The Joe Reis Show

Everyone in tech is telling you to go faster. Eric stepped away from his role to do the opposite.In this conversation, we get into why so many people feel like they're falling behind in AI, and why that feeling is mostly manufactured. Eric makes the case that we're miscalibrated: assuming what's true for the 0.1% (the SF AI inner circle) is true for the 10%, when by definition almost no one is keeping up with that group.
We talk about why judgment, not throughput, is the real bottleneck right now, why most AI products feel boring even as code output explodes, what the "flatten the org" experiments are actually measuring (spoiler: yesterday's stock price), and why people are leaving corporate roles at a rate that's hard to ignore.
We also get into the parts nobody wants to say out loud: layoffs by email, the gap between who people are in private vs. on LinkedIn, the ghost routines after you leave a job, and what walking around San Francisco actually feels like when every billboard is AI and every person you pass looks depleted.If you've felt the FOMO and wondered whether the problem is you or the framing, this one's for you.
Eric Weber is a data and product leader formerly at Grammarly, Yelp, LinkedIn, and Stitch Fix.

In this episode, Tristan Handy and I sit down to unpack a massive shift coming to the data industry: over the next 12 months, the primary consumers of data won't be humans. They will be AI agents. We dive deep into what this means for data infrastructure, compute costs, and the tools we use every day. We also talk about processing high-volume agent queries, building "context stores", and why the industry shouldn't just build "horses with wheels" when designing agentic data engineers. We also take a fun detour comparing the current AI landscape to the early days of dial-up modems and Mosaic browsers , and discuss why stepping away from the screen and going old-school might be the ultimate productivity hack.

NOTE - Sorry for the edits in this video. I used Descript to edit out the umms and uhhs, and it was a bit too aggressive. Will make it less jarring in future videos. Thanks.
Freestyle Friday, May 15, 2026
Walking around Salt Lake City and unpacking the April 2026 data modeling survey results (334 respondents). Across three surveys now: January's State of Data Engineering (1,100), March's AI usage poll (193), and April's data modeling deep-dive. Not surprisingly, the same two pain points keep surfacing: time pressure and lack of clear ownership.90% of respondents have a data modeling pain point. When asked what would actually help, only 4.8% wanted better tools. Training, business requirements, time, and ownership crushed tooling in the rankings.
Will AI improve things or make them worse? Time will tell...Also covered:
Why physical data modeling has become the default (and why that's a problem)
Data modeling vs. schema design - they're not the same thing
Semantic layers (yay or nay?), Lloyd Tabb, and Malloy
Conway's Law, Reis's Law, and what changes when org charts get flattened by AI
Why leadership is under more pressure than ever
The June half-year survey is coming
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Are AI agents silently draining your cloud data budget? With the rise of consumption-based pricing and autonomous AI queries, data teams are facing a perfect storm of skyrocketing costs and operational chaos. In this episode, I sit down with Sanjay Agrawal, CEO and Co-founder of Revefi, to discuss the intersection of data engineering, cloud warehouse optimization, and FinOps in the age of AI.We chat about how legacy on-prem habits are bankrupting modern data platforms, why query optimization is more about ROI than just speed, and how AI agents are changing the landscape of data consumption. Sanjay shares his deep expertise from building world-class databases at Microsoft and ThoughtSpot, revealing how to automate cost management and performance tuning for Snowflake, Databricks, and BigQuery.Key Topics:
The evolution of cloud data warehouse pricing and why it breaks traditional budgets.
How AI agents are causing massive, unpredictable spikes in compute spend.
Real-world horror stories of ""lift and shift"" cloud migrations.
Why database benchmarks focus on speed but ignore the actual ROI of data.
The future of open table formats (Iceberg) and multi-engine routing.

Zach Wilson and I happen to be in Stockholm, Sweden, this evening. In this Freestyle Friday chat, we talk about what it takes to be a data engineer in 2026 and much more.