According to

Andrej Karpathy: AGI is still at least a decade away

The 'Year of Agents' Narrative

While AI agents show promise, current systems remain brittle and limited in scope. True autonomous agents require robust error handling, reliable long-term memory, and the ability to recover from failures—capabilities we're still developing.

  • Most "agents" are still glorified API wrappers
  • Real autonomy requires self-correction and planning
  • We're still in the "demo works, production fails" phase

The Infrastructure Gap

The fundamental challenge isn't model capability—it's the surrounding infrastructure. Agents need reliable tool use, consistent outputs, proper evaluation frameworks, and systems that degrade gracefully when they inevitably make mistakes.

  • Current LLMs lack persistent memory and context
  • Tool use remains fragile and error-prone
  • We need better evaluation beyond benchmarks

AGI Timeline Reality Check

Despite rapid progress, AGI remains at least a decade away. We're getting better at narrow tasks, but general intelligence requires breakthroughs in reasoning, long-term planning, and the ability to learn from experience like humans do.

  • Scaling laws are slowing down
  • We're optimizing for benchmarks, not intelligence
  • True understanding vs. pattern matching gap remains
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Result: Significantly reduced travel time and expenses in distribution to retail chain.

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