> Anyone familiar with basic economics is pulling their hair out reading this, because there's one extremely obvious way to lower the price of building new housing: Reducing or eliminating tariffs on construction equipment and materials and ensuring a robust supply of low-cost labor.
And just in general reducing the restrictions on building in places with high rent to income ratios.
> Game publishers have already publicly floated the idea of not selling their games but charging per hour. Imagine how that impact Call of Duty or GTA.
MMORPGs have had monthly subscription fees for a long time.
For a lot of games if they charged by the hour would probably see less revenue...people buy tons of games and then barely ever play them.
You assume there's only one type of player. Some players fall into a category I lovingly call Madden Guy. These people will play some other games, but they will play one game *a lot*. Call of Duty, Arc Raiders, Destiny, Battlefield, Fortnite, these are the type of games who attract these kind of players. If a game has 600 purchasable items, a seasonal battlepass-type thing, multiple female rappers skins, and a publisher-financed pro scene, it's probably one of those games.
Those games 100% already have game modes you pay by the hour. They will have special modes you access with currency and you need to keep paying to keep playing. Those modes are usually special, with increased and unique drops.
I think it's reasonable to argue something like, "some IP protection is good, but too much is bad, and we probably have too much right now." It would be impossible to calibrate the laws so that the amount of IP protection is socially optimal, but we can look at the areas where the protection is too much and start there.
It's not impossible at all. We should do 5 year copyright - 99% of all commercial profit of all media is collected within 5 years of publishing.
Copyright is granted to media creators in order to incentivize creativity and contribution to culture. It's not granted so as to empower large collectives of lawyers and wealthy people to purchase the rights and endlessly nickel and dime the public for access to media.
Make it simple and clear. You get 5 years total copyright - no copying, no commercial activity or derivatives without express, explicit consent, require a contract. 5 years after publishing, you get another 5 years of limited copyright - think of it as expanded fair use. A maximum of 5% royalties from every commercial use, and unlimited non-commercial use. After 10 years, it goes into public domain.
You can assign or sell the rights to anyone, but the initial publication date is immutable, the clock doesn't reset. You can immediately push to public domain, or start the expanded fair use period early.
No exceptions, no grandfathering.
There's no legitimate reasons we should be allowing giant companies like Sony and HBO and Paramount to grift in perpetuity off of the creations and content of artists and writers. This is toxic to culture and concentrates wealth and power with people that absolutely should not control the things they do, and a significant portion of the wealth they accumulate goes into enriching lawyers whose only purpose in life is to enforce the ridiculous and asinine legal moat these companies and platforms and people have paid legislators to enshrine in law.
Make it clear and simple, and it accomplishes the protection of creators while enriching society. Nobody loses except the ones who corrupted the system in the first place.
We live in a digital era, we should not be pretending copyright ideas based on quill and parchment are still appropriate to the age.
And while we're at it, we should legally restrict distribution of revenues from platforms to a maximum of 30% - 70% at minimum goes to the author. The studio, agent, platform, or any other distribution agent all have to divvy up at most 30%.
No more eternal estates living off of the talent and creations of ancestors. No more sequestration of culturally significant works to enrich grifters.
This would apply to digital assets, games, code, anything that gets published. Patents should be similarly updated, with the same 5 and 10 year timers.
Sure, it's not 100% optimal, but it gets a majority of the profit to a majority of the creators close enough and it has a clear and significant benefit to society within a short enough term that the tradeoff is clearly worth it.
Empowering and enabling lawyers and rent seekers to grift off of other peoples talent and content is a choice, we don't have to live like that.
I'm fairly certain that would not work at all for media such as sci-fi/fantasy books, where a system like this would result in people just forever reading older books which are free and effectively kill the market.
There is a limited amount of time to read in a day and the amount of 10+ year old content that is still amazing is more then anyone could ever read, and it's hard to compete with free.
I think video games is actually kinda an anomaly when it comes to copyright because they have been, on average, getting better and better then games released even in the recent past, mostly due to hardware getting better and better. Also any multiplayer game has the community issue where older games tend to no longer have a playerbase to play with.
Same could be said about movies/tv shows that rely on CGI up until somewhat recently where the CGI has pretty much plateaued.
