The world’s IT workforce drinks an estimated 12 million litres of coffee a day

The world’s core IT workforce probably drinks about 12 million litres of coffee a day. A prudent range is roughly 5 million to 26 million litres per day, because no global database records both the number of IT workers and their actual coffee intake. The central figure is not a measured statistic. It is a transparent model built from observable employment anchors, published coffee-consumption evidence and stated assumptions about drinker prevalence, servings and cup size.

The central model uses a global core IT workforce of about 50 million people. It assumes that 68% drink coffee, that coffee drinkers average two coffee servings a day and that the average finished serving contains 180 millilitres of liquid coffee beverage. The arithmetic is direct: 50 million × 0.68 × 2 × 0.18 litres equals 12.24 million litres per day. The value is best read as an order-of-magnitude answer rather than a census result.

That daily volume is equivalent to about 68 million 180-millilitre servings, or nearly 49 million 250-millilitre mugs. Annualised, the central estimate reaches approximately 4.47 billion litres of coffee beverages a year. It is a surprisingly large number, yet it remains plausible beside the International Coffee Organization’s world-consumption outlook of 177 million 60-kilogram bags for coffee year 2023/24.

The crucial distinction is between coffee volume and coffee material. A 30-millilitre espresso, a 180-millilitre filter coffee and a 350-millilitre latte are not interchangeable beverages, even if each is casually called “one coffee.” This article therefore estimates litres of finished beverages, not tonnes of green coffee, roasted beans, caffeine or milk-based drinks. A second estimate for coffee servings is provided because it is easier to picture and more stable across cup formats.

The final practical answer is deliberately narrow: around 12 million litres a day is a defensible global midpoint for core IT workers; 5–26 million litres is a more honest uncertainty band. Anyone claiming an exact answer is implying a dataset that does not exist. The useful question is not whether the sector drinks exactly 12.24 million litres. It is whether the assumptions are visible, reasonable and easy to revise when better data arrive.

A defensible definition of the IT sector

“The IT sector” is not a single occupational category. It can refer to people employed by technology companies, people doing technology work inside every industry, or a much broader ecosystem that includes telecommunications, hardware assembly, online commerce, digital advertising, consulting, customer support and platform operations. A coffee estimate changes sharply depending on which version is chosen.

This analysis uses a core IT workforce definition: people whose principal work is to develop, operate, secure, analyse, design, administer or support digital systems. It includes software developers, data and AI specialists, cybersecurity staff, cloud and infrastructure engineers, IT managers, systems analysts, network administrators, quality-assurance professionals, technical support staff and closely related digital product roles. It excludes most factory employment, retail sales, general office users and non-technical employees at technology companies.

That approach resembles the way European statistical agencies describe ICT specialists. Eurostat treats ICT specialists as people whose main job involves developing, operating or maintaining ICT systems. The definition is occupational rather than employer-based, which is useful because a cybersecurity engineer at a bank is still part of the practical IT workforce even though the employer is not a technology company.

The definition deliberately excludes “everyone who works with a computer.” A global accountant, teacher, designer, hospital administrator or shop manager may use software all day without working in the IT sector. Including every digitally enabled occupation would turn the estimate into a calculation about the modern white-collar workforce rather than IT. It would also greatly increase the headcount while weakening the link between sectoral identity and coffee habits.

A broader definition remains useful for some business decisions. A company selling office coffee equipment, for example, may care about every employee in digital-intensive firms, including sales, finance, legal and people teams. That population could be well above 100 million globally. For an editorial estimate of the coffee consumed by “the IT sector,” however, a 40–65 million core-workforce band is more defensible than a vague count of everyone employed by technology-adjacent businesses.

The official statistic that does not exist

No international body publishes a global annual measure of coffee consumed by IT workers. Labour statistics classify occupations and industries; coffee statistics track production, trade, apparent consumption and retail markets. The two systems do not meet at the level required to answer this question directly.

The International Labour Organization reports labour-market conditions at global and national levels, but it does not publish a harmonised global count of every core IT occupation combined with workplace beverage data. The OECD and Eurostat provide strong national and regional evidence on ICT specialists, yet their coverage is not a complete census of the world’s technology workforce.

The International Coffee Organization has the complementary problem. Its World Coffee Statistics Database and Coffee Report and Outlook track coffee-market quantities across countries, but they do not split consumption by occupation. The organization’s data are valuable for a plausibility check: an estimate that implies IT workers consume a major share of all coffee would be implausible. They cannot by themselves identify how much coffee is drunk by developers, systems administrators or product managers.

This is an estimation problem, not a fact-retrieval problem. Good estimation requires an explicit denominator, a clearly stated consumption rate and a realistic beverage volume. It also requires resisting the temptation to use one eye-catching national survey as a global average. The United States has high coffee participation and relatively large cup sizes; it is informative but not representative of every labour market.

A transparent model has another advantage: it can be audited. A reader who believes the global IT workforce is 65 million rather than 50 million, or who believes coffee drinkers average 1.5 rather than two coffees daily, can change one number and observe the result. The result is not weakened by that flexibility. It becomes more reliable because its uncertainty is visible instead of buried behind a false claim of precision.

Counting people rather than companies

A workforce estimate should begin with people, not technology-company revenue. Revenue counts are distorted by automation, pricing, cloud-scale infrastructure and intellectual property. A platform can serve billions of users with comparatively few employees, while an outsourced support provider may employ many thousands of people with modest revenue per worker.

Employment data are also imperfect, but they map more closely to the question. Coffee is consumed by people. The relevant denominator is the number of people regularly performing IT work, including those inside banks, hospitals, manufacturers, governments and universities. A systems engineer running a hospital network belongs in a practical coffee estimate even though the hospital’s revenue is reported as healthcare, not technology.

