Developer Productivity Beyond the Hype
The productivity gains are real—but they didn't come from where you'd expect.
Bi-weekly insights on enterprise AI implementation. Evidence over hype.
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Practical insights for practitioners building production AI systems. No fluff, just lessons from real implementations.
The productivity gains are real—but they didn't come from where you'd expect.
We tested six agent frameworks on the same task with 250 samples. The accuracy spread was 4 percentage points. The improvement from fixing bugs was 45%.
"Roughly 80% of the effort is spent not on the agent's core intelligence, but on the infrastructure, security, and validation needed to make it reliable and safe."
40% of successful AI deployments use human-in-the-loop patterns.
"85-90% accuracy per tool call. Four or five calls? It's a coin flip."
Keyword routing handled 69% of our customer triage requests for $0.
Within 5 seconds of deployment, our AI agent tried to run `rm -rf /`.
Parser accuracy varies 45 percentage points or more depending on document type.
80-90% of enterprise data is unstructured (Gartner, 2023). Most companies ignore it.
We analyzed 598 AI case studies published over the past two years. None had rigorous evidence.
Claude has a 200,000 token context window. Quality degrades at 80%.
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