I've been finding AI's suggestions -- even when rather wrong -- help me do that initial step faster. Which, I think, jives with their findings here.
I would suggest we have flexible RAM. Also, we have an awful lot of it. The analogy breaks down as soon as you look at it too seriously!
In IT we largely deal with compute, persistent storage and non-persistent storage. Roughly speaking: CPU, RAM, HDD. In humans we might be considered to have similar "abilities" but unlike IT there is a mostly a single thing that performs all of those functions - the brain. That organ is both compute and storage.
LLMs can be surprisingly useful but they are a tool. As with all tools they can be abused and no doubt you have spotted all those tech blogs that spout the same old thing and often with subtle failings (hallucinations).
Keep your tools sharp and know how to safely use sharp tools.
RAMs differentiating factor is increasingly just that it can handle a lot of read/write cycles, not it's speed. And that doesn't map to anything in biology
7 GB/s is the low end of the DDR3 performance range; DDR3 is 17 years old. Meanwhile, DDR5 performance ranges from about 33.5 GB/s to about 69 GB/s. RAM latency, even on DDR3, is measured in nanoseconds; NVMe latency is measured in microseconds, making it about three orders of magnitude higher.
Then we extended the energy in our calories reserves with crops/livestock.
Then we extended the length of our memories with writing.
Then we extended the breadth of our thinking with AI?
We extend our perception with remote sensors (video, sound).
We extend our muscles with machines.
...
What's your process? Can you give an example? So far for me, I found them to be most useful using LLMs as code copilot.
It's sort of like Cunningham's Law with a party of one. Giving me the wrong answer helps me clarify what the correct answer should look like.
Or, perhaps a better way to put it:
It's easier to criticize than to create. It gives me something to criticize and tinker with. Doing so helps me hone in on the solution I want. (Provided its suggestion was at least in the right universe, of course.)
I have no idea how I could even integrate AI into my workflow so that it's useful. It's even less reliable than search is for basic research and can't even cite its sources....
This argument held a lot more weight when it was a search engine playing the role of our memory.
Generative AI still has a ways to go for other forms of research, and it will never fully replace the utility of a search engine. They're two different tools for different but overlapping tasks.
Even AI code suggestions seem to be only a minor improvement over basic LSP integration. One major exception is tedious formatting of the text—say you want to copy over a table by hand to a domain value, copilot is really good at recognizing values and situating them appropriately in the parent l-value.
If chatbots could serve as my RAM, surely they'd be able to generate code relevant to the rest of the codebase or at the very least not require deep scrutiny to ensure their RAM matches mine (it most often does not).