est31

Right but 2000 to 2010 didn't have similar progress, and especially 2010 to 2020 didn't. Sure, things have gotten better but not as much as the 1990 to 2000 leaps.

And yes, laptop specs haven't changed much and this is partially because the need for spec changes wasn't present, but also during the last 20 years there has been tremendous pressure for efficiency in datacenters.

Despite that, dennard scaling is dead since 20 years. There are physical limits. Already now, the wear effect of electrons jumping is present, and it will only get worse as things scale towards smaller sizes.

There are some benefits to be had, e.g. one can etch models into chips directly so you can pack them more closely, and run more inference on Tensor like chips, but that gives you maybe one order of magnitude improvement in total, at most. Also, of course nobody does that when each 2-6 months a new model comes out.

I'm born and raised German, and above I mostly used the German ways of writing the town names (stripping the umlaut). Which as it turns out are not the same way you'd write it in english, interesting!

I've never been to China either. It's a huge country and it probably depends on where you are (hong kong probably friendlier than a random place in the mainland), but from what I heard/read:

* language issues. Many chinese don't speak english. Also a problem in many european countries (esp latin and slavic speaking ones), but at least the european languages are easier to learn. Compare this to Amsterdam, Goteborg, Berlin-Mitte or Kopenhagen where everyone speaks english.

* citizenship is one of the hardest to get in the world.

* I heard complaints about onboarding into the chinese app/digital ID ecosystem.

China is not very immigration friendly to non-han folks, but I guess chinese researchers won't make it to the US and this already will have a great effect on the chinese economy.

Europe is in its own set of problems and it is not in the same situation that US used to be after WW2 (only major economy not affected by bombing).

Europe's problems:

* active major war in Ukraine (lasting longer than Axis/Soviet war in WW2)

* energy supply issues (unlike US it's not energy sufficient and the places that supply it with energy are involved with wars)

* a wall of people aging away from employment and into doctor's and hospital waiting rooms (forcing less investment into research and roads/bridges/railway, more towards stabilizing pensions, healthcare)

* major pieces of the european export economy are being replaced by China (eg chinese car brands eating the lunch of european car brands).

Yeah, Linux-the-kernel does have a stable ABI indeed, but this is not relevant for most ISV desktop software out there. In my comment above I was referring to Linux-the-OS (aka GNU/Linux). The userspace libs don't have a stable ABI at all, and this is a widely discussed problem. Other operating systems built on top of Linux-the-kernel don't have this problem, Android has a really stable ABI.

GPU/RAM/etc prices could continue to rise. If the world leaders decide it's time to build the robot armies, then that could price out the civilian uses for GPUs.

Those solutions have moats:

1. the cloud moat is mostly around talent really. Try finding people who can self host the alternatives to S3 et al at the HA and the scale the businesses need. Those alternatives are usually not free either, and each product might have its creator acquired (and the product cancelled) or similar. if you're a larger business then the data lock in becomes a moat: getting your data out of the cloud is prohibitively expensive. Furthermore, large businesses have sweet discounts.

2. ms office has immense networking effects due to its formats being quasi standards in many industries. try sending an odt to a government entity. As for gsuite, it uses open formats but it's classical google fashion a large suite of software bundled together and not that expensive for what it offers.

3. Linux is not a free alternative if you're a business, you still need to pay someone to support the computers with linux on it, and operating systems have the strongest network effects ever. Linux also has no stable ABI so one can't easily deploy third party software for it.

What's the LLM moat? Codex is OSS and Claude has gazillions of alternatives. Cursor is a nice app but it's a bunch of patches on top of vscode, a team of 5 people can vibecode it in 6 months.

There is distillation going on where chinese providers give the model lots of outputs. We don't live in a world where chinese providers are not doing this so we can't compare the advantage of this distillation, but there is some advantage to it otherwise they wouldn't do it.

If Anthropic can block distillations somehow (which are fair game imo given that Anthropic et al did the same with the written works of mankind), then they might stop or slow down the chinese from catching up.

Chinese also have like 40% of the AI researchers of the world, plus they have access to a lot of cheap labour for writing training data. I'm sure an hour of training data creation from one of China's 162 million university educated people is much cheaper than an hour of work from one of US's 97 million. Probably still cheaper than someone from the grand area.

China is behind in AI chips/GPUs but they are catching up. One thing where they have a hard dependence on outside is their energy imports: they have to import a lot of stuff from third party countries. The US on the other hand is energy self sufficient.

There is a lot of desertification of farmland going on, including in the USA.

It gets abstracted away for richer countries as they can outbid the poorer countries for food. In developed economies, most of a particular piece of food's price is not the costs that go to the farmer, but costs that come later in the process, so that cost increasing is also felt less.

Heatwaves on the other hand affect the western countries directly.

Infineon still exists as a semiconductor manufacturer. Their stock has gone crazy since start of the year as well.

Removing HEVC support wasn't their choice but probably stems from the licensing pools increasing their prices [1].

Windows media player probably sees very little usage nowadays and probably even less for HEVC, when most content playback happens via streaming and browsers today.

