Slide rules displaced tables, calculators displaced slide rules and calculators themselves were displaced for the most part (they are still in use, in education but not nearly as much as they used to be outside of that) by the PC.
LLMs do that sort of thing for just about every information processing aid.
The harness in my experience makes a huge different too. Out of curiosity I tried having Gemini chat (with Argon) try to solve a level of this new puzzle game I'm linking (Order of the Sinking Star). It just got in an infinite loop and crashed. Then I try Antigravity with Flash 3.8 and it solved it easily. I saw the harness looked at other sokoban puzzles, coded some stuff, tried to replicate the level, etc. All things that a pure Gemini Chat doesn't really do.
LLMs are great for the high level harness, but if you want to count letters for example, it will still rely on "tools" like a piece of code to do it more reliably. Similarly in the above example, you wouldn't have the LLM directly try to analyze the images, but instead fetch and use the best tool for the job. But that can only be as good as the harness/tools it has access to.
Doesn't matter the domain or the task, the answer from some researchers is "LLM" always.
We're messaging each other right now inside a heavily sandboxed virtual environment that interprets XML-like markup and runs a text-based JITted scripting language from the 90s. It doesn't really matter that it's extremely inefficient, since it's still effectively instant.
Effectively instant means nothing, since everything is fast for small n. I love to give this example from a codebase I have worked on during my Ph.D. The problem which took half an hour under Matlab was done in ~40 seconds with mine, however, a thousand-step version of the same problem would take 11 hours. Saving a single second from that 40 second loop would shave my work by ~20 minutes, and this will compound for bigger problems still.
Just because we see the results instantly, doesn't mean it's fast or efficient by any means. It means we might forego optimizing it for sufficiently small n, if we don't care about that part of the code. That's all.
The app is less efficient only in an abstract, impractical way.
It depends on the calculator and task at hand. Consider graphing, integration or any programmable needs, and they might converge. ALU of a processor is pretty efficient at what it does.
> The app is less efficient only in an abstract, impractical way.
Again, depends, as I said above. You tend to brush efficiency away because you only mentally measure time to completion, only, which is understandable.
If it's not, then it's not.