Object Storage

Agents overwhelm storage billing because their unpredictable loops break provisioned pricing.

AI's random access patterns clash with storage systems built for sequential reads.

LLMs already fluently speak bash, making it the universal agent interface.

Models reason better with POSIX file systems than object storage.

How parallel agents can safely share files without losing data to race conditions.

Streaming data directly to GPUs eliminates costly delays before training actually begins.

S3 dominates AI training because its design matches how data spreads across distributed compute.

Storage layers must bridge the gap between ephemeral sandboxes and stateful agent workflows.

Storage performance is now the top bottleneck keeping GPUs idle during AI training.

Agents that remember across sessions outperform those reset every time you open them.

How GPU-native processing and open table formats are reshaping S3 data lake design.

Parallel S3 access patterns solve GPU starvation better than traditional file storage.