Nearly half of software developers are in their first five years

The overview

The integrated market (Europe, the United States, India and the rest of the world outside China) holds approximately 16.5 million professional software developers, up from 4.5 million in 2000.

Growth over the full period runs at 5.38% a year: 5.66% through the 2000s, 4.66% through the 2010s, and 5.86% across the most recent decade.

Expressed as a doubling period, the population takes 13.2 years to double at the full-period rate. The decades differ: 12.6 years at the 2000s rate, 15.2 at the slower 2010s rate, and 12.2 at the rate of the last ten years. No single figure describes the whole span, and the most recent decade is the fastest of the three.

The market is assembled from seven components rather than modelled as a whole. Five rest on statistical instruments; two are estimated.

Estimated components total 26.2%. Provenance for each is set out in part four.

Who is in the room

Four and a half of every ten sit inside their first five years. Fewer than two have passed fifteen. The people who have watched a full technology cycle begin and end are outnumbered better than four to one by those who have not.

Experience distribution

Three features of the shape

It falls steeply and then flattens. From 45% to 25% to 12% to 8% to 5%, then back up slightly to 5% at twenty-five years and beyond, that final band holding every surviving cohort from 2000 backwards rather than a single five-year intake. Median tenure is under six years.

The 20–25 band is the smallest in the distribution, smaller than the band above it. Those are the 2001–2004 entrants, and the US employment series falls outright through exactly those years. This is the one feature that neither the attrition assumption nor the sector cut affects.

Seniority is scarcer than the structures built to absorb it. About 18% of developers carry fifteen years or more. US information-systems managers number 10.1 per 100 technical staff, down from 15.9 in 2015, and industry-standard senior IC ladders place staff-and-above at roughly 10–15%. Those structures now hold roughly as many positions as there are people qualified for them.

Career movement is the lever

One parameter governs this model and it is not the size of the market. Holding separations fixed and changing the estimated market by fifteen percent in either direction moves the tenure distribution by nothing at all — the shape is scale-invariant.

BLS publishes that rate for software developers with every projections release, and has done since 2016–26 when the current methodology replaced the older Replacements approach. Eight releases exist, each estimated from a different window of Current Population Survey data. Applying each published value flat across the period bounds the answer:

Across every rate BLS has published, the share within five years spans 42.4% to 49.2% and the share past twenty years spans 8.0% to 13.3%. That is the honest uncertainty on this parameter: wide enough to matter, narrow enough that the headline holds. No value outside this range is used anywhere in this document, and none should be: arbitrary multiples of a published figure are not evidence about anything.

The rate is not constant, and the movement has a signature

Total separations run from 7.2% at the 2020–30 vintage down to 4.84% at 2024–34, averaging 6.05% across the eight. The movement is directional rather than noisy, which is why the model uses the series rather than any single value, holding the earliest observed rate for years before 2016.

Almost the entire decline is occupational transfers, not people leaving work. Transfers fell 29%, from 4.9% to 3.5%. Labour-force exits held between 1.4% and 2.3% throughout with no trend. Fewer developers are moving into other occupations than a decade ago, while the rate at which they leave working life altogether has not moved at all. Whatever is driving it concerns the attractiveness of remaining a developer, not retirement or demographics.

How the series was verified

The published rate reproduces from the underlying counts. For the 2023–33 vintage, annual openings less annualised growth over mean employment gives 5.14% against a published 5.10%, confirming both the arithmetic and the denominator convention. BLS applies rates to the mean of base and projected employment, not the base year, a distinction worth four tenths of a point.

Occupation codes were traced across two reclassifications. The 2016–26 and 2018–28 vintages predate the 2018 SOC revision and report 15-1132 and 15-1133 separately, both carrying identical rates. The 2019–29 and 2020–30 vintages use 15-1256, which combines developers with quality-assurance analysts. From 2021–31 onward developers are isolated as 15-1252. Where QA is reported separately its rate runs 0.5 to 0.8 points above developers, so the two combined vintages sit marginally high.

An alternative instrument was tested and rejected. Inferring the rate from an observed age structure does not identify it, for reasons set out in the appendix: the method produces results that resemble evidence and are not.

The same number describes different rooms

Sector distributions are derived exactly as the headline is: from each sector’s own employment history and the same measured separations series. No age data enters. Histories come from ACS microdata for 2009, 2013, 2017 and 2023, and they diverge by a factor of six: developer headcount in the tech sector compounded at 8.38% a year while manufacturing managed 1.28%.

Growth rate is the mechanism. The ordering follows employment growth, which is what a cohort model should produce: a workforce growing at 8% a year is mostly recent arrivals, and a flat one is mostly people already there. Manufacturing has 1.9 of every ten developers past twenty years’ experience because manufacturing has barely hired since 2009.

