Voice of the DBA

Tag: career

  • The Quiet Part

    Apparently, Meta did what a lot of employees suspect their management will do: use AI to lay off people. In this case, there is a report (and lawsuit) that Meta used it’s AI-integrated HR platform to make decisions about who to let go in a layoff. The rumor is that the AI used productivity metrics to choose who was the target of the layoff. As with a lot of AI failures, this appears to be another case of poor communication guiding the AI, or the AI not actually taking individual situations into account.

    A number of the people terminated were on maternity/paternity leave, which is a protected activity. Others may have been on other medical leave, though for privacy reasons, the article doesn’t have firm data to prove this. The lawsuit will likely bring more of this out, but this appears to be a case not just of AI making decisions, but of poor behavior from management. Plaintiffs were discouraged from taking leave off, which is something sh****y humans have said to people for decades. We need you; your baby or family isn’t important, so don’t use your leave. It’s one aspect of working in the US that is way worse than overseas, where there are more employee protections.

    Meta disputes the case, saying the AI didn’t make decisions. However, that brings out an interesting point. If the AI recommends things, who’s responsible? The humans, right? They still have to sign off on the decision. If they don’t perform due diligence, they’re still responsible, correct? I think so; after all, I’m responsible for code the AI writes if I commit it. Even if an agent does the work, I have to oversee it and approve (or grant permissions for) the actions.

    I. Am. Responsible.

    Trusting an AI to do a lot of work is like trusting a lot of smart, but very inexperienced staffers to work in your environment. There are always inconsistencies and reasons why we might code something, configure something, or deploy something a certain way. What seems like a good way to tackle the situation from the outside doesn’t always make sense when you have experience. Humans often have experience that AI agents lack.

    LLMs are relatively stateless, and while we can provide context, give them guidance, and provide comprehensive codebases, they still sometimes do silly things. This can be problematic, especially with database changes, which are stateful and disruptive to rollback.

    AI agents can make mistakes much like humans, only faster. Much, much faster.

    Labor is one of the most expensive parts of many organizations’ budgets. Plenty of management would like to replace relatively expensive humans with cheaper tokens. That isn’t working out as well in practice, despite lots of experiments. Hopefully, other organizations realize that AI is a tool, not a replacement for humans, and we can’t trust it or even believe it’s outputs without some human judgment.

    Steve Jones

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  • Are You Working More Hours?

    Recently, I noticed my son was coming home later and going to work earlier. He typically works a 9/80 schedule as a software engineer, but at times he might work extra hours on a deadline. His company tries hard to keep employees working a set schedule without overtime. Unless there is a need, after all, this is software. However, when they are on a deadline, management warns people, and they try to manage overtime to reasonable levels to avoid burning out employees.

    I asked him if he was extra busy or if AI was encouraging extra hours. He told me this was a short-term project with a few more hours, but mostly the team was decompressing a bit after work. I completely understood that as I’ve spent my share of time with co-workers at theend ofday, sometimes sharing a drink or meal nearby, sometimes just chatting in the office or the parking lot.

    There have been a number of studies and reports that AI is making people work more, not less. The time spent on a variety of tasks, especially those where we are reading or interacting with text, has grown. I suspect there is additional pressure from management as well to get more done since everyone has an AI assistant around. There is also the excitement of tackling new projects, which can lead people to spend more time at work. While I appreciate that AI can be exciting, and you might love it, don’t lose yourself in it at work. It’s still just a tool.

    And, of course, there’s the token spend. Some managers are measuring people on the amount of work their AI assistants do, rather than by what gets done. I’ve certainly seen plenty of people using AI to perform busy work just to be seen as using AI tools.

    At times, it seems people are using AI to process more information, which they then use AI to summarize. They post these summaries, which grow in number because the AI can produce more of them. Others use their AIs to read these summaries and produce their own summary of what they should be reading. It certainly seems like AI is producing more copies of information, summarized over and over, giving all of us more things to keep an eye on.

    That seems silly. While I like AI, I fall into that group that uses it lightly for targeted tasks. I certainly try not to create more reports, summaries, or even emails to send to others, especially large groups of people. We have enough to review without AI creating even more tasks to deal with every week.

    Steve Jones

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  • Make It Routine

    The first one is hard. The rest are boring.

    I heard this statement from someone recently, and it sounds like something a technical person would do. It’s been a goal of mine, or maybe a direction to aim for, though I sometimes think that goal is more aspirational than actual. It can be hard to do that for every task.

