Impact of Technology on Economic Growth

My latest in the Financial Times. I make four points: 1) Capitalism's great achievement was moving activity out of the household and into the market — turning domestic production into paid specialisation, creating jobs, and making output visible to the national accounts. AI-enabled self-service might quietly reverse that centuries-long trend. 2) When a technology automates tasks within a service, it can trigger a Jevons paradox: the service gets cheaper, demand expands, and employment grows. That is what ATMs did, and why automation has so rarely produced mass unemployment. But the paradox holds only when the technology makes the existing service model more efficient. When it lets people do the work themselves, demand for the service collapses. 3) AI extends this even to the manual trades, the supposed safe haven of the AI age. If a homeowner can ask a chatbot why their boiler keeps losing pressure, heating engineers lose call-outs. 4) When work shifts to the consumer, it vanishes from the economy statisticians measure. Replace a billing department with a chatbot and a firm records lower costs and higher output per worker; the national accounts register a productivity gain. But the hours patients spend decoding their own test results appear nowhere — not in labour statistics, not in GDP. As self-service spreads into professional domains, that blind spot will grow.

As AI is replacing early-career jobs, the economy's productivity in the short-run increases, but productivity and welfare in the long run may decline. In a new paper, Enrique Ide argues that we may be witnessing "socially excessive automation of early-career work. Such automation may deliver immediate productivity gains, but it also erodes the skills of future cohorts and constrains long-run growth." Here's the abstract of his paper: "Recent advances in Artificial Intelligence (AI) have sparked expectations of unprecedented economic growth. Yet, by enabling senior workers to accomplish more tasks independently, AI may reduce entry-level opportunities, raising concerns about how future generations will acquire expertise. This paper develops a model to examine how automation and AI affect the intergenerational transmission of tacit knowledge—practical, hard-to-codify skills critical to workplace success. I show that the competitive equilibrium features socially excessive automation of early-career tasks, and that improvements in such automation generate an intergenerational trade-off: they raise short-run productivity but weaken the skills of future generations, slowing long-run growth—sometimes enough to reduce welfare. Back-of-the-envelope calculations suggest that AI-driven entry-level automation could reduce the long-run annual growth rate of U.S. per-capita output by 0.05 to 0.35 percentage points, depending on its scale. I further show that AI co-pilots can partially offset lost learning by assisting individuals who fail to acquire skills early in their careers. However, they may also weaken juniors’ incentives to develop such skills. These findings highlight the importance of preserving and expanding early-career learning opportunities to fully realize AI’s potential." What can policy do? In the concluding remarks, the paper offers several ideas: - government subsidies for "mentorship, apprenticeship, and other entry-level training arrangements" - "taxing entry-level automation" - reducing minimum wages for young workers - promoting AI systems that complement rather than replace entry-level jobs. Universities could also play a role by placing "greater emphasis on providing undergraduate students with opportunities to gain practical experience before they formally enter the labor market. Such initiatives would complement the traditional focus of undergraduate programs on codifiable knowledge and help foster the early development of tacit skills." Read the full paper here: https://lnkd.in/eMq3uktX (open access)

"When automation removes the simpler tasks (as accounting software did for bookkeeping clerks), the remaining work becomes more specialized, wages rise, and fewer workers qualify. When it removes the harder tasks (as inventory management systems did for warehouse workers), the job becomes more accessible, employment expands, and wages fall. Same technology, opposite labor market outcomes, depending on which part of the job gets automated." - Alex Imas, The University of Chicago Booth School of Business from What Will Be Scarce? https://lnkd.in/eqdKHk2u

A new paper from David Autor, in collaboration with Neil Thompson, makes an important contribution to explaining how AI is likely to impact labor markets. Based on a rigorous model, confirmed with an analysis of 40 years of data, they provide a nuanced perspective on how automation impacts job employment and wages. Essentially, this depends on the extent to which easy tasks are removed from a role and expert ones are added, and how specialized a role becomes as a result. When jobs gain inexpert tasks but lose expertise, wages decline, but employment may increase. Think of how taxi driving became less specialized, and well-paid, but more common, due to Uber. In contrast, when technology automates the easy tasks inside a job, the remaining work becomes more specialized. Employment falls because fewer people now qualify, but the scarcity of expertise drives wages up. This is what seems to be happening with proofreading, which is now less about spell-checking and more about helping people to write, leading to lower job numbers but higher average wages. Their model helps us to understand the impacts of AI on labor markets. For instance, why AI tools can raise wages for senior software engineers, but decrease employment, while simultaneously reducing earnings, and increasing employment, for more entry level software engineering roles.

