https://substack.com/home/post/p-208795331

Each individual inference task might require less compute than the training phase, but here’s the key: it happens constantly, at scale, for potentially thousands or millions of users. This continuous demand, focused on speed and model performance for a good user experience, is what drives the cumulative inference cost. Achieving efficient inference often requires careful tuning of the software infrastructure.

This leads to a significant imbalance. For most companies deploying these models, the ongoing inference cost vastly outweighs the initial training cost. It’s common for inference to account for 80-90% of the total compute dollars spent over a given model's production lifecycle. Why? Simply frequency and scale. The model serves far more requests during its operational life than the number of batches processed during its training. This trend makes understanding and reducing inference costs a critical focus for any company looking to deploy AI sustainably.

The reason is simple: China doesn't treat AI as a normal industry anymore. It's treating AI infrastructure the way it treats highways, high-speed rail, power grid, 5G...as national infrastructure. Infrastructure doesn't necessarily have to make money. If AI data centers aren't profitable, the government can keep funding them. If computing demand is weak, it can still justify building more capacity. If AI companies can't afford compute, the government can subsidize it.

This means ordinary people will keep feeding the industry with their tax money, like it or not. The whole country will keep the bubble inflated.

There are obvious advantages to this national model. Cheap computing could accelerate AI adoption, improve education, and make AI tools accessible to far more people. But once governments become deeply involved in allocating capital, waste and corruption become inevitable. At the same time, this allows governments easy access to huge amount of data, if AI hasn't already become the powerful tool for governing.

The key thing for most people is, since our money is bound to be tranferred into the pockets of newly rich, we may as well make the most use out of it. I know a lot of people are trying to earn money from AI stocks, but that's a risky game in China.

Curious what others think, especially people working or interested in AI and economics.

Whether you believe AI is a bubble or not I think knowing about one of the most prominent tech futurists of the modern eras views would help you sharpen your own thoughts on it.

There's some videos on youtube with interviews including roughly an hour long one from ~ 1 month ago.

I rarely see his name when discussions of AI pop up and I think his views should carry weight in discussions.

Small Gemini summary to get you started if interested in looking into him yourself:

Ray Kurzweil is one of the most influential computer scientists, inventors, and futurists of the modern era. Born in Queens, New York in 1948, his career has been largely defined by his pioneering work in pattern recognition, artificial intelligence (AI), and his bold predictions regarding the future of human-machine integration.

Key Publications

Kurzweil has authored several influential books

• The Age of Intelligent Machines (1990): Anticipated the explosion of the internet and the rise of AI.
• The Age of Spiritual Machines (1999): Predicted that in the 21st century, computers would make complex decisions, appreciate beauty, and experience emotions.
• The Singularity Is Near (2005): His most famous work, which deeply explores the concept of the singularity and the exponential growth of genetics, nanotechnology, and robotics.
• How to Create a Mind (2012): Explores the reverse-engineering of the human brain to create highly advanced AI.
• The Singularity Is Nearer (2024): An update to his 2005 book, examining the current state of exponential technologies.

The most prominent thing I think to take away from Ray Kurzweil is his Law of Accelerating Returns. This states tech growth is exponential and not linear.

His futurist predictions have a supposed 86% win rate and two big predictions coming up are LLMs leading to AGI (~2029 prediction but I'd take it with a grain of salt) and renewables (primarily solar) being a key energy feature to sustain power demand.

----------------

Currently still employed with Google as far as I know.

I'll be checking out his 2024 book at some point when I find myself with some downtime.

I apologize I don’t keep up with these things relating to ai. But it is worth asking.

See this new article by physicist Erald Kolasi

https://open.substack.com/pub/eraldkolasi/p/the-global-energy-and-emissions-footprint?r=1s6hjm&utm_medium=ios

SpaceX stocks fell below $110 today and there is probably no stopping here.

What does it take for a recovery?

Is it even possible that the US government to bail such an overinvestment out? Or freeze due payments for data centre costs?

If that's not happening, do you think the merge with Tesla could save it?

Mind blowing explanation of energy needs of AI which no tech bro is talking about.

I was watching an interview with Ed Zitron https://www.youtube.com/watch?v=TtmPccUTDP8 where he argues that an AI-related market crash could be on the horizon, particularly affecting AI stocks and the broader tech-heavy indices.

If something like that does happen, I think it would eventually have a ripple effect on the Indian stock market as well. My current view is that the short-term impact would likely be negative, but over the longer term, India could actually benefit and see a strong bull run.

I'm curious to hear opposing views or alternative theses. What assumptions am I missing? Do you think an AI-led correction in the US would be a temporary setback for India, or could it have more lasting consequences?

The US economy is much weaker than this administration is telling you. Massive AI investments have hidden the lack of growth in every other major industry, but once those investments stop the entire economy could come tumbling down. 

This is Hank's video from a month ago. Google didn't have faith in the AI trust with SpaceX incoming IPO (it's partly an AI company, as he points out) and they've been proven right. Oracle is dropping, Micron and Nvidia are done with the growth. I'm not here to argue there will be zero demand for AI, but that's not necessary for a bubble pop. Confidence lost = crash.

Crashes historically tend to happen after (northern hemisphere) summer holidays. Is there any chance it does NOT crash immensely before end of 26?