The OECD distinguishes between ICT specialists and broader ICT task-intensive occupations. That distinction matters. ICT specialists build, operate and maintain systems as the main substance of their work. ICT task-intensive occupations cover a much wider group of workers who perform substantial digital tasks but may not be IT specialists. The central estimate uses the first group, not the second.

A company-based count also risks excluding independent contractors and small firms. Freelance developers, cybersecurity consultants and remote technical workers may work through personal service companies, marketplaces or informal contracts. Their coffee consumption is real, even if it is not captured neatly in payroll headcounts. This is one reason the lower end of the workforce range cannot simply be created by adding official industry tables.

At the other extreme, it would be wrong to count everyone on a technology company’s payroll. Large firms employ facilities workers, recruiters, lawyers, sales teams, finance staff, content moderators, warehouse employees and many other roles. Some are part of digital operations and some are not. The model is designed to estimate an occupationally defined IT workforce, not the coffee demand of every company branded as “tech.”

Employment anchors from Europe

Europe provides one of the clearest official anchors because Eurostat publishes a harmonised ICT-specialist series. In 2025, the European Union recorded 10.45 million ICT specialists, equal to 5.0% of all employed people. Eurostat’s 2023 industry-based series separately counted more than 7.36 million people in the EU ICT sector, showing why occupational and industry measurements should never be treated as interchangeable.

The 10.45 million figure is especially useful because it includes ICT specialists working outside the ICT sector. A software engineer at an insurance company, for example, may be an ICT specialist but not an employee of an ICT-sector business. For a consumption question, that broader occupational treatment is usually more appropriate.

The EU alone accounts for roughly one-fifth of the 50 million central global IT workforce assumption. That is not a claim that Europe represents one-fifth of every technology metric. It is a modelling anchor. Europe combines large formal labour markets, high digital intensity and relatively reliable occupation data, so it gives the global estimate a solid base rather than a speculative starting point.

Coffee habits across Europe vary sharply. Nordic countries, Germany, the Netherlands and several Central European markets have strong coffee cultures, while consumption patterns differ in Southern and Eastern Europe. The European Coffee Federation’s report, which draws on ICO data and population data, illustrates the variation in per-capita coffee consumption across countries. A single “European cup count” would therefore be as misleading as a single global cup count.

For the central scenario, Europe is not assigned a bespoke country-by-country coffee rate. Doing so would create an appearance of accuracy without a global worker-level beverage dataset. Instead, Europe helps establish the workforce scale, while the global consumption model uses a moderate blended assumption. More detailed regional modelling is discussed later as a refinement rather than presented as a false precision upgrade.

The United States as a second anchor

The United States supplies a major, well-documented occupational benchmark. The U.S. Bureau of Labor Statistics projected 5.4167 million workers in computer and mathematical occupations in 2024, rising to nearly 6 million by 2034. This group includes many core IT occupations, though it also contains mathematical roles that are not always counted as IT and excludes some technology managers and technical workers classified elsewhere.

The BLS also reported 5.1774 million workers in computer and mathematical occupations in its May 2023 occupational employment estimates. The difference between occupational datasets, publication cycles and classification boundaries shows why a global workforce estimate should not pretend that every national source measures the same thing.

For this model, the United States contributes a workforce anchor of roughly 5.5–6.5 million core IT workers after allowing for information-technology management, certain technical operations roles and classification differences. That is intentionally broader than a strict “computer and mathematical occupations” count but narrower than the entire U.S. technology sector.

Coffee consumption evidence is unusually rich in the United States. The National Coffee Association reported in 2025 that two-thirds of American adults drank coffee daily and that coffee drinkers averaged three cups a day. Those figures should not be exported directly to the whole world. They are better treated as evidence that a two-serving global average among coffee drinkers is conservative for high-income, coffee-oriented technology hubs.

The United States also illustrates a practical measurement issue: “cups” are large and inconsistent. A survey participant may count a travel mug, cold brew, single espresso or café latte as one cup. Litres should therefore be estimated from an assumed average finished beverage size, not from a kitchen measuring cup. The central 180-millilitre value is a compromise between short espresso drinks and much larger filter, iced and milk-based drinks.

India’s giant technology workforce

India is indispensable to any global IT estimate because its technology sector alone employed about 5.8 million people in fiscal year 2025, according to Nasscom. That number covers the country’s technology sector workforce and is one of the largest directly reported employment anchors outside Europe and the United States.

The figure should not be read as a pure count of software developers. India’s technology sector includes IT services, business-process management, engineering and related delivery functions. Some roles will be squarely inside a core IT definition; others are operational or customer-facing and sit closer to the edge. For a global model, the number is still more informative than attempting to infer India’s workforce from coding-course enrolment, digital exports or technology-company valuations.

Coffee consumption in India also cautions against using U.S. assumptions. Tea remains important, coffee consumption is regionally concentrated, beverage volumes differ and workplace routines vary. A large share of India’s IT workforce may drink coffee, tea, both or neither. The workforce is large, but the per-worker coffee assumption should not be assumed to be North American.

This is one reason the global central model uses a 68% coffee-drinker rate rather than the 66% daily-drinker rate reported for U.S. adults and then adds two servings for drinkers. The model does not assert that 68% of every country’s IT workers drink coffee every day. It uses a blended global assumption intended to balance high-consumption hubs against technology labour markets where tea and other beverages have a larger role.

India’s scale also explains why a narrow definition matters. Counting every employee in IT-BPM would produce a larger coffee total than counting only engineers and infrastructure specialists. Neither answer is inherently wrong, but they answer different questions. For the central estimate, India is treated as a major core-IT anchor while the uncertainty range absorbs classification ambiguity rather than pretending it can be fully removed.

