Germany’s reputation for efficiency hides massive losses from excessive bureaucracy Evidence from trains, bakeries, and schools shows that cutting redundant processes boosts performance and retention.
U.S. protectionism is splitting markets and learning tools into rival stacks China’s AI capex and ASEAN infrastructure create a pull toward its stack Answer with block-biliteracy, pooled compute, and portable standards to keep choice
One number captures the urgency of the situation: 15%. This is the tariff level the United States now applies to European imports, and the effective floor many Asian exporters must plan around. The administration's actions, negotiations, and carveouts are shifting week to week, creating a sense of urgency. Prices are responding, plans are stalling, and universities are discovering that tariffs don’t just reprice goods; they segment ecosystems—chips, cloud credits, textbooks, even accreditation. In the same year, China is projected to pour as much as US$98 billion into AI capital spending, underwritten by a new state-backed US$8.2 billion fund. A hard tariff wall on one side; an industrial-policy engine on the other. In classrooms from Bangkok to Bandung, the choice no longer reads “which supplier,” but “which stack.” That is why the tariff story is, at root, an urgent education story.
The conventional take treats tariffs as macro noise that the education sector can ride out. That underestimates the impact of a durable 10–15% tariff regime on the micro-economy of learning. Executive actions in April and July introduced and then modified a “reciprocal” tariff framework, while parallel deals produced a flat 15% rate for EU goods—a signal that Washington is willing to normalize elevated duties, not simply threaten them. Court fights may prune the legal basis, but the policy thrust is plain enough for procurement officers to notice. When the price of imported lab equipment, developer workstations, or cloud credits rises and the legal basis wobbles, multi-year syllabi and infrastructure plans wobble with it. For ministries budgeting around five-year cycles, the result is not a transient price bump but an incentive to switch suppliers, platforms, and standards toward the stack they can reliably access.
The export-control environment compounds that incentive. In January 2025, the U.S. Commerce Department’s Bureau of Industry and Security moved—then partially reversed course in May—toward an unprecedented control on closed-weight AI models and tighter rules on advanced computing items. Even with rescission of the “AI Diffusion” rule framework, much of the emphasis on controlling chips, large clusters, and specific model weights persists, and the mere signal of possible licensing in future rounds is enough to make universities think twice about relying on a single, foreign-hosted stack. The administrative churn is the point: standards and access are now policy variables. Education systems that plan for volatility will favor multi-homed compute, bilateral recognition of credentials, and courseware that ports across ecosystems.
If we reframe tariffs as a pipeline shock, the stakes sharpen. In the 1930s, block economies hoarded commodities; today, they hoard compute, standards, and trust in credentials. A 1–2 percentage-point price shock imposed on AI-relevant imports would be survivable in isolation. But layered atop export controls and procurement uncertainty, it nudges systems toward the nearest reliable platform. However, with proactive policy and procurement strategies, these shocks can be turned into opportunities. That is how trade policy becomes curriculum policy, and how we can shape a more hopeful future for education and AI infrastructure.
Against that policy weather, the supply landscape in Asia is rapidly diverging. China’s AI capital expenditure is forecast between ¥600–700 billion (US$84–98 billion) in 2025, and authorities have launched a 60 billion-yuan (US$8.2 billion) fund to feed the early-stage pipeline. The result is a dense mesh of cloud options, accelerator vendors, and model providers prepared to court universities with localized pricing in Southeast Asia. Crucially, much of this capacity arrives physically nearby: Chinese cloud providers are building and leasing facilities across ASEAN, putting low-latency, compliant infrastructure within reach of public universities and vocational institutes that cannot afford U.S. hyperscaler pricing at commercial rates. The gravitational pull is not ideological; it is logistical.
The infrastructure map supports that claim. Alibaba Cloud has opened a third data center in Malaysia and plans to open a second in the Philippines; Huawei Cloud and other Chinese providers are expanding across the region, even as they remain constrained in Western markets. On the demand side, ASEAN’s data-center electricity consumption is projected to nearly double by 2030, with Malaysia alone expected to surge from roughly 9 TWh in 2024 to about 68 TWh by decade’s end—a proxy for the compute that education and research could tap if access arrangements are negotiated early. The point is not that these facilities were built for universities, but that their presence expands the bargaining set for public procurement. Systems that move now can secure “education tiers,” a term referring to the levels of access and quality of services that can be obtained through early adoption of new technologies, while capacity is still being allocated.
