Software Developers
Six people did this job in 1945. Today: 1.69 million. every figure cited
Drag the slider to travel 91 years of this work.
Employment of US computer programmers (the sibling SOC 15-1251, kept separate from software developers) falls to its lowest level since 1980 — about half the 2000 peak — with a 27.5% drop in the 12-month average since early 2023, correlating with ChatGPT's release.
The tools that defined the work
Select an era to see how it reshaped the work.
ENIAC and plug-board programming
No programming languages existed. Programs were specified by physically rewiring patch panels and setting thousands of switches by hand. Programming was treated as clerical work and largely assigned to women; the engineers who designed the machine considered hardware (not software) to be the intellectual achievement.
Effect on the workCohort: 6 ENIAC programmers in 1945. The broader US Army "computers" pool — ~200 women doing manual calculation — was the recruiting feeder.
Work toolChanging equipment FORTRAN, LISP, COBOL, ALGOL
First-generation high-level languages decoupled programming from machine wiring. FORTRAN (IBM, 1957) made scientific computation a writeable activity; LISP (McCarthy, 1958) seeded the AI tradition; COBOL (1960) put programming inside enterprise back-offices. Headcount grew from ~15K (1957) toward >100K within a decade.
Mainframe processingComputerized records Structured programming + UNIX + C
Dijkstra's 1968 "GOTO considered harmful" letter triggered a decade of methodological re-tooling. UNIX (Thompson + Ritchie, Bell Labs, 1969) and C (Ritchie, 1972) made portable systems software possible and seeded the open-source tradition. The job title "software engineer" emerged — formalized at the 1968 NATO conference.
Work toolChanging equipment IBM PC + Borland Turbo Pascal
The IBM PC (1981) and the $49.95 Turbo Pascal compiler (Borland, 1983) democratized programming — a single developer could buy a development environment for the price of a textbook. Programming moved out of computer centers and onto individual desks. By 1990 BLS counted ~565,000 computer programmers in the US, ~3.5× the 1980 number.
Work toolChanging equipment Visual Basic + RAD + Windows business apps
Microsoft Visual Basic (1991) created an entire occupational sub-category — "VB programmer" — focused on internal business applications. Rapid Application Development tools (VB, Delphi, PowerBuilder) let business analysts build their own software. This is also when "the programmer who isn't a computer scientist" became the modal hire.
Work toolChanging equipment Open source — Linux, Apache, Perl, MySQL
The LAMP stack (Linux 1991, Apache 1995, MySQL 1995, Perl 1987→1994) made it possible to build commercial software entirely on free tools. Open source flipped the economics of software development; by the early 2000s no startup paid for a database license, a web server, or an OS.
Work toolChanging equipment Web 1.0 — Netscape, Java, JavaScript
The Netscape Navigator browser (1994) and the simultaneous arrival of Java + JavaScript (1995) created an entirely new programming target: the browser. Demand for web developers exploded; "webmaster" and "web programmer" were briefly distinct census-tracked occupations. The dot-com peak in 2000 saw US programmer headcount cross 700,000.
Work toolChanging equipment Agile + Scrum + XP
The Agile Manifesto (signed February 2001 by seventeen developers at Snowbird, Utah) replaced the waterfall planning culture that had dominated since the 1968 NATO conference. Scrum (1995) and Extreme Programming (1996) provided the practice scaffolding. Developer time shifted from up-front specification toward iterative iteration in two-week cycles.
Work toolChanging equipment AWS S3 + EC2 — public cloud
Amazon S3 (March 14, 2006) and EC2 (August 2006) made it possible to ship a production service without buying a single server. By 2010 every Amazon.com retail site had migrated to AWS; by 2012 Netflix had publicly committed 100% of infrastructure to AWS. The "ops" half of the developer job mostly disappeared into AWS bills.
Work toolChanging equipment GitHub + distributed version control
GitHub launched April 2008. Linus Torvalds's git (2005) had already replaced CVS/SVN in serious open-source projects; GitHub built a social layer on top and within a decade had >100M users. Open-source contribution became a public résumé. Hiring funnels reorganized around public commit history.
Work toolChanging equipment DevOps + Docker + Kubernetes
The first DevOpsDays conference (Ghent, October 2009, founded by Patrick Debois) named a practice that had been emerging at Flickr, Etsy, and Amazon. Docker (2013) and Kubernetes (2014) operationalized it. The developer role broadened to absorb deployment, monitoring, and incident response — the "full-stack" era.
Work toolChanging equipment GitHub Copilot — first AI pair-programmer
GitHub announced Copilot (technical preview) on June 29, 2021, built on OpenAI Codex. The first commercial product framed explicitly as "an AI that writes code with you." General availability followed June 21, 2022 at $10/month. GitHub later reported Copilot was "writing nearly 50% of code in files where it's enabled" — the first widely-cited substitution claim that wasn't hype.
AI audit toolsPattern detection ChatGPT — general LLM code assistance
ChatGPT launched November 30, 2022. Within weeks developers were routing Stack Overflow questions through it instead of Stack Overflow; by 2024 the Stack Overflow Developer Survey reported 63.2% of professional developers using AI tools in their workflow (up from 44% in 2023, with 82% using AI for code generation specifically).
Effect on the workSentiment turned mixed fast — favorability dropped from 77% (2023) to 72% (2024); 45% of developers say AI handles complex tasks poorly, 31.2% explicitly distrust AI output.
