Is your engineering team wasting budget and sacrificing latency by pre-computing data that most users never see? Chalk co-founder Elliot Marx joins Andrew Zigler to explain why the future of AI relies on real-time pipelines rather than traditional storage. They dive into solving compute challenges for major fintechs, the value of incrementalism, Elliot’s thoughts on and why strong fundamental problem-solving skills still beat specific language expertise in the age of AI assistants.

Join our AI Productivity roundtable: 2026 Benchmarks Insights

*This episode was recorded live at the Engineering Leadership Conference.

Follow the show:

Subscribe to our Substack
Follow us on LinkedIn
Subscribe to our YouTube Channel
Leave us a Review

Follow the hosts:

Follow Andrew
Follow Ben
Follow Dan

Follow today's guest(s):

Elliot Marx: LinkedIn
Chalk: Website | Twitter/X | Careers

OFFERS

Start Free Trial: Get started with LinearB's AI productivity platform for free.
Book a Demo: Learn how you can ship faster, improve DevEx, and lead with confidence in the AI era.

LEARN ABOUT LINEARB

AI Code Reviews: Automate reviews to catch bugs, security risks, and performance issues before they hit production.
AI & Productivity Insights: Go beyond DORA with AI-powered recommendations and dashboards to measure and improve performance.
AI-Powered Workflow Automations: Use AI-generated PR descriptions, smart routing, and other automations to reduce developer toil.
MCP Server: Interact with your engineering data using natural language to build custom reports and get answers on the fly.

The hidden costs of pre-computing data | Chalk's Elliot Marx

Dev Interrupted