Counterweights exist—and they matter. AWS has committed roughly US$9 billion to expand Singapore capacity by 2028, and Singapore-based GPU-as-a-service has begun offering H100-class clusters region-wide. That means ASEAN ministries are not condemned to single-vendor dependence; they can triangulate between the Chinese cloud in Malaysia/the Philippines, and an allied cloud in Singapore, weighting the mix by cost, latency, and export-control exposure. However, the real game-changer is bilateral recognition of credentials. This can significantly enhance cooperation and standardization, making the education-policy foundations for adoption more robust. On governance metrics, Oxford Insights places Malaysia, Thailand, Indonesia, and Vietnam in the region’s first tier of AI readiness; this is a signal—not a guarantee—that ministries can turn cloud capacity into curricular capacity if they move on teacher training and credential standards.
The Japan–Korea question should be read less as a capability deficit than as a pipeline constraint. Tokyo’s innovation-first AI Promotion Act gives universities a permissive legal frame for testbeds and industry partnerships; Seoul is pursuing scale, publicly targeting procurement of 10,000 high-performance GPUs to anchor a national AI center, while planning a substantial 2026 budget uplift oriented to AI-led growth. Aging demographics will result in tighter student cohorts and salary ladders. Still, in the near term, these systems can supply high-end practicums and faculty exchanges that ASEAN universities can plug into—especially if reciprocal recognition and cross-porting assignments are built into capstones.
The most valuable graduate over the next five years will not be a single-stack specialist but a “block-biliterate” engineer, teacher, or technician who can move fluently between the U.S./allied and Chinese stacks. That is not a rhetorical flourish; it is a curriculum blueprint. Start with the first-year “AI math labs” that teach probability, optimization, and numerical linear algebra alongside GPU programming. Add an intermediate year built around model-lifecycle assignments—data provenance, fine-tuning, safety evaluation—executed twice: once on a U.S./allied platform (CUDA/PyTorch, OpenAI-compatible APIs) and once on a Chinese/open alternative (Ascend/CANN, local API suites). In parallel, embed a shared safety card and documentation standard so that capstones travel. Singapore’s PISA record is instructive here: 41% of students hit the top tiers in mathematics; systems that choose high expectations and aligned supports can absorb technical content at pace. The takeaway is not to imitate Singapore, but to match its clarity on outcomes.
Compute is the binding constraint, so buy it like a region. ASEAN should pool demand for an education-only GPU bank that reserves capacity in Singapore (for allied-stack workloads) and in Malaysia/the Philippines (for Chinese-stack workloads), with a modest sovereign cluster in at least two mainland states for redundancy. The goal is not self-sufficiency; it is predictable access to mixed stacks at education prices. A three-year pooled reservation of a few thousand high-end accelerators—sourced across vendors—would support course-level quotas and research fellowships, with 15–20% capacity carved out for cross-border project teams and teacher-training colleges. The market context is favorable: capacity is being added quickly, and providers on both sides are seeking anchor tenants. If ministries commit early, they can secure telemetry and transparency clauses for model and data handling, and most importantly, price certainty for students.
Standards preserve mobility when politics does not. A narrow standards spine—dataset documentation, reproducibility checklists, incident reporting, and minimal safety tests—should be written once and made stack-agnostic, then embedded into procurement and accreditation. The BIS’s brief foray into controlling “model weights” underscored how quickly access rules can change, and how suddenly a project can become non-portable; ASEAN standards should assume volatility by design. Concretely, capstone projects should be graded on cross-portability: the same model must run on both stacks, with an identical artifacts bundle (weights if permissible, prompts, data sheets) escrowed locally with audit trails. Ministries need not invent all of this in-house; they can draw on existing research reproducibility norms and adapt them to undergraduate and TVET contexts.
