Idle GPUs can still produce an expensive AI bill. In VentureBeat Intelligence’s Q3 survey, 79% of respondents reporting GPU use said their GPUs were busy half the time or less. Yet 70% of that low-use group planned a new, additional or replacement AI platform. A platform change can mean moving workloads, replacing an inefficient stack or buying access to a different model. It does not automatically mean ordering more GPUs. An enterprise can also pay twice: once for owned or reserved compute, then again for a hosted model API. Spare capacity on its own hardware does not automatically offset the API invoice. Moving that work requires a suitable model and a functioning deployment. The fixed-cost arithmetic is unforgiving. At 20% busy time, each busy GPU-hour carries 5x the fixed capacity cost of a fully used hour. At 50%, that falls to 2x. At 80%, it is 1.25x. Moving from 20% to 50% cuts fixed cost per busy hour by 60%, with no change in hardware prices. The total fixed bill stays the same; more work shares it. This is an illustrative model, not a measured saving. The return still needs proving. In September, 58 of 64 respondents planning a platform change lacked finance-reviewed ROI figures. That means unverified returns, not necessarily losses. For investors, follow three different tests: • Hardware suppliers: orders converting into collected cash. • GPU clouds: utilization alongside realized pricing and contract costs. • Enterprise buyers: cost per successfully completed task. A busy GPU can run an unprofitable workload. An idle enterprise GPU does not establish a shortage of demand everywhere else. Survey caveat: self-selected respondents, not a representative census of the world’s GPUs.

the neocloud boom is really an anthropic boom of the $157B in publicly priced deals neoclouds signed this year, anthropic signed $90B (57% of total) anthropic's revenue grew 7x in a year and unlike most companies, they actually can use any chip. and they are. they use amazon's trainium, google's tpus, nvidia's gpus, AND signed a deal with amd because it can use any chip, it can rent from anyone with capacity, which protects them from any shortage meta came second at $48B with @nebiusai and @CoreWeave. but where's openAI? this year it signed just $10B with @cerebras - (but in 2025 openai signed ~$610B with @Oracle, @Microsoft, @awscloud and @CoreWeave, while anthropic only signed ~$80B) as long as anthropic keeps growing, the biggest neoclouds will continue to have a great customer

Shelby is live! Eight  DCs across US and EU. > 500 TB of data stored in a month. 100s of concurrent uploads/downloads. Lower subsecond TTFB. We have been head down building a global data network coordinated on a high performance blockchain, free of cloud silos, regional buckets, egress taxes. Plus cryptographic verifiability baked in. Single address with global access. Customers are live and validating these claims. Developer access coming real soon.

@shelbyserves This is Egress fees, Storage lock-in, Latency to storage, Stateful coupling ( databases, caches, and config baked into a specific environment make workloads sticky even when the app code itself is portable).