China and large unobserved pools

China is the largest source of uncertainty in a global IT workforce estimate. Its software and information-technology services industry is vast, and official statistics confirm continued rapid growth in information transmission, software and IT services. Yet a single internationally harmonised occupational count equivalent to the Eurostat ICT-specialist series is not readily available for a global roll-up.

China’s National Bureau of Statistics describes software and information technology services as including software development, integrated-circuit design, information-system integration, internet-of-things technology services, operation and maintenance, data-processing and storage support, IT consulting and digital-content services. That scope overlaps closely with the core workforce definition used here, but it is still an industry description rather than a complete count of workers in specific occupations.

The model therefore does not insert a single authoritative Chinese headcount. It uses a range-based approach. A plausible central allocation for China and adjacent East Asian technology hubs is roughly 10–14 million core IT workers combined, but this is explicitly an inference from the size of the digital economy, observed national labour-market anchors and the need for the global model to remain consistent with known employment totals elsewhere.

That uncertainty matters more than any small adjustment to average cup size. If the global core IT workforce is 45 million rather than 50 million because China, Southeast Asia and other countries are lower than assumed, the central beverage estimate falls by about 1.2 million litres a day. If the workforce is 60 million, it rises by about 2.4 million litres a day under the same coffee assumptions.

China does not make the estimate impossible; it makes a narrow confidence interval dishonest. The correct response is a range, transparent assumptions and an explicit invitation to update the number when better global occupation data become available. A model that conceals this gap behind a precise decimal point would be less useful, not more.

Regional coverage beyond the largest markets

Europe, the United States and India already account for more than 21 million workers in the model’s major anchors. The remainder comes from China, Japan, South Korea, Taiwan, Southeast Asia, the United Kingdom, Canada, Australia, Latin America, the Middle East, Africa and dispersed technical workforces inside non-technology industries.

Japan and South Korea have mature digital economies, advanced manufacturing, strong software and electronics capabilities and high technology intensity. Taiwan has a globally important semiconductor and hardware ecosystem. Singapore is a regional technology hub. Canada, Australia and Israel have substantial technology workforces relative to population. Brazil, Mexico, Argentina, Colombia, South Africa, Nigeria, Kenya, the United Arab Emirates and Saudi Arabia all contribute growing pools of software, infrastructure, data and digital-services workers.

The ITU reported that 5.5 billion people were online in 2024, or 68% of the global population. That figure is not a workforce count, but it confirms the breadth of the digital infrastructure and services environment that requires technical labour across countries. Digital participation is widespread even where detailed occupational measurement is weak.

A purely formal-sector estimate may miss self-employed developers, contractors, informal repair and support work, small agencies and remote workers serving foreign clients. Yet including every person who repairs a phone, manages a social-media account or operates a small internet café would exceed the intended definition. The model’s 40–65 million range is designed to include meaningful uncertainty without turning “IT” into a catch-all for all digital work.

Regional diversity affects coffee culture as well as employment. Coffee is deeply embedded in some markets, while tea, mate, cocoa, energy drinks, yerba mate or other beverages may be more important elsewhere. The estimate is therefore stronger as a global average than as a prediction for any individual country, office or company. Local procurement decisions should always use local survey or transaction data instead.

Building the global workforce range

The model’s central workforce estimate is 50 million core IT workers, with a low case of 40 million and a high case of 65 million. The purpose of this range is not to guess every country’s exact number. It is to reflect what the available anchors can and cannot establish.

The lower case assumes a strict occupational definition, conservative treatment of technical support and business-process roles, and limited inclusion of hardware and telecommunications workers. It still recognises that IT specialists work throughout the economy, not just at dedicated technology companies. A lower figure below 40 million would be difficult to reconcile with the EU’s 10.45 million ICT specialists, the United States’ roughly 5.4 million computer and mathematical workers and India’s 5.8 million technology-sector employment alone.

The high case assumes a broader but still recognisable core. It includes more technical customer support, digital operations, specialist roles in telecoms, software-adjacent business-process work and workers classified inconsistently across national systems. It does not include every office worker who uses software or every non-technical employee at a technology firm.

A global headcount of 50 million is a modelling midpoint, not a published census. Its usefulness comes from triangulation. The major formal anchors create a floor; the scale of China, East Asia and the rest of the world provides the additional population; the range recognises that national classifications cannot be perfectly merged.

One practical test is proportionality. The world had billions of workers, while formal IT specialists represent a relatively small but growing share of employment in advanced economies. Eurostat’s 5% ICT-specialist share in the EU is a useful reference point, not a global ratio. Applying it mechanically to all global employment would overstate IT staffing in many lower-income economies; applying only formal-sector data would understate technical work in outsourced and remote delivery hubs.

Coffee drinkers are not all coffee consumers

The model separates the share of workers who drink coffee from the number of drinks consumed by those drinkers. This matters because a headline such as “three cups per day” usually refers to coffee drinkers, not every adult or every worker. Multiplying that rate by an entire workforce would overstate consumption.

The National Coffee Association’s 2025 U.S. survey reported that 66% of American adults drank coffee daily and that coffee drinkers consumed an average of three cups each day. This yields an average of roughly two coffee cups per adult across the full U.S. adult population, before accounting for cup-size differences. It does not tell us the global rate for IT workers.

IT workers may be more likely than some occupations to use caffeine because of screen-intensive work, distributed teams, on-call schedules and deadline pressure. Yet there is no robust global dataset establishing a universal “developer coffee multiplier.” The model does not assume that programmers drink coffee because of a stereotype. It assumes that a globally distributed knowledge-worker population has moderately high, but not extreme, coffee participation.

