Technology

If you prompted an LLM to review all of it's database entries, generate a new response based on that data, then save that output to the database and repeat at regular intervals, I could see calling that a kind of thinking.

That's kind of what the current agentic AI products like Claude Code do. The problem is context rot. When the context window fills up, the model loses the ability to distinguish between what information is important and what's not, and it inevitably starts to hallucinate.

The current fixes are to prune irrelevant information from the context window, use sub-agents with their own context windows, or just occasionally start over from scratch. They've also developed conventional AGENTS.md and CLAUDE.md files where you can store long-term context and basically "advice" for the model, which is automatically read into the context window.

However, I think an AGI inherently would need to be able to store that state internally, to have memory circuits, and "consciousness" circuits that are connected in a loop so it can work on its own internally encoded context. And ideally it would be able to modify its own weights and connections to "learn" in real time.

The problem is that would not scale to current usage because you'd need to store all that internal state, including potentially a unique copy of the model, for every user. And the companies wouldn't want that because they'd be giving up control over the model's outputs since they'd have no feasible way to supervise the learning process.

That’s what an LLM is, a database of words using vectors.

You’re still limited by the context window in your example, giving it another source of information doesn’t do anything than give more context.