Not surprisingly companies are willing to get into bed with more and more questionable use cases if it helps show some desperately needed AI adoption revenue.
Companies are getting desperate to show AI adoption as right now the numbers just don’t add up.
Just because you’re struggling to get raw materials for your business doesn’t make it a good business. Without strong enterprise adoption ASAP (which is what’s seriously suffering) things are going to hit the fan real quick.
> While AI training is often the most intense and expensive process for a single model, the majority of total AI compute usage (approximately 90%) is used for inference.
> Here is the breakdown of why this is the case: > Inference as High-Volume
> Activity: Inference occurs every time a user interacts with an AI model (e.g., asking ChatGPT a question, using image recognition, or generating code). While a model is trained once (or updated infrequently), it runs millions or billions of inferences continuously.
> Cost Scaling: Training is a massive, one-time upfront cost, while inference is an ongoing, daily operational cost. As the number of AI users grows, the demand for inference compute scales faster than the need for training new, large models.
> The Shift to Efficiency: While early AI hype focused on the immense compute needed for training, the industry has shifted toward making inference cheaper and faster through specialized hardware and techniques like optimization, quantization, and small language models (SLMs).
https://share.google/aimode/v3Y9P3rYIx1oj9VI2
And I finally figured out how to get links to answers instead of just inlining the content as before. Anyways, there it is. We live in a time where questions like "Does inference or training use more compute?" can be answered quickly by just pasting it into a search box.
Google doesn't need to do anything to make any other numbers work.
Gemini 3.1 pro is really good; Meta just signed a deal with Google for their TPUs.
Nano Banana 2 Pro is alsy very good.
OpenAI numbers might not add up, Antrophic might burn through cash, but not google.
And it doesn't matter anyway because as long as google can afford it, Microsoft HAS TO do this too and Microsoft also can afford it. The same with Amazon.
Microsoft invests in OpenAI and Amazon invests in Antrophic.
Given Anthropic is also funded by them, either they are desperate to not lose or they really don't think Anthropic has a moat.
Even a company like Amazon hasn't just billions on a bank account.
They make enough profit to easily afford this.
Its easier to get a loan instead of getting a lump sump through other means.
And their core business is super stable even with AI.
So the only real risk is increasing operational costs but if they pay of the investment, they literlay could just stop the hardware from running and reduce the operational cost if there is no demand
But not all companies as we have seen over the last week or so.
Irregardless, all companies doing so will have to balance the ethics of their choices against the public perception of their company as all of us are free to make choices that align with our own personal ethics.
(In short, they don't get to hide behind "everyone else is doing it".)
Do you sincerely believe this? This claim seems as wild as those vastly popular podcasters who constantly claim they're being "silenced".
This is further compounded by the fact the DOD, and the administration at large, is headed by some of the most incompetent and morally bankrupt individuals imaginable. I wouldn't trust Hegseth to change a goddamn light bulb, let alone run the DOD.
I do think those who bully those who defend the US online are the morally bankrupt ones.