So how exactly is Anthropic and OpenAI ever going to pay back the trillions that they plan on spending?
It's really simple: if they truly get to human-level AI (or even superhuman AI), then money and debts no longer matter, since our current economic system will be obsolete. They are betting everything on this outcome.
I don't know if they will manage to do it before their debts have to be repaid, but considering the rate of acceleration in the past few months, there is a non-trivial chance that they will, IMHO. We will see.
LLM has nothing to do with AGI.
We do know, that the ceiling is below AGI. And it's not a matter of opinion - LLMs can not achieve AGI due to their design. Anyone telling you otherwise is lying to you.
And it doesn't matter how many or how severe bugs they can find, because it's not about what they produce, but how they produce it.
[citation definitely needed]
> And it doesn't matter how many or how severe bugs they can find, because it's not about what they produce, but how they produce it.
AGI is defined by the practical outcomes, not by the way the outcomes are achieved. You have no way to know that scaled-up Transformers predicting the next token will never result in human-level intelligence, since we currently have no idea where the ceiling of that approach is.
If you're not even familiar with how LLMs work, perhaps you should restrain yourself from confidently talking about this topic until you educate yourself. You're only spreading misinformation.
> AGI is defined by the practical outcomes, not by the way the outcomes are achieved
That for sure would be a very convenient definition, especially for all those AI labs trying to convince investors that they achieved AGI. Unfortunately everyone knows, that knowing the right answer isn't the same as knowing where that answer came from.
I am very familiar with how LLMs work, and I am telling you that there is no consensus that they cannot achieve AGI in the machine learning community. Some people think so (such as Yann LeCun), others disagree. We just don't know yet.
> That for sure would be a very convenient definition, especially for all those AI labs trying to convince investors that they achieved AGI. Unfortunately everyone knows, that knowing the right answer isn't the same as knowing where that answer came from.
AGI is defined by capabilities, not methods.
Those are Musk-like businesses, on steroids.
Not even Tesla has been profitable compared to the capital raised and the debt issued.
OpenAI and Anthropic are already in a ~200bil hole from previous model iterations and are committing to trillions of additional spending
OpenAI spent more TBPN than kimi spent on training K3
They are by all accounts, not. Z.ai for instance is a public company according to wikipedia. Moonshot AI is private but all their investors are private companies. Alibaba, as we all know, is a massive publicly traded tech conglomerate.
Moreover even if we take the more charitable view that they're controlled by the CCP, and therefore will continue releasing models for free, that seems as questionable as the prospect that private investors will continue shoveling money into anthropic/openai.
Don't forget, Jack Ma of "publicly owned" Alibaba, had to go into classroom time out after seemingly forgetting that its classroom capitalism and not real world capitalism.
2. people tend to ignore this, but the salary budget of a US frontier lab and chinese frontier lab is nowhere comparable, the first can easily outdone the later by 100x.
3. us labs, like other US style startups, always throw ton of money to capture the market. I don't see the chinese company doing the same scheme at all.
so, surely chinese AI providers also lost money making new models, but they are not spending nearly as much as US ones.
>2. people tend to ignore this, but the salary budget of a US frontier lab and chinese frontier lab is nowhere comparable, the first can easily outdone the later by 100x.
Both arguments make it seem like there's a double standard for american vs chinese AI companies, where american labs are held up to strict standards for profitability, but chinese labs get a pass because [insert handwaving about how some aspect of chinese labs is different]. Let's do apples to apples comparisons here, what are both sides' run rates and revenue growth prospects?
>3. us labs, like other US style startups, always throw ton of money to capture the market. I don't see the chinese company doing the same scheme at all.
Right, instead they're releasing their models for free so competitors can undercut them on inference. American labs' prospect of "there are open models 90% as good but cost less" might seem bad, but chinese labs' prospect of "there are companies offering the exact same models but aren't on the hook for r&d spend" seems even worse.
Not really, in a way. Things just cost far more in the US than in China; has pretty much always been the case, far back as I can recall. The Chinese state heavily invests in anything it wants to succeed at, and it has the resources to throw. Cost of living is generally wildly lower in China, along with salaries (although it's also pretty location- and role-dependent).
Overall I'd say labs are far cheaper to run in China than in the US, in more than just from the finances angle.
PRC AI have lower opex and capex, i.e. export controls means they couldn't be trillions in the hole on inflated hardware in the first place. They only need to extract a few 10s of billions from domestic market have a healthy runway. If investors/gov wants to throw in a few billion to treat as utility, whatever, it's still rounding error.
This seems like a double standard given that china is still building coal.