AI Has Made Software Engineering Fun Again
Slop in, slop out
I wanted to share some thoughts on AI in the workplace from the perspective of someone who has spent nearly four decades building software. I’m 58 years old, and next year I’ll celebrate forty years as a professional software engineer. I started at nineteen while I was still studying at UC Irvine, and over those four decades I’ve worked as both a software engineer and an engineering manager across companies ranging from startups to large enterprises. During that time I’ve seen the industry reinvent itself several times, from the rise of the web, to mobile computing, to cloud services. AI feels different—not because it’s replacing software engineering, but because it’s changing who can effectively contribute to every other part of building a business.
I recently joined a software company where AI isn’t simply another tool in the toolbox; it’s part of the company’s culture. During a recent company retreat, our CEO published a collection of AI skills that employees use throughout the organization. People use them to review specifications, documentation, engineering tickets, product proposals, and other internal documents to ensure they are clear, consistent, and aligned with the company’s goals. Working in this environment has reinforced something I’ve suspected for a long time: AI is most valuable when it amplifies human expertise rather than attempting to replace it.
The Communication Gap
To explain why I believe that, it’s helpful to describe something I’ve observed throughout my career. Being an excellent engineer and being an excellent communicator are two very different skill sets. Some engineers possess both, but many do not. I’ve worked with brilliant people who could solve incredibly difficult technical problems yet struggled to explain those solutions to anyone outside engineering. As a result, engineering organizations often become isolated from the rest of the company. Product managers translate customer requirements into engineering work. Marketing translates technical capabilities into customer value. Sales translates product features into business outcomes. Every layer adds value, but every layer also introduces another opportunity for misunderstanding or loss of nuance.
For much of my career I’ve naturally gravitated toward that boundary between engineering and product management. I’ve enjoyed helping bridge the gap between technical implementation and business requirements, and I think I’ve been reasonably successful at it. Even so, I discovered that communicating my own ideas beyond my immediate team was surprisingly difficult. I could often see an opportunity, understand the technology required to pursue it, and even build a prototype, but I struggled to package those ideas into something investors, customers, or even colleagues could immediately understand.
The Companies I Never Built
I can think of at least two occasions where that limitation probably prevented me from building a successful company.
The first occurred in the early 2000s while I was consulting for several Fortune 500 companies. They all seemed to struggle with a common problem: they possessed enormous amounts of business information but lacked effective ways to present it to decision makers. I recognized there was an opportunity, but I couldn’t identify the right market, define the right feature set, or clearly articulate the value proposition. I spent months exploring the idea and even spoke with a few venture capitalists I knew, but eventually abandoned the project because I couldn’t explain what I was trying to build.
Looking back with twenty years of hindsight, I now realize I was describing what eventually became known as web-based business intelligence. The market was there; I simply couldn’t frame the opportunity clearly enough to pursue it.
A few years later, as smartphones and social media began transforming the technology landscape, I found myself working on another idea. I was fascinated by the relationship between people, places, and social connections, and I built a mobile application that allowed you to choose a location, tell your friends you planned to be there, and let members of your social circle discover where their friends were gathering. Like many early mobile applications, it suffered from technical limitations—GPS accuracy wasn’t particularly reliable at the time—but that wasn’t the real problem. The larger issue was that I still didn’t know how to describe the business. I couldn’t clearly explain who the customers were, how the company would grow, or why investors should care.
Someone else eventually solved that problem. The company was called Foursquare.
What AI Actually Changes
Those experiences have shaped how I think about AI today. I often hear people speculate that AI will eventually create entire companies from scratch, independently discovering markets, inventing products, and building successful businesses. Perhaps that day will come, but I haven’t seen convincing evidence of it yet.
What I have seen is something that, in many ways, is more immediately valuable.
If I had those same ideas today, I have little doubt I could build a company around them. Not because AI would generate the ideas, but because it would help me develop them. It would help me identify potential markets, evaluate competitors, refine the messaging, prepare financial projections, challenge my assumptions, develop a business plan, and produce investor presentations. None of those activities replace creativity; they allow creativity to reach people who can act on it.
I think that’s the real revolution. AI isn’t replacing expertise—it is removing many of the barriers that prevented expertise from being translated into products, businesses, and ideas that other people can understand.
AI Isn’t the Source of “Slop”
This leads to another criticism I hear frequently—that AI is flooding the internet with low-quality content, or what many people call “AI slop.” There is certainly some truth to that. The internet is already filling up with generic articles, marketing copy, presentations, and documentation generated with very little thought or care.
But I think that criticism misunderstands the technology.
AI doesn’t inherently produce poor content; it reflects the quality of the thinking behind the prompt. Ask a vague question and you’ll receive a vague answer. Ask for generic marketing copy and you’ll receive generic marketing copy. In many cases, what people call AI slop is simply human slop produced more efficiently.
Over forty years in software engineering, I’ve learned that the difficult part of communication isn’t writing sentences. The difficult part is organizing your thoughts, understanding your audience, deciding what matters, and explaining complicated ideas without oversimplifying them. Those are still fundamentally human skills.
When I use AI, I’m rarely asking it to write something for me. I’m asking it to help me think. I provide context, explain my goals, describe my audience, ask it to challenge my assumptions, and iterate on the results. Sometimes I reject everything it produces. Other times it helps me discover a clearer explanation or a stronger structure than I would have found on my own.
The value isn’t that AI is doing my thinking.
The value is that it helps me communicate my thinking.
Why I’m Optimistic
As engineers, we’ve always understood that tools amplify the quality of the person using them. A compiler doesn’t magically produce good software because someone typed code into it, and a database doesn’t produce valuable business insights simply because someone executed a query. The quality of the output depends on the quality of the design, the quality of the inputs, and the judgment of the person using the tool.
AI is no different.
In fact, I think it does something even more interesting: it increases the return on expertise. Forty years of engineering experience still matter. If anything, they matter more than ever because AI allows me to apply that experience in areas where I was never formally trained—product strategy, marketing, business planning, technical writing, and communication.
Looking back, I don’t think AI would have given my younger self better ideas. I think it would have given those ideas a much better chance of succeeding by helping me explain them, test them, challenge them, and communicate them to investors, customers, and teammates. The ideas were always there; what was missing was the ability to translate them into something the rest of the world could understand.
As I near the end of my career, I find that realization surprisingly energizing. I could retire today—and probably could have retired several years ago—but instead I find myself more excited about software engineering than I have been in a very long time.
Simply put, AI has made this job fun again.
Not because it has replaced what I’ve learned over the last forty years, but because, for the first time in my career, I feel like those forty years of experience can finally reach farther than they ever could before.