The 68% coffee-drinker assumption is slightly above the U.S. daily-adult share but below the share implied by treating every coffee consumer as a daily drinker. It is intentionally blended. It allows high-coffee markets and office cultures to lift the rate while leaving room for tea-drinking regions, non-coffee consumers, health-motivated abstainers and workers who obtain caffeine from other sources.

A different model could use 60% coffee drinkers and 2.2 servings each, producing almost the same beverage volume. The decisive quantity is average coffee servings across all IT workers, not the exact split between participation and intensity. In the central case, that average is 1.36 coffee servings per worker per day.

Units decide the answer

A litre estimate requires a serving-size assumption that is often more important than people realise. Coffee may be served as a 25–35 millilitre espresso, a 150–200 millilitre filter coffee, a 250-millilitre office mug, a 350-millilitre takeaway drink or a much larger iced beverage. Counting “cups” without defining volume produces a number that sounds exact but is not operationally useful.

The central model uses 180 millilitres per finished coffee serving. It is not a universal physical cup. It is a blended beverage-size assumption designed for a workforce that includes espresso drinkers, machine coffee, filter coffee, instant coffee and milk-based drinks. A lower 160-millilitre value is used in the conservative case; a higher 220-millilitre value is used in the high case.

Caffeine content varies even more than volume. EFSA notes that coffee caffeine concentrations can vary widely, from 270 to 1,340 milligrams per litre. Litres of beverage should never be converted directly into caffeine exposure without information about brewing method, bean type, extraction, serving size and other caffeine sources.

This distinction also matters for procurement. A company buying beans, capsules or vending-machine ingredients needs an estimate in servings and grams of coffee input. A facilities team planning water, cups, milk, cleaning and waste needs a beverage-volume estimate. A public-health team evaluating caffeine intake needs milligrams of caffeine, not litres. Each question has a different denominator.

For this article, litres are used because the original question asks for litres. The answer is paired with servings so readers can translate it into familiar workplace terms. The central estimate of 12.24 million litres a day corresponds to roughly 68 million 180-millilitre servings, not 68 million identical cups.

The central scenario and its equation

The model’s central scenario is deliberately simple enough to reproduce on a calculator. It uses four variables: number of core IT workers, coffee-drinker share, coffee servings per drinker per day and litres per serving.

The formula is:

Daily litres = IT workers × coffee-drinker share × servings per coffee drinker × litres per serving.

Using the central values gives:

50,000,000 workers × 0.68 × 2.0 servings × 0.18 litres = 12,240,000 litres per day.

The result can also be expressed as average consumption per IT worker, whether or not that worker drinks coffee. The model produces 1.36 servings per worker per day, multiplied by 180 millilitres. That equals 244.8 millilitres per worker per day across the entire 50 million-person population. This is a useful sanity check: it does not imply that every IT worker consumes a litre of coffee daily.

The model does not claim that every worker consumes coffee at work. Many drinks are consumed at home before logging on, during commuting, in cafés, at customer sites or after work. The estimate is about coffee consumed by people in the sector over a day, not coffee dispensed from office machines. That distinction becomes important when the result is applied to office procurement or workplace sustainability.

The central case is conservative relative to the U.S. coffee-drinker average of three cups per day, but it may be high for tea-oriented markets. It is also conservative relative to coffee-heavy Nordic or Central European professional settings, but it does not assume that those markets define the whole world. Its purpose is balance, not cultural generalisation.

Central model inputs

The table makes clear that the result is a model output. Changing any input changes the answer proportionally. That is not a flaw; it is the core discipline of honest estimation.

What the low and high cases mean

The uncertainty band is driven chiefly by workforce size and cultural variation, not arithmetic. A low estimate uses 40 million IT workers, a 55% coffee-drinker share, 1.4 servings per drinker per day and a 160-millilitre average beverage. It produces approximately 4.93 million litres a day.

The high estimate uses 65 million IT workers, a 75% coffee-drinker share, 2.4 servings per drinker per day and a 220-millilitre average beverage. It produces about 25.74 million litres a day. That upper case is not a forecast. It is a plausible boundary for a broader core-IT definition and coffee-intensive behaviour, including larger drinks.

The range should not be interpreted as a 95% statistical confidence interval. There is no random sample, no global occupational beverage survey and no estimated sampling distribution. It is a scenario range. Its purpose is to show how far the result moves under assumptions that a reasonable reader might defend.

The low case is useful for skeptics. It says that even if the global IT workforce is smaller than the central model, coffee participation is lower and drinks are smaller, the sector still likely consumes several million litres a day. The high case is useful for commercial planners. It shows that a broader workforce definition, larger beverages and higher participation can more than double the central volume.

A single answer such as “12 million litres” is memorable. The 5–26 million-litre range is more decision-useful. It tells a coffee supplier, workplace operator, analyst or journalist that the point estimate is a midpoint, not a claim to know the world’s exact coffee tally.

Sensitivity in the global estimate

Workforce size is the largest single driver because every other variable multiplies it. A 10% change in the workforce estimate changes daily litres by 10% if coffee behaviour stays constant. A 10% change in drinker participation, servings or cup size has the same mathematical effect, but workforce uncertainty is more structural because global occupation data are incomplete.

The sensitivity table below shows a low, central and high scenario. It should be read as an explanation of the range, not as evidence that the values are independently measured. The employment anchors are observed; the blended coffee behaviour inputs are analytical assumptions informed by market and consumption evidence.

Scenario range for coffee consumed by core IT workers

The middle estimate should not be rounded to “12.24 million litres” in ordinary prose. The decimal precision comes from arithmetic, not from measurement. “About 12 million litres a day” is the correct editorial formulation. For commercial modelling, the full inputs are more useful than any rounded headline.

