The AI Investment Boom

Why does every hype article start with this. Personally my copilot usage has gone down while coding. I tried and tried but it always gets lost and starts spitting out subtle bugs that takes me more time to debug than if i had written it myself.

I always have this feeling of 'this might fail in production in unknown ways' because i might have missed checking the code throughly . I know i am not the only one, my coworkers and friends have expressed similar feelings.

I even tried the new 'chain of thought' model, which for some reason seems to be even worse.

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).

Given where mobile sits in the hierarchy of interfaces, that is where I would be placing my bets if I were a VC.

Although it's much better when writing standard REST and gRPC APIs

It’s also getting worse because people are poisoning the well.

Look at this nonsense for example: https://intouch.family/en

but i would never push llm generated code. never.

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edit to add some substance:

if it’s someone who

* does a lot of manual local testing

* adds good unit / integration tests

* writes clear and well documented PRs

* knows the code style, and when to break it

* tests themselves in a staging environment, independent of any QA team or reviews

* monitors the changes after they’ve gone out

* has repeatedly found things in their own PRs and asked to hold off release to fix them

* is reviewing other people’s PRs and spotting things before they go out

yea, sure, i’ll release the changes. they’re doing the auditing work for me.

they clearly care about the software. and i’ve seen enough to trust them.

and if they got it wrong, well, shit, they did everything good enough. i’m sure they’ll be on the ball when it comes to rolling it back and/or fixing it.

an llm does not do those things. an llm *does not care about your software* and never will.

i’ll take people who give a shit any day of the week.

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.

But yes, a lot of energy wasted in the growing phase.

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.

When I ask him to write a function that should do something much more complex, it usually do something so bad it takes me more time because it confuses me and now I have to back to my original reasoning (after trying to understand what it did).

What I found useful is to ask him to explain me what a function does in a new codebase I am exploring, although I have to be very careful because a lot of time invents or skips steps that are crucial.

Arguably if the investment here works out we’ll see deflation through extreme technical advancements.

A lot of LLM based software is uneconomical because we don't have enough compute and electricity for what they're trying to do.

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.

I've been in the former since '21 and have seen every single cycle since 2011 in the latter, I can assure their is more dumb money in the former than in the latter; (just by scale alone) at least in the latter whether it was ICO or NFTs or whatever mal-investment was promptly punished (rugpulls/exit scams) companies like INTEL get to stay in Zombie mode because of the corpo-welfare that the US doles out while shaming everyone else to be prudent with their investments while these corps and banks spend like drunken sailors and tries to strangle the former out of existence (rightly so in most cases as most crypto is a total scam).

With that said, what you will see emerge are some incredibly established players in both fields that will have the staying power to change how the Industry is shaped around them: Nvidia and Bitcoin are comparable to one another in that respect.

Both have/had crazy volatility but the staying power and just the fact they remain firmly at the center of both Industry's is rather telling that you simply don't see what these technologies offer because of the hype and boom and bust cycles.

As a person who directly benefits from this: I can assure you most of these VCs are exit liquidity just as most foolish people are for the alt scams of yore, execpt the US economy (likely all of the Western World at this point) isn't entirely reliant on the promise of vapourware with 'crypto' in any capacity, weheras the same cannot be said about the theatrics of Jensen's Nvidia.

Source: I build data center infrastructure for these mega corps doing 'AI' and I'm doing an MSc in CS (Big Data) and been a Bitcoiner since Satoshi was still on BTF.

American Automative filled for Bankruptcy multiple times.

American Government had to step in to back them up and bail them out.

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.

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.

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.

edit: if it isn't clear I'm a staunch opponent of cryptocurrency in any form.

It just does the job that cursor does there, but better.

Maybe us programmers should focus on making higher order programming tools instead of black box text generators for existing tools.

The real limiting factor is not so much task complexity as the level of abstraction and indirection. If you have code that requires following a long chain of references to understand, LLMs will struggle to work with it.

