Maybe AGI is not the goal.

A few days ago, I wrote about whether we are framing the AGI debate the right way. I argued that intelligence may be layered. A general foundation provides breadth. Specialized systems provide depth. A coordination layer orchestrates everything.

A recent paper co-authored by Yann LeCun, Chief AI Scientist at Meta and one of the pioneers of modern deep learning, titled “AI Must Embrace Specialization via Superhuman Adaptable Intelligence,” pushes this idea further.

The core argument is this: instead of trying to build one system that can do everything reasonably well, we should build systems that are extremely good at specific tasks and can adapt within that scope. Not human-level generalists, but superhuman specialists.

The paper questions the assumption that scaling one general model is the only path forward. General systems are powerful, but they are expensive, hard to evaluate, and difficult to guarantee in high-stakes domains. Specialized systems can be easier to train, easier to measure, and easier to align with clear objectives.

If you look at real-world use cases, most of them do not need general intelligence. They need very strong performance within clear boundaries. Fraud detection that rarely misses a case. Risk models that consistently outperform analysts. Planning systems that handle constraints better than humans.

General models help us cover many areas. Specialization is what helps us win in a specific one.

This is not very different from how humans and organizations operate. We all have baseline capabilities, but progress comes from deep expertise and structured collaboration between specialists.

From a product perspective, this has real implications. Instead of building one mega-agent that tries to do everything, we may end up building ecosystems of specialized agents. Each optimized for a clear task. Each measurable. Each accountable.

Perhaps the faster path to impact is not chasing general intelligence, but, it is building superhuman specialists that work together.

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