    The first time I tackle something, it should be hard. It’s new work. It’s a new process/code/thought/action/etc. It’s unfamiliar, and I spend more time on it than I want. After that, I’d hope I could repeat the thing again in much less time. That’s the goal, and that’s what we aim for in a lot of DevOps work. Make the things we think are hard, less hard. Make them boring by codifying things, using automation, and have the computer replicate the task.

    I think the second one is often hard as well. How often have you tried to reproduce something you did and can’t quite get it? Heck, I now depend (and use) SQL History constantly because I will write some code, change it a bunch and then realize that I can’t reproduce the version that did thing thing (or broke the thing) I was working on. I need to rewind things and figure them out again. Usually by the 4th time I’ve done something, it’s starting to become easier. By the 10th its boring.

    Unless I’m playing guitar, in which case, some things take a few more reps than 10.

    I’ve had plenty of developers say never repeat yourself. If you can automate it, you should. In practice, that’s hard. Sometimes I’m unsure of whether I’ll do something again, or often enough to spend the time automating it. There are also times I’m not sure it’s worth the effort. I spent a day once trying to automate a bunch of Outlook appointments, only to realize the whole Office API and deluge of information out there made this much harder than the 15 minutes a year I spend putting in all my Database Weekly reminders.

    I don’t want to discourage you from automating things and making them routine or boring. My database deployments ought to be routine. The daily checks should be so boring and automated that I don’t bother with them because I know the machine is doing the work and will let me know if there is something I should examine. The efforts to refresh dev dbs, or respond to audit requests, or even reset a password ought to be boring and easy. Some of us build ways to smooth these tasks and ease our jobs, and some of us treat every one as an ad hoc thing that we do over and over.

    The stuff you do over and over is going to be handled by AI at some point (maybe soon). Maybe soon, maybe in a few years, but a lot of simple stuff that you keep doing- that mindless, tedious stuff that doesn’t require a lot of thought – is going to be handled by AI agents. Either you’ll direct them, or your boss will ask someone else to do it after you leave. AI can handle things like figuring out when disks are full and cleaning out old log files, shrinking databases, archiving things, and then writing scripts (and scheduling them) to prevent issues. If that’s your job, your days are numbered.

    Make things routine by thinking about the pattern, how we could reduce or eliminate a lot of labor, and how we can use a computer to handle them. Even better, learn how to guide an AI to do that work and prove your worth.

    Steve Jones

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  • Forward Deployed Engineers

    I recently recorded a session with Ken Muse and a Redgate Flyway Solution Engineer. It was a fun session using GitHub and AI, and better managing the code in an automated fashion to bring some determinism to AI coding. I’m hoping it will be released soon, and you can see a vision of how you can better wrangle your AI agents and reduce risk and increase reliability.

    When we started our discussion, Ken noted that he is an AI forward deployed engineer for GitHub. His job is to work with teams in how to use agentic coding. When we were first prepping, I had never heard the Forward Deployed Engineer title, which is apparently getting popular. It was in an issue of the Pragmatic Engineer Deep Dives last year, and I must have missed that issue. Apparently, this is a role that works part of the time with customer teams and part of the time with product or engineering teams.

    In other words, a software engineer with a new title who gets paid more than a software engineer.

    Titles are always a funny thing to me. I’ve seen people who write computer code go from programmers to developers to software engineers to forward deployed engineers. The job is the same. I work with a team of others who write code, or I work with a customer who wants code and might need help writing it. Or, if I’m working at a company that works with outside customers, this is really a consultant role renamed.

    DBAs have had a similar change. I started as a DBA because they made a lot of money at the time. I moved from programmer to DBA, without much change in skill, but with a nice pay rise at a new company. I left that role before we got Data Engineers, Site Reliability Engineers, Database Reliability Engineers, and who knows what else. However, if I had stayed in a company, I’m sure I would have been changing my title periodically to earn more money.

    Certainly, I’d still have been adding skills that give me a reason to change my title, but I’m not sure the job would have changed. Instead, I’d be trying to grow my career not only with seniority and time, but with new skills and title changes.

    Maybe that’s a good reason to keep learning new skills. New skills let you claim you need a new title. Hopefully, you pick a title that HR doesn’t have any data about, so they have to set a new range for the position that’s above your current range.

    Viola! Instant raise.

    Not a bad career plan. Just make sure you’re adding some skills that are asked for in job descriptions, including soft skills. After all, a forward deployed engineer is going to be working with others, so communication is going to be key.

    Steve Jones

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