For similar reasons, they also struggle with:

- generic types

- inheritance hierarchies

- long function call chains

- dependency injection

- deeply nested structures

They're also bad at counting, which can be an issue when dealing with concurrency—i.e. you started 5 operations concurrently at different points in your program and now need to block while waiting for 5 corresponding success or failure messages. Unless your code explicitly uses the number 5 somewhere, an LLM is often going to fail at counting the operations.

All in all, the main question I think in determining how well an LLM can do a task is whether the limiting factor for your task is knowledge or abstraction. If it's knowledge (the intricacies of some arcane OS API, for example), an LLM can do very well with good prompting even on quite large and complex tasks. If it's abstraction, it's likely to fail in all kinds of seemingly obvious ways.

It's very clear, but your exposure to zealots, on both sides, shouldn't deter you from being objective and seeing what these technologies actually offer. Hence why i wrote that part in the 2nd to last paragraph.

HN has such misinfored vitriol for any technology it didn't ordain itself, you sem to be of that cohort, what's odd is that very same people who gave you VC.SV funded startup land are all major backers of this technology.

I can just summarize this in one phrase: you seem to collectively not know, what you don't know and make leaps in logic and misinformed judgments from that POV.

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.

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.

AI is a great tool and does speed things up massively, it just doesn't align with the magical thought that we provide the ideas and AI does all of the grunt work. In general, always better to form mental models about things based on actual evidence as opposed to fantasy (and there is a lot of fantasy involved at the moment). This doesn't mean being pessimistic about potential future advancements however. It is just very hard to predict what the shape of those improvements will be.

GPUs are different, unless things go very poorly, these GPUs should be pretty much obsolete after 10 years.

The ecosystem for GPGPU software and the ability to design and manufacture new GPUs might be like fiber. But that is different because it doesn’t become a useful thing at rest, it only works while Nvidia (or some successor) is still running.

I do think that ecosystem will stick around. Whatever the next thing after AI is, I bet Nvidia has a good enough stack at this point to pivot to it. They are the vendor for these high-throughput devices: CPU vendors will never keep up with their ability to just go wider, and coders are good enough nowadays to not need the crutch of lower latency that CPUs provide (well actually we just call frameworks written by cleverer people, but borrowing smarts is a form of cleverness).

But we do need somebody to keep releasing new versions of CUDA.

i gave it a yaml and asked it to generate a json call to rest api . It missed a bunch of keys and made up a random new key. I threw out the whole thing and did it with awk/sed.

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.

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.

But raising interest rates and keeping them high in an environment where runaway government deficits and high government debts are causing inflation runs the risk of exacerbating inflation.

You have high interest rates on a large amount of government debt which continues to push _more_ money into the economy.

The Fed doesn’t have any real options at this point but to lower rates.

> AI people sound more dug in to be honest from my perspective. But I guess that's cause the crypto stuff tends to be less overtly religious and more overtly batshit crazy politics and economics, which I'm much more used to dealing with haha. And mostly, everyone has figured out the scam by now on the crypto side.

>>The AI people freak me out cause they are all talking eschatology and shit as if they have stumbled upon the literal ark of the covenant like in raiders of the lost ark or something.

>>>It's a really great act to be honest. They've been clearly studying a lot of the more dishonest American religious culture of the last couple of decades.

I'm going to commit a HN faux pas to prove a point and show you why I think Bitcoin has a valid use case here alone: I decided to repost what he said because there are valid points here and are worth discussing.

Had I the inclination I can hash this into the blockchain for all to see what was written from this poster for the aforementioned reasons for as long as the mainchain continues to be maintained, protected and supported .

This has great utility, and the mere suggestion that you cannot get over that is because those 'crazies offend me and my disposition' and stop there you fail to see why and what this technology can already do--create an actual immutable archive of all Human history if we desire it.