A further sensitivity test is possible. Holding workforce size at 50 million but reducing average coffee servings across all workers from 1.36 to 1.0 cuts the estimate to 9 million litres a day. Raising it to 1.8 raises the estimate to 16.2 million litres a day. Reasonable assumptions still place the sector’s daily coffee volume in the millions of litres.

Annualising the daily consumption

The central estimate implies around 4.47 billion litres of coffee beverages per year. The calculation is 12.24 million litres a day multiplied by 365 days. The low scenario produces about 1.80 billion litres annually; the high scenario reaches about 9.40 billion litres.

Annual figures are visually striking but should not be mistaken for annual workplace coffee procurement. The calculation includes coffee consumed by IT workers on weekends, holidays, sick days, travel days, office days and remote-work days. It estimates personal sector-associated consumption, not a corporate purchase order.

This distinction matters because an individual’s identity as an IT worker does not disappear on Saturday morning. A software engineer may drink coffee at home while reading, travelling or spending time with family. That coffee belongs in a sector-consumption estimate if the question is about what the workforce drinks, but it does not belong in an office-machine calculation.

Office-only demand is likely materially smaller than total daily consumption by IT workers. The split depends on remote-work patterns, local commuting culture, workplace catering, household brewing, café use and employer-provided beverages. The National Coffee Association reported that 82% of U.S. past-day coffee drinkers consumed coffee at home in 2025, illustrating why home consumption cannot be ignored even for office-based occupations.

Annualising nevertheless serves two purposes. It helps compare the result with national beverage markets and it makes input requirements easier to understand. A sector consuming around 4.5 billion litres of finished coffee drinks yearly needs a large supply chain of beans, water, milk or alternatives, cups, machines, energy and labour. The coffee is a small item in the digital economy, but at global workforce scale it becomes a large physical flow.

The global coffee market plausibility test

A useful estimate should fit inside the known global coffee market. The International Coffee Organization projected world coffee consumption at 177 million 60-kilogram bags in coffee year 2023/24. That is roughly 10.62 million tonnes of green-coffee equivalent.

The IT model’s 12.24 million litres a day does not directly translate into 12.24 million kilograms of coffee beans. Drinks have different recipes. A 30-millilitre espresso may use roughly 7–10 grams of ground coffee; a 180-millilitre filter coffee can use more or less depending on brew strength; a latte’s finished liquid volume is mostly water and milk. Any bean estimate should therefore be treated separately from beverage litres.

A broad coffee-input check can still be made. If the central 68 million daily servings used roughly 7–12 grams of roasted coffee each, the implied daily input would be about 476–816 tonnes of roasted coffee. Across a year, that is roughly 174,000–298,000 tonnes. It is a small share of the global coffee market after allowing for the difference between roasted and green coffee weights.

That proportionality is reassuring, not conclusive. The model does not claim that IT workers consume a precisely measured percentage of world coffee. It shows that a 12-million-litre daily beverage estimate does not demand an implausible share of global coffee supply. An estimate in the hundreds of millions of litres a day, by contrast, would quickly fail this market reality check.

Europe’s scale offers another reference point. The Centre for the Promotion of Imports from developing countries reports that Europe accounted for 30.7% of world coffee consumption in 2023/24, or 3.26 million tonnes. The IT workforce is globally distributed, but its total coffee demand is still far smaller than the consumption of an entire major region.

Workdays versus all days

A “per day” answer can mean two different things. It may mean the coffee consumed on an average calendar day by people working in IT, or it may mean the coffee consumed on a typical workday. The central figure in this article uses calendar-day consumption because the user’s question asks how much the sector drinks, not how much office kitchens serve from Monday to Friday.

A workday-only estimate might be higher per active day but lower when averaged across the calendar year. Many people drink more coffee on workdays, especially during early meetings, commuting and concentrated technical work. Yet weekends, public holidays, leave and sick days do not necessarily reduce personal coffee consumption to zero.

For workplace operations, the right denominator is usually occupied-office days. A company with 1,000 IT workers should not budget coffee for 1,000 people every calendar day if hybrid attendance means 400 people are in the office on a typical Tuesday. Conversely, it should not assume every remote employee consumes less coffee; they may simply brew at home.

The global model could be converted into an indicative workday total by assuming workdays raise coffee intake by, for example, 15–30% above the all-day annual average. But that would introduce another layer of unmeasured assumptions. The article does not add a workday multiplier because the evidence is weaker than the value it would create.

The practical interpretation is simple. Around 12 million litres is the best estimate of daily personal coffee consumption by the world’s core IT workforce, averaged over the year. It is not an estimate of office coffee dispensed, and it is not a count of coffee consumed only during paid working hours.

Home, office and hybrid work

Hybrid work changes where coffee is consumed more than it changes whether it is consumed. A worker who once used an office espresso machine may now drink filter coffee at home before a video call. The consumption moves from corporate procurement to household purchasing, café spending or subscription deliveries.

This shift makes anecdotal office observations unreliable at global scale. A technology office may report lower bean use after adopting hybrid schedules and conclude that employees drink less coffee. The conclusion could be wrong. Employees may have transferred their habits to home machines, convenience stores or cafés. The total caffeine and beverage volume may be unchanged.

The National Coffee Association’s 2025 survey found that most U.S. past-day coffee drinkers consumed coffee at home. Home consumption is therefore not a marginal category for knowledge workers; it is likely a central part of the pattern. The same may be true elsewhere, though local data vary.

Hybrid work also changes cup size. Office vending machines and small espresso drinks may be replaced by larger home mugs, insulated travel cups or café takeaway beverages. A liter-based model is more sensitive to that shift than a serving-count model. If a worker replaces two 150-millilitre office coffees with two 300-millilitre home mugs, the number of servings is unchanged but beverage volume doubles.

