The work
above us.

The argument beneath the illustration

A transition is something people have to live through.

The important question is what happens between one arrangement of work and the next. A company can become more productive while an employee loses a reliable income. A customer can receive a better roof while a new worker finds fewer ways to learn the trade. Those outcomes can coexist. Understanding them requires looking past the job title to the tasks, the economics of completing them, and the people carrying the transition.

Our concern is an overlap: digital systems may change office employment while physical systems change the work into which some displaced people might move. That overlap is possible, not inevitable. We do not have an established date for widespread autonomous roofing. We do have reason to examine what would happen if the period of protection offered by physical work proved shorter than the time people needed to retrain.

This paper combines occupational evidence, bounded robotics reports, and an original conditional model. Its purpose is to make the assumptions visible enough to challenge. Optimism has a useful role here: it can motivate investment in a better outcome. It cannot substitute for the evidence that the outcome is being achieved.

01 · The unit of change

Less time on a task does not settle the fate of a job.

A production manager might delegate the first draft of a takeoff while retaining the inspection, the unusual substitution, and the conversation with the crew. A roofer might use a machine for a repeated installation task while continuing to diagnose damage and resolve awkward transitions. In either case, some work changes hands. The amount of paid employment depends on what happens to the rest of the job and how much work customers purchase.

The opening uses task descriptions from O*NET’s roofing occupation and a composite of its construction-management and construction-supervision roles. We assigned the time weights. Each illustrated week begins at 40 hours; it is not an observed time diary. A commercial membrane crew would need a different allocation from the residential reroofing and repair mix used here.

Colored portions preserve the original workload. They show baseline-equivalent work assigned to different execution modes, not machine operating hours. Assistance can reduce human effort on the work that remains. Supervision and new responsibilities then add human hours of their own. Their presence does not guarantee enough new work to replace every hour saved. Accountability for an accepted, durable roof also remains a separate question from which system executed a task.

02 · The receiving occupation

The trades offer opportunity. Their capacity is finite.

Anthropic’s original scenarios explicitly exclude rapid robotics and recognize that workers may struggle to move between occupations. Our extension examines that stated boundary. A destination occupation may experience growing labor supply at the same time that technology changes its demand for human effort. Treating physical work as a permanent refuge would require an additional assumption about how long that work remains scarce.

Roofing is a substantial trade, but a small receiving market relative to the national workforce. BLS reports 166,900 roofer jobs in 2025 and projects approximately 12,000 openings annually over 2025–35, many replacing people who leave. Its May 2025 median annual wage is $55,440. Employment includes self-employed workers; that wage estimate excludes them. These figures establish scale, not a forecast of how many displaced office workers could enter.

An occupational move involves learning, local vacancies, physical suitability, and a period of earning under different conditions. BLS describes training that progresses through practical tasks and experience. We should therefore ask a second question: if repeated beginner tasks become easier to automate, how will the next generation acquire expertise? A narrower entry route is a hypothesis to investigate. Experienced judgment retaining value would not, by itself, solve that problem.

03 · Capability, reliability, deployment

The useful question is what the machine can finish.

Physical automation in roofing need not arrive in a human-shaped body. In October 2024, Saint-Gobain announced a partnership with Renovate Robotics targeting asphalt-shingle installation and prospective contractor pilots. Renovate’s public program makes specialized roofing systems part of the relevant evidence. These sources do not establish whole-roof autonomy, independently verified reliability across varied sites, or a complete installed-square cost.

Broader robotics reports explain why this boundary deserves attention. Figure reports more than 90,000 sheet-metal parts loaded and 1,250 runtime hours in a BMW deployment. In a separate household experiment, Figure reports complete-task success increasing from 9% to 56% across 30 unseen homes with a different pretraining approach. These are vendor-reported results in specific workflows; neither measures roofing productivity. Likewise, a hand capable of gripping a triggered tool is a component capability, not an accepted roof installation.

The economic threshold is cost per accepted task. Include transport, setup, supervision, downtime, maintenance, financing, rework, and warranty exposure. Compare the same roof system, access conditions, and finish quality. A low purchase price or many powered-on hours cannot answer that question. Public evidence can justify a conditional scenario before it justifies an adoption forecast; keeping those standards separate makes the research more useful.

04 · The overlap

Two changes can put pressure on the same paid hours.

Consider a deliberately simple example with unchanged order volume. If qualified labor availability rises 10% while required human hours fall 20%, available labor per required position rises by 37.5%: 1.10 divided by 0.80. This arithmetic does not predict a 37.5% wage cut. It identifies a source of pressure that hiring, pay, working time, and occupational exit might absorb in different ways.

