# From COBOL to Copilot: 30 Years of Data, BI, and AI with David Langer

DevFeed: [From COBOL to Copilot: 30 Years of Data, BI, and AI with David Langer](<https://devfeed.tech/articles/from-cobol-to-copilot-30-years-of-data-bi-and-ai-with-david-langer-38709.md>)

Original publisher: [Read original article](<https://dataengineeringcentral.substack.com/p/from-cobol-to-copilot-30-years-of>)

Author: Daniel Beach

Published: 2026-07-01T13:43:11Z

Content type: article

Language: en

Sources: [Data Engineering Central](<https://devfeed.tech/sources/data-engineering-central.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [cobol](<https://devfeed.tech/topics/cobol.md>), [Data Science](<https://devfeed.tech/topics/data-science.md>), [mainframes](<https://devfeed.tech/topics/mainframes.md>), [Software Engineering](<https://devfeed.tech/topics/software-engineering.md>), [AI Strategy](<https://devfeed.tech/topics/ai-strategy.md>), [self-service](<https://devfeed.tech/topics/self-service.md>), [dashboards](<https://devfeed.tech/topics/dashboards.md>), [jupyter notebooks](<https://devfeed.tech/topics/jupyter-notebooks.md>)

Tags: [adoption](<https://devfeed.tech/tags/adoption.md>), [ai](<https://devfeed.tech/tags/ai.md>), [business-intelligence](<https://devfeed.tech/tags/business-intelligence.md>), [cobol](<https://devfeed.tech/tags/cobol.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [enterprise](<https://devfeed.tech/tags/enterprise.md>), [jupyter-notebooks](<https://devfeed.tech/tags/jupyter-notebooks.md>), [mainframes](<https://devfeed.tech/tags/mainframes.md>), [programming](<https://devfeed.tech/tags/programming.md>), [self-service](<https://devfeed.tech/tags/self-service.md>), [semantic](<https://devfeed.tech/tags/semantic.md>)

## AI overview

A podcast conversation with Dave Langer about nearly three decades spanning COBOL, enterprise architecture, business intelligence, analytics, data science, machine learning, and AI. It discusses persistent data-industry problems, self-service analytics, dimensional modeling, AI adoption, semantic layers, governance, and career advice for data professionals.

## Source excerpt

What happens when someone who started programming on a Commodore 64 watches AI reshape the entire data industry?