Apologies for the tangent, it wasn't related to this thread. I've been in the habit of bookmarking predictions on HN to revisit them later, and your username caught my eye. I'm a bit surprised you maintain it, but the final verdict remains to be seen.

What have you seen since the new year that makes you surprised I'd change an opinion I had then?

OpenAI's GPT4. Bing's AI chat. Google's bard. Midjourney v5. GitHub copilot X. etc.

I was deeply involved in the last AI hype cycle. I'd want to see lasting applications giving clear benefits before I change my opinion. More foundation models don't change that, nor do the startup launches based around llms I've seen so far. It all has the same 2016 "deep learning" vibe, lots of apparent potential but no pathway to actual use cases.

I don't necessarily agree with your prediction, but I agree that we're still in a demo-ish phase for this new round of AI and it's likely that this hype cycle might fizzle out. However I think there are a few key differences this hype cycle around:

1. The generality of this current crop of models is much higher than before which makes it really easy for non-experts to get started. 2016 deep learning still required lots of data cleaning, training, hyperparameter tuning, retraining, etc. This can lead to a lot more experimentation with lower barrier to entry.

2. The cost of using these models has become cheap enough where a lot of tasks that 2016 AI would have worked well on but were too expensive (due to labor required to build the models or compute required to train the model) are now within reach for a lot of businesses.

3. The internet of 2023 is a lot more varied than the internet of 2016. Using ML models to generate content wouldn't have made sense in 2016 where traditional media still captured a lot of people's interests but now with the rise of influencers and medium-sized fandoms, there's large markets to generate content associated with medium-sized brands.