I think the sales of books is pretty much uncoupled from the supply or price, as piles and piles of great books are available for free online or at the local library.
More recorded shows exist than any one man can watch in a lifetime, and yet there are multiple concurrent series ongoing right now.
I think the real kicker is that IP law was built around things like books, that don't suddenly stop working or need to be maintained, etc. Modern laws should take software into account and deal with it differently.
If it were modified to "99% of media has commercial profit collected within five years" it's probably pretty close to the mark, given how much is released and never reprinted/etc.
However, even 1% of a very large market is a huge tail, which is valuable.
Regardless, change the game. If you have a valuable, useful platform, and compete with other platforms for quality and delivery of service, then you're optimizing for the right things. If you have valuable media and the platform only serves to collect fees for the privilege of accessing the media, then you're optimizing the thing that is net negative for society, and ends up with adtech and degraded service and gotchanomics to try to nickel and dime you at every opportunity.
Imagine a world in which spotify and youtube and netflix had to compete on product and service quality, instead of network effects and legal technicalities. In which you could vibe code an alternative platform and have it be legally feasible to start your own streaming service merely by downloading a library of public domain content, then boot-strapping your service and paying new studios for license to run content, and so on.
The entire ecosystem would have to adapt, and it would be incredibly positive for creatives and authors and artists. There wouldn't be a constant dark cloud of legal consequences hanging over peoples heads, with armies of lawyers whose only purpose in life is to wreck little people who dare "infringe" on content, and all the downstream nonsense that comes from it.
Make society better by optimizing the policies that result in fewer, less wealthy, and far less powerful lawyers.
>> A few billionaires might have additional vacation homes, but they are not going to consume a million homes, much less 10 million.
> Sumner is somehow unfamiliar with the concept of a landlord or vacant property investment.
I'm sure he is not unfamiliar with either...
Not sure what landlords have to do with anything since washing machines are often included as part of a rental (or the apartment doesn't have a washing machine, but what does that have to do with landlords?).
And vacant property investment is a small fraction of total property ownership in the US. It's more common that people have a vacation home and rent it out part of the year.
>> progressive consumption taxes
> When someone proposes one, let me know.
They have been proposed...many times. In fact, the US's system has elements of a progressive consumption tax already since people can put retirement savings in IRAs/401ks. What would make it a more complete progressive consumption tax would be to either raise the limits on contributions to these retirement accounts (and remove income limits), and also introduce accounts like these that are meant as more universal savings vehicles. This is preferred (in my view at least) to just cutting dividend and capital gains rates to 0% since that would benefit existing rich people.
> It is also important to note that, until recently, the GenAI industry’s focus has largely been on training workloads. In training workloads, CUDA is very important, but when it comes to inference, even reasoning inference, CUDA is not that important, so the chances of expanding the TPU footprint in inference are much higher than those in training (although TPUs do really well in training as well – Gemini 3 the prime example).
Does anyone have a sense of why CUDA is more important for training than inference?
NVIDIA chips are more versatile. During training, you might need to schedule things to the SFU(Special Function unit that does sin, cos, 1/sqrt(x), etc), you might need to run epilogues, save intermediary computations, save gradients, etc. When you train, you might need to collect data from various GPUs, so you need to support interconnects, remote SMEM writing, etc.
Once you have trained, you have frozen weights/feed-forward networks that consist out of frozen weights that you can just program in and run data over. These weights can be duplicated across any amount of devices and just sit there and run inference with new data.
If this turns out to be the future use-case for NNs(it is today), then Google are better set.
Won't the need to train increase as the need for specialized, smaller models increases and we need to train their many variations? Also what about models that continuously learn/(re)train? Seems to me the need for training will only go up in the future.
This is a very important point - the market for training chips might be a bubble, but the market for inference is much, much larger. At some point we might have good enough models and the need for new frontier models will cool down. The big power-hungry datacenters we are seeing are mostly geared towards training, while inference-only systems are much simpler and power efficient.
