stult

You're talking past the OP commenter. Not all companies whose owners enjoy the benefits of limited liability are Limited Liability Companies (LLCs). The other commenter was discussing the general concept of a company whose owners enjoy limited liability, meaning they cannot be held responsible for the company's debts or other financial obligations beyond the value of their ownership stake, i.e. their _liability_ for the company's obligations is _limited_. There are many corporate forms that provide this benefit to their owners, including common US corporate structures like LLCs, C Corps, S Corps, and Limited Liability Partnerships (LLPs). It's important to note that the terminology varies across jurisdictions, so the specific structure we call an LLC might be called something else entirely or might not exist at all.

Which is precisely why the OP commenter framed their argument in terms of the general concept of limited liability. The argument applies to any company structured with joint ownership and limited liability, whether we happen to label that corporate structure C Corp, Private Limited Company (Ltd.), Gesellschaft mit beschränkter Haftung (GmbH), or even Limited Liability Company (LLC).

There is no model of the market that can remain stably accurate because the market will inevitably incorporate the insights of any model that is accurate until those insights are no longer accurate

I didn't know they had Panda Expresses in the wild

I assume in that situation they would be targeted specifically with ASATs at the beginning of the conflict, with the denial of LEO then preventing us from launching replacement sats.

We also have visual navigation systems that can use satellite imagery to produce guidance packages. They will become increasingly less useful the longer the imagery goes without updates after a Kessler syndrome type event, though. Another strategy, which I believe the Ukrainians have been experimenting with, is scattering small radio beacons throughout the battlespace to provide a network of navigation aids.

In a true Kessler scenario where LEO is denied for an extended period of time, I expect a variation of that strategy is how we would replace GPS for most civilian use cases. Likely by using cell towers to triangulate, possibly enhanced with additional transmitters to improve accuracy and coverage. Pure cell tower triangulation would have much worse coverage than GPS, obviously especially in rural areas where cell service is often already poor, but also anywhere that getting a signal from multiple towers isn't easy or possible. Cell towers are never distant enough to use the relativity-based timing tricks GPS relies on, so multiple signals would be required to triangulate. We'd likely end up with substantially less precise positioning, maybe something more on the order of the 10-20m of error we used to see with GPS before the US stopped limiting civilian access to the deaccuratized version. Cell tower triangulation also obviously doesn't work for aviation or oceanic shipping, where trident-like inertial+stellar nav would probably be the only workable option.

This happened after I stopped working for the DOD, but I am 99% sure the program in question during this incident was something I worked on. It was effectively an anomaly detection system for identifying any weird behaviors detectable in WAMI (wide area motion imagery, which is high resolution, low framerate drone video data that covers a large geographic area). As an example from the unclassified sample project they asked us to do as part of the bidding process, our code flagged a car doing donuts in a parking lot (the sample data was just from a random US city on a random day because this was an unclassified open bidding process). The system was not and never was intended to be a "terrorist" detection system. The idea was always to flag potentially interesting events for a human analyst to follow up on. The disconnect it took for someone to blindly trust the program as a target acquisition system boggles my mind. Someone doing donuts in a parking lot should not be automatically targeted with hellfire missiles, yet in practice that is how they were using the tool. I still lose sleep over it.

Bullshit. Even if you don't think LLMs alone are enabling that level of advancement, GPUs in general very much are. They are the critical enabling technology underlying self-driving cars, autonomous drones, camera-based robotics controllers, faster/better drug discovery, faster/better modeling protein structure and design, ML weather forecasting, materials discovery, and many other applications.

You are not disproving but rather reinforcing my point. Amazon survived the dotcom bubble precisely because they could hope to do more than deliver physical books. It was a more flexible, generally applicable business model than pets.com, and they started out targeting a market more susceptible to ecommerce conversion than pet food delivery proved to be. Similarly, a company selling a product that can only be used for the limited use cases we have found for chatbot-style text generating LLMs will be more likely to fail than a company selling something that is more generally useful. Data centers are more generally useful than LLMs, and the companies building them are more likely to survive than those whose fortunes are pinned entirely to unrealistically high expectations for replacing white collar workers with LLMs.

I think actually it's quite the opposite and you're the one conflating the impacts on a narrow market space with the larger economy. Right now, AI dominates growth in the stock market and demand for chips, but it is by no means encompassing the whole economy. Neither the stock market nor the chip sector are the entire economy. My grocery store isn't going to go out of business if Anthropic does.

A lot of investors may lose money as a result of the bubble bursting, but that does not mean the underlying asset will be forever worthless, just that it didn't provide sufficient returns sufficiently quickly to justify the upfront investment for the investors that funded it, at the time they made that investment. A different investor who could afford to ride out a period of reduced demand might have an entirely different experience.

Imagine you take out a five year loan to buy a truck to deliver packages, but then for the first two years you operate it, gas prices are elevated so you struggle to make the payments on your loan and end up not making as much money as you had hoped to originally, perhaps even to the point you need to declare bankruptcy and sell of the vehicle. But if not and then gas prices drop back down and demand shoots up for the last three years of the loan, you could then make up the difference. If the first two years drive you into bankruptcy, that is difficult for you, but amortized over the entire five year loan period, the truck may actually have been a profitable investment for someone who could have afforded to ride out the first two years. And just because you go bankrupt doesn't mean the truck stops being a valuable asset, it's just that you don't end up benefiting personally from that value because your timing was bad. From a macro perspective, the overall economy doesn't suffer, except to the extent that it might have been more efficient to invest the capital that went into procuring the truck elsewhere during those first two years. But only possibly and only on the margins, because the truck remains a profitable investment over the course of its entire lifetime.

