Theory Ventures' Head of AI drops real data on the GPU shortage narrative: it's not binary.
Actual workload breakdown at Theory:
• 5-10% of agent jobs need real-time inference
• 40-60% can tolerate latency until next sync (hours)
• Rest can wait days
This urgency distribution means different compute profiles even inside one org. A trillion-param training run demanding 1000x B300s on standby? Yeah, that's genuinely capacity-constrained. But an agent doing text summarization on a small node? Plenty of options.
The GPU market isn't universally tight—it's segmented by workload latency requirements and scale. If you're not running massive synchronous training, you're probably fine.
Actual workload breakdown at Theory:
• 5-10% of agent jobs need real-time inference
• 40-60% can tolerate latency until next sync (hours)
• Rest can wait days
This urgency distribution means different compute profiles even inside one org. A trillion-param training run demanding 1000x B300s on standby? Yeah, that's genuinely capacity-constrained. But an agent doing text summarization on a small node? Plenty of options.
The GPU market isn't universally tight—it's segmented by workload latency requirements and scale. If you're not running massive synchronous training, you're probably fine.