# mainframes

Mainframes are large, high-performance computers designed for continuous operation and high-volume transaction processing.

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## Enterprises are sweating legacy IT assets as AI investment grows

DevFeed: [Enterprises are sweating legacy IT assets as AI investment grows](<https://devfeed.tech/articles/enterprises-are-sweating-legacy-it-assets-as-ai-investment-grows-31542.md>)

Original publisher: [Read original article](<https://www.theregister.com/systems/2026/09/16/enterprises-are-sweating-legacy-it-assets-as-ai-investment-grows/5296896>)

Author: Dan Robinson

Published: 2026-09-16T16:15:00Z

Content type: article

Language: en

Sources: [www.theregister.com - Articles](<https://devfeed.tech/sources/www-theregister-com-articles.md>)

Topics: [legacy](<https://devfeed.tech/topics/legacy.md>), [Hardware](<https://devfeed.tech/topics/hardware.md>), [mainframes](<https://devfeed.tech/topics/mainframes.md>), [data](<https://devfeed.tech/topics/data.md>), [business logic](<https://devfeed.tech/topics/business-logic.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [business-logic](<https://devfeed.tech/tags/business-logic.md>), [data](<https://devfeed.tech/tags/data.md>), [ensono](<https://devfeed.tech/tags/ensono.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [it-modernization](<https://devfeed.tech/tags/it-modernization.md>), [legacy](<https://devfeed.tech/tags/legacy.md>), [legacy-systems](<https://devfeed.tech/tags/legacy-systems.md>), [mainframes](<https://devfeed.tech/tags/mainframes.md>), [systems](<https://devfeed.tech/tags/systems.md>)

### AI overview

The article examines how enterprises are dealing with legacy IT assets as AI investment grows. It reports that mainframes and other hardware may contain the data and business logic needed to build AI services.

### Source excerpt

Hardware such as mainframes found to hold the data and business logic needed to build those AI services

## 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?