Because their core product is not a long-term sustainable business strategy. Local hardware and models will continue to improve to the point of not needing the hosted solutions. And if you do need a hosted solution, remember that the big cloud providers already offer these solutions, so signing up for OpenAI/Anthropic _and_ AWS/GCP/Azure is not a sound business decision compared to just signing up with 1 of them that offers your cloud infra + GenAI infra. (Which is why the long-term benefits for cloud companies will probably be for the likes of AWS and not the likes of OpenAI).
They'll continue to burn money for marginal model improvements in the next few years all the while having no moat _and_ having Open-Weight / Local models eat their lunch.
The only way for them to stay relevant as a company is to expand beyond simply providing the models.
I'm old enough to remember the arrival of RDBMS, once IBM primed the space with DB2.
There was a pitched battle over features like row-level locking as competitors like Sybase, Ingress and Oracle scrapped it out. New features arrived on a monthly cadence, with immense engineering effort behind them. The winners (Oracle mostly) won a great moat which led to them to where they are today.
The fact that so many AI companies can produce amazing coding tools so quickly shows there is no moat, supporting your theory.
That is impossible to fund. At some point someone will decide to stop throwing money on the firepit that's the current business model and then hardware prices crash back to earth as 60-70% of the global demand disappears overnight.
The hardware miniaturization gains have finally dried up, though.
I am pretty certain that the current state of the art silicon feature size won't shrink again for at least another decade or two.
It normally takes about a decade to mature a tech which can create a smaller feature size into something commercially viable for mass production scale, and no further improvements have been in the pipeline for that long now.
So it's like the "next piece" indicator while playing Tetris is just blank.
It is more of a regulatory capture byproduct to prop up an artificial token driven Ponzi scheme.
There is a serious alternative to NVIDIA "AI" hardware dropping out of China in February 2027. There is no moat, but a whole lot of unpaid debts in the near future.
I honestly have no expert knowledge about this stuff. What I say is based only on intuition.
- China is heavily, heavily incentivised to enhance their own chip making
- Looking at the rate Chinas has expanded into just about every single other
space, and from quantity to quality, I just think it is impossible that they
don't compete on equal grounds pretty soon.
- I don't buy the insurmountable moat of TSMC
Reminiscent of tank warfare in WW2. The soviet T-34 was not remarkable in any particular way, but the sheer volumes it was produced in made it a very serious enemy to German tanks. As Stalin said "quantity has a quality all of its own".
The companies who control the compute resources will ~always control the greatest "amount" of intelligence. They can lease that intelligence out, or they can use it themselves. Currently the "total amount of intelligence" or perhaps "total amount of ability-to-do-stuff" is split between humans and machines at a ratio that means it still makes sense to lease the machine intelligence to the human intelligence - plus there are things that humans are still better at. In maybe 2 more years that will stop being true, due to the availability of more physical compute resources, and far greater model intelligence per unit compute. At that point, the point at which the substantial majority of ability-to-do-stuff is controlled by machine intelligence, then the entities who control all the compute will control all the ability-to-do-stuff, i.e. "the economy."
So I agree that the core product is not long-term sustainable as a product but this is because the whole world will look so different in the near future that the framing of intelligence as a "product" breaks down.
Open-Weight models, of course, are fine and useful, but if you have one million times less compute than your competitor (the lab), then you're not really playing the same game. You can only tackle the problems that they have decided they're not interested in.
There are fewer obstacles. That's the main reason it works, if you tried flying at 1 m above the ground it would become a lot more like driving, but without the benefit of friction. Flying requires less strict constraints on latency because it happens in straight line segments that are rather longer than the segments that you use when controlling a vehicle.
That’s close to whataboutism.. just because other technology can also be dangerous doesn’t absolve OpenAI/ChatGPT.
Also, ChatGPT is clearly exerting a massive (potential) influence on people if their MAU numbers are to be believed, so rightfully it should get attention on the potential harm.
Full GLM-5.3 needs a beast of a system, but you can run GLM-5.3 Flash on the 2x Spark setup the GP comment mentioned. If benchmarks are anything to go by, Flash is like having a local Terra-tier coding model: https://artificialanalysis.ai/models/comparisons?compare=glm...
I’m also pretty happy with GLM 5.3 Flash (for coding, navigation and german language it sucks at). Incredible that you can run it on a fairly practical (seeming) home setup.
But here’s the standard question: At what speeds/other limiting factors?
Not Singapore specifically but in today's AI/tech world if you're not constantly working you're on the firing line.
I'm in the Bay Area. It's Sunday morning and I'm working becasue I still need to pay for food, rent, and enormous medical bills and don't want to get fired, and I got yelled at by my director this morning on Slack because I'm not delivering fast enough despite squashing 12 months' worth of work into 4.
I don't have time for books. Finding time for even exercise is hard.
This sounds like mostly a USA problem and not something that extrapolates well to other countries. I had a suspicion it’d come from a US point of view lol.
EDIT: while more common in the US, if you are actually regularly working weekends, look for a better job. In my part of FAANG I rarely have to work weekends, although the occasional crunch time can happen.
But I think that your experience is actually the useful one that proves a point. Most people consume content on their phone (e.g instagram) and would usually just scroll by photos. They wouldn’t deeply analyze them.
Not entirely whataboutism from my part (in my opinion), the thread above was talking about China and US imperialism in (now deleted) comments. It's not like I brought in the US out of nowhere although it looks that way due to deleted comments.
Same for the first part you're commenting on, that was in direct response to the thread.
"Maintain economic dominance" is a bit of a distorted view of late 19th century US colonialism. It was not a dominant economic power compared to colonial Europe. It was to boost the local economy for sure, with the guano islands. But colonizing the Philippines after the "war" with Spain was mostly to prevent European colonial powers from dominating that part of the world.
The US didn't become economically dominant until after the first World War, when it already had an overseas empire for half a century.
I assumed the comment I was replying to was in reference to our constellation of military bases and the economic machinations post ww2.
If it was referencing history prior to that then I would reply with the as you mentioned guano mines, fruit production, and keeping European powers out of our regional backyard after several scuffles with Spain and Britain at minimum.
I’d also mention the boat touching still.
The Marine’s Hymn doesn’t mention the Halls of Montezuma and the Shores of Tripoli for no reason.
All these companies have a nauseating narrative around AI. I want to believe they are intentionally deceitful and not actually delusional. Hard to tell though which one it is.
Clearly with a Butlerian Jihad and everyone getting high on spice while riding giant worms. Silly you need to even ask that question, didn’t you read the sci-fi books. :D
They'll continue to burn money for marginal model improvements in the next few years all the while having no moat _and_ having Open-Weight / Local models eat their lunch.
The only way for them to stay relevant as a company is to expand beyond simply providing the models.
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