For the global estimate, this effect supports using a moderate 180-millilitre average rather than treating every drink as an espresso or every drink as a large latte. Remote work adds uncertainty to volume, not a reason to abandon the estimate. The range already reflects plausible differences in cup size and daily beverage routines.

Geography moves the answer

Coffee consumption is not a universal cultural constant. Per-capita coffee demand differs substantially across countries, shaped by income, climate, tea traditions, local café culture, home-brewing habits, commuting, pricing and the availability of alternatives. IT work is globally distributed across precisely those different consumer environments.

European markets remain especially important in the global coffee economy. The European Coffee Federation documents high per-capita consumption in several European countries, while the European Union imported 2.7 million tonnes of coffee from non-EU countries in 2023. These data do not show what IT workers drink, but they show why Europe cannot be represented by a low coffee-prevalence assumption.

North America is also coffee-intensive. The ICO forecast North American coffee consumption at 30.9 million bags in 2023/24, while U.S. survey data show high daily participation and multiple daily servings among drinkers. Latin America combines coffee-producing countries with significant domestic consumption, though patterns are diverse. Asia contains large tea-drinking populations alongside substantial and growing coffee markets.

A global average is not an average country. It is a weighted abstraction. The central model does not try to assign one coffee rate to each region because that would require reliable occupational headcounts and beverage surveys for all major labour markets. Instead, it uses a moderate blended rate and keeps the uncertainty band wide.

For a local market, geography should replace the global average. A Scandinavian software company, an Indian services campus, a Japanese data centre, a Brazilian fintech and a Kenyan engineering hub should not all use the same per-capita coffee budget. The global answer is for scale; local data are for procurement.

Role mix inside IT matters

Not every IT job has the same schedule, work setting or beverage routine. A remote front-end developer, a night-shift network operations analyst, a field support technician, a security incident responder, a data scientist and a customer-success engineer may all be included in a core IT definition, yet their daily patterns differ substantially.

On-call operations and incident-response roles may have unusually intense caffeine demand during outages, night work or rapid-response events. A regular daytime developer may have a stable morning-and-afternoon pattern. Field technicians may buy coffee while travelling. Call-centre and support workers may have scheduled breaks and access to vending machines. Data-centre staff may work shifts that do not resemble conventional office hours.

The model does not apply occupation-specific consumption rates because reliable global evidence is lacking. It recognises role variation through the scenario range rather than pretending that every IT worker has identical habits. This is a recurring principle: a more complicated model is not automatically a better model when the additional inputs are unmeasured.

Role mix also affects beverage type. Engineers working near large office campuses may use bean-to-cup machines and espresso bars. Contractors and remote workers may use instant coffee, capsules or home filter brewers. Support staff may have more access to tea or vending beverages. The finished volume per serving can therefore differ even when the number of coffee occasions is similar.

For businesses, this means coffee planning should follow actual site-level attendance and role patterns. A global 12-million-litre estimate should never be divided by the number of technology companies to create a “typical company” number. The distribution is highly uneven. Large campus employers, outsourcing centres and 24/7 operations can account for much more coffee per location than small distributed software teams.

Specialty coffee, tea and energy drinks

Coffee is only one part of the stimulant and comfort-beverage mix used by IT workers. Tea, energy drinks, matcha, yerba mate, cola and caffeine tablets can substitute for coffee, while decaffeinated coffee may preserve the ritual without the stimulant effect. A coffee-only estimate should not be treated as a total caffeine estimate.

This substitution is especially important in regions with strong tea traditions. It is also relevant for younger workers, shift workers and people who prefer cold, sweetened or functional beverages. The more strongly these alternatives are used, the lower the share of IT workers who drink coffee daily.

The central model’s coffee-drinker share and serving assumptions intentionally leave room for substitution. It does not assume that the IT sector runs exclusively on coffee. It estimates coffee because coffee is the requested beverage and because coffee-market data provide a useful external plausibility benchmark.

Specialty coffee complicates volume. A specialty espresso may be small but intense; a cold brew may be large; a latte may contain a modest amount of coffee relative to total drink volume. The National Coffee Association reported that specialty coffee consumption in the United States has risen, with espresso-based beverages among important categories.

For a litre estimate, the rise of specialty and milk-based drinks may increase finished beverage volume without a proportional increase in coffee grounds. That is another reason the article reports litres of beverages and does not equate litres with bean consumption or caffeine exposure.

Coffee machines, pantries and vendors

The global estimate describes people’s consumption, not the number of machines required to serve them. Equipment demand depends on concentration. Ten thousand remote workers may consume large amounts of coffee with almost no corporate machine demand, while ten thousand workers on one campus can require dozens of machines, water connections, milk systems, cleaning routines and service contracts.

A workplace facilities manager should model three separate numbers: daily office attendance, coffee uptake among attendees and drinks per attendee. The relevant cup volume should be based on local machine data, not the 180-millilitre global average. Cashless vending records, coffee-machine telemetry, bean purchases and milk usage can produce much stronger estimates than employee surveys alone.

The 12-million-litre figure is therefore not a market-size estimate for office coffee equipment. It includes home brewing, café consumption, travel and personal purchases. It is a useful illustration of the physical scale associated with global IT employment, but it cannot determine how many machines a region needs.

Machine type also influences waste. Capsule systems create different material flows from bean-to-cup machines. Filter coffee can create batch waste when pots sit too long. Espresso systems may use more energy for heating and cleaning. Milk-based drinks increase refrigeration, dairy or plant-milk logistics and spoilage considerations.

At company level, the best operational rule is straightforward: use observed drinks-per-day data for sites and use workforce models only for markets where direct records do not exist. Global estimation belongs in strategy, journalism and high-level forecasting; telemetry belongs in procurement.