Today I published one of the most important analyses I’ve written this year. For decades, the “real economy” — restaurants, trucking fleets, farms, construction firms — failed to digitize. Not because they lacked ambition, but because the economics never made sense. Thin margins, high turnover, fragile workflows, and million-dollar IT projects simply couldn’t coexist. That era is over. AI has finally changed the math. Not gradually. Not theoretically. But decisively — in cost structure, workflow design, and failure tolerance. In this deep-dive, I break down: 🔹 Why software failed these industries for 40 years 🔹 What changed at the architecture + compute level 🔹 The cost collapses making automation viable 🔹 The real bottlenecks: vision, workflow entropy, multi-step reliability 🔹 Which sectors will be transformed first — and why If you care about AI’s impact on the actual economy — not just office workflows — this is the shift to watch. Full article: https://lnkd.in/dwANsSJ7 Curious to hear your thoughts: Which low-tech sector becomes unrecognizable first?

European Commission: #Work in the #Digital Era This report provides a comprehensive analysis of the impact of digital technologies on work and occupations in Europe, critically reassessing dominant narratives of mass unemployment and job polarisation. The report synthesises work done by the JRC Employment team over the last years. Drawing on a wide range of empirical research, the report introduces an analytical framework distinguishing three main vectors of change: automation, the replacement of labour by machines; digitisation, the increasing use of digital tools in work processes; and platformisation, the use of digital platforms for coordinating work. Contrary to widespread fears, our research finds that the impact of automation, such as industrial robots, on net employment levels in recent decades has been modest and often positive. While specific tasks are automated, this has primarily boosted productivity and led to a reallocation of labour rather than a net destruction of jobs. The most profound transformation stems from digitisation. This process, while enhancing efficiency, has fundamentally altered work organisation by enabling unprecedented levels of standardisation, monitoring, and managerial control. This creates a central paradox: while employment shifts away from routine occupations, work processes within many non-routine professional roles are becoming increasingly routinised and subject to digital control, impacting worker autonomy and job quality. Finally, the report identifies the rise of platformisation, not just in the gig economy, but as a logic of algorithmic management and surveillance extending into traditional workplaces. This trend is reshaping the nature of workplace control across the economy. Analysis of occupational structures reveals that job upgrading, rather than job polarisation, has been the most common pattern of change across the EU, driven largely by the growth of high-skilled service sector jobs. The report concludes that the primary impact of the digital era on work is a qualitative transformation in its nature, focusing on coordination, control, and job quality. The effects of technology are not deterministic; they are strongly mediated by institutional frameworks, with regulation and collective bargaining playing a crucial role in shaping outcomes for workers in the digital age.

Businesses are laying off workers as AI expands. Cutting costs through technology may deliver short-term gains, but it risks hollowing out the very capability economies need to adapt. That’s because human capital remains one of the most decisive factors for growth and resilience. According to IMF, 40% of jobs worldwide are affected by AI. Finance, manufacturing, and transportation face especially high exposure. But whether societies benefit or lose out depends on workers’ skills, education, and the strength of institutions. We already know that technology delivers higher returns for workers with stronger skills. For example, digital platforms that reduce remittance costs mainly benefit migrants who already have financial literacy. In agriculture, new technologies only deliver results when farmers have training and extension support to adopt them, and when governments can design and implement effective programs for adoption. Meanwhile, shocks are eroding human capital. Climate stress, conflict, and malnutrition undermine productivity and livelihoods, while crises interrupt education. Without action, AI could widen inequalities within and across countries. This is why AI adoption has to advance in parallel with investment in people through education, nutrition, skills, and capable public institutions, especially for women and youth. The long-term payoff from AI will depend less on the technology itself and more on how societies invest in human capital. Sources: Chart based on illustrative estimates from OECD (2023), ILO (2023), McKinsey Global Institute (2023), World Economic Forum Future of Jobs Report (2023).

The jobs AI eliminates first determine who gets to become a senior leader later. This is the strategic risk technology leaders must consider when evaluating AI deployment decisions. If automation removes the routine tasks that junior professionals traditionally performed : document review, basic research, initial analysis. Where do future CTOs, creative directors, and strategic planners gain their foundational experience? McKinsey's analysis of UK job postings since May 2022 reveals a troubling acceleration. Overall vacancies are down 31%. But roles highly exposed to AI and large language models have declined by 38%, nearly twice the rate of positions with low AI exposure. Software development, data analysis, management consulting, and graphic design vacancies have declined by over 50% in some categories. These are not low-skill positions. These are professional entry points that historically provided the experiential learning required for senior leadership roles. Bank of England Governor Andrew Bailey calls this "talent pipeline disruption." The concern is not just displacement of current workers but the erosion of development pathways for future leaders. When efficiency gains eliminate the apprenticeship stage of professional development, where does the next generation of strategic capability come from? In fashion retail, this pattern carries particular weight. The industry has always combined efficiency-driven operations with cultural judgment and creative direction. If AI eliminates the junior operational roles that traditionally fed senior creative and strategic positions, the talent pipeline for distinctly human leadership functions begins to hollow out. Automation may improve short-term margins in operational areas. But it risks sacrificing the experiential learning that develops leaders who understand customers, products, and brand at the intuitive level that data analysis alone cannot provide. This is not an argument against AI adoption. It's a call for deployment strategies that consider talent development alongside productivity metrics. The question for CTOs: are you measuring only what AI gains deliver today, or also what capabilities it may erode over time? The choices we make now about which tasks to automate and which to preserve for human learning will determine whether we're building organisations for sustainable competitive advantage or extracting short-term efficiency at the cost of long-term capability. #AI #FutureOfWork #Leadership

