You have a medium memoruy. I remember how USENET was, prior to the web forums you complain about. Reddit has blocked the possibility of a real discussion forum that works for complicated topics, locking in a short term popularity contest with ever-shifting "hot topics".
At present, no. I've done a number of experiments with LLMs' capabilities with generating fiction, and while they are impressive for a beginner writer -- because fo the lack of spelling and grammar mistakes, they are nowhere near being able to write competent fiction.
This is because they don't model reader or character minds, they're based on word probabilities, like a predictive text keyboard. The engineers can compensate for for this to a surprising degree by feeding it data to recognize specific situations (e.g. the car wash is 100 feet from my driveway; should I drive or walk?). But this is turd-polishing.
AI technology practice isn't foundational. Critical thinking is. The closer AIs come to being able to do something that looks like them doing our thinking for us, the more capable we have to be at thinking without them. A human who can't outthink an AI adds nothing to an AI-human partnership.
This proposed school is a typical scheme by people who think because they have been educated, they understand how the education works. This is like thinking because you eat, you know how to cook. Project work in education is nothing new; it goes back well over a century. And in real life, you may get a STEM degree, but your real education in engineering comes from real life work. But that doesn't mean classes in calculus and physics don't do anything for you.
Or English classes, for that matter. Nobody doubts the value of project work in education, particularly *androgogy* -- the education of adults. But for *pedagogy* you have to establish foundational skills as the student matures intellectually and socially. Entering the high school years, a typical student has had *no* training in critical thinking skills. To develop critical thinking skills in an area, you need both general skills (epistemologicla literacy: research, location of orignial sources, dealing with conflicting evidence) and domain specific training in that area. In other words, *liberal arts* training is foundational in an AI-saturated world. "Liberal Arts" -- from *artes liberales*: literally the skills necessary to be a free person.
But I think these people have at least got the problem right: as AI drives the cost of elaborate projects down, we all have more projects in our future. Big, elaborate, faultlessly plausible-looking projects which may or may not have actual value.
Where they are completely wrong is the solution, which is to organize education around practicing using AI to make impressive projects. In a word of AI-enamored dunces this will put you at the head of a very sorry class of ignoramuses. The answer is to build fundamental cognitive and intellectual skils at every stage of a student's development. Project work is surely part of that, but it's just a technique, not a solution.
In a nutshell, the academy is training students to be the next step down the evolutionary scale from code monkeys. More like the pigeons B.F. Skinner trained in WW2 to peck at a screen, in an attempt to create a primitive guided missile without computers. They want to create "main characters"; and they'll probably create a few. There are always a few natural autodidacts in any moderately large group. But what they are set up is to produce a bunch of fragile, economically privileged narcissists trained to cover their lack of education with impressive-looking AI slop.
The compute cost for DeepSeek V3 was $5.6M. Training costs for traditional US frontier labs model is about $70 to $200M+, with estimates of up to $1B+ to train the next generation models. It's expensive, but it's not even in the ballpark of "There is no viable economic model to do so." Google revenue in Q2 was $1.32 billion *per day*. $1B is trivially less than a day; $5.6M is just 6 minutes of revenue.
The cost problem is in the compute buildout and the subsidized compute to capture users.
> It remains to be seen if even using their models can be profitable,
I presume we can stipulate there exists some tasks that are cheaper to do using an LLM than doing it "by hand". As a such, using an LLM model can be profitable. To me, it is also obvious that the business of serving an LLM model can be profitable. There's large benefits to being able to get tokens quickly, and you can expect people to pay for that, even if we presume they could run the model locally with no maintenance overhead. There's even cases where using the cloud is cheaper than you can serve locally at all - I saw one guy that measured his added cost of electricity when his computer ran a specific LLM, and it was more expensive to run it locally than to buy tokens from the cheapest inference provider for the same model, presumably because they had better hardware and cheaper electricity.
It's a way of reframing sloppy practices as a signal to your investors that your software has become powerful. Most of the horror stories about AI showing signs of becoming hyperintelligent are coming from the companies themselves, which should tell us something.
While the energy source of wind and solar are free, building and maintaining the plants is not free.
If all they were building were a single 100MW data center, sure: renewables would be cheaper to build. But they evidently are planning a major campus that will consume most of the plant's 615 MW output over the next 25 years.
A solar installation, in a favorable location, capable of supplying 600MW around the clock (with battery backup) would have to be roughly 1800MW in capacity. It would be among the largest inthe world, on the order of ten thousand acres in size at a build cost of maybe 2 billion. The battery backup system to ensure 99.9% uptime would be 9x the size of the largest li-ion grid storage system ever built, and set you back on the order of 4-5 billion dollars.
While it's probably cheaper to go renewable than build a *new* nuclear plant, if you can reactivate and one for just two billion that looks like a bargain, if you have a use for all that power. Inability to save money by load following is the financial Achilles' heel of this generation of reactors, but if you have a guaranteed customer for most of your output, years in advance, that's as close to an ideal economic case for them as anything could be.
ToS is the strongest argument, but the distillers are not parties to the ToS. They get their data from data brokers. It is possible that data brokers are violating the ToS, but it would be hard to write ToS that precluded running queries for third parties without creating problems for consultancies and other businesses. Even presuming the ToS could be written to preclude the data brokers doing that, it doesn't affect the resulting model.