Demand can offset the pressure. Better execution might make more projects affordable, shorten waiting times, or allow a company to undertake work it previously could not serve. Additional maintenance, repair, supervision, and training may also create paid work. Each channel needs a mechanism and a customer or institution willing to pay for it. A larger theoretical market is not the same as enough completed orders at sustainable margins.

Our scenarios make both sides explicit. They vary task substitution, assistance, new responsibilities, qualified entrants, and the speed of matching people to positions. The roofing module then combines fewer hours per project with an assumed expansion in demand. Its wage response is a chosen rule, not an estimate from observed roofing markets. The exercise asks which assumptions produce pressure and what would have to change to relieve it. It cannot tell us which case is most likely.

The timing matters within a company as well. A saved hour can become an additional project, a shorter working day, time to teach an apprentice, or a reduction in paid work. Those are different uses of the same technical improvement. The model represents selected demand and wage responses; it does not choose a contractor’s staffing policy. Employers and crews still have decisions to make before a productivity gain becomes an employment outcome.

05 · Reading the result

A larger economy can contain a harder transition.

In the Aggressive case, the year-6 national output proxy reaches 138.0 from a baseline of 100, while aggregate labor income falls to 85.8. The separate roofing wage index reaches 85.0. This is an internally consistent possibility under the selected assumptions, not evidence that these changes will occur. The proxy omits many constraints on production and investment that a national forecast would need.

The counterexamples matter as much as the headline. Aggressive national-model unemployment is 12.2% with direct robot substitution and 12.6% without it. The modeled demand response and changing labor availability can offset displacement elsewhere. Robotics does not worsen every aggregate measure in this experiment. The comparison changes both required hours and demand, so it does not isolate a mechanical replacement effect.

Stronger demand changes the roofing result as well. Raising roofing volume 25% above each default path lifts the terminal wage indices to 108.2, 103.8, 93.4 in Mild, Moderate, and Aggressive, respectively. That assumption is not a program we can promise to deliver. It makes the size of the offset visible. A constructive response should examine how useful demand could grow and where the economics would still leave people exposed.

06 · Who receives the gains

Productivity creates a possibility. Distribution is a decision.

Output, wages, and household security measure different things. A wage index describes the jobs in an occupational group; it does not track the lifetime earnings of the people who started there. Someone moving into another occupation may earn less even if wages in that destination rise. Employment and paid hours also matter. Looking only at the wages of people who retain work would miss part of the transition.

Labor share is compensation divided by output. A worker can receive capital income without increasing that share. Ownership and profit participation may therefore matter to household outcomes, but their value depends on purchase costs, risk, dilution, governance, and actual distributions. Our model does not estimate those arrangements. Its gross nonwage residual includes capital costs and depreciation; it is not cash available to distribute.

We think workers should have a meaningful part in the gains created with their experience and effort. That is a Foundation position. Whether a particular arrangement delivers it has to be tested against individual outcomes. Founder ownership, a training announcement, or an expanding company is insufficient evidence on its own. The relevant questions are what workers receive, what risks they bear, and whether people affected by the transition can participate.

07 · A positive path

Build the transition with the people doing the work.

We support automation that makes good roofs easier and safer to deliver. A responsible approach begins with crews helping identify the work worth changing and the failure cases a demonstration can miss. Decisions about quality acceptance and stopping unsafe work need clear human authority. Removing exposure to difficult tasks is valuable; the result still needs evidence from the full operating workflow.

Training and income continuity belong in the deployment plan before paid work disappears. New roles in diagnosis, maintenance, quality, customer care, and machine supervision need real demand, adequate pay, and a route for people to qualify. Employers should examine how newcomers will learn when machines take on some of the repeated tasks that previously supplied practice. These are design obligations, not guaranteed effects of buying equipment.

Responsibility also extends beyond any one employer. Education providers, customers, insurers, and public institutions influence which changes can work in practice. Income support or public training could alter the transition, but those choices have costs and eligibility questions that this model does not calculate. Company-level success should not be presented as proof that the economy-wide adjustment has been solved.

Measure customer and worker outcomes together: accepted work, price, rework, injury exposure, paid hours, earnings, retention, and participation in gains. Our concern would weaken if economical automation remained narrow, few qualified entrants arrived, or demand and new responsibilities absorbed the saved hours. It would strengthen if deployment outran those adjustments. A better future remains possible. Taking that possibility seriously means publishing adverse results and changing course when the evidence calls for it.