"First, writing code isn’t, and never was, the bottleneck. For example, a 2019 paper summarized existing studies with the conclusion that “developers spend surprisingly little time with coding, 9% to 61% depending on the study”. This finding was consistent with the paper’s own data from 6,000 developers at Microsoft. As coding agents began to be taken up, there was an explosion of blog posts in late 2025 pointing out that writing code isn’t the bottleneck, as developers realized that using agents to write most of the code led to little impact on overall productivity [1, 2, 3, 4, 5, 6, 7, 8].If writing code isn’t the bottleneck, what is? The task-breakdown surveys point at things like meetings or debugging. This just leads to more questions: what are developers doing in those meetings and why can’t it be done by AI? Won’t debugging get automated as capabilities improve? To understand the real bottlenecks, we have to get qualitative, and dig into software engineers’ own understanding of what it is they do that resists automation.When we did this analysis, it revealed three things as the real bottlenecks (1) deciding and specifying what to build, (2) verifying and being accountable for what is delivered, and (3) the deep human understanding — of the codebase, the business, and the environment — required to carry out both of these.In other words, software engineers’ work consists of a “decide-execute-deliver” sandwich (with understanding being a prerequisite for all three). AI has compressed the middle of the sandwich, but has left the two ends largely unchanged. As long as software development teams are in charge of decision making and accountable for what they deliver, engineers still need to spend time building up a deep understanding of the system. These are the three bottlenecks."normaltech.ai/p/why-ai-hasnt-r#AI #GenerativeAI #LLMs #Programming #SoftwareDevelopment #Automation #Productivity
"Some commentators argue that a future with humans staying in control is unlikely because it is too costly to pay people to do so. There have already been a few viral stories of poorly-supervised coding agents deleting production databases or causing other types of damage. But we view these as “man bites dog” stories rather than an emerging norm. They go viral precisely because they represent such irresponsible and unusual behavior that they have shock value, and serve as regular reminders and learning moments helping the community guard itself against over-reliance on AI. As the aphorism goes, “if it’s in the news, don’t worry about it”. Still, being able to detect whether there is an uptick in poorly-supervised use of AI for high-stakes tasks — across the economy, not just in software engineering — remains one of the most critical data gaps we have today.By the way, the sandwich getting squished is a new trend and it is not uniquely due to AI. Over two decades ago, the Bureau of Labor Statistics started tracking programming separately from software engineering. Roughly speaking, programmers are responsible only for execution while software engineers manage a bigger part of the sandwich. Not only has programming been shrinking, it is also pays much less because it is seen as grunt work. AI merely accelerates this long-existing trend, further devaluing purely technical skills."