The common critiques can be answered. “Isn’t this code for dependence on China?” Not if procurement is deliberately multi-homed and academic outputs must be cross-portable. “Won’t U.S. tariffs fade in court?” Perhaps—but today’s policy environment is already reshaping behavior, and even an adverse ruling in Washington won’t repeal the logic of export-control cycles or the sunk investments ASEAN states are making in nearby data centers. “Can Japan and Korea really anchor regional training if their cohorts shrink?” Yes, if ASEAN leverages them for advanced practicums and faculty exchanges rather than volume teaching. The deeper objection—that building biliteracy cedes ideological ground—misreads the purpose. The aim is not to normalize either stack; it is to teach students to translate between them, and thereby keep options open for the decade ahead.
Turning the agenda into procurement lines. Set three visible deadlines. First, publish a 12-course spine for block-biliteracy by mid-2026, with sample labs released under open licenses and an explicit mapping to TVET certificates and bachelor’s degrees. Second, negotiate an ASEAN Education Compute Facility that reserves capacity across at least three jurisdictions and two vendor families, with telemetry and queueing rules published up front. Third, issue micro-credential standards for “portable capstones” and agree on reciprocal recognition among willing ASEAN, Japanese, and Korean universities. The data-center boom is not a distant prospect; on most forecasts, ASEAN’s sector will double by 2030, and Malaysia’s power demand alone is slated for multi-fold growth. Education can contribute to that growth—but only if it moves while capacity is being allocated.
Where the money meets the model, the U.S. still leads in private AI investment by an order of magnitude; in 2024, U.S. private AI funding was roughly US$109 billion, nearly twelve times China’s US$9.3 billion. That gap indicates where cutting-edge tools will continue to emerge. But capital expenditure is a different variable: Beijing’s ¥600–700 billion AI capex this year pays for the hard things—compute, fabs, power, parks—that make access cheaper next door. For ministries, the lesson is to arbitrage the difference: teach on both stacks, research where you can secure credits and GPUs at scale, and align teacher training to the standards that travel regardless of geopolitics. If tariffs are the tax on indecision, then biliteracy is the hedge against it.
Implications for schools below the university tier. The split will reach secondary schools via content filters, cloud-delivered tutoring, and the identities of the firms that underwrite assessments and teacher PD. We should maintain universal and straightforward guardrails: privacy-preserving analytics, dataset provenance in plain language, and a relentless emphasis on statistical thinking over rote “prompting.” In systems that can afford it, a small, ring-fenced share of pooled compute should be dedicated to teacher colleges and vocational institutes; the payoff is immediate, as industry utilizes the graduates these institutions produce. When procurement officers ask whether such ring-fences are realistic in an era of energy-hungry data centers, point to the region’s expansion numbers and the rise of GPU-as-a-service: capacity is growing, and governments retain leverage if they buy together and publish usage telemetry.
What matters now is that the temptation is to wait for the litigation cycle to conclude and for export-control guidance to be finalized. However, higher education does not operate on quarterly horizons; it operates on cohort horizons. A student who starts a diploma in 2026 will graduate into a world of stacked toolchains and political contingencies. The systems that act now—designing biliterate curricula, buying pooled compute, and locking in portable standards—will ship graduates who can work anywhere, with anyone, on anything that meets baseline safety and documentation. The systems that delay will inherit syllabi tied to whichever vendor offered a quick discount in 2024–2025, and will spend the next decade unpicking those decisions. The policy north star is not autarky; it is freedom to choose, class by class and project by project. That is what sovereign education looks like in a blocked world.
We began with two numbers: a tariff floor and a capex surge. The first is already rewriting procurement spreadsheets and cross-border contracts; the second is already reshaping where ASEAN’s compute sits and who can afford to rent it. Treat the combination as an opportunity, not a trap. Ministries should commit—before this academic year ends—to a biliterate spine that forces students to ship on both stacks; to a regional GPU bank that buys time, literally, for classrooms; and to a minimalist standards passport that keeps work portable no matter how court rulings or export rules swing. The U.S. investment engine will keep throwing off new tools; China’s industrial machine will keep laying concrete and cables; and ASEAN’s data-center curve will keep racing upward. If educators act now, they can transform tariff noise into a valuable human capital signal. The right graduates—fluent in both dialects of AI, trained on shared standards, comfortable switching stacks—will insulate schools from politics by making their students indispensable to both sides. That is how public education converts a trade shock into a decade of strategic advantage.
