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Eight Myths on Software Engineering and GenAI
Examining the most common misconceptions

Generative AI is reshaping software engineering—but the narrative has gotten ahead of the evidence. Marketing claims, anecdotal wins, and misread studies have given rise to a set of persistent myths that are quietly driving poor decisions about AI adoption, tooling, and how to measure success.

This article examines eight of the most common misconceptions. We already know developers don’t actually spend most of their time writing code, with studies at Microsoft and elsewhere showing it’s closer to 14 percent. That means AI code generation, even when it works well, touches a surprisingly small slice of the actual job. And yet organizations are doubling down on lines-of-code metrics to track AI’s impact, which is a measure that is neither statistically valid nor meaningfully connected to outcomes such as software quality or delivery speed.

The reality is messier and more interesting than the headlines suggest. AI works better for some tasks, some developers, and some contexts than others. Productivity gains don’t flow automatically from handing engineers a license—they require rethinking workflows at the organizational level. Adoption stalls when developers don’t trust the tools, lack time to learn them, or worry about de-skilling. And the “startups move fast with AI” narrative ignores the compliance, legacy systems, and reliability constraints that define enterprise software.

This article isn’t skeptical, but rather provides practitioners, team leads, and engineering leaders a clearer, research-backed picture so the decisions organizations make about AI are grounded in evidence, not just enthusiasm.

Eight Myths on Software Engineering and GenAI - ACM Queue Generative AI is reshaping software engineering—but the narrative has gotten ahead of the evidence. Marketing claims, anecdotal wins, and misread studies have given rise to a set of persistent myths that are quietly driving poor decisions about AI adoption, tooling, and how to measure success.

Climbing the Generative AI Mountain
A “hitchhiker’s guide” for product managers

Most product managers working in software today feel it—that dizzying sense of standing at the base of a mountain, staring up at a new way of working and wondering where to even begin. Generative AI has fast arrived in the software engineering workforce, with little guidance and high expectations. PMs are expected to be more productive almost overnight, without a map for how to get there.

Microsoft has been at the center of this transformation. Drawing on interviews, surveys, and telemetry from 885 PMs on software teams at Microsoft, we studied how practitioners at every stage of the climb are actually navigating this shift—from those still finding their footing at base camp to those who have reached the summit and fundamentally redesigned how they work. From this data, we developed a set of personas that capture the real texture of the ascent: where people get stuck, what moves them forward, and what traps are easy to fall into.

This article is not a feature overview or a list of prompting tips. It is a practitioner’s guide built from real behavior, real struggles, and real wins—with verbatim accounts from PMs doing this work every day. Whether you are still packing your bags or looking to push past the next ridge, this article offers concrete, experience-backed guidance for redesigning your workflows with AI, one step at a time.

Climbing the Generative AI Mountain - ACM Queue Most product managers (PMs) working in software today feel it—that dizzying sense of standing at the base of a mountain, staring up at a new way of working and wondering where to even begin. Generative AI has fast arrived in the software engineering workforce, with little guidance and high expecta...

Kode Vicious: KV the Apostate

Faith-based computing versus the unnatural science

Whether we ask an LLM or a recent graduate to type the code is less important than knowing what the code does, how it was built, and when to look under the hood

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The Secret Formula for Choosing the Right Next Role:
The best careers are not defined by titles or resume bullet points.

- Kate Matsudaira

Changing jobs—especially the higher up you get in your career—is a complex process. There are so many factors to consider, and often the factors that stand out most are the ones that matter the least: fancy titles, exciting projects, tempting promises of future success

But those factors that seem so valuable in the moment are just that—they are momentary. Your career isn't just about this one next step you're taking. Your career is a journey that will last a long time.

It is smarter to invest in your long-term success. Focus on factors that will increase your career capital and make you a more valuable hire in your next role, and the one after that, and the one after that.

When you are looking at the options for your next role, there are smarter choices that you can make. Here are the most important factors to consider when picking your next opportunity.

The Secret Formula for Choosing the Right Next Role - ACM Queue Changing jobs—especially the higher up you get in your career—is a complex process. There are so many factors to consider, and often the factors that stand out most are the ones that matter the least: fancy titles, exciting projects, tempting promises of future success