# ai impact Published articles for ai impact. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Your AI coding spend bought 25% more output. Duplication rose 81%. DevFeed: [Your AI coding spend bought 25% more output. Duplication rose 81%.]() Original publisher: [Read original article]() Author: Steve Fenton Published: 2026-09-14T14:39:14Z Content type: article Language: en Sources: [The New Stack]() Topics: [ai-coding](), [Claude Code](), [cursor](), [datasets]() Tags: [agentic](), [ai](), [ai-coding](), [ai-impact](), [ai-operations](), [ai-strategy](), [ai-tools](), [claude-code](), [coding](), [contributed](), [contributed-octopus-deploy](), [cursor](), [dataset](), [software-development]() ### AI overview The article examines the return on investment from AI coding tools. It reports that heavy users gained 25% over their previous velocity, while code duplication rose 81%, and argues that output measures such as lines of code, pull requests, and feature counts do not reliably represent business value. ### Source excerpt Since they arrived on the scene, a great swathe of the software industry has pinned its hopes on AI tools, The post Your AI coding spend bought 25% more output. Duplication rose 81%. appeared first on The New Stack. ## How do you actually measure the impact of AI on design? DevFeed: [How do you actually measure the impact of AI on design?]() Original publisher: [Read original article]() Author: Shane Johnston Published: 2026-08-28T19:30:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [Artificial Intelligence](), [Figma]() Tags: [ai](), [ai-impact](), [ai-tools](), [collaboration](), [design](), [developers](), [product-development](), [productivity](), [research](), [workflows]() ### AI overview Figma describes a multi-year study measuring how AI affects design and product development. Its AI impact index compares expectations with reported experience across collaboration, personal productivity, projects, and products, using survey responses from designers, developers, and product managers. ### Source excerpt In 2024, we kicked off a study to understand how AI would reshape design and product development. Now, AI is helping us run it. ## Good relationships, automation, and weekly readings 💡 DevFeed: [Good relationships, automation, and weekly readings 💡]() Original publisher: [Read original article]() Author: Luca Rossi Published: 2026-07-27T07:00:37Z Content type: article Language: en Sources: [Refactoring]() Topics: [Automation](), [Artificial Intelligence]() Tags: [ai](), [ai-impact](), [automation](), [relationships](), [report](), [weekly]() ### AI overview This edition of Monday Ideas discusses AI spending outpacing returns, recommends building professional relationships before an immediate need arises, and argues that teams should automate repetitive copy-and-paste work while preserving human judgment. ### Source excerpt Monday Ideas -- Edition #218 ## Platform engineering makes a difference. Here's how to prove it DevFeed: [Platform engineering makes a difference. Here's how to prove it]() Original publisher: [Read original article]() Author: Liz Coolman Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [Platform Engineering](), [internal developer platform](), [AI Engineering](), [Developer Platform](), [configuration](), [Security](), [telemetry]() Tags: [ai](), [ai-engineering](), [ai-impact](), [cognitive-load](), [configuration](), [developer-platform](), [encryption](), [engineering](), [idp](), [infrastructure](), [least-privilege](), [metrics](), [performance](), [platform](), [platform-engineering](), [platforms](), [security](), [software](), [speed](), [tooling]() ### AI overview This article explains how platform engineering teams can prove the value of an internal developer platform (IDP) to executives. It describes how IDPs reduce developer friction and cognitive load through standardized golden paths, configuration, and tooling; provide guardrails for security, encryption, and least-privilege access; and help organizations manage the effects of AI on engineering work. It recommends measuring velocity indicators such as cycle time and lead time to change, along with AI adoption, impact, and other metrics, to quantify platform performance and guide improvements. ### Source excerpt Learn how to prove the value of platform engineering to executives. This article outlines the essential metrics--from velocity and AI impact to developer sentiment--needed to quantify the success of your Internal Developer Platform (IDP) and keep pace with evolving AI capabilities. ## The State of AI Impact in Engineering DevFeed: [The State of AI Impact in Engineering]() Original publisher: [Read original article]() Author: Luca Rossi Published: 2026-07-22T12:06:40Z Content type: article Language: en Sources: [Refactoring]() Topics: [Artificial Intelligence](), [data]() Tags: [ai](), [ai-impact](), [data](), [engineering](), [velocity]() ### AI overview The article examines data from more than 500 teams to explore what it reveals about velocity gains associated with AI in engineering. ### Source excerpt What data from 500+ teams is revealing about velocity gains. ## How we brought agentic workflows to Cloud SIEM with the Datadog MCP Server DevFeed: [How we brought agentic workflows to Cloud SIEM with the Datadog MCP Server]() Original publisher: [Read original article]() Author: Chelsea Xu; Eddie Cai; Romain Kirszbaum; Mohamed Hachem Ouertani Published: 2026-07-17T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [SIEM, Security](), [MCP Server](), [Model Context Protocol](), [Security & compliance, Cloud security](), [real user monitoring]() Tags: [agent-observability](), [agentic](), [ai](), [ai-impact](), [cloud-security](), [context-window](), [eval](), [mcp](), [mcp-server](), [real-user-monitoring](), [security](), [tool](), [workflows]() ### AI overview This article explains how Datadog built MCP tools for Cloud SIEM to support agentic security workflows. It covers tool scoping based on user behavior, progressive disclosure for managing a shared context window, custom evaluation of non-deterministic agent behavior, and governance of a growing multi-team toolset. ### Source excerpt See how we built MCP tools for Cloud SIEM, using usage data, progressive disclosure, and a custom eval framework to keep a multi-team agentic toolset reliable. ## 5 pitfalls to avoid when measuring DevEx in the AI era DevFeed: [5 pitfalls to avoid when measuring DevEx in the AI era]() Original publisher: [Read original article]() Author: Candace Shamieh; Teddy Gesbert Published: 2026-06-30T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [devex](), [code productivity](), [engineering-leadership](), [Artificial Intelligence](), [Code review]() Tags: [ai](), [ai-adoption](), [ai-impact](), [ci-cd](), [ci-visibility](), [collaboration](), [developer-experience](), [devex](), [devops](), [dora-metrics](), [engineering-management](), [internal-developer-portal](), [metrics](), [performance](), [productivity](), [review](), [software-delivery](), [test-optimization]() ### AI overview This article examines common pitfalls in measuring developer experience during the AI era. It argues that individual output metrics, including token consumption, lines of code, pull request counts, commits, and story points, can undermine trust and collaboration when treated as measures of personal productivity. ### Source excerpt Don't mistake AI adoption for productivity. Learn how to avoid 5 common pitfalls when measuring DevEx, with practices from Datadog engineering ## Choco automates food distribution with AI agents DevFeed: [Choco automates food distribution with AI agents]() Original publisher: [Read original article]() Published: 2026-04-27T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [OpenAI](), [API](), [Automation](), [Large Language Model](), [multimodal](), [Inference](), [Latency]() Tags: [ai](), [ai-impact](), [apis](), [automation](), [customer](), [growth](), [inference](), [latency](), [llms](), [multimodal](), [openai](), [productivity](), [supply-chain]() ### AI overview Choco uses OpenAI APIs, production-ready LLMs, and multimodal systems to automate food-distribution order processing. Its OrderAgent converts emails, SMS messages, images, and documents into structured ERP orders, while VoiceAgent uses the Realtime API for phone ordering with sub-second latency. The article reports increased productivity, reduced manual order entry, and large-scale processing across the food supply chain. ### Source excerpt How Choco used OpenAI APIs to streamline food distribution, boost productivity, and unlock growth--an in-depth customer story on real-world AI impact.