Don't confuse the success or failure of individual investors or businesses with the success or failure of the overall economy. Current data center build outs premised on fanciful projections of demand for LLMs may end up not being profitable in the short term while still being profitable over the entire productive lifetime of the asset, if sufficient demand is found elsewhere or if demand for LLMs picks up later. Similarly, I anticipate at worst we will see chip prices plateau for a while if there is a pullback in LLM demand, but we won't see them fall and they will continue to rise over the longer term as more demand is generated elsewhere.

As one small example: we have barely begun to scratch the surface of what we can achieve with robotics. Think about the demand for video processing if you have tens of thousands of robots stocking shelves in supermarkets generating video all day long. On board processing will of course be the obvious primary demand for chips, which doesn't benefit data centers, but central processing of video to extract useful data from the entire fleet will generate demand for data centers. As will large scale training jobs. Now multiply that thinking across the entire scope of industries where robotics may be useful for replacing human labor, and you're talking about an extremely significant amount of valuable computational work.

That math is very much general purpose. It's just linear algebra under the hood, and linear algebra is a wildly useful toolkit with an unbounded set of potential applications, including many applications other than LLMs. That's how something developed for gaming evolved into something used for text processing and generation. For example, we have barely begun to scratch the surface of what we can achieve with robotics, and I have no doubt that further advances in robotics will generate an enormous amount of computer vision work that GPUs are extremely well designed to handle.

Fair, but my point is that a data center is not pets.com. They are a much more flexible asset, and are even more flexible than AI itself. Pets.com failed because the physical delivery infrastructure and consumer buying habits that support Chewy today did not yet exist and took years to create. Data centers can be repurposed from supporting the current generation of LLM-based AI models to an infinite variety of use cases. Pets.com could only ever hope to deliver pet food.

I have generally been bullish on data centers independent of how AI demand/load evolves. People will find a way to use that compute, even if it isn't the precise use we expect. As scale increases and the price per computation comes down, we will be able to brute force solutions to problems that otherwise would be intractable or prohibitively expensive to solve. And there are effectively an infinite set of those problems.

This tracks the evolution of how we use cloud computing and GPUs over the last 20 years. Cloud computing was originally just about saving companies from needing to maintain their own server racks, but has unlocked previously unserviced demand by allowing people to build an app or service and scale it to meet rapidly rising use without needing to invest a ton upfront in hardware. Suddenly a hobbyist could spin something up in their spare time that previously required many thousands of dollars of investment. Or when someone wants to run a single large scale computation, they can do it without needing to waste capital on maintaining idling servers to meet an occasional demand spike, like when a company I used to work at moved from running atmospheric calculations on a server in a closet to the cloud and were able to achieve double digit accuracy increases with the increased scale, while spending less overall on batch computing jobs.

GPUs, as the name implies, were created for graphics, and primarily for gaming graphics, but then more or less accidentally ended up enabling the present AI boom, which depends on a scale of computation that would have been impossible with older CPU architectures. Maybe someone at some point predicted this, but I think for the vast majority of people, it was extremely surprising that a niche gaming product would enable an industrial revolution level technological leap forward.

LLMs are just one way that increased compute scale unlocks seemingly magical results, but they are far from the only example and I have no doubt that there are many unknown examples remaining to be discovered yet.

Do you mean is there anyone who thinks that the future is NOT local models anyway? Since that seems to be the gist of your other points

Humanity might be able to, humans cannot

I'm shocked Netflix hasn't just settled to keep this quiet, because the entire situation makes them look not only like an abusive, discriminatory employer but even worse, horrifically incompetent at being abusive and discriminatory. Plenty of companies treat their employees like shit, that's not news. But any competently managed company knows how to do so without incurring massive legal liability like this, and it speaks to a remarkable lack of professionalism and basic competence at the leadership level and within the legal and HR teams.

If the accusations are true as reported, their in-house counsel fucked up terribly by assuming all ketamine use is illegal and recreational without bothering to conduct the very cursory investigation required to determine that it is in fact a perfectly legitimate option for treating treatment-resistant depression. That single error drove them to fire an otherwise exemplary employee for an obviously illegal reason in a manner that screwed him out of significant compensation including severance.

This was an obvious, unforced error by their legal team. Competent lawyers and competent corporate leadership would recognize that the relatively small amount of money they may save by litigating this issue is not worth the damage it is very predictably inflicting on their corporate culture and public brand. The lawyer who publicly admitted that ketamine factored into their decision should be immediately fired because he is demonstrably a terrible fucking lawyer, and he seems to be representative of the quality of legal advice Netflix is receiving, because any attorney with half a brain would have immediately recognized both the underlying legal error that incurred liability for the company and the business context that makes immediate heavily NDA'd settlement the optimal strategy for defending, even were the original legal error not so egregious.

My main takeaway from this situation is that Netflix is managed by remarkably incompetent leaders who are advised by even more remarkably incompetent lawyers.