But to his point, yes it's roots in Crypto-Anarchism (which started in CA at the inception of the rise of modern SV btw) has many of you questioning the ''sanity' and 'motives' behind this technology, and you assume they are all the same but rest assure their is a reason for the brain drain from all of tech/STEM/finance during my era and time in Bitcoin.

Most of whom are now incredibly wealthier than they ever were working in academia or private industry if you think money is a measure of one's success--I don't, but most of you do.

The AI people strike me more as a range of the introduction corpos from banking and academia into bitcoin (Gavin's, Hearn) to total con men like Veer and sprinkled in there are the cult memebers you mentioned who honestly think that their techno-utopian trans-humanist dreams are being built one LLM update at a time. It's sad... it's the same thing just different names/faces.

I'm just here because it happens to be where like-minded people (_sometimes_) hang out.

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.

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.

Ohh, well... I'm guilty of drinking that kool-aid, unfortunately and was a boot-strapping fintech founder with the battle scars to show--those scars appear in this discussion thus far by the way.

But I get it, I'm trying to be amicable about this as much as I can, especially fbecause I know no one can deny us anymore in the BTC side at this point, and in the AI side... well, money-hype boom and AI-doomer pr0n cycles aside we are actually building amazing amounts of compute that hopefully can yield amazing results, something like Starship recovery system levels of advancement in many other Industries/Sectors one day, probably very far off in the future but that's the start, right?

Every forest starts with a sappling kind of a thing, and these two technologies emerged from a time when I was in my formidable development period so I saw them as something more than just the marketing side--AI is marketing, ML is really just stats with code to back it up after all. Bitcoin is just a FOSS network using token based cryptographic key encryption.

Stop using throwaways if you want real conversations, it might help you have those conversations you're looking for. :)

As a back end developer I am not familiar with the latest trends in JavaScript and CSS, and frankly I do not want to spend my time studying these. A LLM can generate an interactive web game based on my description. I review the code, it is usually okay, sometimes I suggest an improvement. I could have done all of that -- but it would take me a week, and the LLM does it in seconds. So it is a difference between a hobby project done or not done.

I also tried a LLM at work, not to code, but to explain some complex topics that were new to me. Once it provided a great high-level description that was very useful. And once it provided a great explanation... which was a total lie, as I found out when I tried to do a hello-world example. I still think the 50% success rate is great, as long as you can quickly verify it.

Shortly, we need to know the strengths and the weaknesses, and use the LLMs accordingly. Too much trust will get you burned. But properly used, they can save a lot of time.

Another possibility is that the rate is still not high enough and needs to be raised much much higher to stop inflation. I think rates need to be in the 6 to 7 percent to really stop inflation. The is just a pause. It will come back like a vengeance.

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.

Every single example was completely useless. The code wouldn't compile, it would invent methods and variables and the instructions to go along with it were incoherent. All whilst gaslighting along with the way.

I have also previously tried using it with some Golang code and it would constantly add weird statements e.g. locking on non-concurrent operations.

LLMs are great when you are doing the same things as everyone else. Step outside of that and it's far more trouble than it's worth.

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.

Sonnet can absolutely get very confused and break things. And there were tasks where I had a really hard time getting it to do the right thing, or understand what I wanted. But I need you to understand: Sonnet made this thing for me in two and a half days of part-time prompting. That is probably ten times faster than it would have taken me on my own, especially as I have absolutely no design ability.

Now, is this a big project? No, it's like 2kloc. But I don't think you can call it "simple" exactly. It's potentially useful technology. This sort of "just make this small tool exist for me" is where I see most of the value for AI in the next year. And the definition of "small tool" can stretch surprisingly far.

Throwing dozens of articles, social media posts and why not even videos. Hallucinations really don't matter at scale. And enough content is already generating enough views to make it somewhat viable strategy.

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/

That and tech's status inflation means when we are talking about "mid level" engineers, really we are talking about engineers with a couple years of experience who have just graduated to the training wheels phase of producing production code. LLMs are still broadly aimed at removing the need for what I would just call junior engineers.