The views expressed in this article are those of the author(s) and do not necessarily reflect the official position of the Swiss Institute of Artificial Intelligence (SIAI) or its affiliates.
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Tariffs act as hidden taxes, falling mainly on U.S.
Masculinity norms shape labour supply, health, and political preferences Germany’s 1940s and Korea’s 2020s show contrasting trajectories Education policy can recalibrate norms and stabilise economies
Asia’s coalition strength lies in education and knowledge, not tariffs Patent dominance shows the region’s leverage in IP and skills An education-first compact is harder to divide than a trade bloc
The most decisive
AI translation reshapes the labor market, eroding low-skill roles while rewarding domain expertise Education must shift toward “language plus” skills—pairing translation with data, law, or health Policy should teach students to work with machines, not against them
Over the ten years following the emergence of neural machine translation from research labs to everyday use on our devices, a quiet transformation occurred in the hiring of translators. A recent study examining the spread of machine translation across various sectors suggests that for every one-percentage-point rise in usage, translator job growth slowed down by approximately 0.7 percentage points—leading to an estimated 28,000 lost new translator jobs between 2010 and 2023 compared to what would have happened otherwise. The same research indicates significant drops in job postings requiring foreign language proficiency, particularly in industries most affected by automated translation, even for widely spoken language pairs like English and Spanish. These figures are not indicative of a catastrophic outcome; instead, they represent consistent, incremental changes that alter the profession's trajectory. If we continue to teach languages and translation as though no significant changes have occurred, we are preparing students for a job market that is rapidly evolving. Conversely, if we recognize “good-enough” machine translation as a foundational tool and educate individuals to build upon it, we can create new opportunities on a more stable foundation.
We are reframing the familiar “translation jobs are dead” lament into a policy argument about complements. When the baseline cost of getting a passable translation falls toward zero, value migrates to what machines still do badly or cannot be trusted to do alone. That includes domain-specific precision, liability-bearing use cases, quality assurance at scale, and multilingual data stewardship. The practical implication for education is not to abandon language learning or to double down on a nostalgic defense of artisanal translation. It is to couple language with domain expertise and with the tooling, methods, and judgment needed to supervise automated systems—what we might call “language operations.” The goal is neither to resist automation nor to yield to it, but to teach students how to place the machine in the loop and keep themselves in charge of outcomes.
The timing matters. In the last two years, research groups and vendors have demoed real-time, expressive speech-to-speech systems that preserve tone, and large language models increasingly match or surpass dedicated MT systems on specific WMT evaluations. At the same time, human translators report material income losses in parts of the market most exposed to automation. The pattern is classic technological diffusion: rapid capability gains at the frontier, uneven enterprise integration, and sharp price pressure in commoditized segments long before “replacement” is complete. Educators who wait for the dust to settle will find their graduates already competing on the wrong margin.
Across the commercial language industry, the work has already been unbundled and re-bundled, showcasing the industry's adaptability. Post-edited machine translation (MTPE) is now the default production baseline for a large share of vendors, with industry surveys showing adoption rates rising steeply from 2022 to 2024. Shared tasks at WMT have folded LLM systems into head-to-head evaluations with traditional MT providers, while research programs like Meta’s Seamless line push low-latency voice translation into everyday tools. In other words, production workflows now assume that a machine produces the first draft and that humans specialize in triage: deciding when the draft is safe, what to escalate, and how to achieve publication-grade accuracy in contexts where errors carry legal, financial, or clinical risk. The pedagogy that matches this world does not start with replacing bilingual drafting; it begins with training students to measure quality, select and tune systems, maintain domain glossaries, and document the line between acceptable and unacceptable risk.
This reconfiguration is evident in labor data that, at first glance, appears contradictory. The U.S. Bureau of Labor Statistics reports a 2024 median annual wage of $59,440 for interpreters and translators, with employment projected to grow 2% from 2024 to 2034—slower than average but not collapsing. Meanwhile, industry and union surveys in publishing report that over a third of translators have already lost work or income due to the use of generative AI. Both can be true when aggregate employment is propped up by growth in specialized and public-sector roles (e.g., courts, hospitals, government), even as prices in commoditized segments compress. That is precisely why education policy should pivot away from preparing generalists for one-off document work and toward roles that attach language competence to regulated domains, enterprise systems, and accountability frameworks.
