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That's a whole lot of twisting to avoid admitting "it usually doesn't work, and even when it does work, it's usually not cost-effective even at the heavily-subsidized prices." Or maybe it's more about refusing to admit that executives are out of touch with concrete reality and are just blindly chasing trends instead.
Which science is responsible for the answer that if you can't establish the veracity of the premise for the question, economics can't help you find the missing outcome that shouldn't be there?
I witness it with my developer friends. Most of them try for 5 minutes to get AI to code something that takes them an hour. Then they are annoyed that the result is not good. They might try another 5 minutes, but then they write the code themselves.
My thinking is: Even if it takes me 2 hours to get AI to do something that would take me 1 hour it is worth it. Because during those 2 hours I will make my code base more understandable to help the AI cope with it. I will write better general prompts about how AI should code. Those will be useful beyond this single task. And I will get to know AI better and learn how to interact with it better. This process will probably lead to a situation where in a year, it will take me 30 minutes with AI to do a task that would have taken me an hour otherwise. A doubling of my productivity with just a year of work. Unbelievable.
I see very few other developers share this enthusiasm. They don't like putting a year of work into something so intangible.
In contrast to the Economist blaming inefficient workers sabotaging the spread of this wonderful technology, make sure to check out https://pivot-to-ai.com/ where David Gerard has been questioning whether people are prompting it wrong or AI is just not that smart.
That's a whole lot of twisting to avoid admitting "it usually doesn't work, and even when it does work, it's usually not cost-effective even at the heavily-subsidized prices." Or maybe it's more about refusing to admit that executives are out of touch with concrete reality and are just blindly chasing trends instead.
Another issue, one that you alluded to, is imagine AI actually was reliable. And a company does lay off e.g. 30% of their employees to replace them with AI systems. How long before they get a letter from AI Inc 'Hi, we're increasing prices 500x in order to enhance our offerings and and improve customer satisfaction. Enjoy.' The entire MO of big tech is trying to create a monopoly by the software equivalent of dumping…
Literally every shitty corporate behaviour is amplified by this technology fad.
That's a whole lot of twisting to avoid admitting "it usually doesn't work, and even when it does work, it's usually not cost-effective even at the heavily-subsidized prices." Or maybe it's more about refusing to admit that executives are out of touch with concrete reality and are just blindly chasing trends instead.
* Determine what is happening in a scene/video * Translating subtitles to very specific local slang * Summarizing scripts * Estimating how well a new show will do with a given audience * Filling gaps in the metadata provided by publishers, such as genres, topics, themes * Finding the most "viral" or "interesting" moments in a video (combo of LLM and "traditional" ML)
There's much more, but I think the general trend here is not "chatbots" or "fixing code", it's automating stuff that we used armies of people to do. And as we progress, we find that we can do better than humans at a fraction of the cost.
One reason is that humans have a strong tendency to optimize for the short term. I witness it with my developer friends. Most of them try for 5 minutes to get AI to code something that takes them an hour. Then they are annoyed that the result is not good. They might try another 5 minutes, but then they write the code themselves. My thinking is: Even if it takes me 2 hours to get AI to do something that would take me…
I hope your doubling of productivity goes well for you, I'll believe it when I see it happen.
> such as datasets that are not properly integrated into the cloud I believe this is a core issue that needs to be addressed. I believe companies will need tools to make their data "AI ready" beyond things like RAG. I believe there needs to be a bridge between companies data-lakes and the LLM (or GenAI) systems. Instead of cutting people out of the loop (which a lot of systems seem to be attempting) I believe we need…
The Economist is filled with writers easily gulled by tech flim-flam. They went big on Cryptocurrency back in the day as well.