The Machine Tool Industry Is Reinventing Itself! If you’ve been in manufacturing long enough, you’ve seen cycles before. But this time, the shake-up isn’t just about the economy, it’s about a fundamental shift in how machine tools are sold, serviced, and integrated into shops. What’s really happening? It’s not just layoffs for the sake of cost-cutting. The entire machine tool supply chain, from builders to dealers, software providers to automation integrators, is going through a restructuring phase to adapt to: ✅ The Rise of Automation – Shops are investing in automation and self-sufficient systems, meaning dealers and software companies are having to shift their focus from traditional machine sales to full-scale automation solutions. ✅ A Shift in How Machines Are Bought – Machine tool sales have traditionally been relationship-driven. But today? Shops are doing their own research, watching YouTube, following LinkedIn influencers, and making more data-driven purchasing decisions—which means the role of the traditional machine dealer is changing. ✅ The Digital Revolution in Manufacturing – CAD/CAM, ERP, IIoT, and AI-driven production are no longer “nice to have”—they’re essential. But software companies that aren’t innovating fast enough are losing ground, leading to workforce cuts and major restructuring. ✅ Global Economic Adjustments – High interest rates, supply chain fluctuations, and reshoring efforts mean manufacturers are delaying major machine purchases—putting pressure on machine dealers and OEMs to rethink how they operate. ✅ Mergers, Acquisitions, and Consolidation – We’ve seen major industry shakeups with machine tool builders merging, dealerships being acquired, and software companies combining forces. The result? Some roles become redundant, while new opportunities are created in companies that are future-focused. While some companies are making cuts, others are doubling down on AI-driven manufacturing, digital sales strategies, and automation-forward machine tools. The ones adapting are positioning themselves for long-term success. For those in the industry, the real question isn’t “Will my job be safe?” but rather “Am I positioning myself to be part of the next evolution in manufacturing?” Change is happening, but those who stay ahead of the curve will be the ones leading it. What are your thoughts?

Why does the same technology raise wages in one job, but lower them in another? A new theory of expertise, unveiled by David Autor and Neil Thompson. Last week, I wrote about David's Paris School of Economics/CEPR - Centre for Economic Policy Research keynote on expertise, but now the full working paper is out in National Bureau of Economic Research. Worth a read for not only labor economists, but any economist and scholar. Not all automation is created equal -- and neither are its effects on different occupations. David and Neil relate the effects with the role of expertise, referring to scarce and valuable human capital. A new framework, called the expertise model, helps explain why workers in jobs that look similarly “automatable” may experience radically different outcomes. Take two examples: accounting clerks and inventory clerks. Both were heavily exposed to automation in recent decades. Traditional economic models would predict similar effects -- declining wages or reduced labor share -- due to a loss of routine, codifiable tasks. But that’s not what happened. Instead: a. Accounting clerks’ wages have risen, while their employment declined. b. Inventory clerks’ wages declined, but employment rose. Why? Because automation eliminated inexpert tasks (like data entry) in accounting, pushing the role toward higher-skill, decision-oriented functions. Fewer people can do that work -- hence higher wages, but fewer jobs. In contrast, automation eliminated expert tasks in inventory roles (like pricing and flagging stock anomalies), reducing the skill barrier and opening the job to a broader labor pool -- hence lower wages, but more employment. This “expertise framework” is a powerful tool for understanding how technological change reshapes labor markets -- not just by eliminating tasks, but by changing who is qualified to do what remains. When asked about technological change, I used to think about things in terms of the task-based model, but now it's clear that we also need to consider how technology affects the optimal composition of expertise required within each job. And what's more, then how does AI affect the degree of augmentation versus automation of expertise within jobs? Organizations will need to respond accordingly (e.g., compensation, recruitment). #FutureOfWork #Automation #LaborEconomics #AI #WorkforceDevelopment #OccupationalShifts #HumanCapital #Productivity