January 16, 2026

We Let AI Control Our Data Warehouse — The Results Were SHOCKING

25 minutes

Can AI actually reduce cloud costs — or does it just create better dashboards?

In Episode 27 of Built This Week, Sam Nadler and Jordan Metzner are joined by Ben, CEO of Espresso AI, to break down a real production system that uses machine learning to actively optimize data warehouse compute in real time.

We walk through a live demo built specifically to expose hidden inefficiencies inside Snowflake and Databricks environments — from over-refreshing dashboards to duplicated queries and underutilized clusters. Then we go deep on how Espresso AI works under the hood: proxy-based routing, workload-aware ML models, and fine-grained compute orchestration that runs without changing application code.

This is not FinOps theater. This is AI actively rewriting how compute is allocated.

We also discuss:

Why most teams overpay for convenience in the cloud
How real-time query routing beats manual cost controls
Where AI helps engineers — and where it absolutely does not
The limits of vibe coding for serious infrastructure
Gemini powering Siri and what it means for voice assistants
Meta’s massive GPU buildout and the future of hyperscalers

No hype.
No theory.
Just what happens when you put AI in control of real infrastructure.

New episodes every Friday.

Timestamps

(0:00) Why modern AI understands code differently
(0:45) Episode 27 kickoff and guest introduction
(1:30) Live demo: diagnosing hidden warehouse inefficiencies
(3:00) Why dashboards refresh far more than they are viewed
(4:30) The real cost of duplicated queries across teams
(6:00) What Espresso AI actually does (in plain English)
(7:45) Kubernetes for data warehouses, powered by ML
(9:30) How real-time query routing works
(11:30) Why most companies are not “doing it wrong”
(13:00) Transformers and deep code understanding
(15:00) Where AI helps engineers today
(16:30) Why AI cannot yet run core infrastructure autonomously
(18:00) Productivity gains without replacing engineers
(19:30) Gemini, Siri, and the next generation of voice assistants
(21:00) Meta’s massive GPU investments explained
(23:00) Will Meta become a hyperscaler
(24:30) Final thoughts and closing