orange website, these days someone will sometimes 'helpfully' post a summary generated by a magic robot. Have a look at these, sometime. They _often_ leave out key details, and sometimes outright make shit up.
Interesting article on someones' experiences with this recently: https://ea.rna.nl/2024/05/27/when-chatgpt-summarises-it-actu...
Original article: https://osa1.net/posts/2024-10-09-oop-good.html
LLM (ChatGPT o1-preview) results: https://chatgpt.com/share/67166301-00d8-8013-9cf5-e8a980aca7...
LGTM!
I'd like to know which model is used in the article you've referenced as well as the prompt. I also suspect that 50 pages is pushing up to the limits of the context window and has an impact on the results.
---
As for the article itself... Use a language like F# or OCaml and you get a functional-first language that also supports OOP!
We will also get some nice things, like more intelligent IDE at affordable cost, think CursorAi costs $20/month(240/year), while whole JetBrain's package costs only 25/month(290/year).
However, I am a bit worried about all these data center and AI and energy use/scaling. While consumers are being pushed to more and more efficient energy usage and energy prices are definitely high(to what I would expect with massive renewable energy production), large corps and such will continue scaling energy usage higher and higher.
Also, the AI fad will eventually spook out a lot of free knowledge sharing on the open and everything will get behind paywall, so random poor kid in some poor country will no longer have access to some nice tutorial or documentations online to learn cool stuff because in some countries, what we call a price of "morning coffee" is actually could be a day's earning of an adult but not for non-privileged people. Without ability to pay for AI services, no more access to knowledge. Search engines will eventually drown in slop, I mean even google now frequently gives me "no resoult found" page and I need to use ddg/brave/bing to fish out some results still.
1996 - Cisco was 23.4B or 0.3% of US GDP
2000 - Cisco peaked at 536B or 5.2% of US GDP
2020 - Nvidia was 144B or 0.7% of US GDP
2024 - Nvidia is 3.4T or 11.9% of US GDP
Numbers very rough and from different sources, but I'd be surprised if Nvidia doesn't pop within 1-2 years at most.
There's a reason Microsoft just outright purchased the entire output of 3-mile island (a full sized nuclear power plant).
At some point, people will stop buying GPUs because we've simply run out of power.
My only hope is that we don't suffer some kind of ill effect (ex: double the cost of consumer electricity or something, or local municipalities going bankrupt due to rising energy costs). The AI boom has so much money in it we need to account for tail wags dog effects.
More importantly, Cisco's margins and PE were much higher than Nvidia's today.
You should use actual financial measures and not GDP national accounts which have zero bearing on business valuation.
There's a relation between P/E and future actual revenues of a company.
Imagine that a similar comparison would imply that it's projected that in a few years NVidia's revenues shall represent 10% of the US's GDP: do we really believe that's going to happen?
The Mag 7 + Broadcom have a market cap that is now 60% of the US's GDP. I know you think it's silly but... Doesn't that say something about the expect revenues of these companies in a few years?
Do we really think the Mag 7 + Broadcom (just an example) are really to represent the % of the actual US's GDP that that implies?
Just to be clear: I'm not saying it implies the percentage of the US GDP of these 8 companies alone is going to be 60% but there is a relation between the P/E of a company and its expected revenues. And revenues of companies do participate in the GDP computation.
I don't think it's as silly as several here think.
I also don't think GP should be downvoted: if we disagree, we can discuss it.
I think the renewables would have been built at the same rate anyway precisely because they're so cheap; but nuclear power, being expensive, would not be built if this bubble had not happened, and somehow nuclear does seem to be getting some of this money.
After Fukushima (https://news.ycombinator.com/item?id=41768726), Japanese reactors were shut down and there was a glut of uranium available in the spot market. Simultaneously, Kazatomprom flooded the market with cheap ISR uranium. The price of uranium fell far below the cost of production and the mining companies were obliterated. The few miners that survived via their long-term contracts (primarily Cameco) put their less efficient mines into care and maintenance.
Now we're seeing the uranium mining business wake up. But after a decade of bear-market conditions the miners cannot serve the demand: they've underinvested, they've lost skilled labor, they've shrunk. The rebound in uranium supply will be slow, much slower than the rebound in demand. This is because uranium mining is an extremely difficult process. Look at how long NexGen Energy's Rook 1 Arrow mine has taken to develop, and that's prime ore (https://s28.q4cdn.com/891672792/files/doc_downloads/2022/03/...). Look at Kazatomprom's slowing growth rate (https://world-nuclear-news.org/Articles/Kazatomprom-lowers-2...), look at the incredible complexity of Cameco's mining operations: https://www.petersenproducts.com/articles/an-inflatable-tunn...
