Thoughts on AI After My Panel Discussion

Today at Day of Data Orlando, I moderated a panel discussion with Bob Ward, Celia Brusatori, Kevin Feasel, Rob Volk, and Tim Radney talking about AI in your career, and whether it’s your sidekick, hero, or villain.

A few really interesting things came up that I wanna share with you because I know a lot of y’all don’t get out to meet your peers in person at regional or national conferences. I feel you. I try to bring you along with me.

We were talking about AI brands, and out of curiosity, I asked for a show of hands for each brand of AI to see who was using each model. Almost everyone in the audience raised their hand for almost every brand, Anthropic, Google, Microsoft, and OpenAI. That completely blew me away. This wasn’t a management or executive conference – these were developers, DBAs, BI professionals, and college students. AI isn’t just pervasive, but all of the brands were pervasively used.

Shortly later, one of the audience questions was, “How do we stop the AI arms race? For example, right now people are using AI to apply for jobs, and companies are using AI to filter out those AI-written job applications. That seems like a waste – how do we get back to real humans doing this stuff?” The panel had a discussion about how the HR and job application processes have been broken for decades, and that going back to real humans wouldn’t fix anything, but the questioner persisted – that’s just one example of the arms race, he said. (Which is a great point – AI is disrupting all kinds of stuff.)

When you place that question alongside the fact that almost everybody in the audience is already using all kinds of AI tools, though, I don’t think it’s realistic to roll back that arms race. It’s just too late. This is the new reality.

At the event, I was talking to a friend about what it’s like to interview people in the age of AI, and he commiserated about how some candidates just give hand-wavy answers now about how they do things. There’s now a generation of employees who put things on their resume as if they’ve done ’em, but … all they really did was prompt those things, and they don’t know the underlying mechanics of how the thing was done. They can even talk a good game, reciting some of the challenges encountered during the project, but if you drill a little deeper as to how they overcame those obstacles, well … it falls apart.

I don’t know what the right answer there is, either, because to some extent, these people did do that task, and if they were gonna do it again today, as long as they were allowed to use AI to accomplish that task – is that good enough? I’m not sure. It’s like if I asked, “Have you ever configured an Availability Group?” If they used the setup wizard, well… they did do it. I think the underlying question is wrong, and it’s time to start turning the questions into the post-prompting work that people have to do – like how do you know if an existing AG setup is correct, or how do you troubleshoot a broken AG. But are people even going to do that without AI? I’m not sure. In some environments, yes – but it’s also a question of how long that will go on, given how good local models are becoming.

Another audience question was, “What happens if a giant AI company’s IPO fails, like Anthropic or OpenAI?” Rob Volk had a succinct answer: it’d be just like when Lehman Brothers failed back in 2008. There’d be a recession, and the US government would try to contain the failures. Things would be financially rough for a while.

I pointed out, though, that that still wouldn’t stop AI: open source local models like Qwen and DeepSeek can already be run locally with open source apps like DwarfStar as long as you’ve got a reasonably powerful Mac or an NVIDIA GPU. If there wasn’t ever another open source local model release, I’d still be quite happy using Qwen and DeepSeek locally on my laptop, and the way I work would never go back to the old way.

Several questions came up that could all be reworded as, “How do I avoid getting laid off?” The audience was almost all using AI already, so they knew firsthand that they were much more productive in less time. They had valid concerns that their companies would see that, and start laying people off because the remaining staff could pick up the work.

Nobody had a silver bullet for that one.

Tim Radney had a good point: the first sign of trouble would be if you can finish all your work in a 40-hour week. If you can, you need to try to become more proactive, to look further down the road at the next problems the company wants you to solve. If you run out of work, the company’s gonna notice that. You also need to be visible in the company: you need to be seen as the one everybody turns to for quick, accurate solutions.

Multiple folks on the panel pointed out, though, that the days of company loyalty to employees is long, long gone. (AI didn’t introduce this phenomenon.) Companies exist not for your benefit, but for the owners’ benefit, and if they can save money by doing something more efficiently without you, they will. Don’t think that by putting in long hours, they’re going to reward it.

I half-jokingly suggested that attendees look to /r/overemployed for tips on how to stay employed. That subreddit is all about getting multiple full-time remote jobs at once, staying employed at all of them, and avoiding detection. It’s hilarious to read their stories about nearly getting caught. I’m sure a good chunk of it is just fiction, but … some of it clearly isn’t. <sigh>