Q&A with Chang She, CEO, LanceDB: Why the 20-year-old data stack is breaking under AI workloads

For 50 years, the pattern has been the same: store the data in the database, keep the big files somewhere else, link them with a pointer. It's powered most production systems we've ever built. Chang She thinks AI is about to break it.

In this episode, Pete Soderling sits down with Chang She ahead of AI Council SF 2026 to talk about why the old data stack wasn't built for what's coming, what agents are doing to database throughput, and why anyone with a serious background in performance "starts to shake in their boots a little" when they think about agentic data access at scale.
About Chang

Chang She is CEO and co-founder of LanceDB, building modern data infrastructure for AI. Previously, he architected the ML and experimentation stack at TubiTV as VP of Engineering. In the mythical pre-pandemic epoch, Chang was the second major contributor to pandas, CTO/co-founder of DataPad, and a recovering financial quant.

Timestamps
00:00 — Storing blobs inline vs. as pointers: the trade-offs
02:34 — When you've blown past the bandwidth limit on object storage
04:03 — Six months trying to make Spark on Parquet work, and why it didn't
06:04 — The moment Chang decided to build something new from scratch
07:35 — Why Chang wasn't worried about adding another tool to the AI ecosystem
11:34 — Agents are firing 100,000 QPS, and most stacks weren't built for it
13:32 — Latency, scale, and the new ceiling for production AI workloads
14:54 — Pipelines written by agents, not humans
16:12 — From co-authoring pandas to rebuilding the stack on top of it
17:27 — Why Chang predicts "multimodal by default" within three to five years
19:48 — What Chang is most looking forward to at AI Council

Catch Chang and the rest of the speaker lineup at AI Council SF, May 12–14, 2026 in SOMA: https://aicouncil.com/sf-2026
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Видео Q&A with Chang She, CEO, LanceDB: Why the 20-year-old data stack is breaking under AI workloads канала AI Council