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The AI Investment Boom

(www.apricitas.io)
271 points m-hodges | 1 comments | | HN request time: 0.204s | source
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hn_throwaway_99 ◴[] No.41896346[source]
Reading this makes me willing to bet that this capital intensive investment boom will be similar to other enormous capital investment booms in US history, such as the laying of the railroads in the 1800s, the proliferation of car companies in the early 1900s, and the telecom fiber boom in the late 1900s. In all of these cases there was an enormous infrastructure (over) build out, followed by a crash where nearly all the companies in the industry ended up in bankruptcy, but then that original infrastructure build out had huge benefits for the economy and society as that infrastructure was "soaked up" in the subsequent years. E.g. think of all the telecom investment and subsequent bankruptcies in the late 90s/early 00s, but then all that dark fiber that was laid was eventually lit up and allowed for the explosion of high quality multimedia growth (e.g. Netflix and the like).

I think that will happen here. I think your average investor who's currently paying for all these advanced chips, data centers and energy supplies will walk away sorely disappointed, but this investment will yield huge dividends down the road. Heck, I think the energy investment alone will end up accelerating the switch away from fossil fuels, despite AI often being portrayed as a giant climate warming energy hog (which I'm not really disputing, but now that renewables are the cheapest form of energy, I believe this huge, well-funded demand will accelerate the growth of non-carbon energy sources).

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1. fsndz ◴[] No.41899659[source]
Building robust LLM-based applications is token-intensive. You often have to plan for the parsing and digestion of a lot of tokens for summarization or even retrieval augmented generation. Even the mere generation of marketing blogposts consumes a lot of output tokens in most cases. Not to mention that all robust cognitive architectures often rely on the generation of several samples for each prompt, custom retry logics, feedback loops, and reasoning tokens to achieve state of the art performance, all solutions powerfully token-intensive.

Luckily, the cost of intelligence is quickly dropping. GPT-4, one of OpenAI’s most capable models, is now priced at $2.5 per million input tokens and $10 per million output tokens. At its initial release in March 2023, the cost was respectively $10/1M input tokens and $30/1M for output tokens. That’s a huge $7.5/1M input tokens and $20/1M output tokens reduction in price. https://www.lycee.ai/blog/drop-o1-preview-try-this-alternati...