Why not just apologize and have Claude rewrite any code that's similar? Whether you did it intentionally or not, you probably agree that you can't keep using his code and that it would pretty bad if someone did something similar to you.
À 2 bit quant will (at best) get you about 80% of the full models memories. That's from a purely information theoretical sense. IRL it's worse than that.
Capability can still be better than 80%, but that depends on extensive post-quant recovery training to essentially rebuild the models internal manifold to route around the damage.
So, 3.8 Flash Next is better than GLM 5.3 for some things. This version is not.
FP4 is as low as you want to go if you want to retain most function and recall. Below that the noise gets too high and information becomes unretrievable. If it's a full quant you don't get to choose which info is lost. Just 20% randomly.
One interesting thing about this model is that it uses engrams. Meaning you can separate much of the storage from the compute and quantize them differently. That's not what they did here though. Here it was indiscriminate.
"The Congress shall have Power To ... promote the Progress of Science and useful Arts, by securing for limited Times to Authors and Inventors the exclusive Right to their respective Writings and Discoveries." - The United States Constitution
Copyright is a government mandated monopoly that was only granted in order to advance the arts and science. Any interpretation that runs contrary to that is bollocks being used by the religiously or financially motivated to serve their own petty interests to the detriment of societies.
More seriously, that needs to be done once and then it can be used by millions of people. Divide the cost by all the users and it's trivial. It's certainly not enough to lose sleep over.
When the bubble bursts it will depress valuations across the board, including the valuations of the good companies that will survive to eventually own significant market share. Buy those at those depressed prices and wait a decade.
The trick will be learning to tell the difference between the Google's and the pet.com's of the AI era. The trick during the dotcom situation was to look for companies that had actual gross profit and were reinvesting it, rather than companies that only had theoretical profit based on nonsense like market share and eyeballs.
Take Amazon as an example. They were $107 a share in 1999. By late 2001, it had fallen to under $6. Now they are $250. It still took almost 10 years to recover.
Your Amazon example is misleading. That $250 price today really undersells the growth because that’s post split. Their dot com high was like $4 in today’s terms. Or multiply today’s price by 20 for the 1999 equivalent ($5000).
It doesn’t negate your point about 10 years to recover but it’s important to highlight how substantial the growth was after it did.
Is there anyone in the AI sector that is making profit and re-investing it? I guess that really only applies to Microsoft, Apple, Google, etc. that other lines of business to prop up their AI divisions, vs. companies like OpenAI and Anthrophic. I wonder where NVIDIA will land...
Google is making money on it. However, that's coming at the expense of the very free web they built it from. So they have a different problem that may be unsolvable.
Worst part is that some of the companies that survived the dot com era are the new "...but on the internet" companies except now it's "...but with chat bot"
The problem is twofold. One, even a monopoly AI provider wouldn't have pricing power against its suppliers. Its suppliers are energy, semiconductors, and real estate. Semiconductors maybe they could get some leverage on but energy and real estate have plenty of other buyers. Two, there's still no evidence of a runaway scenario (ie a small lead turns into a big lead over time) and there's still no evidence that there's some resource that you can deny everyone else that they can't build your product also. You can't hoard energy, compute, memory, data, human talent, or customers.
The net effect is that the most likely scenario is if one big lab fails, they will likely all fail. Their revenues are all correlated.
To go to your dotcom comparison, the winner will be the ones picking through the assets that were written down by orders of magnitude and trying new products with the technology until one sticks to the wall. But I don't know if a dramatic crash is guaranteed either.
>
The problem is twofold. One, even a monopoly AI provider wouldn't have pricing power against its suppliers. Its suppliers are energy, semiconductors, and real estate. Semiconductors maybe they could get some leverage on but energy and real estate have plenty of other buyers.
Concerning the leverage on energy and real estate: don't forget that the AI companies have quite a lot of choice where to build their data centers. So AI companies have lots of opportunities to play several parties off against each other (in particular also for real estate and energy).
"but it can stay there so long as the balance sheet doesn't deteriorate."
Uhm, what? LOL.
People dont value firms based on balance sheets fella. Have you taken a basic valuation class?
Tesla is a nice stock for traders - they like the volatility. Nobody holds Tesla as stock for investing. If you were to truly value it on an intrinsic value basis you'd have to bring in failure risk.
I suspect this is going to end up like most services provided e.g. cloud stuff, balkanized between a couple major players and an assortment of DIY or less popular options if you don't like those ecosystems, plus some UX/DX focused wrappers that use the big players under the hood.
I think that would be a pretty satisfactory outcome compared to one hypercompany consuming trillions of dollars of the world economy.
THe problem with analogies is that they are imperfect.
I would argue those who already rule the world, will continue to do so.
What happens to OAI and Anthropic? No idea, probs go bust. Google just has to offer a half-decent offering in the long run and have a cost-advantage and it'll eventually knock OAI and Anthropic out as firms figure out what combination of models they want to be best for their economics and generating returns. Enterprises trust google over OAI and Anthropic. A clear signal of this was the Apple deal.
Dont forget those sweet returns fellas! CEO's are hired to make the owners wealthier. That is not gone.
“Divide the world” sounds ominous. Here’s another scenario to consider:
Internet access is not really unlimited, but for many people with fiber at home, it effectively is and we pay a flat rate.
Perhaps by the end of next year, most programmers will stop thinking about metered access for AI? For many people, the cheaper models (about as good as today’s frontier models) will be good enough.
Which might sound good, but the downside is that it will also be easier to build an AI botnet without the users paying for it noticing. Particularly when people are running AI inference on their own hardware.
Hardware is still insanely hard to get a hold of, and the stuff that's being built doesn't really work for home use. Maybe if it crashes Nvidia will adjust the hardware flow.
My guess is even if the AI market busts there is still a massive demand for hardware as models are solving all kind of problems now.
But ya, lots of hardware everywhere not managed well is how you get sovereign AI.
Or, like airlines, the ones that are left will have great technology but be not so great from a business and financial perspective. To me AI seems like a commodity service.
reply