General Compute CTO and co-founder Jason Goodison argues that faster AI inference could sharply reduce the time expensive software engineers spend waiting for coding agents to finish their work.
AI coding agents · Inference speed
Waiting for agents is becoming a line item on engineering costs
General Compute CTO and co-founder Jason Goodison argues that faster AI inference could sharply cut the time expensive engineers spend waiting for coding agents to finish multi-step work.
$300K+
Annual pay of the engineer in Goodison's example
1 hour
Time an engineer may spend waiting for one agent task
0
Published benchmarks or measured speed gains cited in the material
How one hour of waiting compares to a workday
Illustrative 8-hour day; each block is one hour of engineer time.
One waited hour is one-eighth of a standard day. Repeated across many tasks, the time adds up quickly for a highly paid developer.
Why waiting grows as agents take on more work
A multi-step agent loop repeats, and each pass adds to the wait.
Edit several files
→
Run tests
→
Fix errors and iterate
→
Result returned
The core question
Can inference providers cut latency without hurting code quality or raising costs?
Lower latency must hold up without sacrificing the soundness of the code produced.
Speed has to come without a rise in cost per task.
If both hold, speed could become a competitive factor alongside model capability.
The case for speed
Agents now run longer, multi-file jobs where waiting compounds.
For high-cost engineers, small time savings add up across a workday.
Speed shifts from a convenience feature to a productivity question.
The caveats
The claim is the company's perspective, not independently verified data.
No specific benchmarks or measured speed improvements were provided.
Faster output is only useful if the code it produces is sound.
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