The tech sector is the junior outlier, not the norm. Anyone whose working life has been spent at technology companies has been sampling the most junior corner of the profession and reasonably mistaking it for the whole.

Institutional memory is inversely proportional to growth, which means it is real but was purchased by stagnation rather than by retention policy.

Education, healthcare and government are absent because their developer populations are too small to measure from this data. Software development accounts for 70% of technical employment in the tech sector and 63% in manufacturing, but only 26% in government, 18% in education and 16% in healthcare, so technical staff in those sectors are mostly support and administration, and the developers among them number fewer than 250 unweighted records apiece across four states.

Where each number comes from

Each component is graded by what actually underlies it: measured where a statistical agency publishes the quantity, derived where a measured quantity is transformed by a documented ratio, and estimated where no measurement exists.

Open questions

Scientific brief

Estimand

The count and tenure distribution of people whose primary occupation is software development, within labour markets that interoperate, meaning markets whose developers are mutually hireable, work on shared codebases, and draw on a common toolchain and hiring pool. Tenure is years since first professional entry, not years since first writing code.

Scope decisions

1. China excluded. Chinese developers operate in a substantially separate ecosystem. GitHub accounts in China grew 1.9× between 2020 and 2026 against a world figure of 4.2×, the lowest multiplier of any major economy; China’s share of world accounts fell from 12.9% to 5.9%; per-capita accounts stand at 8.1 per 1,000 against 18.4 in India and 94.8 in the United States. Domestic platform Gitee grew from roughly 3 million users in 2018 to over 14 million by December 2025 with 40 million repositories, having been commissioned by China’s Ministry of Industry and Information Technology in 2020 to build an independent national code hosting platform. Hong Kong registers 464.7 accounts per 1,000 residents, 46% of its population, a proxy-routing artefact absorbing part of the mainland deficit.
2. Management excluded. Managing technical work is a distinct occupation. US SOC already separates them: information-systems managers sit in 11-3021, outside the 15-1xxx computer occupations. Eurostat’s ICT specialist definition does not, explicitly including ISCO 133, so the European component is adjusted.
3. No available labour statistic counts software developers directly at national scale. Every source instrument (BLS occupational series, Eurostat ICT specialists, NASSCOM industry employment) reports a wider technical population containing them. A software share is therefore applied to each, measured for the United States and derived or estimated elsewhere. This is the largest single manipulation applied to the source data.
4. Occupation frame, not industry frame. Industry classifications cannot observe a developer becoming a manager at the same employer.
5. Tenure frame, not age frame. Cohort tracking on ACS microdata for 2013 and 2023, following identical birth cohorts on matched occupation baskets, shows technical cohorts growing until roughly age forty: the cohort aged 25–29 in 2013 was 75% larger by the time it reached 35–39, and combined cohorts aged 25–54 grew 1.127× across the decade. That requires substantial entry well past the twenties: best-fit mean entry age is 29, with roughly a quarter of entrants arriving in their thirties and one in eleven at forty or older. A developer aged fifty who entered at forty-two is a junior developer. No quantity in this document is derived from age.

Sources

Transformations, and how each value is arrived at

US ×1.096, measured. The CPS series covers full-time wage and salary workers only. ACS 2023 PUMS gives the exact composition of computer occupations: 4.07% self-employed (2.03% unincorporated, 2.04% incorporated), 5.86% part-time, 91.20% full-time wage and salary. The multiplier is 1/0.912. Measured self-employment sits well below the level practitioners typically assume, most likely because many software contractors are W-2 employees of staffing agencies and therefore already inside the CPS count.

US software share 55.2%, measured. Software occupations (programmers, software developers, QA analysts, web developers) as a share of all computer occupations, ACS 2023. Rising from 44% in 2009.

Europe ×0.918, derived. Removes ISCO 133 from the Eurostat basket using the measured US ratio of 636k managers against 6,297k technical staff, or 10.1 per 100. Span of control is a structural property of team organisation rather than a national characteristic, which is what licenses the transfer.

Europe software share 39.7%, triangulated from three European observations. Eurostat’s ICT specialist basket is substantially wider than the US occupational one: beyond core computing it includes electronic engineers, telecommunications engineers, graphic and multimedia designers, information technology trainers, ICT sales professionals, electronics engineering technicians, electronics mechanics and ICT installers. In US SOC every one of those falls outside 15-xxxx.

