The Buzzword Industrial Complex

The Weekend Windup #25 - Reflections, Cool Reads, Events, and More

Eisenhower famously warned of the Military Industrial Complex. I’m here to warn about something almost as insidious.

The Buzzword Industrial Complex is at it again. Last year, the industry declared it was the “Year of Agents.” Never mind the fact that barely anyone is actually running agents in production in the real world. But forget that, or the other dozen or so hype cycles we’re still trying to implement. Now, the powers that be have decreed that this is the “Year of Context”.

Slightly mixing metaphors, it reminds me of the fashion industry: every year, we have to invent a new “season” and a new trend just to keep up appearances, even if it’s entirely unnecessary. It’s gotten so absurd that I recently made a parody article and buzzword bingo game just to mock how predictable the keynotes have become.

The reality is that this hype cycle is actively harming companies. Here is why the constant churn of new buzzwords is a massive problem:

• We’re stacking new trends on broken foundations. We haven’t even finished the last several “seasons” of buzzwords, and now we’re onto a new one. We keep throwing new concepts onto the pile before anyone has actually implemented the foundational stuff we’ve been talking about for ages.

• We’re still barely doing BI, let alone AI. But let’s throw yet another buzzword to prey on the insecurities of those who feel like they’re not keeping up. Data leaders are getting increasingly confused by all this noise. “Do I do agents? How do I give them context? Why do I suddenly have a receding hairline?!” They go back to their executives and spout a bunch of stupid buzzwords because they feel they have to sound smart, relevant, and hip.

  The dark irony here? A lot of these teams can barely get their f*cking dashboards working. But AI will do away with dashboards, right? Just throw an LLM on top of your data, give it context, and away you go. Nevermind the underlying data is probably in dire shape. But hey, data modeling doesn’t matter because AI, right? The circular arguments are insane.

• Trend-chasing over execution. This cycle forces companies into ridiculous contortions just to play along with the hype. It is trend-chasing at the direct expense of just getting shit done. And like clockwork, the cycle begins anew. “There I go, turn the page,” as Bob Seger once sang.

Let’s look specifically at this new obsession with “context.” The idea is that we need to suck every last bit of tacit knowledge out of the business (and fire them, “because AI”) to feed the machines. But what does that actually look like?

If you just grab unstructured data from everywhere, are you pulling in the random Slack channels where people just goof off and send emojis and animated GIFs all day? As Matt Housley pointed out in our latest podcast, if you try to build context without proper data governance, especially without knowing which data in your organization is actually correct and valid for decision-making, you are going to get a lot of nonsense flowing out. It’s just a new generation of data governance problems applied to the unstructured data world.

That doesn’t even begin to account for the carnage of most data models in organizations large and small. It’s largely a mess, and although I definitely see AI speeding up the drudgery of right-sizing one’s data model, it’s not as simple as slapping semantic layers, ontologies, and knowledge graphs on top of a questionably modeled database and calling it a day. Database modeling is relatively easy to implement compared to the newer context approaches I just mentioned, simply because it’s more widespread as a practice, more tooling is available, and far more practitioners have learned it. That said, how often do you see “good” data models in the wild? And now we’re going to impose extra work on data teams because AI agents (largely not implemented in production today) need context (also not widely implemented and arguably an order of magnitude harder than vanilla data modeling). Cool.

I’m all for doing new stuff, but let’s not forget the foundations upon which we’re implementing this cool new stuff. If each hype cycle magically solved everything, we wouldn’t still be having the exact same conversations about core challenges, like people and processes, that we’ve been having for decades. Yet the core challenges remain year after year.

The Buzzword Industrial Complex will try to convince you otherwise, mostly to keep its flywheel of vendor rankings and hype cycles spinning. But you know deep down that buying a shiny new toy doesn’t atone for past architectural sins.

Have a great weekend,

Joe

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Awesome Upcoming Events

All technical. All data. All day.

On March 26, 2026, there’s a very cool single-track, deeply technical data engineering event in San Francisco.

Capped at 100 practitioners.

No salespeople. No sponsors. Just talks + the hallway track, followed by happy hour.

When: March 26, 2026 · 10am–4pm + happy hour

Where: L4, 945 Market St, San Francisco

→ Register here.

Thanks to Confluent for partnering on this event and newsletter.

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🇸🇪 Sweden! See you at the Data Innovation Summit in Stockholm.

I’m doing a keynote and workshop on Mixed Model Arts: Data Modeling in the Age of AI.

• May 7 - keynote

• May 8 - workshop

Here’s 10% off: SD10OFF (good for the event. Workshop is not included)

Register here

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Cool Videos and Reads

The steady work of the white-collar tech industry isn't what it used to be, and anyone could be on the chopping block at a moment's notice. With tens of thousands of highly skilled people getting laid off from Big Tech on a seemingly bi-weekly basis, competing in the traditional job market is brutal right now.

In this episode, Jody Hesch and I discuss why building a freelance consulting business isn't just a career pivot, but a necessary Plan B.

Paul Blankley and Ryan Janssen, founders of Zenlytic, drop in to discuss the massive shift in how we build software and handle data. We trace their journey from studying early NLP and Transformers at Harvard right when the BERT paper dropped, to building a company that relies on cutting-edge LLMs. As far as I know, they're the first to use LLM's for analytics. We chat about the evolution of LLMs for analytics and much more.

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Here are some things I read this week that you might enjoy.

DOGE employee stole Social Security data and put it on a thumb drive, report says | TechCrunch

Go is the Best Language for AI AgentsCursor Goes To War For AI Coding Dominance

Claude Code isn’t going to replace data engineers (yet)

Open Sores

Silicon Valley’s Image Takes a Dark Turn in Pop Culture - The New York Times

The Structure of Engineering Revolutions

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