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Computer programmers

Also called Programmer, Programmer analyst, COBOL programmer, RPG programmer, Mainframe programmer

In brief

In May 2025, BLS counted 92,230 US computer programmers, with median pay of $100,390 a year. Of the BLS counts since 2021, the highest was 152,610, in May 2021. The earliest figure here is an estimate of 15,000 from 1957, not measured the same way as today's count. The last full technology era for computer programmers: OpenAI Codex, 2021 to 2022.

What the sources say

Official outlook

BLS projects employment to decline 7.3% from 2025 to 2035.

BLS Employment Projections 2025-35

AI exposure studies

  • 68.3% of this job's tasks are exposed to large language models. Not a forecast of job losses. Eloundou and others, 2023
  • 0.48 estimated probability that the whole occupation could be computerised, over an unspecified number of years. Not a forecast of job losses. Frey and Osborne, 2013

Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. BLS publishes these counts as yearly survey snapshots and cautions against reading them as a precise trend.

Last updated .

Cite this page
APA

Future History. (2026, October 11). Computer programmers: jobs and pay since 1957 [Data set; data as of BLS OEWS May 2025; BLS Employment Projections 2025-35]. https://futurehistory.earth/timeline/15-1251

Chicago

Future History. "Computer programmers: jobs and pay since 1957." Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. Last modified October 11, 2026. https://futurehistory.earth/timeline/15-1251.

BibTeX

@misc{futurehistory_15_1251, author = {{Future History}}, title = {Computer programmers: jobs and pay since 1957}, year = {2026}, month = oct, howpublished = {\url{https://futurehistory.earth/timeline/15-1251}}, note = {Last updated 2026-10-11. Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35}, }

The access date is today's date on your device.

drag to travel through time
195019752000now
2026
Known then as Computer Operator / Machine Coder
Known then as Programmer / Coder
Known then as Computer Programmer
Known then as Programmer / Programmer-Analyst
Known today as Computer Programmer
Projected to still be called Computer Programmer
Data from the May 2025 survey
US Employment
92K
Est.

Stated in source: BLS OEWS

Official statistic
Report an error
Median Annual Wage
$100,390
Est.

≈ $97,816 in 2024 prices (derived by us, CPI-adjusted)

Official statistic
Report an error
Beat · 1945

ENIAC goes online and its six women programmers (Jean Bartik, Kathleen Antonelli, Marlyn Meltzer, Betty Holberton, Frances Spence, and Ruth Teitelbaum) invent subroutines, nesting, and conditional branches without manuals, professors, or precedent. They are not invited to the February 14, 1946 public unveiling dinner, and are cropped out of the photographs that circulated in the press. The profession begins in invisibility.

Beat · 1968

Edsger Dijkstra's letter "Go To Statement Considered Harmful", submitted to Niklaus Wirth at CACM and published in the March 1968 issue, launches the structured-programming movement and begins the shift of programming from an ad-hoc craft to an engineering discipline with formal methodology. Dijkstra wins the Turing Award in 1972 for this work. The same year, the NATO Software Engineering Conference coins the term "software engineering" and articulates the "software crisis": systems costing 10× their estimates and shipping years late.

Beat · 1975

Frederick Brooks publishes "The Mythical Man-Month: Essays on Software Engineering", drawing on his experience managing IBM's OS/360 project. The book's central insight ("adding manpower to a late software project makes it later") becomes one of the most-cited principles in software management and articulates why programmer productivity is not linearly scalable. Brooks later estimated OS/360's failure modes would cost $500M; the book's second essay, "No Silver Bullet" (1986), explicitly argues that there is no single technique or tool that will deliver an order-of-magnitude productivity improvement in software development: a prediction that AI coding tools are now actively testing.

Beat · 2001

The dot-com bust triggers the largest peacetime layoff wave in the history of the US tech sector: over 168,000 tech layoffs are announced in 2001 alone. Computer programmer employment falls from the 700,000 peak in 2000 toward 499,000 by 2002: a 28% decline. University enrollment in computer-science degree programs drops noticeably in subsequent years. The BLS, which had forecast continued programmer-employment growth through 2010, is caught projecting growth while the actual workforce shrinks. By 2006, programmer employment settles at 435,000-130,000 below the 1990 level despite 16 years of internet expansion.

Beat · 2021

OpenAI publishes the Codex paper (arXiv:2107.03374, August 2021), showing a GPT model fine-tuned on 54 million GitHub repositories can solve 28.8% of competitive programming benchmarks at first try and 70.2% with repeated sampling. GitHub Copilot's technical preview launches June 29, 2021 using Codex as its engine. This is the moment an AI tool first produces plausible production code at scale in a real programming environment, not a research demo. Within a year, 1.3 million developers are on the Copilot waitlist.