Three European values inform the share. The United Kingdom is measured: ONS publishes SOC2020 at four digits, permitting a basket built occupation by occupation to match Eurostat’s definition: core computing less managers at 1,354.2 thousand, peripheral occupations at 274.3 thousand, and a software basket of programmers, quality and testing professionals and web designers at 607.2 thousand, giving 37.3%. Germany and France are inferred by dividing published national developer counts by their Eurostat ICT specialist totals, giving 41.2–45.8% and 38.2% respectively; those published counts are of unattributed provenance and are treated as weak. The mean of the three is applied to the bloc.

A single share for the bloc is a convenience rather than a measurement. Eurostat’s own occupational structure varies enormously across member states: the professional share of technical staff in high-technology services runs from 49.1% in Italy to 83.9% in Poland, a 35-point spread. No European country is representative of the aggregate, and Eurostat publishes nothing below one-digit ISCO that would allow the bloc to be disaggregated.

United Kingdom, separated and measured. Eurostat reported the UK through 2019 and not since. The series carries it forward on rest-of-Europe growth, which reconstructs the European total to within 1.4% in every year. Because the UK sits outside the reporting bloc and its own statistical office publishes at four-digit occupational detail, it is booked as its own component at its measured 37.3% share rather than folded into a European average it no longer contributes to.

Europe pre-2011 ×0.82, derived. The ISCO-88 to ISCO-08 transition produces a discontinuity at 2011, 6,768k to 5,681k, a ratio of 0.839. Earlier years are scaled to the later basis; the applied factor is marginally more conservative.

India ×1.115, software share 55%, estimated from external triangulation. NASSCOM measures the IT-BPM industry rather than an occupation, so a multiplier is needed to reach a developer count. No Indian source supplies one. The value used is the median of the only two independent external estimates available: SlashData’s regional population sizing implies 1.26, and the ratio of Indian to American GitHub accounts implies 0.97. The software share of 55% has no Indian basis. See open questions.

Brazil, measured from a business register. IBGE’s Cadastro Central de Empresas reports employment by CNAE activity code, and CNAE division 62 resolves to five digits, separating software development from consulting and technical support. Codes 62.01 (custom development), 62.02 (customisable licensed software) and 62.03 (non-customisable licensed software) give a directly software-specific count: 131,878 in 2006 rising to 479,310 in 2024, a rate of 7.18% a year across eighteen years and 7.10% across the last ten. This is the only source found anywhere that isolates software development inside a national employment register.

Two adjustments are needed to make it comparable. The CEMPRE figure is an industry count, so reaching an occupation-frame number requires an industry-to-occupation ratio that Brazil does not publish; the value applied is 1.43, the average of the only two such ratios with any basis: the US measured at 1.75 and India’s derived 1.115. Years before 2006 are back-extrapolated at Brazil’s own 2006–2011 rate. The resulting 2025 figure is 0.69M; the unadjusted register count of 0.48M is a hard floor.

Rest of Latin America, estimated and anchored to Brazil. Brazil accounts for 45.1% of Latin American GitHub accounts and the remaining eighteen economies for 54.9%. That split is the only regional splitter available and it is an account proxy rather than an employment measure, so this component is scaled from Brazil’s measured series rather than measured in its own right.

Rest of world, estimated on an averaged growth path. Roughly 150 countries with no reachable occupational statistics: Japan, South Korea, Canada, southeast Asia, the Gulf, Africa, Russia and Australasia among them. Two decisions define it. Its level is set as the residual after the six identified components, calibrated at a software share averaging the places where that share is measured: the United States at 55.2% from ACS microdata, Brazil at 52.1% from the IBGE register, and the United Kingdom at 37.3% from ONS occupational detail. Its shape is the year-by-year mean growth of the quantified components rather than a constant rate. Earlier versions used a fixed 6.24% every year, which gave a quarter of the model a standard deviation of zero and no response to any historical event; the averaged path carries a standard deviation of 2.34 points and reproduces the 2001–2004 and 2008–2009 contractions that every measured region experienced. Whether those downturns reached the long tail of countries as hard as they hit the measured ones is not known, and averaging assumes they did.

Pre-2000 back-extrapolation, estimated. Cohorts entering before the series begins are reconstructed at growth rates rising toward an 18% ceiling in 1975. Material only to the 25-plus band.

Instruments rejected

• GitHub account counts as headcount. Across 480 country-quarter transitions the account series never declined, while git pushes declined in 152, the signature of a cumulative stock rather than a population. Accounts per worker widened monotonically from 1.49× in 2020 to 3.67× in 2025. Retained as an overlay: corrected denominators narrow the cross-region spread from 2.68× to 2.25×, weak support in the right direction, though the United States remains an outlier in a way indicating the ratio conflates statistical coverage with platform adoption intensity.
• Static age structure as an attrition instrument. Does not identify the parameter; see appendix.
• ILOSTAT. Public dataflows expose only one-digit ISCO, and the bulk and API endpoints are not retrievable.
• Graduate counts as a level instrument. Education pipelines explain under half of gross entry; bootcamp, self-taught, career-change and immigration channels dominate and are unmeasured. Useful as a rate constraint only.

