Viewed from historical perspective, big tech is really reaping the benefits of the intellectual wealth accumulated over many thousands of years by humanity collectively. This should be recognized to find a better path forward.
How? They are all losing tens of billions of dollars on this, so far.
Open source models are available at highly competitive prices for anyone to use and are closing the gap to 6-8 months from frontier proprietary models.
There doesn't appear to be any moat.
This criticism seems very valid against advertising and social media, where strong network effects make dominant players ultra-wealthy and act like a tax, but the AI business looks terrible, and it appears that most benefits are going to accrue fairly broadly across the economy, not to a few tech titans.
NVIDIA is the one exception to that, since there is a big moat on their business, but not clear how long that will last either.
I'm not so sure thats correct. The Labs seem to offer the best overall products in addition to the best models. And requirements for models are only going to get more complex and stringent going forward. So yes, open source will be able to keep up from a pure performance standpoint, but you can imagine a future state where only licensed models are able to be used in commercial settings and licensing will require compliance against limiting subversive use or similar (e.g. sexualization of minors, doesn't let you make a bomb etc.).
When the market shifts to a more compliance-relevant world, I think the Labs will have a monopoly on all of the research, ops, and production know-how required to deliver. That's not even considering if Agents truly take off (which will then place a premium on the servicing of those agents and agent environments rather than just the deployment).
There's a lot of assumptions in the above, and the timelines certainly vary, so its far from a sure thing - but the upside definitely seems there to me.
If that's the case, the winner will likely be cloud providers (AWS, GCP, Azure) who do compliance and enterprise very well.
If Open Source can keep up from a pure performance standpoint, any one of these cloud providers should be able to provide it as a managed service and make money that way.
Then OpenAI, Anthropic, etc end up becoming product companies. The winner is who has the most addictive AI product, not who has the most advanced model.
What’s the purpose of licensing requiring though things though if someone could just use an open source model to do that anyway? If someone were going to do those things you mentioned why do it through some commercial enterprise tool? I can see maybe licensing requiring a certain level of hardening to prevent prompt injections, but ultimately it still really comes down to how much power you give the model in whatever context it’s operating in.
Nvda is not the only exception. Private big names are losing money but there are so many public companies seeing the time of their life. Power, materials, dram, storage to name a few. The demand is truly high.
What we can argue about is if AI is truly transforming lives of everyone, the answer is a no. There is a massive exaggeration of benefits. The value is not ZERO. It’s not 100. It’s somewhere in between.
The opportunity cost of the billions invested in LLMs could lead one to argue that the benefits are negative.
Think of all the scientific experiments we could've had with the hundreds of billions being spent on AI. We need a lot more data on what's happening in space, in the sea, in tiny bits of matter, inside the earth. We need billions of people to learn a lot more things and think hard based on those axioms and the data we could gather exploring what I mention above to discover new ones. I hypothesize that investing there would have more benefit than a bunch of companies buying server farms to predict text.
CERN cost about 6 billions. Total MIT operations cost 4.7 billions a year. We could be allocating capital a lot more efficiently.
I believe that eventually the AI bubble will evolve in a simple scheme to corner the compute market. If no one can afford high-end hardware anymore then the companies who hoarded all the DRAM and GPUs can simply go rent seeking by selling the computer back to us at exorbitant prices.
The demand for memory is going to result in more factories and production. As long as demand is high, there's still money to be made in going wide to the consumer market with thinner margins.
What I predict is that we won't advance in memory technology on the consumer side as quickly. For instance, a huge number of basic consumer use cases would be totally fine on DDR3 for the next decade. Older equipment can produce this; so it has value, and we may see platforms come out with newer designs on older fabs.
Chiplets are a huge sign of growth in that direction - you end up with multiple components fabbed on different processes coming together inside one processor. That lets older equipment still have a long life and gives the final SoC assembler the ability to select from a wide range of components.
That makes no sense. If the bubble bursts, there will be a huge oversupply and the prices will fall. Unless all Micron, Samsung, Nvidia, AMD, etc all go bankrupt overnight, the prices won't go up when demand vanishes.
That assumes the bubble will burst, which it won't if they succesfully corner the high-end compute market.
It doesn't matter if the AI is any good, you will still pay for it because it's the only way to access more compute power than consumer hardware offers.
There is a massive over-buying of compute, much beyond what is actually needed for the current level of AI development and products, paid for by investor money. When the bubble pops the investor money will dry up, and the extra demand will vanish. OpenAI buys memory chips to stop competitors from getting them, and Amazon owns datacenters they can't power.
I agree with your point and it is to that point I disagree with GP. These open weight models which have ultimately been constructed from so many thousands of years of humanity are also now freely available to all of humanity. To me that is the real marvel and a true gift.
It's turning out to be a commodity product. Commodity products are a race to the bottom on price. That's how this AI bubble will burst. The investments can't possibly show the ROIs envisioned.