Beat · 2022

GitHub Copilot becomes generally available on June 21, 2022 at $10/month for individuals and $19/seat/month for business. A September 2022 GitHub-sponsored controlled study of 95 developers finds Copilot users complete tasks 55% faster (1h 11min vs 2h 41min, p=.0017) and report higher job satisfaction. Within a year Copilot reaches 1.3 million paid subscribers: the fastest adoption curve of any developer tool in GitHub's history.

Beat · 2025

Fortune publishes "Employment for computer programmers in the US has plummeted to its lowest level since 1980 — years before the internet existed" (March 17, 2025). BLS OEWS data shows 109,870 programmer jobs in May 2024, down from 199,540 in May 2019. CPS rolling-average data shows a 27.5% drop in programmer employment since approximately 2023, coinciding with ChatGPT's November 2022 launch. Researchers begin describing this as "one of the earliest visible AI labor-market effects" in occupational data: the first white-collar occupation where the AI productivity shock is legible in census-level employment statistics rather than just survey responses.

How to read this number

OEWS is a point-in-time survey snapshot, not a continuous time series; BLS advises against using it for year-over-year trend comparison.

Each dot is a cited figure over time; the dotted line only links them (values between aren't measured). Hollow dots are estimates.

Official statistic: Copied by a script from a statistics agency table or a published study's table, not retyped by hand. BLS figures and the forecast and exposure tables are also re-checked against that table on every data run. Researched context: Researched from the cited source rather than copied from it by a script, so it is not machine-checked yet. Spot a mistake? Each figure has a “Report an error” link.

Cited forecasts · present → 2035

Will AI replace computer programmers? What the research says

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, we show the disagreement rather than an average.

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, the spread is the honest signal.

What published forecasts say
Forecast change in the number of people doing this work, as published by each named source. Bars share one scale on every role, centred on zero and reaching the end at ±30%. Grey marks a small change, 3% or less either way.
BLS Employment Projections 2025-35
→ 2035
-7.3%
BLS National Employment Matrix, 2025-35 cycle, line for SOC 15-1251 (Computer programmers): 110.8 thousand jobs in 2025, projected 102.7 thousand in 2035, a change of -8.1 thousand (-7.3%). Matrix employment counts all jobs in the occupation, including self-employed workers (14.6% of this occupation in 2025). The average for all occupations in this cycle is +3.5%.
Official statisticReport an error
How exposed is this work to AI (research measures)
Scores as published by each study. Each row says what it measures. These measure what AI could technically do, not forecasts of jobs lost.
Eloundou et al. (2023), "GPTs are GPTs"
2023 study · Share of tasks exposed to LLMs (β, human-rated)
68.3%
of tasks
What this measures

Published occupation-level exposure from the paper's occ_level.csv for O*NET-SOC 15-1251.00 Computer Programmers. Shown: human-rated β = 0.683, the share of the occupation's tasks an LLM could do in at least half the time at equal quality, counting tasks that need LLM-powered software at half weight (β = E1 + 0.5 × E2). Other published measures for this occupation: human-rated α (LLM alone) 0.433 and γ (all LLM-assisted) 0.933; GPT-4-rated β 0.95. This measures how much of the work an LLM could touch, not how many jobs disappear.

Official statisticReport an error
Frey & Osborne (2013), "The Future of Employment"
2013 study · Probability of computerisation (2013 model)
48%
probability
What this measures

Published appendix value for SOC 2010 15-1131 Computer Programmers: probability of computerisation 0.48, rank 293 of 702 (rank 1 is least computerisable), mapped one-to-one to SOC 2018 15-1251 by the BLS 2010-to-2018 crosswalk. Frey and Osborne estimated in 2013, with a Gaussian-process classifier on O*NET variables, how likely the whole occupation is to be technically automatable over "some unspecified number of years, perhaps a decade or two". It is a probability for the occupation, not a share of tasks and not a forecast of jobs lost.

Official statisticReport an error
Today, in this role

What's changing day to day for computer programmers

Three parts of your work where AI is already doing real lifting, and what stays yours.

AI is taking this onUse AI coding agents (Copilot, Cursor, Amazon Q Developer) to generate boilerplate, scaffold repetitive modules, and autocomplete routine code blocks, then review, test, and integrate the output into the existing codebase.

Use AI coding agents (Copilot, Cursor, Amazon Q Developer) to generate boilerplate, scaffold repetitive modules, and autocomplete routine code blocks, then review, test, and integrate the output into the existing codebase.[3]

Where your edge is

Shift focus from typing code to critically reviewing AI output: verify logic, edge cases, and security before committing. Build prompt-engineering fluency for your specific tech stack.