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.

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."

In practice, the vast majority of blockchain ledgers record the history of scams, penny stock style trading and money laundering attempts.

Also your Google Drive API key is easily discoverable with about 15 seconds of looking at the JS source code -- this is something a professional software developer would (hopefully) have picked up without you asking, but an LLM isn't going to tell you that you shouldn't ship the `const API_KEY = ...` code as a file to the client, because you didn't ask.

> Even the software publishers and computing infrastructure industries at the forefront of this AI boom have seen functionally zero net employment growth over the last year - the dismal job market that has beleaguered recent computer science graduates simply has not improved much.

... which may explain, in broad brush, the polarised HN attitude: bitter cynics on one side and aggresive zealots on the other.

We bought a lot of shovels. Even if we don’t find more gold, we can dig holes for industry elsewhere.

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 mean, it would have taken me a lot longer on my own. Sure it's not a huge project, I agree; I wouldn't call it entirely trivial.

> Also your Google Drive API key is easily discoverable with about 15 seconds of looking at the JS source code

No, I'm aware of that. That's deliberate. There's no way to avoid it for a serverless webapp. (Note that Guesspage is entirely hosted on Github Pages.) All the data stored is public anyways, the key is limited to only have permission to access the stored data, and you still have to log in and grab a token that is only stored in your browser and cannot be accessed from other sites. Literally the only unique thing you can do with it is trigger a login request on your own site that looks like it comes from Guesspage; and you can do that just as easily by creating a new API key and setting its name to "Guesspage".

The AI actually told me that was unsafe, and I corrected it. To the best of my understanding, the only thing that you can do with the API key is do Google Drive uploads to your own drive or that of someone who lets you that look to Google as if my app is triggering them. If there's a danger that can arise from that, and I don't think there is, then it's on me, not on Sonnet.

(It's also referer domain limited, but that's worthless. If only there was a way to cryptographically sign a referer...)

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...

Has your barber/hairdresser recommended you buy NVDA?

The railroads have lasted decades and will remain relevant for many more decades. They slowly wear out, and they are the most efficient form of land transport.

These hardware investments will all be written off in 6 years time and won't be worth running given the power costs and relative output. They will be junked.

There's also the extra risk that for some reason future AI systems just don't run efficiently on current gen hardware.

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 every dollar invested in hardware, we expect 8-to-20 times the amount to be spent on software... While the initial wave of AI investment lays the foundational infrastructure, the next wave is clearly set to capitalise on the burgeoning AI software market."

Regardless, I think this makes a lot of sense; there's no clear scientific consensus on the path forward for these models other than "keep going?", so building out preparatory infrastructure is seen as the clear, safe move. As the common refrain goes: "in a gold rush, sell shovels!"

As a big believer in the upcoming cognitive era of software and society, I would only add a short bit onto the end of that saying: "...until the hydraulic mining cannons[3] come online."

[1] https://www.bain.com/insights/ais-trillion-dollar-opportunit...

[2] https://www.privatebankerinternational.com/comment/is-ai-sof...

[3] https://en.wikipedia.org/wiki/Hydraulic_mining

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.

On the more pessimistic end, AI will replace us and we'll be sent to the coal mines.

On the possibly most optimistic end, living standards are a composite of many things rooted in reality, so I'd say the actual cap is about doubling of life quality, which is not nothing, but not unprecendented if we look at the past century and a half.

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.

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".

At some point we need to address the elephant in the room and ask people specifically what they mean by "high" rates, because 5% isn't particularly high by historical terms, it's only high for people who never paid attention to interest rates before 2010.

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.

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.

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...

We have been terrified to whisper the words "nuclear power" for decades now, but the AI boom is likely to put enough demand on the power grid that it forces us to face this reality and make appropriate buildouts.

