The cost of intelligence collapsed 280x in 18 months. Yet most people are still... chatting.
Something strange is happening in AI.
The models have gotten absurdly good. Claude Opus 4.5 scores 80.9% on SWE-bench - the first AI to break the 80% threshold on real-world software engineering tasks. Boris Cherny, who built Claude Code at Anthropic, recently shared that he landed 259 pull requests in 30 days. Every single line was written by Claude. He doesn't write code anymore. He guides it.
Meanwhile, over the holidays, developers discovered what Opus 4.5 could actually do. The reaction was visceral. "This is the first model that makes me actually fear for my job," wrote one engineer on Reddit, collecting nearly a thousand upvotes. They're calling it getting "Claude-pilled" - that moment when you hand your work to the AI and witness, as one observer put it, "a thinking machine of shocking capability."
And yet.
Most people are still using AI like it's a search engine with attitude. A slightly smarter autocomplete. A chatbot you ask questions to, one at a time, waiting for each response before typing the next query.
There's a gap here. A massive one. And it's not about the models.
The Collapse No One Feels
Let's talk numbers.
According to the Stanford AI Index 2025, the cost of querying an AI model equivalent to GPT-3.5 dropped from $20 per million tokens in November 2022 to $0.07 by October 2024. That's a 280-fold reduction in eighteen months.
Epoch AI's research found that depending on the task, inference prices have fallen anywhere from 9x to 900x per year. And the trend is accelerating. Before January 2024, the median annual decline was 50x. After January 2024, it jumped to 200x.
To put this in perspective: Moore's Law - the north star of the semiconductor industry for fifty years - delivered roughly 2x improvement every eighteen months. AI inference costs are dropping 10x every twelve months. We're watching decades of economic shift compressed into quarters.
This should feel like a revolution. For most people, it doesn't.
The Two Worlds
Here's what I see every day:
World A: An engineer opens ChatGPT. Types a question. Waits. Reads the response. Types a follow-up. Copy-pastes some code. Tries it. It doesn't work. Back to ChatGPT. This is how most people use AI. One conversation. One thread. One thought at a time.
World B: A different engineer spins up eleven agents - Gemini, Codex, Claude - in parallel. An orchestrator allocates twenty bugs across them simultaneously, routing work so dependencies resolve smoothly. Some agents tackle independent issues. Others coordinate on shared code. When conflicts arise, agents work them out together. If they can't, the orchestrator resolves it at the end and verifies everything works. The engineer doesn't write code - they orchestrate systems that write code.
Same underlying models. Same API prices. Radically different outcomes.
The gap between these two worlds isn't 10%. It's not even 10x. It's closer to 100x. And it's widening every month as the models improve and the tools... don't.
Why the Gap Exists
The problem is the interface.
Not just the buttons and text boxes - although that's part of it. The entire product experience. The input and output mechanisms through which human and machine collaborate.
Right now, that layer is a maze. To get real power from AI, you need to understand MCP servers, authentication clients, API keys, prompting patterns, agentic prompting, RAG pipelines for custom documents, agent skills, context windows, token limits. You need to wire up integrations yourself. You need to know which model to use for which task, and how to chain them together.
This is deeply technical knowledge. Software engineers can navigate it. Everyone else is locked out.
Chat was a brilliant innovation for introducing AI to the world. It's intuitive. It's familiar. Anyone can type a question and get an answer. But chat is also a bottleneck. It assumes a single thread of conversation. It assumes you're doing the thinking and the AI is responding. It assumes one task at a time.
When intelligence costs $20 per million tokens, that's fine. You use it sparingly, for important questions.
When intelligence costs $0.07 per million tokens - when it's essentially free - the constraint flips. The bottleneck is no longer "can the AI do this?" It's "how do I direct enough AI at enough problems simultaneously?"
Chat doesn't scale. You can't run twelve chat windows and context-switch between them productively. You can't spawn a research team in ChatGPT while a coding team works in parallel. The current product experience wasn't built for abundance. It was built for scarcity.
From OS to OS
Here's the shift that's coming: from Operating System to Orchestration System.
For decades, the operating system was about managing hardware resources - CPU cycles, memory allocation, disk access. The user was the source of intent, and the computer was the executor of instructions.
In the intelligence age, the operating system needs to manage cognitive resources. The user is still the source of intent, but now there are agents - dozens of them, potentially - that can reason, plan, and execute. The computer isn't just an executor anymore. It's a team.
This requires a fundamentally different interface. Not a chat window. Not a copilot that suggests the next line of code. An orchestration layer that lets you spin up agents, assign them tasks, coordinate their work, and synthesize their outputs.
(See The AX Paradigm for a deep dive on agent interfaces.)
The models are ready. Opus 4.5 proved that. The economics are ready. $0.07 per million tokens proved that. What's missing is the system that puts it all together.
The Intelligence Gap
There's a term economists use: "technology diffusion." It describes how innovations spread through a population. First the pioneers, then the early adopters, then the mainstream. The gap between pioneers and mainstream can be years, sometimes decades.
With AI, the gap is forming in real-time. Some people are running agent swarms, extracting 100x the value from the same models everyone has access to. Most people are chatting, getting maybe 1% of what's possible.
This isn't about intelligence or technical skill. It's about tools. The pioneers built their own orchestration systems, or cobbled them together from developer frameworks. The mainstream is waiting for tools that don't require a PhD in prompt engineering to use.
The intelligence revolution has arrived. The economics prove it. The benchmarks prove it. The developers who "fear for their jobs" prove it.
The missing piece isn't smarter models. It's the interface that lets everyone participate.