As for the RAM increase, well that's probably a consequence of the general trend of doing frontend engineering via JS/TS instead of using OS native frontend APIs. The advantages are more on the development side of those apps, i.e. you can hire JS UI devs way more easily, and probably LLMs know way better how to deal with a react app than an UML one.

[1] https://arstechnica.com/gadgets/2026/04/lawsuits-licensing-a...

In order to kill all cancer cells in the body, it probably needs to be delivered to every single cell in the organism, and scan the nucleus of that cell. Viruses usually don't infect every single cell, just a small percentage.

So one needs to figure out a delivery method that is efficient enough, and that doesn't elicit an immune response. But I guess one can analyze the cancer in the lab and figure out which receptors it expresses, and then bind to those? We could have a toolkit of different delivery methods, tailored for each patient's cancer.

Similar times and the Rust originator went on to work on Swift after it.

A new generation of AI companies is out there to take over blue collar jobs as well. Check recent YC batches.

Software engineering was a nice target because inputs and outputs are just data and you don't need to figure out robotics. But idk, 3 years ago it seemed illusory (at least for me) that LLMs could take over software engineering, but now here we are. They are still not 100% there yet (software engineers still have jobs), but we are getting ever closer.

Companies are in the process of figuring out robotics, and even if it's not figured out, then we might introduce a gig-ified blue collar economy where an unskilled, underpaid gig worker implements instructions by AI. Plus a lot of blue collar work already today involves robots (cranes, excavators, trucks, etc).

The stable Linux ABI is Win32 provided by Wine.

These deals are part of how the AI economy operates. Amodei has explained this in his recent Patel podcast.

1. Building datacenters takes time. Months, if not years. They take billions of investment.

2. AI revenue is highly unpredictable. Sure, you can make predictions, but maybe your competitor releases a better model 2 weeks after your release, maybe the new model you built isn't as much better, maybe the chinese models steal your show, etc.

3. AI revenue grows a lot. Anthropic's case is 10x per year.

4. So if you are off by just a year in terms of how much GPU you actually need, then that means a 90% of your compute capacity is wasted, and you go bankrupt.

As a solution, companies buy compute from each other! If one company's model did well, they can buy compute from the company whose model didn't do well (like in the case of grok). It's beneficial for both sides, so positive sum game. So deals like this aren't something bad in itself.

It's nothing new either. In SAAS deals, you often commit to a certain revenue and then pay extra if your revenue exceeds that amount. And power market is cut in two as well: longer term deals plus spot markets. Spot prices are way higher than the longer term deal prices.

Given it's SpaceX of course there is financial engineering involved: the GPUs aren't actually owned by SpaceX but a daughter company, and it's been financed via loans that are backed by pension funds. So it's already the case that pension funds back bear the risks associated with SpaceX's operations.

Right now, the bulk of the AI bubble sits in such debt statements and not in public markets.

Yeah, among other factors, that "figure it out" mentality put me off in the end. Especially because often you need to show the same mentality unless you want to overkill proofs and spend more time on them than assigned to you. I sometimes miscalibrated and pointed out some details that didn't need pointing out in my proofs while in other proofs, I skipped over too many details for the TA.

Of course I agree that if the student just asks LLM to do their homework, they have not learned anything. But it's sad if one can't ask questions about a proof or such. Having the LLM around to review the homework submission is also useful, to make sure that the arguments are solid.

One of the reasons why over a decade ago, I dived deeply into the OSS world instead of mathematics was that it was so much more accessible: there were docs for everything, and I got direct feedback when something worked vs when something didn't work. Most of my questions had answers on stack overflow, and once I joined Rust (which back then in 2015 didn't have a big stackoverflow presence) I had a community who answered them for me (and in maths I didn't have that).

AI makes the math world more accessible than before. If you have a question about a proof in the lecture, you can just ask it. Of course, one can't trust it blindly, but fundamentally it's amazing.

I think that's a good thing, but of course this means that a lot has to change in culture and behaviors, also in the research world.

The software engineering world is more or less in the same situation, it's also changing. But for now I think it still holds true that someone who knows maths plus an LLM is better than someone who doesn't know maths plus LLM. At least in software it does.

Horrible to hear this news. Neurological diseases are the worst because we understand so little about them and usually there is no cure, just management.

What have your experiences been with using AI for medical advice? Especially for such rare diseases I suspect that very little shows up in the training data. Personally I'm using AI only for work and only recently started using it for non-work non-coding stuff too.

It's fine to make mistakes, that's how you learn. The problem here was that they didn't announce to the host that they are doing a test of their in-development equipment.

So the host wasn't able to add the additional risk and hassle to the price, which in this instance would have been a quite legitimate ask as the robot damaged their revenue generating property.

It's very ironic that Airbnb itself has done similar practices in the past where it ignored hospitality regulations to establish their business model, i.e. not asking for permission but for forgiveness.