As an LLM I use whatever is free/cheapest. Why pay for ChatGPT if Copilot comes with my office subscription? It does the same thing. If not I use Deepseek or Qwen and get very similar results.
Yes if you're a developer on Claude Code et al I get a point. But that's few people. The mass market is just using chat LLMs and those are nothing but a commodity. It's like jumping from Siri to Alexa to whatever the Google thing is called. There are differences but they're too small to be meaningful for the average user
They are wasting tens of billions on something that has no business value currently, and may well never, just because of FOMO. That's not what I would call an investment.
>Open source models are available at highly competitive prices for anyone to use and are closing the gap to 6-8 months from frontier proprietary models.
What happens when the AI bubble is over and developers of open models doesn't want to incinerate money anymore? Foundation models aren't like curl or openssl. You can't have maintain it with a few engineer's free time.
If the bubble is over all the built infrastructure would become cheaper to train on? So those open models would incenerate less? Maybe there is an increase of specialist models?
Like after dot-com the leftovers were cheap - for a time - and became valuable (again) later.
No, if the bubble ends the use of all that built infrastructure stops being subsidized by an industry-wide wampum system where money gets "invested" and "spent" by the same two parties.
Training is really cheap compared to the basically free inference being handed out by openai Anthropic Google etc.
Spending a million dollars on training and giving the model for free is far cheaper than hundreds of millions of dollars spent on inference every month and charging a few hundred thousand for it.
The recipe of coke is not a copyright, it is a trade secret. Trade secrets can remain indefinitely if you can keep it secret. Copyrights are "open" by their nature.
I was agreeing it could last a very long time, even longer that copyright. But specifically because it is not copyright. But as an AI model, it just won't have value for very long. Models are dated within a 6 months and obsolete in 2 years. IP around development may last longer.
The recipe to Coca Cola is not copyrighted (recipes in general can't be) but is protected by trade secret laws, which can notionally last forever.
The recipe also isn't that much of a secret, they read it on the air on a This American Life episode and the Coca Cola spokesperson kind of shrugged it off because you'd have to clone an entire industrial process to turn that recipe into a recognizable Coke.
imo there are actually too few answers for what a better path would even look like.
hard to move forward when you don't know where you want to go. answers in the negative are insufficient, as are those that offer little more than nostalgia.
It's interesting that the prosperity maximum of both the United States and China happened at "market economy kept in line with a firm hand" even though we approached it from different directions (left and right respectively) and in the US case reversed course.
But op is frankly absurd. It sounds reasonable for about 1 second before you think about it. What sets tech apart from every other area of human innovation? And why limit it to that? What about mineral exploitation? Oil etc.
It's just not a well thought out comment. If we focus on the "better path forward", the entrance to which is only unlocked by the realisation that big techs achievements (and thus, profits) belong to humanity collectively... After we reach this enlightened state, what does op believe the first couple of things a traveller on this path is likely to encounter (beyond Big Techs money, which incidentally we take loads of already in the form of taxes, just maybe not enough)?
Tech is the most set apart area of innovation ever.
First you have tech's ability to scale. The ability to scale also has it creep new changes/behaviors into every aspect of our lives faster than any 'engine for change' could previous.
Tech also inherits, so you can treat it as legos using, what are we at, definitely tens, maybe hundreds of thousands of human years of work, of building blocks to build on top of. Imagine if you started every house with a hundred thousand human years of labor already completed instantly. No other domain in human history accumulates tens of millions of skilled human years annually and allows so much of that work to stack, copy, and propagate at relatively low cost.
And tech's speed of iteration is insane. You can try something, measure it, change it, and redeploy in hours. Unprecedented experimentation on a mass scale leading to quicker evolution.
It's so disingenuous to have tech valuations as high as they are based on these differentiations but at the same time say 'tech is just like everything from the past and must not be treated differently, and it must be assumed outcomes from it are just like historical outcomes'. No it is a completely different beast, and the differences are becoming more pronounced as the above 10Xs over and over.
Is this technically a form of retroactive mind rape? If so, at least we have the right oligarchic friends experienced in this running the big show. (Apologies if I just any broke rules here).
This seems to be a touchy subject for YC people with 500+ karma. Not a repudiation but an 'invisible hand' downvote to avoid a response or exposure of an opinion. My ancestors fought in the revolutionary war and like them, I'll die on this very subtle rolling hill of a question. I loved you all as brothers, this may be the end for mrwaffle.
Every single open source model I've used is nowhere close to as good as the big AI companies. They are about 2 years behind or more and unreliable. I'm using the large parameters ones on a 512GB Mac Studio and the results are still poor.
Hope folks involved in this type of exploration have it clear in mind that what they are reasoning about it’s strictly the model of the real world only. It’s far from obvious that nature follows anything remotely computational.
