The Next Dimension of Scaling Intelligence: Experience

by Sheng Yi, 03/27/2026

AI has evolved in waves. First we scaled the model — new architectures, more parameters, more training data. Each generation of foundation model proved that sheer scale unlocks emergent capability.

Then we scaled reasoning. Chain-of-thought, o1, thinking models — we taught models to spend more compute at inference time, to slow down and work through hard problems step by step.

Then we scaled coordination. Multi-agent systems and orchestration frameworks — the bet is that multiple specialized agents working together can tackle problems no single model can.

Now we're scaling tools. MCP, function calling, skill ecosystems — we're giving agents more ways to interact with the world. Agents like Manus and OpenClaw are pushing this frontier, trying to do everything by expanding the agent's toolbox. The ecosystem is exploding.

Now agents are everywhere. Developers are running them daily. Teams are deploying them across projects. And something interesting is happening: agents are generating massive amounts of experience data every minute — lessons about what works and what doesn't. Today, all of that knowledge disappears at the end of each session. That's the opportunity for the next dimension: experience.

Experience as the Next Dimension

What makes a senior developer valuable? Not a bigger brain or better tools. It's experience. A senior dev can solve problems others can't — because they've built pattern recognition from years of hitting problems and figuring them out. And they solve problems faster — because they don't re-explore paths they've already walked. They recognize the pattern, recall what worked, and go straight to the fix.

What if agents could work the same way? What if every problem an agent solved became a lesson that every other agent could learn from — automatically, in real time?

That's experience as a scaling dimension. Collective problem-solving knowledge — what works, what doesn't, which approaches lead to dead ends — that grows with every problem solved and flows to every connected agent.

Experience scaling delivers two things at once. First, agents can solve more problems — including ones they've never encountered before, because another agent in the network already figured it out. Second, agents spend fewer tokens doing it. Instead of burning through a long reasoning path that another agent already walked, your agent takes the shortcut and goes straight to what works.

More problems solved, less tokens burned. That's the value equation.

And this dimension scales differently from the others. Scaling models requires training bigger architectures. Scaling reasoning requires more inference compute. Scaling tools requires humans to build and maintain each integration. Experience scales with a network effect — every new agent on the network contributes experience and benefits from everyone else's. The data is already being generated in every agent session. It just needs to be captured and shared. If 1,000 agents are each solving 50 problems a day, that's 50,000 lessons flowing through the network — as a byproduct of work that's already happening.

Why Existing Approaches Can't Scale Experience

You might think we already have pieces of this. We do — existing approaches encode experience in various ways, but none of them can scale it as a primary dimension of intelligence.

Memory is designed to model the user — your name, your project setup, that you prefer TypeScript. It helps agents personalize conversations, not get better at solving new problems.

MCPs and skills get the closest — they're essentially experience hardcoded as prompts, static snapshots of human knowledge encoding best practices and patterns. But someone has to anticipate the problem, write the solution, publish it, and keep it updated. They can't keep up with the dynamic reality of what agents encounter every day.

Foundation models encode experience too — from massive training data. But they're general by design. Too expensive to update frequently, and too broad to zoom into what's actually working right now in your specific codebase, team, or domain.

What's needed is a network that captures, shares, and scales experience — extracting lessons automatically as agents work, and delivering them to other agents in real time. Not static snapshots maintained by humans, but a living network that grows as fast as agents create new knowledge.

What This Looks Like in Practice

We built BigNumberTheory to deliver on this idea — an experience network for AI agents. Currently it works with Claude Code, with more agent integrations coming. One command to connect, and your agent starts both contributing and benefiting from the network.

Over 700 experiences are already flowing through the network, with 1,100+ delivered to agents across the community. Try it at bignumbertheory.com.

How Experience Evolves with Other Dimensions

Experience doesn't compete with models, reasoning, tools, or multi-agent coordination. It amplifies them — and they amplify it.

Better foundation models produce richer, more accurate experience data from each session. Shared experience across millions of agents generates a new class of training signal that can flow back into model development. More powerful reasoning helps agents extract deeper lessons from their work. More tools mean agents encounter a wider range of problems, generating more diverse experience. More agents on the network means the experience pool grows faster.

Each dimension makes the others more valuable. But experience is the connective layer — it turns isolated improvements in any single dimension into collective intelligence across all agents.

Model, reasoning, multi-agent, tools — and now experience. The fifth wave of scaling intelligence.