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[Sethbacca]

These companies are going to find out eventually that outsourcing all of your work overseas and importing millions of visa holders to save money eventually causes your product to go to crap, including AI.

Who do they think are even going to buy these products when nobody has a job and thus no money. They're putting the cart before the horse out here.

 

[FalsettoVibe]

Good.

 

[Angry_Megalodon]

Some AI stuff is really great - speech to text, summarization, image recognition, translation etc
Some AI stuff is reasonable - better search engine, casual image generation etc
Some AI stuff just bad - complex stuff, stuff requiring memory or rare knowledge etc

The problem of AI companies is that good/reasonable stuff not worth even tens of billions and they aim for trillions, so they try to upsale stuff that obviously bad. And not eveyone buying this shit.

Even translation is bad. I mean, proper translation, not superficial level stuff. I ran a few tests on my books (from Spanish to English) and the results were appalling. Each page needed dozens corrections.

The problem is that execs and AI preachers make the wrong assumption that those easy tasks in which AI is good at can easily lead to the next level of complexity. Applied to videogames, generating random landscape images is not "a previous step" to achieving a full world map by typing in a few prompts. The gap between these two tasks is huge. Same as believing that a chatbot can write a (good) novel or script. It's nonsense.

 

[TheDreadLord]

Microsoft's approach to AI feels underwhelming, whereas Google appears to have a more compelling strategy in this area. The integration seems to make sense and is actually useful which is a shame since Office and OneDrive are quite good tools.

 

[Felessan]

Even translation is bad. I mean, proper translation, not superficial level stuff. I ran a few tests on my books (from Spanish to English) and the results were appalling. Each page needed dozens corrections.

It's one uses of AI I actually do and find it reasonable. Much better than what MTL was before.
For common stuff it's good, for specific/rare stuff it might trip itself, but humans tend to do it too.
So basically it's on the level of an average translator, not high skilled pro or one with extensive knowledge in your field. But generally it's more than enough.

 

[Loxus]

Even translation is bad. I mean, proper translation, not superficial level stuff. I ran a few tests on my books (from Spanish to English) and the results were appalling. Each page needed dozens corrections.

The problem is that execs and AI preachers make the wrong assumption that those easy tasks in which AI is good at can easily lead to the next level of complexity. Applied to videogames, generating random landscape images is not "a previous step" to achieving a full world map by typing in a few prompts. The gap between these two tasks is huge. Same as believing that a chatbot can write a (good) novel or script. It's nonsense.

This is the very reason why prices will not drop anytime soon, chatbots still have room to improve.

Even if the AI bubble burst, the largest players are still committed to multi-year AI/AGI roadmaps requiring massive compute. (GPUs, CPUs, HBM, Advanced packaging (CoWoS, 3D stacking) and networking)

This demand isn't driven by hype but long-term strategy.

Companies like X (xAI), OpenAI, Meta, Google, Microsoft, and Amazon plan to deploy tens of millions of GPUs over the next decade to pursue self-improving models (AGI research) and operate global inference networks.

This creates a baseline demand that stays high regardless of "bubble" conditions.

 

[Felessan]

This is the very reason why prices will not drop anytime soon, chatbots still have room to improve.

Even if the AI bubble burst, the largest players are still committed to multi-year AI/AGI roadmaps requiring massive compute. (GPUs, CPUs, HBM, Advanced packaging (CoWoS, 3D stacking) and networking)

This demand isn't driven by hype but long-term strategy.

Companies like X (xAI), OpenAI, Meta, Google, Microsoft, and Amazon plan to deploy tens of millions of GPUs over the next decade to pursue self-improving models (AGI research) and operate global inference networks.

This creates a baseline demand that stays high regardless of "bubble" conditions.

They plan until investors happy with plans. And investors happy if there is a profit in sight. And if there is none - plans can be changed.
There is already a noise against as we see in Oracle and MS cases.

Companies reports to boards and boards ain't there for charity "better humanity" projects