AI is taking this onWrite and update inline code comments, README files, and API documentation by prompting AI tools with the source code, replacing manual documentation passes that are chronically deferred in maintenance shops.

Write and update inline code comments, README files, and API documentation by prompting AI tools with the source code, replacing manual documentation passes that are chronically deferred in maintenance shops.[6],[1]

Where your edge is

Review AI-generated docs for accuracy: auto-generated descriptions often miss business context and exception handling rationale. Add the "why" that AI cannot infer from the code alone.

AI is taking this onParticipate in agentic software development workflows where autonomous AI agents (Devin, Cursor Agent) handle multi-step tasks (plan, implement, test, and open a pull request) while the programmer acts as reviewer and approver rather than implementer.

Participate in agentic software development workflows where autonomous AI agents (Devin, Cursor Agent) handle multi-step tasks (plan, implement, test, and open a pull request) while the programmer acts as reviewer and approver rather than implementer.[7],[8]

Tools picking this up
Where your edge is

Build skills in agentic workflow design: learn to decompose tasks into clear sub-goals, write effective agent prompts, and catch failure modes before they propagate. The emerging role is "AI-orchestrating programmer," not "absent programmer."

Where computer programmers go next

Natural next steps for someone with your foundation: not exits, evolutions.

What each move takes

Computer and Information Systems Managers lead entire technology functions, setting strategy, managing teams, and aligning IT investments with business goals. Long-tenured programmers with cross-functional relationships and institutional system knowledge are credible candidates, especially as AI compresses the programmer headcount they would have previously supervised.

What you'd add
  • · Technology strategy and IT governance (ITIL, COBIT fundamentals)
  • · Budget ownership and vendor contract negotiation
  • · People management: hiring, performance reviews, team building
  • · Business-case writing and C-suite communication
What it takesA real upskill, but a natural one
Tools of the era

How has the work of computer programmers changed since 1945?

The tools that defined the work, one line per era. Select an era to see how it reshaped the work.

  • Machine code and assembly language (punch cards, plugboards)

    The first programmers worked entirely in machine language: binary or hexadecimal opcodes entered via patch panels, punch cards, or toggle switches on the front panel. ENIAC used plug-board wiring to route signal paths; programs were the physical configuration of cables. There was no distinction between writing a program and physically rewiring the machine.

  • Assemblers and early compilers (Grace Hopper's A-0, 1952)

    Grace Hopper at Remington Rand developed the A-0 system in 1952: the first compiler, which translated mathematical notation into machine code. This was the conceptual bridge between machine-level programming and high-level languages. Hopper also pioneered the concept of machine-independent code, arguing (against widespread skepticism) that a computer could be instructed in something closer to English.

  • FORTRAN (1957) and COBOL (1959): first high-level languages

    FORTRAN (FORmula TRANslating system), released by IBM in April 1957 under John Backus, was the first high-level language to achieve widespread commercial use: it dramatically reduced the time to write scientific and engineering programs and opened programming to non-specialists. COBOL followed in 1959, designed by a DoD committee that included Hopper, specifically for commercial data-processing applications. COBOL would become the most widely deployed business programming language in history and remains so by lines-of-code count today.

    Effect on the work

    FORTRAN is documented as having "greatly increased programmer productivity and significantly lowered costs": IBM's own history credits it with making programming accessible beyond a small group of experts, which expanded the market for programmers rather than displacing them.

  • Time-sharing systems (MULTICS 1964, IBM TSS, BASIC 1964)

    Time-sharing allowed multiple users to interact with a single mainframe simultaneously via terminals, replacing the batch-card-deck paradigm. BASIC (Beginners All-purpose Symbolic Instruction Code), designed at Dartmouth in 1964, made interactive programming accessible to non-specialists and students for the first time. The shift from batch to interactive development fundamentally changed the feedback loop for programmers: errors were visible in seconds rather than hours.

  • Structured programming: Pascal, C, software engineering discipline

    Dijkstra's March 1968 letter to CACM, "Go To Statement Considered Harmful," launched the structured-programming movement, arguing that programs should use structured control flow (if/else, for, while) rather than arbitrary goto jumps. Niklaus Wirth's Pascal (1970) and Dennis Ritchie's C (1972) codified structured programming in widely adopted languages. This era also produced the software-engineering discipline: the 1968 NATO Software Engineering Conference coined the term and articulated the "software crisis", complex systems were taking far longer and costing far more than estimated.

  • Personal computer: IBM PC (1981), Turbo Pascal, Visual Basic

    The IBM PC's 1981 launch and the explosion of PC clones created an entirely new programming market: shrinkwrap software. Microsoft's Visual Basic 1.0 (1991) brought event-driven GUI programming to non-specialists. The PC era split the profession into enterprise/mainframe programmers (still running batch COBOL on IBM iron) and a rapidly growing PC/shrinkwrap cohort working in C, Pascal, and then C++.

