A recent report by Epoch AI suggests that scaling AI models up to 10,000 times the capability of today's models by 2030 is technically possible but not certain. The report identifies four major constraints to this scaling: power, chips, data, and latency. OpenAI has ambitious plans to surpass Google's infrastructure through multi-datacenter training, utilizing gigawatt clusters, telecom networking, long haul fiber, and hierarchical and asynchronous SGD. The feasibility of achieving 2e29 FLOP training runs by 2030 is also highlighted. The high costs of scaling are a significant consideration.