In March, Andrej Karpathy’s autoresearch project lit up the AI community. A 630-line script turned a GPU into a tireless research assistant – running hundreds of experiments overnight, keeping what works, and discarding everything else. The community called it a glimpse of recursive self-improvement, but few asked the harder question: What does the infrastructure it’s built on look like at enterprise scale? The truth is that scaling autonomous AI systems is not a compute challenge. It’s a connectivity, latency, and data gravity challenge. The ambition to innovate at machine speed, indefinitely and autonomously, places extraordinary demands on infrastructure. Every iteration of the…

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