Even if the AI Boom crashes, these power plants will have positive impacts on the country for decades, likely centuries to come. Keeping bountiful power available and likely low-cost.

I guess humans are worthless as well since they are notoriously unreliable. Or maybe it just means that artificial intelligence is more realistic than we want to admit, since it mimics humans exactly as we are, deficiencies and all.

This is kind of like the self-driving car debate. We don't want to allow self-driving cars until we can guarantee that they have a zero percent failure rate.

Meanwhile we continue to rely on human drivers which leads to 50,000 deaths per year in America alone, all because we refuse to accept a failure rate of even one accident from a self-driving car.

And I'm saying that as someone who finds all this stuff cool and would like to see it used in international shipping.

Yeah I think you’re right about that. But what about GPU’s? Will they benefit from economies of scale or the opposite?

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.

https://x.com/ayooshveda/status/1847352974831489321

https://x.com/Gloraaa_/status/1847872986260341224

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!

Similarly I think people will be ok with other AI if it performs well.

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.

only 6 were AI, the highest being "OpenAI to become for-profit" coming at number 10. Top story was "Bop Spotter" followed by Starship and click to cancel.

Only if that knowledge is sufficiently represented in the training data or on the web. If, on the other hand, it’s knowledge that isn’t well (or at all) represented, and instead requires experience or experimentation with the relevant system, LLMs don’t do very well. I regularly fail with applying LLMs to tasks that turn out to require such “hidden” knowledge.

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...

What an inspiring vision for the future of news and entertainment.

Even poor people can enjoy 8k gaming on a phone soon.

It just doesn't represent a realistic codebase. It is significantly smaller than a lot of college projects.

The current software system I'm working on now is ~2 million lines of code split across a dozen services.

AI has been pretty good for search across the codebases and absolutely hopeless for code gen.

LLMs just aren't that good yet for writing code on a decent sized system.

[0] https://github.com/Guesspage/guesspage.github.io/blob/master...

It doesn't take long before they make themselves present. Thanks for proving my point.

There is no long-term time scale in which humans running things do not obliterate each other or end up sitting on a planet filled with trash.

Either we figure out how to colonize other planets or we hand over the reigns to something that can plan long term and not be irrational.

Maybe if we figure out immortality it might work, but with the short life span of a human there is no way to not be short-sited or eventually end up with the wrong person in charge the button.

The same is going to happen with AI. Yes, Nvidia and their competitors are going to do well. But most of the value will be in software ultimately.

GPUs and data centers are just infrastructure. Same for the electricity generation needed to power all that. The demand for AI is causing there to be a lot of demand for that stuff. And that's driving the cost of all of it down. The cheapest way to add power generation is wind and solar. And both are dominating new power generation addition. Chip manufacturers are very busy making better, cheaper, faster etc. chips. They are getting better rapidly. It's hard to see how NVidia can dominate this market indefinitely.

AI is going to be very economical long term. Cheap chips. Cheap power. Lots of value. That's why all the software companies are busy ramping up their infrastructure. IMHO investing in expensive nuclear projects is a bit desperate. But I can see the logic of not wanting to fall behind for the likes of Amazon, Google, MS, Apple, etc. They can sure afford to lose some billions and it's probably more important to them to have the power available quickly than to get it cheaply.

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)

Climate related tech needs more money.

Like religion or nationalism fueling some wars.

For Europe overall is 22%.

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.

I believe that is so far off the mark for a couple reasons:

1) It's possible to work around hallucinations in a more cost effective way than relying on humans to always be correct.

2) There are many use cases where hallucinations aren't such a bad thing (or even a good thing) for which we've never really had a system as powerful as LLMs to build for.

There's absolutely very large use cases for LLMs and it will be pretty disruptive. But it will also create net new value that wasn't possible before.

I say that as someone who thinks we have enough technology as it is and don't need any more.

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 kind of like the Chipotle approach. I have a problem with my order, it just refunds me instantly and sometimes gives me a add-on for free.