A real shame, BTW, all that silicon doesn't do FP32 (very well). After training ceases to be that needed, we could use all that number crunching for climate models and weather prediction.
it's already the case that people are eeking out most further gains through layering "reasoning" on top of what existing models can do - in other words, using massive amounts of inference to substitute for increases model performance. Whereever things plateau I expect this will still be the case - so inference ultimately will always be the end game market.
It's just more common as a legacy artifact from when nvidia was basically the only option available. Many shops are designing models and functions, and then training and iterating on nvidia hardware, but once you have a trained model it's largely fungible. See how Anthropic moved their models from nvidia hardware to Inferentia to XLA on Google TPUs.
Further it's worth noting that the Ironwood, Google's v7 TPU, supports only up to BF16 (a 16-bit floating point that has the range of FP32 minus the precision. Many training processes rely upon larger types, quantizing later, so this breaks a lot of assumptions. Yet Google surprised and actually training Gemini 3 with just that type, so I think a lot of people are reconsidering assumptions.
This is not the case for LLMs. FP16/BF16 training precision is standard, with FP8 inference very common. But labs are moving to FP8 training and even FP4.
When training a neural network, you usually play around with the architecture and need as much flexibility as possible. You need to support a large set of operations.
Another factor is that training is always done with batches. Inference batching depends on the number of concurrent users. This means training tends to be compute bound where supporting the latest data types is critical, whereas inference speeds are often bottlenecked by memory which does not lend itself to product differentiation. If you put the same memory into your chip as your competitor, the difference is going to be way smaller.
Training is taking an enormous problem and trying to break it into lots of pieces and managing the data dependency between those pieces. It's solving 1 really hard problem. Inference is the opposite, it's lots of small independent problems. All of this "we have X many widgets connected to Y many high bandwidth optical telescopes" is all a training problem that they need to solve. Inference is "I have 20 tokens and I want to throw them at these 5,000,000 matrix multiplies, oh and I don't care about latency".
I think it’s the same reason windows is inportant to desktop computers. Software was written to depend on it. Same with most of the software out there today to train being built around CUDA. Even a version difference of CUDA can break things.
CUDA is just a better dev experience. Lots of training is experiments where developer/researcher productivity matters. Googlers get to use what they're given, others get to choose.
Once you settle on a design then doing ASICs to accelerate it might make sense. But I'm not sure the gap is so big, the article says some things that aren't really true of datacenter GPUs (Nvidia dc gpus haven't wasted hardware on graphics related stuff for years).
That quote left me with the same question. Something about decent amount of ram on one board perhaps? That’s advantageous for training but less so for inference?
inference is often a static, bounded problem solvable by generic compilers. training requires the mature ecosystem and numerical stability of cuda to handle mixed-precision operations. unless you rewrite the software from the ground up like Google but for most companies it's cheaper and faster to buy NVIDIA hardware
Let w be the vector of weights and S be the comformable matrix of covariances. The portfolio variance is given by w’Sw. So just minimize that with whatever constraints you want. If you just asssume weights sum to one, it is a classic quadratic optimization with linear equality constraints. Well known solutions.
The fix for this is for the AI to double-check all links before providing them to the user. I frequently ask ChatGPT to double check that references actually exist when it gives me them. It should be built in!
Gemini will lie to me when I ask it to cite things, either pull up relevant sources or just hallucinate them.
IDK how you people go through that experience more than a handful of times before you get pissed off and stop using these tools. I've wasted so much time because of believable lies from these bots.
Sorry, not even lies, just bullshit. The model has no conception of truth so it can't even lie. Just outputs bullshit that happens to be true sometimes.
I have found my self doing the same "citation needed" loop - but with ai this is a dangerous game as it will now double down on whatever it made up and go looking for citations to justify its answer.
Pre prompting to cite sources is obviously a better way of going about things.
It's bad when they indiscriminately crawl for training, and not ideal (but understandable) to use the Internet to communicate with them (and having online accounts associated with that etc.) rather than running them locally.
It's not bad when they use the Internet at generation time to verify the output.
I don't know for certain what you're referring to, but the "bulk downloads" of the Internet that AI companies are executing for training are the problem I've seen cited, and doesn't relate to LLMs checking their sources at query time.
And just in general reducing the restrictions on building in places with high rent to income ratios.
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