# future of software Published articles for future of software. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Big Tech Laid Him Off. He Realized He Didn't Need a Job. -- Asian Dad Energy DevFeed: [Big Tech Laid Him Off. He Realized He Didn't Need a Job. -- Asian Dad Energy]() Original publisher: [Read original article]() Author: Daniel Beach Published: 2026-09-09T14:07:55Z Content type: opinion Language: en Sources: [Data Engineering Central]() Topics: [Job](), [future of software](), [Software Engineering](), [Artificial Intelligence](), [cobol](), [mainframes](), [ibm]() Tags: [ai](), [career](), [cobol](), [future-of-software](), [ibm](), [job](), [layoff](), [mainframes](), [software-engineering](), [tech]() ### AI overview A Data Engineering Central Podcast episode features AsianDadEnergy discussing his career from BASIC, IBM 386 systems, and COBOL mainframes to consulting and a chief architect role in Big Tech, followed by a layoff. The conversation covers financial independence, AI's effects on software engineering, communication skills, identity beyond work, and content creation. ### Source excerpt Data Engineering Central Podcast ## How Coding Agents Are Shifting the Focus of Software Engineering DevFeed: [How Coding Agents Are Shifting the Focus of Software Engineering]() Original publisher: [Read original article]() Author: Dr Milan Milanović Published: 2026-09-03T15:00:51Z Content type: opinion Language: en Sources: [Tech World With Milan Newsletter]() Topics: [Software Engineering](), [AI-assisted coding](), [future of software](), [AI Development]() Tags: [agent](), [architecture](), [coding-agents](), [future-of-software](), [guardrails](), [junior-developer](), [software-engineering]() ### AI overview This commentary argues that coding agents are making code generation, testing, refactoring, and bug investigation cheaper and faster. As a result, software engineering increasingly emphasizes judgment, specification, verification, architecture, ownership, team scope, and managing technical debt. ### Source excerpt Since the beginning of writing software, it has been a hard thing to do. ## What is the future of software engineering with Adam Bender, Principal Software Engineer at Google DevFeed: [What is the future of software engineering with Adam Bender, Principal Software Engineer at Google]() Original publisher: [Read original article]() Author: Dr Milan Milanović Published: 2026-07-02T15:01:06Z Content type: opinion Language: en Sources: [Tech World With Milan Newsletter]() Topics: [future of software](), [Software Engineering](), [AI-assisted coding](), [Testing](), [Integration testing](), [Google]() Tags: [ai](), [ai-coding](), [code-generation](), [conway-s-law](), [future-of-software](), [integration-testing](), [software-engineering]() ### AI overview The article discusses Adam Bender's view that the AI coding debate focuses too narrowly on speed. It argues that faster code generation increases pressure on testing, review, engineering culture, system understanding, and long-term maintainability, with integration testing becoming a particular challenge. ### Source excerpt Most of the AI coding debate is about speed. ## Community visions for more human ways to interact with AI-enabled software DevFeed: [Community visions for more human ways to interact with AI-enabled software]() Original publisher: [Read original article]() Author: Jenny Xie Published: 2026-06-24T12:00:00Z Content type: opinion Language: en Sources: [Figma Blog]() Topics: [Interaction Design](), [Artificial Intelligence](), [User interface design]() Tags: [ai](), [design](), [figma](), [future-of-software](), [interaction-design](), [software]() ### AI overview This opinion article presents community ideas about how AI could make software interactions more human and context-aware. Examples include controls that appear only when relevant and tools that combine voice, gestures, and cursor movement to support creative work. ### Source excerpt Our community imagines how AI might bring about more human ways to interact with software. ## What Is Software, and Will LLMs Replace It? DevFeed: [What Is Software, and Will LLMs Replace It?]() Original publisher: [Read original article]() Author: Federico Tomassetti Published: 2026-06-23T08:56:06Z Content type: opinion Language: en Sources: [Federico Tomassetti]() Topics: [Large Language Model](), [Software](), [data](), [integrity](), [Software as a service]() Tags: [ai](), [business](), [chatbots](), [consistency](), [data](), [future-of-ai](), [future-of-software](), [integrity](), [interfaces](), [language-engineering](), [llms](), [mcp](), [opinion](), [patterns](), [reflections](), [saas](), [software](), [software-development](), [sql](), [structure](), [visualization]() ### AI overview The article argues that large language models are unlikely to replace software. Instead, they may provide more flexible interfaces while software's underlying structures--organized data, schemas, constraints, consistency rules, visualizations, and guided processes--remain essential. ### Source excerpt Software isn't being replaced by LLMs, it's being fronted by them, with the deterministic