Honestly I only use LLM for one thing - I give it a set of TS definitions and user input, and ask it to fit those schemas if it can and to not force something if it isn't 100% confident.

I know some people whose whole company is based around the use of AI to send emails or messages, and in reality they're logged into their terminals real time fixing errors before actually sending out the emails. Basically, they are mechanical turks and they even say they're looking at labor in India or Africa to pay them peanuts to address these.

No one is getting excited about "AI slop", just the models that generate it. Funny situation.

What happens when everyone constructs a shovel factory, the shovels become dirt cheap, but there are no buyers?

> 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.

If you're doing something in a way it's not in the training data set, maybe your way of approaching the problem is wrong?

Huge by itself isn't the same as huge winner.

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.

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.

The problem is all the most reliable code it can give you is stuff which ought to be (or already is) a documentation example or a reusable library, instead of "copy paste as a service".

There is no kind of power plant that takes longer to build than a nuclear plant.

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.

Honestly, I feel like with the recent progress of AI, it's a realistic scenario to assume it will replace mpst knowledge workers in the next 5 to 10 years, probably won't replace researchers and other elite intellectuals, and won't even make a dent in the world of physical labor.

In that world, I see AI as harmful, but the people in charge won't, as they are directly benefiting from it.

We live in number-go-up capitalism. A good analog is the housing situation. The ever increasing price of real estate means that the total amount of wealth goes up, so it's seen as a beneficial process by the elites. The rest however will find that they need to dedicate a larger proportion of their income towards getting a roof over their heads, and think this process is bad.

Nowadays, the possibility building a life that would've been considered middle class half a century ago from scratch is available to like 10% of workers, working mainly intellectual jobs.

AI in the future will reduce the proportion of these people by taking away their high paying jobs.

I think for some niches, the former can for a brief period precede the latter. But eventually the market catches up and roots out that which lacks actual value.

More concretely, I suspect the advertising apparatus is going to increasingly devalue unattributed content online, favouring curated platforms and eventually resembling a more hands on media distribution with human platform relationships (where media == the actual medium of distribution not content).

That is already a thing, where for example an instagrammer promoting your product is more valuable than the automated ad-network on instagram itself.

At which point, hopefully, automated content and spam loses legitimacy and value as ad-media.

My point is more that LLMs can handle (some) projects that are useful. It's not just oneliners and hello worlds. There's a region in between "one-page demos" and "medium-sized codebases and up" where useful work can already happen.

    > 2006 property crash

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.

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...

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.

Commercial: https://lotsahelpinghands.com

Non-profit: https://www.caringbridge.org

SuccessFactors is a popular HR platform and I was asking it any question and getting the wrong answer every time.

GM was America's largest employer as recently as the 90s.

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.

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

I could not find them on TechCrunch

Isn't one of the points of AI to make democratize the act of writing software? AI isn't like other software inventions which make a product from someone's intelligence - long term its providing the raw intelligence itself. I mean we have NVDA's CEO saying to not learn to code, and lot of non-techies quoting him these days.

If this is true the end effect is to destroy all value moats in the software layer from an economic perspective. Software just becomes a cheap tool which enables mostly other industries.

So if there isn't long term value in the hardware (as you are pointing out), and there isn't long term value in the software due to no barriers of entry - where does the value of all of this economic efficiency improvement accrue to?

I suspect large old stale corporations with large work forces and moats outside of technology (i.e. physical and/or social moats) not threatened by AI, who can empower their management class by replacing skilled (e.g software dev's, accountants, etc) and semi-skilled labor (e.g call centre operators) with AI. The decision makers in privileged positions behind these moats, rather than the do'ers will win out.

The amount of interest payments therefore long term (not short term) isn't really affected by the IR rate but more by politics and the amount of IR payments/debt burden they can politically get away with - in the US it is a LOT - in other countries the political appetite can be less.

