AI is actually a rolling sequence of bubbles rather than one

The idea that we are in a single "AI bubble" is too simplistic because it ignores how technology actually evolves. Instead of one massive peak followed by a total collapse, we are seeing a series of overlapping hype cycles. One bubble pops—like the initial frenzy over basic chatbots—but the underlying infrastructure remains, fueling the next wave of specialized agents or autonomous workflows. This pattern means the "crash" people fear isn't a death sentence for the industry, but a pruning process that clears out the fluff and leaves behind the tools that actually work.

Why the rolling bubble theory makes sense

Most people confuse a market correction with the end of a technology. When the dot-com bubble burst in 2000, the internet didn't disappear; the companies that didn't have a real business model did. AI is following a similar trajectory but moving much faster. We move from the "LLM as a novelty" bubble into the "AI-integrated software" bubble, and we're already sliding into the "Agentic AI" phase. Each single wave of hype creates a massive over-investment in hardware and energy, which then provides the cheap, scalable foundation for the next, more practical application.

The shift toward a real-world AI workflow

The danger isn't that AI is fake, but that the current valuation of companies is based on the promise of general intelligence rather than the utility of specific tools. We are seeing a transition from pure prompt engineering—where the goal was just to get a coherent answer—to building a complex LLM agent system that can actually execute tasks.

If you're looking to move past the hype and build something durable, focus on these technical shifts:

• From Chat to Agency: Stop treating the LLM as a search engine and start treating it as a reasoning engine that triggers external tools.
• RAG over Fine-Tuning: Most "bubbles" burst when people realize fine-tuning a model on proprietary data is expensive and brittle. Retrieval-Augmented Generation (RAG) is where the real-world value lives because it's verifiable and up-to-date.
• Infrastructure Lag: The hardware bubble (chips and data centers) often peaks before the software bubble. We have the compute; now we need the killer apps that justify the spend.

Surviving the cycle

The best way to navigate this is to treat AI as a utility rather than a miracle. Instead of betting on which "AI company" will win, it's better to focus on a practical tutorial for integrating these models into existing pipelines. Whether it's using Claude Code for rapid iteration or deploying a local Llama instance for privacy, the winners are the ones building actual deployment pipelines while everyone else is still arguing about whether the bubble has popped. The volatility is just noise; the capability growth is the only metric that actually matters.