AI audit toolsPattern detection Cursor + AI-first IDEs
Cursor (Anysphere, 2023) was the first IDE rebuilt around an LLM rather than retrofitted with one. Within a year it had displaced VS Code + Copilot as the modal "AI coding workflow" for ambitious developers — the editor where the AI is the primary interface, not a sidebar.
Work toolChanging equipment Autonomous coding agents arrive (Devin)
Devin (Cognition Labs, announced March 12, 2024) was the first product pitched as a fully autonomous software engineer: take a ticket, write code, run tests, open a PR, with no human in the loop. Cognition raised at a $350M valuation within weeks and turned down larger offers. Independent reviewers (Internet of Bugs) showed Devin failing on real Upwork tasks, and the autonomous-engineer pitch ran well ahead of what the tools could actually deliver. But the direction of travel was set, and within a year every major vendor shipped an "agent mode."
Effect on the workThe 2024 story was substitution: a machine pitched to do the whole job. In practice the early agents still needed close supervision, so the immediate effect was hype and anxiety far more than displacement.
Work toolChanging equipment Agentic coding goes multi-vendor: Claude Code, OpenAI Codex, Cursor, Copilot
By 2025 and 2026 the daily reality was not one autonomous engineer but a crowded, multi-vendor field of agentic assistants a developer drives. The JetBrains AI Pulse survey (January 2026, over 10,000 professional developers) found roughly 90% regularly using at least one AI tool at work, led by GitHub Copilot (29%), with Cursor and Claude Code tied at 18% (Claude Code reached 24% in the US and Canada and posted the highest loyalty on the market at 91% satisfaction). OpenAI Codex was a striking case of how fast position can change. It launched early, an open-source terminal agent in April 2025 and a cloud agent in May 2025 on the codex-1 model, yet as late as January 2026 it sat at just 3% workplace usage and 27% awareness. Then it surged: GPT-5-Codex (September 2025) could run unattended for hours on a single task, GPT-5.3-Codex and a Cerebras-hosted "Spark" variant (February 2026) cut latency about 15x, a desktop app shipped to supervise several agents at once, and Codex crossed 2 million weekly active users by mid-March 2026, roughly fivefold growth in a quarter. The incumbents moved too: Cognition, the maker of Devin, bought the Windsurf IDE (closed December 2025) and shipped its own SWE-1.5 model, consolidating rather than replacing engineers, and Google entered with the Antigravity editor (November 2025, 6% adoption by January 2026). The common thread: the editor or terminal becomes a place you delegate work and review it, not type it line by line.
Effect on the workUsage rose while trust fell. The Stack Overflow 2025 Developer Survey found 84% of respondents use or plan to use AI and about half of professional developers use it daily, yet favorability slid to 60% (61% among professionals), down from over 70% in 2023 and 2024. Only about a third trust the accuracy of AI output (33%) against 46% who actively distrust it, and the most-cited frustration was "AI solutions that are almost right, but not quite" (45%), with 66% saying they now spend more time fixing that almost-right code. Underneath the sentiment the substitution is measurable: by late 2025 the Anthropic Economic Index found that "modifying software to correct errors," debugging, was the single most common task in Claude API transcripts, and Computer and Mathematical tasks made up 46% of first-party API traffic. The work is shifting from writing code to specifying and reviewing it.
AI audit toolsPattern detection
What credible sources project
Scrub the slider past now to anchor each scenario on the scrubber. The spread is the range of futures credible sources project for this role.
What's shifting in the work right now
The historical view above shows how this role has moved. This is the present-day detail: which AI tools are picking up which tasks, where the edge still is, and the natural directions this work can grow.
What's changing in your day
Three parts of your work where AI is already doing real lifting, and what stays yours.
AI is taking this onMigrate and modernize legacy codebases — refactoring deprecated patterns, upgrading dependencies, and translating between languages or frameworks — tasks where autonomous agents (Devin, Copilot Workspace) now handle most repetitive transformation work.
Migrate and modernize legacy codebases — refactoring deprecated patterns, upgrading dependencies, and translating between languages or frameworks — tasks where autonomous agents (Devin, Copilot Workspace) now handle most repetitive transformation work.[7],[10]
Redirect effort from mechanical refactoring to post-migration validation: define the correctness criteria, own the test harness, and perform the business-logic review that verifies the automated agent preserved intended behavior.
AI is sitting alongside you herePrototype new features or proof-of-concepts rapidly using AI-native editors and agent tools, iterating on natural-language specifications before committing to a full implementation plan.
Prototype new features or proof-of-concepts rapidly using AI-native editors and agent tools, iterating on natural-language specifications before committing to a full implementation plan.[11],[12]
Become fluent in prompt engineering for code — writing precise, scoped natural-language specs that produce useful first drafts; treat AI-generated prototypes as spike artifacts requiring design review before production use.
AI is sitting alongside you hereWrite and maintain automated test suites (unit, integration, end-to-end), delegating test-case generation to AI tools while curating edge cases that reflect real production failure modes.
Write and maintain automated test suites (unit, integration, end-to-end), delegating test-case generation to AI tools while curating edge cases that reflect real production failure modes.[6],[3]
Focus on test strategy rather than test authorship: design the test pyramid, define coverage standards, and own the decision of which failure modes matter most — AI can generate hundreds of tests but needs human guidance on what to cover.
Where this role is heading
Natural next steps for someone with your foundation: not exits, evolutions.
Computer and Information Systems Managers
The 2025-2026 labor market shows rising demand for managers who can evaluate AI tooling, set adoption strategy for agentic coding agents, and lead teams navigating AI-assisted development. Experienced developers who build management skills command the highest compensation premiums in the sector.
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