this post was submitted on 15 Apr 2025
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It is definitely here to stay, but the hype of AGI being just around the corner is definitely not believable. And a lot of the billions being invested in AI will never return a profit.
AI is already a commodity. People will be paying $10/month at max for general AI. Whether Gemini, Apple Intelligence, Llama, ChatGPT, copilot or Deepseek. People will just have one cheap plan that covers anything an ordinary person would need. Most people might even limit themselves to free plans supported by advertisements.
These companies aren't going to be able to extract revenues in the $20-$100/month from the general population, which is what they need to recoup their investments.
Specialized implementations for law firms, medical field, etc will be able to charge more per seat, but their user base will be small. And even they will face stiff competition.
I do believe AI can mostly solve quite a few of the problems of an aging society, by making the smaller pool of workers significantly more productive. But it will not be able to fully replace humans any time soon.
It's kinda like email or the web. You can make money using these technologies, but by itself it's not a big money maker.
Does it really boost productivity? In my experience, if a long email can be written by an AI, then you should just email the AI prompt directly to the email recipient and save everyone involved some time. AI is like reverse file compression. No new information is added, just noise.
If that email needs to go to a client or stakeholder, then our culture won't accept just the prompt.
Where it really shines is translation, transcription and coding.
Programmers can easily double their productivity and increase the quality of their code, tests and documentation while reducing bugs.
Translation is basically perfect. Human translators aren't needed. At most they can review, but it's basically errorless, so they won't really change the outcome.
Transcribing meetings also works very well. No typos or grammar errors, only sometimes issues with acronyms and technical terms, but those are easy to spot and correct.
As a programmer, there are so very few situations where I've seen LLMs suggest reasonable code. There are some that are good at it in some very limited situations but for the most part they're just as bad at writing code as they are at everything else.
I think the main gain is in automation scripts for people with little coding experience. They don't need perfect or efficient code, they just need something barely functioning which is something that LLMs can generate. It doesn't always work, but most of the time it works well enough