The Airbnb style response would be to gig-ify this model where you ask an independent contractor to buy the test robot, rent the Airbnb, and test it out instead of you doing it yourself. Then the contractor bears the risk of damages to the property.

Well Bezos did actually state that he wants to turn Earth into a natural park.

But yeah, the robot armies don't need grain so why hike up the price of bread? Lack of grain makes those people resentful which means you need to deal with their anger. Sure, it can be dealt with but it's just cheaper to give the humans grain so they are docile. This is basic governance 101 that goes back to the romans (and further).

They also didn't slaughter all horses immediately. You can't eat that much horse meat anyways. It happened piece by piece.

The only good reason for an abrupt mass culling of the 99% (for a coldly calculating rich person with no empathy) would be game theory, i.e. them not being a contender for power any more. If there are no humans, there is nobody who can question the control of the 1%. It would be thus less about economics and more about power.

I am really rooting for the bottom 99%, myself being a part of it, but I really don't know what will happen to us.

If you give it $290 of input tokens for $10 of output tokens, you are doing something wrong. I.e. you paste the whole CI output into the prompt instead of giving it a link to the file, and then the AI greps its way through it (using a fraction of the tokens).

Sometimes AI overdoes things and it re-runs the whole testsuite because the tail command didn't have enough lines, but the other way round messes up the context so much so that in the end all that context is useless.

There is build.rs, proc macros are unsandboxed, and lastly you install the binary so that you can run it. Even if the build and install were fully sandboxed, the binary could still do malicious stuff if ran.

Re vendor lock in point: this is a harness issue really. Sure, CC is restricted to Anthropic models, but it's not the only harness out there. So if one vendor has an outage or botches the quality of their models due to compute shortage, you can switch to another vendor. LLMs are the easiest to switch. Of course, if hardware costs go up, so will all AI vendors. The only way out for the employer would be to directly buy the hardware (or do a fixed price deal with a cloud provider).

Re the understanding code point: you can still use LLMs to understand code. If you write the spec without knowing anything about the code, of course the architecture might suck. Maybe there is already a subsystem that you can modify and extend instead of adding a completely new one for the new feature you are adding, etc.

I use LLMs for my daily workflows and they do understand code perfectly and much more quickly than if I read it.

This is a general fear for me whenever I take a taxi or something like it: i always remind the driver of my luggage in the back when we arrive and ask them whether they can help me get it.

It's unpleasant for me at normal speed settings, but on fast mode it works really well: the AI does changes quickly enough for me to stay focused.

Of course this requires being fortunate enough that you have one of those AI positive employers where you can spend lots of money on clankers.

I don't review every move it makes, I rather have a workflow where I first ask it questions about the code, and it looks around and explores various design choices. then i nudge it towards the design choice I think is best, etc. That asking around about the code also loads up the context in the appropriate manner so that the AI knows how to do the change well.

It's a me in the loop workflow but that prevents a lot of bugs, makes me aware of the design choices, and thanks to fast mode, it is more pleasant and much faster than me manually doing it.

Not a lawyer but the German constitution, Article 12a, speaks of men above 18, not of citizens, or even residents of Germany.

https://www.gesetze-im-internet.de/englisch_gg/englisch_gg.h...

So that article can in theory be used to conscript any man, citizen or not, living in Germany or not.

The Wehrpflichtgesetz, which is a simple law and requires just the 50% Bundestag majority to have it changed, refines this very wide constitutional power in article 1, to require men who hold German citizenship above 18.

https://www.gesetze-im-internet.de/wehrpflg/BJNR006510956.ht...

Article 3 refines it even further to folks below 45 or 60, depending on the severity of the situation.

But yes, in theory it can be changed to include any non-German citizen man, people aged 80, living inside of Germany since a while or never having been to Germany ever, or just random men who happen to change flights at FRA.

This one might last longer. The AI race is on, and the US tries its best to make it as expensive for China as possible to participate in it. Every dollar China spends on GPUs they get at markup is one not spent on building navy ships.

If there is an escalation over Taiwan, then that will cause the loss of most of the world's high grade chip manufacturing capacity. TSMC is busy doing technology transfers into the US, but it is going to take time, those fabs won't have capacity for the whole world, and they still heavily depend on Taiwan based engineers if something goes wrong etc.

Just like with COVID you don't know how long this shortage will last.

It's a loss leader but this is normal. Same has happened with Uber, Airbnb, Amazon, etc. Using VC money to buy marketshare and once you have it, you can milk it.

The question is more around the moats that these companies have and it seems to me while their models are amazing technology, they don't really have a moat. The open/chinese models still continuously catch up to the american ones.

And how this end is closer with LLMs?

The blog post of this thread argues that now, even average users have the ability to modify GPL'd code thanks to LLMs. The bigger advantage though is that one can use it to break open software monopolies in the first place.

A lot of such monopolies are based on proprietary formats.

If LLM swarms can build a browser (not from scratch) and C compiler (from scratch), they can also build an LLVM backend for a bespoke architecture that only has a proprietary C compiler for it. They can also build adobe software replacements, pdf editors, debug/fix linux driver issues, etc.