Here is a discussion of the uranium mining situtation: https://news.ycombinator.com/item?id=41661768 (including a very risky method of profiting from the undersupply of uranium, stock ticker SRUUF, not recommended). Note that Numerco's uranium spot price was put behind a paywall last week. You can still get the intra-day spot uranium price for free here: https://www.yellowcakeplc.com/
Even the peak of that graph (136… er, USD per lb?) is essentially a rounding error compared to everything else.
0.00191 USD/kWh? Something like that, depends on the type of reactor it goes in.
The fuel is a tiny fraction of the cost of running the plant. See discussion here, contrasting with natural gas: https://news.ycombinator.com/item?id=41858892
It is also important that the fuel is physically small so you can (and typically, do) store years of fuel on-site at the reactor. Nuclear is "secure" in the sense that it can provide "energy security".
And I'm saying that as someone who finds all this stuff cool and would like to see it used in international shipping.
I already linked this above, twice. I know it's a hassle to read, it's Sunday afternoon, so don't worry about it.
It's not important whether you as an individual get this right or not, as long as society reaches the correct conclusion. Thankfully, we're seeing that happen, a worldwide shift toward the adoption of nuclear power.
Have a pleasant evening!
The US has zero commercial reactors under construction and this is for one reason: economics.
The recent announcements from the hyperscalers are PPAs. If the company building the reactor can provide power at the agreed price they will take it off their hands. Thus creating a more stable financial environment to get funding.
They are not investing anything on their own. For a recent example NuScale another SMR developer essentially collapsed when their Utah deal fell through when nice renders and PowerPoints met real world costs and deadlines. [2]
[1]: https://www.lazard.com/media/gjyffoqd/lazards-lcoeplus-june-...
[2]: https://iceberg-research.com/2023/10/19/nuscale-power-smr-a-...
I'm on PG&E, I wish I could get my electricity for only $0.14/kWh
From what I’ve understood PG&E’s largest problem is the massive payouts and infrastructure upgrades needed from the wildfires, not the cost of the electricity itself.
For Europe overall is 22%.
For modern nuclear power the only nuclear reactor under construction in France is Flamanville 3 which is 6x over budget and 12 years late on a 6 year construction timeline.
Hinkley Point C in the UK is in a similar quagmire and Olkiluoto 3 finally got finished last year after a near 20 year construction timeline.
Politically there's some noise from conservative politicians who can't hold a climate change denial position anymore, but still need to be contrarians.
The problem is the horrendous economics.
https://news.ycombinator.com/item?id=41860341
Basically, nuclear fission is clean baseload power. Wind and solar are not baseload power sources. They don't really compete. See discussion here: https://news.ycombinator.com/item?id=41858892
Furthermore, we're seeing interest (from Google and Amazon and Dow Chemical) in expensive but completely safe TRISO (HALEU) reactors (https://www.energy.gov/ne/articles/triso-particles-most-robu...). These companies want clean baseload power, with no risk of meltdown, and they're willing to pay for it. Here's what Amazon has chosen: https://x-energy.com/fuel/triso-x
TRISO (HALEU) reactors use more than 1.5 times the natural uranium per unit of energy produced because the higher burnup is offset by higher enrichment inputs (see page 11 at https://fuelcycleoptions.inl.gov/SiteAssets/SitePages/Home/1...), and the fuel is even more expensive to manufacture, but they are completely safe. This is a technology from the 1960's but it's attractive now because so much money is chasing clean baseload nuclear fission for data centers.
These "impossible to melt down" TRISO small modular nuclear fission reactors are what Elon Musk was talking about on the campaign trail last week, when he said:
ELON MUSK: "The dangers of nuclear power are greatly
overstated. You can make a nuclear reactor that is
literally impossible to melt down even if you tried to
melt it down. You could try to bomb the place, and it
still wouldn't melt down. There should be no regulatory
issues with that. There should be significant nuclear
reform."
This means you don't understand how the grid works. California's baseload is ~15 GW while it peaks at 50 GW.