Cost at global scale

Coffee expenditure is much harder to estimate than coffee volume. A litre of home-brewed instant coffee, a workplace espresso and a café latte can differ in price by an order of magnitude. Currency, wages, hospitality rents, taxes, ingredients and brand positioning all matter.

A rough expenditure calculation based on a single global price would be misleading. It would mix home coffee in lower-income markets with specialty café drinks in high-cost cities and employer-subsidised office beverages. A 12-million-litre daily volume could represent a relatively modest retail value under one consumption mix and a vastly larger one under another.

The useful insight is not a single global spending figure. The sector’s coffee volume creates a substantial recurring expense even when the cost per worker is small. At the company level, a worker consuming one or two subsidised office coffees each day can create meaningful annual costs once beans, milk, cups, service, cleaning, waste and machine depreciation are included.

The global figure also shows why coffee has become a persistent workplace benefit. It is a low-friction daily amenity that supports routine, social interaction and hospitality. But it should not be romanticised as a productivity solution. The effect of caffeine depends on dose, timing, tolerance and sleep, while the value of free coffee may be cultural rather than pharmacological.

A budget model should separate coffee consumption from coffee spending. Volume can be estimated with people and servings. Spending requires prices and channel mix. These are different exercises and should not be collapsed into one confident but unsupported number.

Beans, milk and waste

Millions of litres of finished coffee beverages imply real material flows. Even a modest estimate of 7–12 grams of roasted coffee per serving suggests a large annual requirement for coffee grounds. Milk-based beverages add dairy or plant-based ingredients; takeaway cups add packaging; offices add water, machine cleaning and electricity.

The central model’s 68 million daily servings do not all use the same recipe. A short espresso can use one dose of ground coffee with little water. A drip coffee can use more water and a different grounds-to-water ratio. A latte can contain milk as its largest liquid component. The physical footprint therefore depends more on preparation mix than on beverage litres alone.

Coffee grounds deserve attention because they are often treated as waste even though they can be collected for composting, anaerobic digestion or specialist reuse. Such programmes must be judged locally: transport, contamination and processing capacity can erase benefits if they are poorly designed. Reusable cups also reduce some waste but require washing, water and energy.

The global coffee sector has broader supply-chain questions involving producer income, climate risk, land use, transport and roasting. The International Coffee Organization’s Coffee Development Report addresses sustainability and circular-economy issues in the coffee sector. A large IT coffee estimate should not be used to make unsupported claims about the sector’s exact carbon footprint. That would require lifecycle data by beverage, origin, brewing method, milk choice and waste handling.

The responsible conclusion is narrower: global IT coffee consumption is materially large enough that sustainable purchasing, reusable-service systems and waste reduction can matter at major employers. The actions should be measured locally, not inferred from a global estimate.

Caffeine, sleep and health boundaries

Coffee volume is not a health recommendation. A model of what workers probably consume should never be confused with advice about what an individual should drink. Caffeine response varies by person, health status, pregnancy, medication use, timing, genetic factors and other sources of caffeine.

EFSA states that habitual caffeine intake up to 400 milligrams a day does not raise safety concerns for healthy non-pregnant adults in the general population, while single 100-milligram doses close to bedtime may affect sleep in some adults. Coffee caffeine concentration can vary substantially by preparation, so a serving count is a poor substitute for milligrams.

Research on caffeine and behaviour finds that moderate doses can affect alertness and performance, but the relationship is not a blank cheque for more consumption. Studies also link higher caffeine consumption with poorer sleep quality in some adult samples, though causality and personal context matter.

The IT sector’s coffee culture should not normalise sleep deprivation. A late coffee used to compensate for chronic long hours can worsen the conditions it is trying to solve. Managing on-call rotations, workload, meeting times and cross-time-zone expectations is more durable than treating caffeine as an operational control.

The global estimate is compatible with both healthy and unhealthy individual patterns. It describes aggregated demand, not ideal behaviour. A person drinking one small morning coffee and a person drinking several large late-day energy beverages may both appear in a consumption total, but their risk profile differs. The right unit for health is individual dose and timing, not sector-level litres.

AI’s effect on workforce and consumption

Artificial intelligence may change the composition of IT work without producing a simple one-way effect on coffee consumption. Some tasks may be automated, some roles may grow and new work may emerge in model operations, data infrastructure, security, governance and integration.

OECD analysis notes continued demand for ICT specialists as digital technologies reshape work, while U.S. BLS projections show strong expected growth for several computer occupations. That evidence supports continued relevance of IT labour, not a precise forecast of the global IT headcount.

If AI expands the number of technical workers, the coffee estimate rises in proportion to workforce size. If AI reduces staffing needs in certain support or programming tasks, it may lower the estimate. Yet job substitution is not the only mechanism. Changes in remote work, working hours, shift patterns, productivity pressure and office attendance may affect coffee behaviour more rapidly than the total number of workers.

The key point is that coffee consumption is a second-order outcome. It is influenced by employment, but also by culture, office attendance, local pricing and health preferences. A forecast of IT coffee demand should not be built solely from AI headlines. It needs labour-market and consumption evidence.

For now, the central estimate is best treated as a current-scale model, not a forecast to 2030. Any future projection should present separate scenarios for workforce growth, hybrid work, coffee participation and drink size. The result could easily vary more because of beverage habits than because of modest annual changes in employment.

Better data and a better estimate

A stronger estimate would require data that are not currently collected together. The ideal dataset would contain harmonised national counts of ICT specialists, representative beverage-consumption surveys by occupation, serving-size information and a way to distinguish home, office, café and travel consumption.

