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.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

1. they were trained on FLOSS repositories without consent of the authors, including GPL and AGPL repos

2. the best models are proprietary

3. folks making low-effort contribution attempts using AI (PRs, security reports, etc).

I agree those are legitimate problems but LLMs are the new reality, they are not going to go away. Much more powerful lobbies than the OSS ones are losing fights against the LLM companies (the big copyright holders in media).

But while companies can use LLMs to build replacements for GPL licensed code (where those LLMs have that GPL code probably in their training set), the reverse thing can also be done: one can break monopolies open using LLMs, and build so much open source software using LLMs.

In the end, the GPL is only a means to an end.