So while it is true that higher IR payments do increase the money supply, generally with lower IRs governments are encouraged to "borrow more" by many stakeholders to their capacity under the low rate anyway. For example I saw many newspaper articles around our local media stating things like "rates are low, the government should invest that in infrastructure/disability programs/{insert favorite idea here}, etc when rates were low with politicians happy to spend accordingly.

In addition under low IR's the private sector will borrow more increasing the amount of credit in the economy as well - also inflationary money supply.

There's always nuances; these black and white theories can be dangerous. They assume all else is equal which is rarely ever is.

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.

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.

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.

I don't think AI is ultimately even an application, it's a feature we will use in applications.

Not really much for gaming, especially here in third world cou tries, OLD or Abandoned Gpu's are basically all that is avalaible for use, from anything from gaming to even video editing.

Considering how many new great games are being made(and with the news of nvidia drivers possibly becoming easily avalaible on linux.), and with tech becoming more avalaible and usefull on these places, i expect there to be a somewhat considerable increase in demand for GPU in say here in africa or southeast asia.

It probably wont change the world or us economy, but it would pribably make me quite happy if the bubble were to burst, even as a supporter of AI in research and Cancer detection.

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.

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.

Eventually we have to either give up on the hopes of what could come from LLMs with enough investment, or give up on very loud but apparently hollow arguments related to the damage we are causing to the planet.

With good RAG, hallucinations are non-existent.

The people driving AI investment will simply not be significantly affected by climate change. They don't care that hundreds of millions in the tropics will die, and that much of organised human activity will collapse, because up until the last possible moment they'll be insulated from the consequences.

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.

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.)

While it usually takes decades to payback rail investments, it usually happens within few years in the IT industry.

Spend some more time working with them and you might realize the value they contain.

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.

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.)

You're just burning money bothering to rewrite the rest of the stack when off the shelf will save you years.

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.

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.

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.

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.

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.

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

Which 'hollow arguments' are you referring to?

I call them apparently hollow in this context because we can't both chase the resource behemoth that is LLM tech and make any meaningful change to reduce our impact.

Its a reasonable hope that we could discover a new energy source that can produce orders of magnitude more energy with even less impact than today's sources, but that is just a hope. In the meantime we would be committing ourselves to a new, much higher baseline of energy needs whether we make that discovery or not.

Someone really important or really rich needs to build that demand so we get something for the wrong reasons but for potential good intent. FWIW, I'm not really optimistic that the bubble lasts long enough to even get these plants off the planning stage, though.

Big tech companies have a long list of promises made over the last 5-10 years promising huge cuts in their environmental impact. Those same companies and their leaders largely abandoned those goals.

I didn't really have political leadership in mind when writing that comment, though they could be part of that "we" as well.

> The people driving AI investment will simply not be significantly affected by climate change. They don't care that hundreds of millions in the tropics will die, and that much of organised human activity will collapse, because up until the last possible moment they'll be insulated from the consequences.

We've spent 80 years globalizing economies in an effort to avoid another world war. We'll all be impacted by it if some of the climate predictions are accurate.

Edit: to add that many of the same leaders developing LLMs make claims that LLMs and AI (if we get there) may be our only hope for finding ways of reversing our environmental impact. Either they are making that up as a sales pitch or they do in fact fall into the "we" here of people that care deeply about our impact while simultaneously burning massive amounts of resources on the hope that LLMs may fix it for us.

-Elliot Aronson, "The Rationalizing Animal"

Or to be less philisophical: the people with the money and power to say what's important are rarely the ones thinking long term, nor in an audience of other powerful, rich people thinking long term. US congress' median age is over 60: most aren't thinking about how to keep the Earth alive in 20-30 years. They won't be around to suffer the consequences.

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...

So the games that work are probably out of support anyway. So there's no money being generated for anyone.

Though the US did do a pretty big rate cut. I imagine that will at least stall such a bubble burst into 2026 instead.

