文章探讨了AI产业的现状，包括高额投入、数据问题、用户兴趣减退及竞争导致的产品商品化等，提及AI产业与互联网泡沫的相似性，并设想了行业崩溃的情景及可能的变化。

🎯AI公司为争夺人才和资源投入巨额资金，但面临诸多问题。如支付高薪聘请顶级工程师，建设数据中心等，同时面临数据获取的法律风险，且用户对相关产品的新鲜感逐渐消失，市场竞争激烈导致部分产品商品化。

💡AI产业现状与互联网泡沫有相似之处。当时互联网公司虽获大量投资，但多数缺乏可行商业策略，泡沫破裂后非可行公司被淘汰。如今谷歌、微软等利用互联网主导美国经济，这与AI产业情况有所不同。

🌊文章设想了AI产业崩溃的情景。如投资大幅下降，公众对生成式AI失去兴趣，政客不再关注AI科技公司等，还探讨了在此情景下人们是否会采取不同行动。

Published on August 19, 2024 10:56 AM GMT

'The AI bubble is reaching a tipping point", says Sequoia Capital.

AI companies paid billions of dollars for top engineers, data centers, etc. Meanwhile, companies are running out of 'free' data to scrape online and facing lawsuits for the data they did scrape. Finally, the novelty of chatbots and image generators is wearing off for users, and fierce competition is leading to some product commoditisation. 

No major AI lab is making a profit yet (while downstream GPU providers do profit). That's not to say they won't make money eventually from automation.

It looks somewhat like the run-up of the Dotcom bubble. Companies then too were awash in investments (propped up by low interest rates), but most lacked a viable business strategy. Once the bubble burst, non-viable internet companies got filtered out.

Yet today, companies like Google and Microsoft use the internet to dominate the US economy. Their core businesses became cash cows, now allowing CEOs to throw money at AI as long as a vote-adjusted majority of stakeholders buys the growth story. That marks one difference with the Dotcom bubble. Anyway, here's the scenario:

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How would your plans change if we saw an industry-wide crash? 

Let's say there is a brief window where:

Investments drop massively (eg. because the s-curve of innovation did flatten for generative AI, and further development cycles were needed to automate at a profit).The public turns sour on generative AI (eg. because the fun factor wore off, and harms like disinformation, job insecurity, and pollution came to the surface).Politicians are no longer interested in hearing the stories of AI tech CEOs and their lobbyists (eg. because political campaigns are not getting backed by the AI crowd).

Let's say it's the one big crash before major AI labs break even for their parent companies (eg. because mass-manufacturing lowered hardware costs, real-time surveillance resolved the data bottleneck, and multi-domain-navigating robotics resolved inefficient learning).

Would you attempt any actions you would not otherwise have attempted? 
 

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