The developer productivity paradox: Why faster coding doesn’t mean faster software delivery
The paradox is this simple gap: high individual confidence in AI speed, versus stubborn organizational metrics that just won’t budge:
- Perceived speed is high: Adoption is near-universal (90% usage reported), and confidence is overwhelming (over 80% believe AI has increased their productivity). AI is great at handling cognitive toil and boilerplate, which lets engineers generate bigger code batches and feel genuinely productive.
- Systemic failure persists: The reality, confirmed by DORA in their 2025 report, is that the system often fails to carry or amplify these individual gains. The challenge is that AI models, as massive generative systems, inherently produce failures (mispredictions). As code volume increases, this constant misprediction rate impacts systemic stability.
Interestingly, even leading providers of AI solutions like OpenAI and Anthropic continue to be challenged by the issue of hallucinations and mispredictions, as well as the risks generated by AI. Speaking at a university in India, Sam Altman recently said “I probably trust the answers that come out of ChatGPT the least of anybody on Earth”.
Without strategies and tools for alleviating the issues AI code produces downstream — such as improved observability to understand where something is going wrong — the “much bigger engine” of AI may not actually speed up software delivery after all.
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