this post was submitted on 22 Dec 2024
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The answer is that it's all about "growth". The fetishization of shareholders has reached its logical conclusion, and now the only value companies have is in growth. Not profit, not stability, not a reliable customer base or a product people will want. The only thing that matters is if you can make your share price increase faster than the interest on a bond (which is pretty high right now).
To make share price go up like that, you have to do one of two things; show that you're bringing in new customers, or show that you can make your existing customers pay more.
For the big tech companies, there are no new customers left. The whole planet is online. Everyone who wants to use their services is using their services. So they have to find new things to sell instead.
And that's what "AI" looked like it was going to be. LLMs burst onto the scene promising to replace entire industries, entire workforces. Huge new opportunities for growth. Lacking anything else, big tech went in HARD on this, throwing untold billions at partnerships, acquisitions, and infrastructure.
And now they have to show investors that it was worth it. Which means they have to produce metrics that show people are paying for, or might pay for, AI flavoured products. That's why they're shoving it into everything they can. If they put AI in notepad then they can claim that every time you open notepad you're "engaging" with one of their AI products. If they put Recall on your PC, every Windows user becomes an AI user. Google can now claim that every search is an AI interaction because of the bad summary that no one reads. The point is to show "engagement", "interest", which they can then use to promise that down the line huge piles of money will fall out of this pinata.
The hype is all artificial. They need to hype these products so that people will pay attention to them, because they need to keep pretending that their massive investments got them in on the ground floor of a trillion dollar industry, and weren't just them setting huge piles of money on fire.
I know I'm an enthusiast, but can I just say I'm excited about NotebookLLM? I think it will be great for documenting application development. Having a shared notebook that knows the environment and configuration and architecture and standards for an application and can answer specific questions about it could be really useful.
"AI Notepad" is really underselling it. I'm trying to load up massive Markdown documents to feed into NotebookLLM to try it out. I don't know if it'll work as well as I'm hoping because it takes time to put together enough information to be worthwhile in a format the AI can easily digest. But I'm hopeful.
That's not to take away from your point: the average person probably has little use for this, and wouldn't want to put in the effort to make it worthwhile. But spending way too much time obsessing about nerd things is my calling.
Being able to summarize and answer questions about a specific corpus of text was a use case I was excited for even knowing that LLMs can't really answer general questions or logically reason.
But if Google search summaries are any indication they can't even do that. And I'm not just talking about the screenshots people post, this is my own experience with it.
Maybe if you could run the LLM in an entirely different way such that you could enter a question and then it tells you which part of the source text statistically correlates the most with the words you typed; instead of trying to generate new text. That way in a worse case scenario it just points you to a part of the source text that's irrelevant instead of giving you answers that are subtly wrong or misleading.
Even then I'm not sure the huge computational requirements make it worth it over ctrl-f or a slightly more sophisticated search algorithm.
Well an example of something I think it could solve would be: "I'm trying to set this application up to run locally. I'm getting this error message. Here's my configuration files. What is not set up correctly, or if that's not clear, what steps can I take to provide more helpful information?"
ChatGPT is always okay at that as long as you have everything set up according to the most common scenarios, but it tells you a lot of things that don't apply or are wrong in the specific case. I would like to get answers that are informed by our specific setup instructions, security policies, design standards, etc. I don't want to have to repeat "this is a Java spring boot application running on GCP integrating with redis on docker.... blah blah blah".
I can't say whether it's worth it yet, but I'm hopeful. I might do the same with ChatGPT and custom GPTs, but since I use my personal account for that, it's on very shaky ground to upload company files to something like that, and I couldn't share with the team anyway. It's great to ask questions that don't require specific knowledge, but I think I'd be violating company policy to upload anything.
We are encouraged to use NotebookLLM, however.