New built nuclear power is wholly unsuitable for load following duty due to the economics. It is an insane prospect when running at 100% 24/7, and even worse when it has to adapt.
Both nuclear power and renewables need storage, flexibility or other measures to match their inflexibility to the grid.
See the recent study where it was found that nuclear power needs to come down 85% in cost to be competitive with renewables, due to both options requiring dispatchable power to meet the grid load.
> The study finds that investments in flexibility in the electricity supply are needed in both systems due to the constant production pattern of nuclear and the variability of renewable energy sources. However, the scenario with high nuclear implementation is 1.2 billion EUR more expensive annually compared to a scenario only based on renewables, with all systems completely balancing supply and demand across all energy sectors in every hour. For nuclear power to be cost competitive with renewables an investment cost of 1.55 MEUR/MW must be achieved, which is substantially below any cost projection for nuclear power.
https://www.sciencedirect.com/science/article/pii/S030626192...
> These companies want clean baseload power, with no risk of meltdown, and they're willing to pay for it. Here's what Amazon has chosen
The recent announcements from the hyperscalers are PPAs. If the company building the reactor can provide power at the agreed price they will take it off their hands. Thus creating a more stable financial environment to get funding.
They are not investing anything on their own. For a recent example NuScale another SMR developer essentially collapsed when their Utah deal fell through when nice renders and PowerPoints met real world costs and deadlines.
https://iceberg-research.com/2023/10/19/nuscale-power-smr-a-...
> with no risk of meltdown
Then we should be able to remove the enormous subsidy the Price Anderson act adds to the industry right? Let all new reactors buy insurance for a Fukushima level accident in the open market.
Nuclear powerplants are currently insured for ~0.05% of the cost of a Fukushima style accident and pooled together the entire US industry covers less than 5%.
https://en.wikipedia.org/wiki/Price%E2%80%93Anderson_Nuclear...
Eh, it's generally SMRs, which remain kinda vapourware-y. I'd be a little surprised if anything concrete comes of it, tbh; I suspect that it is mostly PR cover for reactivating coal plants and the like. (The one possibly-real thing might be the Three Mile Island restart, but that in itself isn't particularly consequential.)
In the same way the rights of way obtained with all the railroads were, even if the rails / engines themselves had to be replaced every decade or so
But some hardware does last quite a long while. Fiber laid from 25 years ago is still pretty useful.
We bought a lot of shovels. Even if we don’t find more gold, we can dig holes for industry elsewhere.
For example, there’s a ton of room for developing all kinds of low latency, highly reliable, embedded classifiers in a number of domains.
It’s not as gee-whiz/sci-fi as an LLM demo, but I think potentially much bigger impact over time.
My favourite example is the astonishing pace with which reverse-rendering technology has progressed. It started with a paper by NVIDIA showing projections of 2D photos being "fitted" into a 3D volume of differentiable hashtables, and then the whole thing exploded when Guassian Splats were invented. I full expect this niche all by itself to generate a huge variety of practical applications. Computer games and movie special effects, obviously, but also AR/VR, industrial uses, mapping, drone navigation, etc...
Luckily, the cost of intelligence is quickly dropping. GPT-4, one of OpenAI’s most capable models, is now priced at $2.5 per million input tokens and $10 per million output tokens. At its initial release in March 2023, the cost was respectively $10/1M input tokens and $30/1M for output tokens. That’s a huge $7.5/1M input tokens and $20/1M output tokens reduction in price. https://www.lycee.ai/blog/drop-o1-preview-try-this-alternati...
No one is getting excited about "AI slop", just the models that generate it. Funny situation.
Everyone knows that panning for gold is a fools game so we have a gold pan/shovel bubble.
It is like having a massive lumber bubble and calling it a real estate bubble because someday we might actually build those houses.
> I think that will happen here.
Why? The rail network, road network and fiber network that was laid could be used for decades after their original investors went bust.
The current datacenters full of AI compute can't really be used for anything else if AI companies go bust.
That's the problem with investing in compute infrastructure - you need to have a plan to use it all up in the next 5 years, because after that you wouldn't even be able to give it away.
People will be able to buy those used GPUs cheap and run small local LLMs perhaps. A 10 year old computer today won't do state of the art games or run models, but is entirely acceptable for moderate computing use.