  • Internet era: Java (1995), JavaScript, scripting languages, the web stack

    The commercial internet created a third programming paradigm alongside mainframe and desktop: the web application. Java (released May 1995), JavaScript (September 1995), Perl, PHP, and later Python became the dominant languages of the dot-com boom. Every company suddenly needed a website, then a web application, then e-commerce; programmer demand surged to the 700,000 peak by 2000. The bust in 2001-2002 wiped out the speculative layer (more than a hundred thousand tech layoffs were announced in 2001 alone) but the web stack and internet-oriented programming became permanent.

    Effect on the work

    The 2001-2003 bust collapsed employment back toward 435,000 by 2006 as the speculative capacity unwound.

  • Open source + GitHub (2008) + Stack Overflow (2008): networked knowledge

    GitHub launched in April 2008, making Git version control trivially accessible and turning open-source contribution into a public portfolio signal. Stack Overflow launched in September 2008, creating a searchable, reputation-weighted repository of programming Q&A that dramatically reduced the cost of learning and debugging. Together they changed the craft: the "lone coder in a library" archetype was replaced by the connected networked developer who could find a working solution to almost any standard problem in minutes.

  • Cloud computing: AWS (2006), Azure (2010), GCP (2011)

    Amazon Web Services launched its first commercial cloud services (S3 and EC2) in 2006, eliminating the need to provision physical servers. For programmers, this meant infrastructure became code: deployment, scaling, monitoring, and fault tolerance were now programming problems, not sysadmin problems. Cloud also accelerated the offshoring of routine programming work by making remote collaboration frictionless: a programmer in Bangalore could push to the same codebase as one in Seattle with identical tooling.

  • OpenAI Codex (August 2021): LLMs trained on code, powering GitHub Copilot

    OpenAI published "Evaluating Large Language Models Trained on Code" in August 2021, introducing Codex: a GPT model fine-tuned on millions of GitHub repositories. GitHub used Codex as the engine for its Copilot technical preview (launched June 29, 2021), which was the first AI tool that generated plausible code inline in the programmer's editor at production quality.

  • GitHub Copilot GA (June 21, 2022) + Cursor + Claude Code: AI-native coding

    GitHub Copilot became generally available on June 21, 2022: the first widely-deployed AI coding tool with a subscription model. A GitHub-sponsored controlled experiment of many developers found Copilot users completed tasks 55% faster (p=.0017). Cursor launched in 2023 as the first IDE built from scratch around large-context AI, enabling multi-file editing and autonomous feature completion. Anthropic's Claude Code (2025) extended the agentic model to full software engineering tasks including planning, implementation, and pull-request creation. In November 2025 the Anthropic Economic Index matched 34% of sampled Claude.ai conversations and 46% of sampled first-party API transcripts to computer and mathematical tasks, and "modifying software to correct errors" was the most common task on both.

    Effect on the work

    CPS employment for computer programmers fell sharply between 2023 and 2025, as GitHub Copilot rolled out; multiple researchers cited the drop as one of the earliest visible AI labor-market effects in occupational data.

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Computer Programmers employment and pay by year

On record since1945
Latest US employment92,230 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Latest US median pay$100,390 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Outlook-7.3% by 2035Source: BLS Employment Projections 2025-35Official statisticReport an error
View all 13 years of cited US data, each linked to its source
YearUS employmentAnnual pay (median unless marked)SourceReport
195715,000n/aEst., Wikipedia Researched contextReport
1970200,000n/aEst., Census Bureau / ACS-35 Researched contextReport
1980310,000n/aEst., Fortune Researched contextReport
2000700,000$57,590Est., Fortune (employment) Researched contextBLS OEWS 2000 (pay) Official statisticReport
2006435,000n/aEst., Smash Company Researched contextReport
2011n/a$74,280Est., Estimated 2011 median annual wage… Researched contextReport
2019199,540$86,550BLS OEWS May 2019 Official statisticReport
2020178,140$89,190BLS OEWS May 2020 (employment) Official statisticBLS OEWS May 2020 (pay) Official statisticReport
2021152,610$93,000BLS OEWS May 2021 Official statisticReport
2022132,740$97,800BLS OEWS May 2022 Official statisticReport
2023120,370$99,700BLS OEWS May 2023 (employment) Official statisticBLS OEWS May 2023 (pay) Official statisticReport
2024109,870$98,670BLS OEWS May 2024 Official statisticReport
202592,230$100,390BLS OEWS May 2025 Official statisticReport
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