There can be uses, but if you you're falling on deaf ears as a B2B if you don't solve this problem. Consumers accept inaccuracies, not businesses. And that's also sadly where it works best and why consumers soured on it. It's being used to work as chatbots that give worse service, and make consumers work more for something an employee could resolve in seconds.

as it's worked for millenia, human have accountability, and any disaster can start the PR spin by reprimanding/firing a human who messes up. We don't have that for AI yet. And obviously, no company wants to bear that burden.

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.

The economy bubble will be popped post-election (you will know when the Fed starts raising rates again), but CRE (commercial real estate) is likely the catalyst this time.

Within CRE, datacenters are the only item in the green in investors eyes, even more now because of the AI boom. I fully expect that class of investor to then dump more money than ever into energy generation and other sectors related to AI in order to escape the planned crash.

The biggest variable is if the supranational oligarchs are wanting to use this crash to cause a much more major shift in monetary policy such as CBDCs.

So you see the issue. and the intent.

If you're not confident enough in your tech to be held liable, we're going to have issues. We figured out (sort of) human liability eons ago. So it doesn't matter if it's less safe. It matters that we can make sure to prune out and punish unsafe things. Like firing or jailing a human.

It's a gold rush and they are inspectors. They have an incentive to keep the rush flowing.

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.)

But alas, this rush means they want to pitch to replace people like me, not actually make me more productive.

I have no clue how you get 5 years of experience in any meaningful way on any given tech. You sure won't get that only from the workplace's day to day activities. YoE is more a metric of how much of a glutton for punishment you have more than anything.

I don't really expect the current LLM bubble to last long enough to stand up new nuclear plants, though I don't expect that to actually stop the new energy projects unless the bubble popping has a massive economic impact.

Power plants are slow moving projects. Even if LLMs as they are don't live up to expectations they have seemed to open the door for the idea that amazing things are coming and we need to make sure the energy supply is ready for it.

Everyone's dreams will differ, but I got into tech to make people more efficient, and in turn enable more of the human element and less pencil pushing. Not replace it entirely.

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.

in my industry, the "training data set" won't get much farther from public code than the barebones, generated doxygen comments we call "documentation".

But in a way you're also right. The industry's approach is fundamentally wrong, making 20 solutions to a problem with plenty of room to standardize a proper approach (plenty of room where you need proprietary techniques, but that's getting less true by the month). But an LLM isn't going to fix that cultural issue and will suffer from it.

As a start, let me know when an AI can fail test cases, re-iterate on its code to correct the test case, and re-submit. But I suppose that starts to approach AGI territory.

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!

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.

Im more than happy to play 2010 to 2015 ganes right now at low settings, it would be even better to play games that are 5 years away rather than 10.

The same can be said for rendering, professional work, and server making, something is better than nothing, and most computers here dont even have a seperate gpu and opt for integrated graphics.

I find it quite annoying because for every company that is doing something where AI would actually be useful, there are 10 that are shoving it into their existing app in some capacity to make their software "AI Powered". A perfect example of this is my company recently evaluated Zenhub. Their sales team was very eager to point out that their app was using AI though when we actually looked, all it did was generate story descriptions from a prompt, the most basic of AI integrations.

AI is very useful but my god not everything needs to have it baked in.

Tech companies decided to respond with lower consumer demand by using price hikes, though. And of course letting go of labor, adding to the issue.

These kinds of companies aren't the ones being slowed by increased rates. They can just whether the storm and drain blood out of the rocks they have left on board.

>Private sector investment can continue to increase but at some point that too will hit a brick wall

At this point I'm betting the economy hits an objective recession before that brick wall happens. But I suppose we'll see.

I don't know what the "new normal" is though. I suppose 2025 will be used to figure that out. I don't think 4.75% will be enough.

I only see 4 on my front page, but I dont think 8AM UTC-7 is the right timeslot to record "today's news".

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.