Occupational data are the first priority. Eurostat’s ICT-specialist statistics show what a consistent regional series can look like, but comparable coverage is needed across major technology labour markets. National statistical agencies could publish crosswalks between occupation codes and internationally comparable ICT categories.

The second priority is consumption data. Consumer surveys often record coffee frequency but do not identify occupation. Employer studies may track office beverage use but exclude home consumption and lack representative sampling. Payment data can reveal café purchases but miss home brewing, employer-provided drinks and cash transactions.

The most useful future survey would ask workers both what they drink and where they drink it. It would separate coffee, tea, energy drinks and decaffeinated drinks; record serving type and size; capture work schedule and remote-work frequency; and report results by occupation and country. Even a multi-country survey covering major IT hubs would materially improve the model.

Until such data exist, transparent estimation is better than fabricated certainty. The inputs can be improved one by one. A credible country-level coffee survey can replace a generic participation assumption. A better workforce count can narrow the headcount range. Machine telemetry can replace office assumptions for a company. The model is designed to be revised, not defended as permanent truth.

The decision-ready number

For a headline, use 12 million litres a day. For serious work, use 5–26 million litres a day. The first is clear enough to communicate. The second retains the uncertainty that decision-makers need.

A journalist can write that the world’s core IT workforce likely drinks roughly 12 million litres of coffee a day, based on a transparent estimate rather than an official count. A coffee company can use the range as a top-down market context, then replace it with regional figures before making investment decisions. An employer can ignore the global number and use direct office data for procurement.

The central estimate equals about 68 million medium-sized coffee servings daily. That is a more intuitive figure for people who think in cups rather than litres. It also makes clear that the result does not depend on a fantastical amount per person: the model assumes just 1.36 coffee servings per day across every worker, including non-coffee drinkers.

The answer is not that “IT runs on coffee.” That phrase is a cultural shorthand, not a measurement method. The evidence supports a more disciplined claim: a large, globally distributed technical workforce almost certainly consumes coffee in the tens of millions of servings a day, producing beverage volume in the millions of litres.

Any future revision should start with the assumptions, not the headline. If the global core IT workforce is re-estimated at 60 million, if surveys show materially lower coffee participation, or if larger home servings become dominant, the result should move openly. That is the standard an estimate should meet.

The answer without false precision

Estimated global IT coffee consumption is about 12 million litres per day, or around 4.5 billion litres per year. The best defensible range is approximately 5 million to 26 million litres daily.

That result assumes a core IT workforce of 50 million people, 68% coffee participation, two daily coffee servings among drinkers and 180 millilitres per serving. The most uncertain input is the worldwide headcount because international occupational statistics are incomplete and classifications differ.

The estimate does not measure office coffee alone, bean purchases, caffeine intake, café spending or environmental impact. It measures finished coffee beverages consumed by people working in core IT roles across all settings. It is a credible scale estimate, not an official tally.

A concise answer in Slovak would be: Celý svetový IT sektor pravdepodobne vypije približne 12 miliónov litrov kávy denne, pričom rozumný odhadovaný rozsah je asi 5 až 26 miliónov litrov denne.

Questions readers ask about global IT coffee consumption

Author:
Jan Bielik
CEO & Founder of Webiano Digital & Marketing Agency

This article is an original analysis supported by the sources cited below

Eurostat ICT specialists in the EU
Official European Union update reporting the number and employment share of ICT specialists in 2025.

Eurostat ICT sector employment
Official data distinguishing employment in the ICT sector from employment in ICT specialist occupations.

Eurostat ICT specialist metadata
Methodological metadata on the indicators used to measure ICT specialists in employment.

U.S. Bureau of Labor Statistics occupational projections
Official U.S. employment and projection data for computer and mathematical occupations.

U.S. Bureau of Labor Statistics occupational employment data
Detailed occupational employment estimate for computer and mathematical occupations.

Nasscom Strategic Review 2025
Indian technology-industry employment estimate used as a major workforce anchor.

China National Bureau of Statistics 2024 national economic report
Official definition and reporting context for China’s software and information-technology services industry.

International Labour Organization World Employment and Social Outlook
Global labour-market context and limitations relevant to international employment measurement.

ILO report on ICT skills shortages and labour migration
International evidence on the global demand for ICT specialists and measurement challenges.

OECD Digital Economy Outlook 2024
Analysis of demand for ICT specialists and digital labour-market trends.

OECD ICT task-intensive jobs indicator
Explanation of the difference between ICT specialists and broader ICT task-intensive occupations.

International Telecommunication Union Facts and Figures 2024
Global connectivity data used to frame the international digital economy’s reach.

International Coffee Organization Coffee Report and Outlook
World coffee-consumption forecast used as a market-scale plausibility check.

International Coffee Organization World Coffee Statistics Database
Official source describing the International Coffee Organization’s consumption and market-statistics coverage.

European Coffee Federation European Coffee Report 2023/2024
European coffee-market and per-capita consumption context.

National Coffee Association 2025 consumer survey release
U.S. evidence on daily coffee participation and average servings among coffee drinkers.

National Coffee Association home-consumption update
U.S. survey evidence on the importance of home coffee consumption.

European Food Safety Authority caffeine opinion
Scientific opinion on caffeine safety thresholds for healthy adults.

European Food Safety Authority caffeine topic page
Accessible summary of EFSA’s conclusions on daily caffeine intake and sleep-related timing.

PubMed review of caffeine effects on human behaviour
Scientific review addressing behavioural effects associated with moderate caffeine consumption.

PubMed study on caffeine consumption and sleep quality
Research examining associations between caffeine intake and sleep quality in Australian adults.

Centre for the Promotion of Imports coffee market analysis
Market context on regional shares of global coffee consumption.

Eurostat International Coffee Day data release
Official European Union coffee-import data illustrating the scale of European coffee demand.