core (schemas, constraints, processes) staying as essential as ever. The post What Is Software, and Will LLMs Replace It? appeared first on Federico Tomassetti. ## Responsible Loop Engineering DevFeed: [Responsible Loop Engineering]() Original publisher: [Read original article]() Author: Kaushik Gopal Published: 2026-06-22T20:39:22Z Content type: opinion Language: en Sources: [Kaushik Gopal's Site]() Topics: [Loop Engineering](), [Artificial Intelligence](), [Software Engineering](), [future of software](), [Agent Skills]() Tags: [agent](), [agents](), [ai](), [engineering](), [future-of-software](), [loops](), [software-engineering]() ### AI overview The author argues for responsible loop engineering: designing and operating agent systems that can run continuously while controlling costs. The article distinguishes capped one-shot loops from autonomous loops that select tasks, use subagents, research, test, and return results for review. It argues that autonomous loops require bespoke engineering, integrations, execution strategies, and queueing. ### Source excerpt Loop engineering convinced me. Not because it's clever -- because done right, it doesn't bankrupt you. This post captures where I've landed: a responsible way to run loops at scale without burning a hole in your pocket. Naysayer -> Believer ## I've been a vocal naysayer. Not because the approach doesn't work -- it works. The costs never justified it. No surprise -- the people singing its praises usually aren't the ones paying the API bills. But when Peter & Boris tell you something, you look closer. Same thing happened with agent skills -- Simon W saw something early, and that became the biggest hammer in our AI toolbox. Types of loops ## The public discourse mixes loops with loop "engineering," so let's disambiguate. One-shot loops ### Today, an agent can execute a task, have an independent judge review the result1, apply the feedback, and repeat. You put a cap on the number of loops. Or you let it run until it's "satisfied" -- a bad idea. These are easy to set up. Many people are already demonstrating them. I call these one-shot loops. They're easy enough that I'll focus on the other kind. Autonomous loops ### But when Peter Steinberger and Boris Cherny talk about loops, I think they mean autonomous loops. You set up agents to run continuously. They decide when to act, pick up the right tasks, spin off subagents, research, test theories, and send results back for review. Or ship, if confidence is high enough. An entire system running on its own -- you shovel tasks at the speed of thought or voice. These loops are self-sustaining and take real engineering to get right. I'll go out on a limb: Future of Software Engineering is loop engineering Most of the software engineers of tomorrow are going to be spending their time setting up and engineering loops. Because it's hard and it's going to require skill -- there's no one loop we can template for all solutions. From my experimenting so far, this feels bespoke in the way good software is bespoke. You can't just use an agent sk ## Kent Beck and Michael Grinich Discuss AI Adoption and the Future of Software Engineering DevFeed: [Kent Beck and Michael Grinich Discuss AI Adoption and the Future of Software Engineering]() Original publisher: [Read original article]() Author: Kent Beck Published: 2026-05-20T14:15:38Z Content type: opinion Language: en Sources: [Software Design: Tidy First?]() Topics: [Artificial Intelligence](), [future of software](), [Software Engineering](), [engineering-culture](), [dev-tools]() Tags: [ai](), [developer-tools](), [discussion](), [engineering-leadership](), [future-of-software](), [leadership](), [software-engineering]() ### AI overview Kent Beck and Michael Grinich discuss how AI adoption is affecting the broader technology ecosystem, software costs, competition, and engineering leadership. They also discuss enterprise developer tools and the motivation to build software. ### Source excerpt Michael Grinich & Kent Beck on AI adoption, the future of software engineering, and what enterprise developer tools reveal about where tech is headed. ## An Ethos for Using AI Coding Tools: Ownership and Exploiting High-Value Opportunities DevFeed: [An Ethos for Using AI Coding Tools: Ownership and Exploiting High-Value Opportunities]() Original publisher: [Read original article]() Published: 2025-09-15T00:00:00Z Content type: opinion Language: en Sources: [Tim Kellogg]() Topics: [Artificial Intelligence](), [ai-coding](), [Software Engineering](), [Data analysis](), [future of software]() Tags: [ai](), [ai-coding](), [coding](), [data-analysis](), [future-of-software](), [software-engineering]() ### AI overview The author describes an ethos for using AI coding tools, centered on owning and understanding the generated code. They also argue that effective AI coding involves finding opportunities where limited AI effort can produce substantial value, such as proof-of-concept work and rapid data analysis. ### Source excerpt A few people have asked me how I use AI coding tools. I don't think it's a straightforward answer. For me it's not really a procedure or recipe, it's more of an ethos.