Data engineering gurus Joe Reis and Matthew Housley once again led a closing town hall at Data Day Texas. Rather than opining from the front, they turned the session over to the wisdom of the crowd. Housley seeded the conversation with a single question – “what is the elephant in the room?” – and the room was ready with an answer: AI. In particular: what is AI going to do to my job?
Given a room full of strangers, some participants were remarkably open about their fears. Perhaps knowing that the audience is composed of fellow data geeks helped to establish a sense of vulnerability. These fears were being expressed by the people who, in theory, should be the ones developing expertise in using AI tooling. But that’s how disruptive the technologies may be: even the data experts are uncertain and afraid.
Perhaps 20 years from now we’ll look back and wonder what the fuss what all about, just as I wonder what the backstory was on this balloon-based scarecrow protecting raspberries in my backyard circa 2006. By Stephen A. Fuqua.
Or at least some of them are. Some in the audience already see themselves as the ones leading the AI wave in their companies, and others feel more confident due to the (perceived?) productivity boost experienced with their own applications of the technology. Many embrace the idea that AI Won’t Replace Humans — But Humans With AI Will Replace Humans Without AI.
Reading the room, the fear may have been equally about the jobs of the future as about the participants’ own jobs. Some noted that many companies have already stopped hiring junior engineers because the LLM assistants are already as good. A few weeks after the conference, Salesforce announced no new hiring of software engineers in 2025 due to LLM-based productivity boosts. A question on many minds: who will be the engineers of the future if we do not develop new engineers today?
Sidenote: it will be interesting to see if the hiring freezes are temporary or lasting pauses. Is this perhaps as much a PR ploy as it is an engineering management strategy?
Some remarked on the need for humans to provide knowledge engineering: structuring knowledge to provide context and more intelligent capability. Hence the continuing need for data cataloging. In discussing education and training, others commented on the need to focus on semantics – algorithms and problem solving – over syntax. LLM’s can help you write the proper code in whatever language, but you as an engineer need to know how to diagnose a problem and describe a solution. Another observation is that these tools may help us collectively fix the massive tech debt found in mission critical, but obsolete, software systems.
One individual suggested embracing product management. Learn to identify and prioritize features and functionality on your own, weaving them into your AI-assisted tools and products, and then learn how sell and support those tools for your colleagues’ benefit. But no one dared offer the suggestion of turning themselves into salespeople for AI-assisted software!
Both fear and excitement were palpable. Perhaps the only certainty we can rely on is that our jobs, and even more so those of the newcomers to the field, will continue to evolve at an accelerating pace. Which makes me all the more appreciative of the fantastic learning environment that is Data Day Texas.