But the general shape of the argument brings us right back to unclean hands: we worked hard on this model and it's not fair for you to profit off our work in a way that doesn't have our permission.
I think the fair-minded position is to see what people think when the documentary comes out. You're essentially arguing a negative here -- that there *can't* be anything of value that hasn't been said yet.
Also, I don't think saying something *new* is necessarily critical. Sometimes saying something obvious but in an interesting way is worthwhile.
In this case, Holmes gave access to the filmmaker. Obviously she has an agenda. If the filmmaker is smart, he has an agenda that's different from hers. If he knows his business he'll find something interesting to show you from that conflict.
Now looking at the trailer -- holy shit that woman gives off batshit crazy vibes. That's new to me. I expected her to be slick, persuasive, like Saruman in Lord of the Rings: someone. you'd need real strength of mind to resist being persuaded by. If I'd just handed someone like that my business card, I'd get it back on some pretext then head as fast as I could for the door.
It doesn't make it *right*, but it does make claiming it is *wrong* inconsistent with their own behavior. This could prevent the US companies from suing the Chinese companies seeking an injunction (due to the "unclean hands" doctrine), and probably blocks them from seeking monetary damages in most US jurisdictions.
And suing may undermine the US companies own intellectual property claims by exposing their shaky foundations. An AI model isn't *expression*, so it can't be copyrighted. Insofar as the service allows the underlying model to be deduced through regular usage, trade secret protections don't apply because that's *reverse engineering*, which trade secrets don't prevent.
This leaves violations of terms of service. But setting aside the dubious enforceability of anti-reverse engineering provisions, the Chinese companies may not even be parties to the ToS agreement if they are obtaining the model data through a network of contractors and shell companies.With only moderate paranoia, they can effectively shield themselves from some kind of US tort claim.
Which leaves them with the model they've created from the US company's model, but which the US company has no IP claim upon. If I download a DeepSeek model that's been (hypothetically speaking) trained on Claude, Anthropic might not like that, but what can they do about it?
It's a strange situation. US investors are spending cumulatively on over half a trillion dollars a year in the hope of owning a breakthrough frontier model that is the end of the economic world as we know it. But the essence of the model might not be intellectual property at all.
Think of the categories as fixed, and the people moving *through* them. Over 65s are exiting the category at a lower rate.
I love a term a friend of mine used to use when talking about AI: "Narrowly superhuman" (before "superintelligence" got to be a thing).
AI is narrowly superhuman. It is able to do specific things that used to be defined as a "hallmark of intelligence" much better than a human. The easiest one? Summing numbers. It's both faster and more precise than a human.
AI gets more and more such areas it can do better than a human.
If we define "Superintelligent" to be "A computer system able to do absolutely everything we can somehow call intelligence better than any human", we will *never* have superintelligence. The things we define as intelligence will be in conflict; we'll land at the system being better at one at the cost of another.
I'm in favor with being careful about this development. But "prohibiting the development of superintelligence" isn't an option. It's already here.
Dividing the yearly US spend on Internet advertising by the number of people in the US that has Internet access gives $900-$970 spend per person per year, or $75-$81 per month.
Please choose the option you want:
1. I want to pay about $75 per month per member in my family into the ad auctions to not see any ads. Ie, keep the infrastructure, but let you opt to pay your share through money rather than attention.
2. I want to prohibit ads, and accept that everybody will have to pay for the sites they want access to, that there will be much fewer services, that there will be much higher transaction costs, and that the poor will have much less ability to access services. (Microtransactions won't solve this, due to the need for trust evaluation of same.)
3. I want some combination of 1 and 2, where some sides of ads are prohibited so it is possible to buy my way out of them at a lower price,. I accept that this will result in the loss of some services and lower access to others for the poor.
4. I want legislation that makes Internet ad blockers work more consistently. I accept that this just shifts the attention cost to other people, and have me getting the benefit without paying the cost.
5. I want to remove Internet ads and subsidize the sites through taxes.
6. I want to increase the requirements of Trust and Safety checks and format restrictions for ads, potentially accepting higher overhead (in what goes to content vs infrastructure) for less annoying/bad ads. I accept that doing this creates significant advantages of scale for ad networks.
7. Other, please describe, including where costs gets shifted with your proposal.
The above are my best attempt at listing possible options and their correct caveats. In most situations, I find ads annoying (who doesn't). Unfortunately, they are the main money highway of Internet content.
DISCLOSURE: I used to work for Google, where I rang infrastructure for stopping bad ads/advertisers and for removing fake clicks. I've also been interested at the use of sub-cent microtransactions to pay for content since the 1990s, though I've become disillusioned as I've learned more about the attention economics involved. Approving individual microtransactions has a higher attention cost than dealing with the ads, and bulking them per-site (through micro-subscriptions) will limit the number of sites that can have content.
It's more a case of the fox being in charge of the chicken coop; or bank robbers gaining control of the bank.
The job of the press in a democracy is to hold officials up to scrutiny.
In any problem, if you find yourself doing an infinite amount of work, the answer may be obtained by inspection.
