minware

AI is about as close to replacing senior engineers as I am to replacing the NY Yankees' shortstop. Volpe, the Yankees' shortstop, fields 97% of plays without making an error. I played baseball in high school and could maybe field 50-60% of those that are routine. A good college player could probably field 95% of those plays, and a minor leaguer maybe within 1%. However, that last percentage point is worth millions and can only be delivered by a few uniquely talented people. AI doing 50% of someone's job doesn't mean it's 50% of the way to replacing them. Each step up is exponentially more difficult, and those difficult things are where experts add all of their value.

Welcome Susan Ashley!

Reviews that “don’t use data” really just rely on the manager’s memory, which is often biased. Quantitative metrics are essential for writing a fair engineering performance review with the greatest impact on career growth. Previous abuses like stack ranking engineers by lines of code have unfortunately created a stigma against metrics in performance assessments. Metrics should assist and enrich a manager’s understanding, **not replace or supersede their judgement.** This article looks at several metrics I’ve used to better recognize contributions, assess the pace of work, and evaluate work quality for engineers on my team. If you want to attract and develop the best talent, you need to know what the numbers say and where they disagree with your intuition.

This is the reality of hiring in the age of AI. minware received more job applications in one hour referencing our "commitment to innovation" than can fit in a single screen shot.

Engineers are like inventors. No quantity of mediocre talent can replace a Thomas Edison or a Linus Torvalds.

2024 was the year of reckoning for AI. My most-read posts by far (50k and 20k impressions for #1/#2 vs. 5k for #3) were about how AI is only as good as a very junior engineer, and how we’d be better off automating easier tasks (like labeling Jira tickets) than hard ones (like building software). My friends and family range from total disdain (“I hate the tab suggestions in gmail — I know what I want to write!”) to extreme optimism (“AI can’t replace software engineers today, but just wait until next year!”) LLMs are unquestionably the most powerful advancement in computer science in the past decade, but are clouded by hype. People don’t understand how to use them yet, and many billions of venture capital are flushing down the drain in a wave of FOMO and euphoria. In 2025, winners will emerge who use this new tool well, and those running on hype capital with vague promises of replacing engineers will be seen for what they are. Which side are you on?

People talk about replacing engineers with AI — what about engineering managers? Managers identify anti-patterns (e.g., not reaching out for help soon enough), provide coaching, and track progress. This seems easier to do with AI than building software itself. Of course, AI can’t deal with politics or human problems like lack of motivation. But, it could do a lot of the job, especially for junior-to-mid-level engineers who face similar issues.

Tell ChatGPT to write a performance review based on what you know about your own work this year. If it’s better than your actual performance review from your manager, then it’s time to look for a new job.

The difference between a good and a great engineer is completing the same project with half the features and 90% of the value. Boards and CEOs who drive their teams to increase velocity miss this point: what you do is more important than how quickly you do it. (And if you don’t give your engineers a say in what you do, then you are beyond help…)

If you're worried about people "cheating" with LLMs during your interview process (beyond the initial screening), then your process is horribly broken to begin with.