Artificial intelligence tools can now write code, analyse data, and generate creative output within seconds. Yet, even after investments running into hundreds of billions, AGI that can reason like humans remains out of reach. According to OpenAI Codex lead Alexander Embiricos, the main hurdle is no longer computing power or model design, but the pace at which humans interact with machines.
Speaking on an episode of a technology podcast, Embiricos said that “human typing speed” and “human multi-tasking speed on writing prompts” are the “current underappreciated limiting factor” slowing progress toward AGI.
He explained that most AI workflows still depend heavily on people to write prompts and review outputs produced by AI agents. This reliance creates a bottleneck, even as AI systems become more capable. Embiricos said, “You can have an agent watch all the work you’re doing, but if you don’t have the agent also validating its work, then you’re still bottlenecked on, like, can you go review all that code?”
According to him, the constraint has shifted from what AI systems can do to how quickly humans can validate and manage their output. Even with agents capable of observing tasks, the need for manual verification continues to slow meaningful progress.
To overcome this challenge, Embiricos argued for a redesign of how AI systems are built and deployed. He said, “We need to unburden humans from having to write prompts and validate AI’s work, since we aren’t fast enough.” His view is that systems should be structured so that AI agents are “default useful” without constant human intervention.
He added, “If we can rebuild systems to let the agent be default useful, we’ll start unlocking hockey sticks,” referring to sharp and rapid productivity growth.
However, Embiricos acknowledged that fully automated workflows will not emerge overnight. Each use case will require its own approach, and there is no single solution that fits all applications.
Looking ahead, he expects early adopters to see major productivity gains first, followed by wider adoption across large technology companies. He said, “Starting next year, we’re going to see early adopters starting to hockey stick their productivity, and then over the years that follow, we’re going to see larger and larger companies hockey stick that productivity.”
Once this level of automation feeds back into AI research, Embiricos believes it could finally open the door to AGI. He said, “That hockey-sticking will be flowing back into the AI labs, and that’s when we’ll basically be at the AGI.”
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