Maybe; I find it unlikely though, because unlike CPUs, there's a large difference in compute/watt in subsequent generations of GPUs.[1]
I would imagine that, from an economics PoV, the payback for using a newer generation GPU over a previous generation GPU in terms of energy usage is going to be on the order of months, not years, so anyone needing compute for more than a month or two would save money by buying a new one at knockdown prices (because the market collapsed) than by getting old ones for free (because the market collapsed).
[1] Or maybe I am wrong about this - maybe each new generation is only slightly better than the previous one
That's not really how SaaS works these days. It will be a "cheap" subscription or expensive and focused on enterprise. Both those require maintance costs, which ruin the point of "cheap and small run LLM's".
And they sure aren't going to sell local copies. They'd rather go down with their ship than risk hackers dissecting the black box.
People will locally run the open models which are freely released, just like they do today with Llama and Whisper.
Most of the AI SaaS companies won't be around to have anything to say about it, because they will be casualties of the bust that will follow the boom. There will be a few survivors with really excellent models, and some people will pay for those, while many others simply use the good-enough freely available ones.
That's hard to know from this vantage point in the present.
Who knows what ideas will spring forth when there are all these AI-capable data-centers sitting out there on the cheap.
You still have to pay for power to run them. A lot of power. It won't be that cheap.
That said, I'm not sure the effect of digital infrastructure will be the same as physical infrastructure. A road has a clear material impact on all businesses in the area and their capacity to produce physical goods. But do data centres have the same effect? An extra lane on the road means you can get a greater volume of goods in and out to broaden operations to a larger area, but I don't see what positive effect two data centres could have on the average business. For as great as the internet is, I don't know how much value is created here. The question of what to do with a railroad is quite easily answered, but I'm not really sure what you can do with a datacentre. I guess whoever works it out will be decently rich.
But I feel we already have enough computing power, and the bottleneck in the whole process is making software that efficiently uses it (or knowledge of how to operate such software), rather than the power of devices themselves. Though perhaps as the bubble bursts, the price of programmers will also decrease significantly and the software issue will be resolved.
Maybe we will get a nuclear energy renaissance out of this, who knows.
If it doesn't meet those sky-high expectations it's a flop.
The same happened with metaverse, blockchain etc. Those technologies are kinda shitcanned now which is unfair too because they have excellent usecases where they add value. It was never going to be for everyone, and no we weren't all going to run around with an oculus quest 24/7.
I think these investors break it more than they do good.
debatable depending on what "fine" means. In any case, DSL really doesn't go far.
The old version from the early 2000's could work out to a few miles / km but only with absolutely perfect condition copper. The newer DSL versions are limited to much less distance even with good quality cable.
Each neighborhood has a head-end that does the copper <-> fiber transition. Unless you lived _really_ close to the Central Office, your DSL service was probably copper only for a few blocks before it transitioned to fiber going from TelCo central office to all the individual DSLAMs scattered about.
It'll either prompt serious investment in small modular reactors and rescuing older nuke plants about to retire, or we'll see a massive build out in gas. These companies want the power to be carbon free, so they're trying to do the former, but we'll see how practical that is. Small modular reactors are still pretty new and nobody knows how successful that will be.
At the end of the day, I feel like this will all crash and burn, but we may end up with some kind of nuclear renaissance. We're also expanding the transmission grid and building more wind, solar, and storage. However, I don't think that alone is going to satisfy the needs of these data centers that want to run nearly 24/7.
Computing infrastrucuture that's even one decade old is essentially obsolete. Even desktop PCs are often life-cycled in 5 years, servers often the same.
If it takes AI a decade to find its way, most of today's investment won't be useful at that point.
It’s interesting to observe who is making the counterpoint - it’s often very vocal fundraisers.
Of course you can argue they are raising because they believe, and I don’t (necessarily) doubt that in all cases.
What asset from the AI bubble will still be valuable 5 years later? Probably not any warehouses full of 5 year old GPUs. Maybe nuclear power plants?
I think that will happen here. I think your average investor who's currently paying for all these advanced chips, data centers and energy supplies will walk away sorely disappointed, but this investment will yield huge dividends down the road. Heck, I think the energy investment alone will end up accelerating the switch away from fossil fuels, despite AI often being portrayed as a giant climate warming energy hog (which I'm not really disputing, but now that renewables are the cheapest form of energy, I believe this huge, well-funded demand will accelerate the growth of non-carbon energy sources).