In 5-10 years it will be very costly to use a token as the price will increase dramatically. Investors are already beginning to demand ROI in an industry that's likely almost a trillion in debt. As a result the already small usage rates plummet and the industry collapses on its ass.

inshallah in five years all of this horseshit will be distant fever dream memory

AI is not good for humanity. It's like asking

What exciting new variations of covid will we see in the next 10 years?

I hope in 10 years we have robo household assistants who do the laundry, dishes, a car that autonomously gets the children to football practice. Something that organizes my 120TB harddrive without making any mistakes... I'd be pretty lame if we're stuck with Gemini and ChatGPT by then. And I hope we don't go the dystopian scifi route from the movies of the 80s and 90s.

Getting a downvote for shit grammar.

Exactly the same as now. The ceiling has been reached. The possibilities explored. Nobody learned from it, including you. The future is now. This is it. This is peak AI.

The same as aim chatbots 20 years ago...

I really think the reason kids today think it's impressive, is there was a period we all decided chatbots were useless and just collectively forgot them.

Most of what they get excited about, they don't realize has been around long then they've been alive.

https://en.wikipedia.org/wiki/SmarterChild

Like, when kids saw Sting or NWO on WCW back in the day, they ate it up because they never saw The Crow or NWA. A ripoff in a shiny package sounds cool when you think it's original.

Modern chatbots are mostly advertising successes, when you look at how much every else has advance in 2026, the shit people freak out about ain't much more than Smarterchild.

If anyone wanted this tech, it would have advanced much further than where it's at today.

and will have huge context menus you could talk to them for hours and they will remember every single detail and be able to pull it up instantly

So, the problem is that the way this is generally handled today --- at least via local LLM stuff that I've looked at, haven't looked at the bleeding edge at cloud providers, and they probably don't talk about it --- is via one of two semi-incompatible approaches.

Large context window

This requires a model trained for it and enough memory to work with it I use a Framework Desktop 128GB in significant part to have enough VRAM to run queries that use this approach with mid-sized models (70b) and a 128k token window, which is larger than most people working on typical GPUs can do.

The way this works is that each time you send something to the LLM, your client also resubmits the entire conversation history (or, IIRC, depending upon the API used, does something functionally comparable that only sends the most-recent message). You probably can't afford to recompute the entire conversation history, but you can cache it using K-V caching.

The problem here is that this requires video memory that scales linearly with the size of all prior conversation. It also, IME, has processing time that scales up linearly as well. You can only have a conversation get so big before you're burning a lot of compute time.

Retrieval-Augmented Generation (RAG)

https://aws.amazon.com/what-is/retrieval-augmented-generation/

Here, you have some primitive, not-very-smart software that looks at the text in your message, looks through the conversation history (and potentially other sources of data) and then, in the background, builds a prompt that contains text that it thinks might be relevant. For example, if you mention the name "death" and "Superman" in your message, maybe it looks to see if there is any past text that also contains "death" and "Superman", and inserts that into the prompt.

This has some advantages. For one, it can scale up to enormous amounts of information. And it doesn't need to just look at your conversation history as a source of data, but it can also query other databases and the like.

This also has a number of limitations.

First, the software doing that retrieval is not very "smart". It isn't always fantastic at determining what is relevant.

Second, the way current LLMs work --- at least insofar as I've looked --- is to try to generate text that would follow subsequent to the prompt by predicting tokens that are probable based on what it's seen.

But...what it's getting, in the background, in the prompt is a bunch of discombobulated snippets of text. And my experience is that if you want to generate text in some style, like that of a novel, seeing a bunch of snippets isn't great for that, since that doesn't look much like a novel. It may be that there are techniques for dealing with that.

Third, this also doesn't work well with the K-V cache approach, since each prompt that actually goes to the LLM can look completely different from the previous one, so you'll constantly be getting cache misses and having to recompute stuff.

My own bet is that what we're going to wind up doing is having some form of new data structure --- possibly a neural net, but might look somewhat different from the multilayer model used by the LLM to store static memories, IMHO. I wouldn't be terribly surprised if this is something more like a directed acyclic graph, and I'd guess that it'll also do something akin to how red-black tree rotations see constant accesses restructure the data structure to bring more-frequently-used nodes closer to each other (though I don't personally think that it'll just be doing specifically a rotation. I'm just providing an example of another datastructure that dynamically restructures itself based on use). That'll be a mutable memory, to act like the "context" that human minds have when they're working on a problem. It'll provide properties that today human minds have and LLMs don't, like where repeatedly doing something makes you better at it. Just as we mentally associate things that we've experienced in the past together, so too will future AIs.

It may also be that something like this replaces in part the multilayer neural nets that are LLMs today. My own personal bet is that ultimately, a lot of the way advanced AIs work will not be LLM-based; LLMs just don't have properties that mirror how human minds in a number of ways. Maybe advanced AIs will incorporate an LLM as a component.

But --- and I haven't been following the research closely here --- I don't think that that's currently something being worked on, or at least not widely.

But I do think that, yes, advanced AIs are going to need the ability to do something like that as they head towards approach human capabilities.

Ai will be running bunkers for the billionaires by then.