ChatGPT made LLMs mainstream, but it isn't the endpoint

I recently wrote an article on AI evolution, and one thing kept standing out while researching it.

It feels like the conversation is still centered on LLMs themselves, while a lot of the interesting work has shifted to what happens around the model.

A couple of years ago, the question was basically, "Which model is best?"

Now it feels more like:

• How does the model retrieve current or private information?
• What tools can it call?
• How does it handle long-running tasks?
• What level of autonomy should an agent have?
• How much orchestration belongs outside the model?

In other words, the model is becoming one component in a much larger system.

That doesn't make LLM quality any less important, but RAG, multimodal inputs, tool use, MCP, memory, and agent frameworks seem to be where many of the meaningful improvements are happening.

That's the angle I explored in the article if anyone is interested:

https://aigptjournal.com/explore-ai/ai-guides/ai-evolution/

I'm interested in hearing what this community thinks.

Do you see foundation models continuing to be the primary area of innovation over the next few years, or do you think orchestration, retrieval, tool use, and agents will drive most of the progress from here?