The Data + AI Summit sessions on organizational readiness and AI culture had a consistent message: teams that are succeeding with AI aren't the ones with the most sophisticated technology. They're the ones that got the people, process, and platform working together. I've been saying
Every major conference has a set of announcements that get the keynote coverage and a set that get buried in session tracks and don't make the press release summary. At DAIS 2024, one announcement got almost no coverage relative to its long-term significance: the Unity Catalog lineage
A year in, it's worth being precise about what changed and what didn't. The AI-assisted development story in tech media tends toward two equally wrong poles: either these tools are transformative and everything is different now, or they're overhyped and nothing works as
It's December and I've had six months to see which DAIS 2024 announcements actually mattered and which were conference-stage noise. The scorecard is clearer now. Here's my honest read on what actually changed for the lakehouse in 2024. What Genuinely Moved the Industry
Designing a system is more interesting than implementing one, which is probably why I spent two weeks in October drawing architecture diagrams before I wrote a single line of code for the new memory layer. The diagrams were useful. They forced decisions that are much harder to make once you
GM presented their data intelligence architecture at DAIS 2024, and it deserved more attention than it got. Most of the conference coverage focused on startup architectures and platform vendor announcements. GM is a 116-year-old manufacturer with hundreds of thousands of employees, decades of legacy systems, and data operations
Every year or so someone publishes a take claiming that feature engineering is dead — AutoML will do it, foundation models will make it irrelevant, end-to-end deep learning will learn the representations directly from raw data. And every year, the teams doing serious ML work on enterprise tabular data
Nine months of building a context system for AI-assisted development had produced one clear lesson: the problem is not storage, it's retrieval. Every approach I had tried — flat markdown, structured markdown, a PostgreSQL schema with a JSONB blob — was a better or worse answer to the question
DAIS 2024 had multiple sessions on efficient model training, distillation, and domain-specific fine-tuning. I want to pull on the thread that connects all of them, because it points to a conclusion that's going to be uncomfortable for anyone who has been telling their organization they need
There's a quiet exhaustion settling into data teams that have spent 2024 chasing GenAI use cases that haven't shipped, building RAG pipelines that don't quite work well enough, and defending to stakeholders why the AI chatbot isn't ready yet. Meanwhile, the gradient
Databricks highlighted cost governance at DAIS 2024 — GPU efficiency, cost monitoring, inference optimization. It's worth talking about why they felt they needed to say this out loud. The reason is that enterprise teams building GenAI systems in 2024 are consistently surprised by the cost. Not pleasantly. The Surprise
Every system has a wall — the point where its design assumptions stop holding and the cracks start showing. For markdown-as-memory, the wall appeared around month nine. It wasn't a dramatic failure. It was an accumulation of small frictions that added up to a clear signal: the