Validation

• Separations arithmetic. Published rate 5.10% for the 2023–33 vintage against 5.14% derived independently from openings, growth and mean employment.
• Endpoint corroboration. IDC estimated roughly 13 million developers in 2001 against 12.4 million on the broad technical basket; SlashData estimated 36.5 million professionals in 2025 against 38.5 million.
• Sector age structures. Six of seven explained by sector-specific growth alone once each sector is tested against its own employment history rather than the national one.
• Cohort tracking basket integrity. Matched occupation baskets across the 2018 code revision; the two newly split codes account for 3.1% of the 2023 basket, so the measured cohort ratios are not a basket artefact.
• Attrition-shape sensitivity. Flat, tenure-rising and front-loaded hazards summing to the same effective rate produce headline figures within one point of each other. Shape is not load-bearing; level is.

Known limits

• The separations rate is the dominant parameter. It is measured for 2016 onward across eight vintages and held at the earliest observed value for 1975–2015. Each vintage is a ten-year forward projection rather than a contemporaneous measurement, so mapping a vintage to its base year is an interpretation, though BLS estimates the rates from a recent window of Current Population Survey data, which makes the mapping approximately right.
• Estimated components, rest of Latin America and rest of world, total 26.2% of the market. India adds a further 15.4% resting on a multiplier derived from external sources rather than Indian ones. Europe, the United States and Brazil, together 59.6%, rest on national statistical instruments.
• The rest-of-world component assumes the long tail of countries experienced the same growth pattern as the measured regions, including the 2001–2004 and 2008–2009 contractions. Whether those downturns propagated equally is not known.
• Cross-border contract developers, an engineer in one country invoicing a company in another, are invisible to every national statistical system simultaneously. Structural, not a reporting-quality issue.
• ACS microdata covers four states chosen for technical employment density. They are not nationally representative, and the direction of that bias differs by question.
• Education, healthcare and government software-developer samples fall below 250 unweighted records and are omitted from sector results rather than reported at low confidence.
• The 25-plus band depends more heavily on the pre-2000 back-extrapolation than any other; bands to 20 years rest on measured employment.

Approaches that do not work

Three methods appear serviceable for this problem and are not. Each is recorded because a reader reconstructing this analysis would plausibly attempt it, and because the first produces results that resemble evidence about the separations rate when they carry none.

Inferring attrition from a static age structure

Given an employment history and an assumed entry-age distribution, an observed age structure appears to pin down the separations rate. It does not. Fitting the observed distribution with a free entry-age curve yields a residual of 0.0245 at a rate near 5% against 0.0588 at a rate four times lower: the higher figure fits the age data better, requiring only that 22% of entrants be aged forty or over, which cohort tracking shows is realistic. Any rate can be reproduced by choosing an entry-age curve, and that curve is not observed. Constraining it to a plausible-looking range does not solve the problem; it hides it, because the answer is then determined by the constraint rather than the data.

Using industry-frame data to observe occupational movement

A developer promoted to engineering manager at the same employer never leaves the industry classification. The European software industry’s over-fifty share of 19.8% appears to indicate heavy senior attrition until it is compared against a zero-attrition counterfactual, which predicts 18.3%: growth alone accounts for the age structure. Occupational separation and near-total industry retention are simultaneously true because the movement is lateral. Any question about role change requires occupation-coded data.

Treating platform accounts as headcount

Across 480 country-quarter transitions the GitHub account series never declined once, while git pushes declined in 152, the signature of a cumulative stock rather than a population. Accounts per worker widened monotonically from 1.49× in 2020 to 3.67× in 2025. The series remains useful as an overlay but diagnoses rather than measures.

Origin of the question

The proposition that motivated this work is Robert C. Martin’s: that the number of programmers doubles roughly every five years, with the consequence that half the profession has under five years of experience at any moment, leaving the field in what he calls a state of perpetual inexperience. He sets it out at blog.cleancoder.com/uncle-bob/2014/06/20/MyLawn.html and has repeated it in talks and interviews since. The measured doubling period for professional software developers is 11.7 years over 2000–2025 and 10.3 years over the 2010s. The junior share is nonetheless high, at 45% within five years, because gross entry covers replacement as well as growth, which the doubling argument alone does not capture.

A note on reading the central estimate