We can quantify aspects of the shift while acknowledging uncertainties, particularly regarding price signals. Freelance translator rates hover around $20/hour or $0.10–$0.12/word, but crowd-sourced and vendor sites also show $0.06/word tiers, with MTPE priced at 50–70% of human rates. Using conservative throughput estimates—400–600 words/hour for human translation and 800–1,000 for light post-editing—implied hourly revenues can fall below a living wage in commodity segments. At the same time, premium rates exist in legal, medical, and technical fields. These estimates, derived from industry guidance and empirical studies, reveal critical differences by language pair, client type, and risk profile, highlighting that automation drops prices where quality tolerance is high and liability is low.
A broader education system question sits upstream of wages: Are students still choosing to study languages at all? In the United States, higher-education enrollments in languages other than English fell 16.6% between 2016 and 2021, and roughly 29% since 2009’s peak. In Europe, the picture differs: in 2023, 60% of students in upper-secondary general education studied two or more foreign languages. However, primary-level multi-language study remains rare outside a few countries. If we teach as if every graduate will work in cross-border teams, Europe’s data justify continued investment. If we teach as if automated translation removes the need for language learning, America’s enrollment declines suggest students already believe that story. Policy must rebuild the case for language study on the ground where value now accrues: language plus something, with explicit attention to supervising AI.
Designing curricula in a world with free and instant machine translation (MT) requires a focus on key skills. First and foremost, quality evaluation must be taught as a core competency. Students should conduct human assessments, interpret automated metrics with caution, and utilize error analysis to evaluate systems—skills that reflect current research in MT meta-evaluation. Literacy should encompass the ability to judge the appropriateness of translation models for specific applications, reinforcing the crucial role of human judgment in the translation process.
Second, post-editing should be formalized as a professional skill rather than a fallback option. Evidence suggests post-editing can be faster than starting from scratch, but quality can vary significantly based on approach and familiarity with content. Structured training in post-editing helps students avoid monotonous tasks and enhances their decision-making capabilities.
Third, early integration of domain specialization—such as Language + Law or Language + Biomedicine—can align education with job market demands, emphasizing terminology management and regulatory understanding. Capstone projects should involve real-world partnerships for practical experience.
Fourth, students must learn data stewardship, which includes ethical curation of parallel data and understanding legal considerations in translations. These skills are increasingly relevant to emerging roles in localization and multilingual content management.
For K-12 and general education, machine translation should be seen as a tool rather than a threat to language classes. Students should learn to critically engage with machine translation to verify information and enhance their media literacy.
Employers must clearly define when expert human translation is necessary versus when post-editing or machine-only processes are acceptable. Transparent standards can optimize language work and help educators prepare students for realistic negotiation around language service quality.
Critics may argue that advancements in AI translation will render language roles obsolete; however, evidence suggests that adoption often lags behind capability. Additionally, as translation systems evolve, the need for judgment and error detection will remain crucial, ensuring durable opportunities in the field.
This perspective is supported by data on translator employment trends, labor statistics, enrollment figures, and industry practices, each with its limitations, yet collectively outlining the landscape of evolving language needs.
The 28,000 “missing” translator jobs are a blunt measure of a subtler transformation. We do not live in a world that has abolished translation; we live in a world where mediocre translation is abundant. That abundance drains value from generalist workflows and restores it to the edges: to the legal contract whose ambiguity matters, to the clinical discharge summary that cannot be wrong, to the cross-border dataset whose labels must be audited, and to the editorial line where voice and meaning, not just words, carry the freight. Education policy should meet the world as it is. Keep languages, but teach them with systems. Build majors and minors that yoke language to law, finance, health, and energy. Make post-editing, evaluation, and data stewardship as familiar as verb conjugations. Public institutions that deploy MT should publish human-oversight thresholds and train their staff to meet them. If we do, we stop arguing about whether translation is dead and start preparing graduates for the work that only they can do: the work of deciding what counts as accurate, safe, and fit-for-purpose in every language we use.
The views expressed in this article are those of the author(s) and do not necessarily reflect the official position of the Swiss Institute of Artificial Intelligence (SIAI) or its affiliates.
Acemoglu, D. (2024). Interview in NPR Planet Money newsletter (media clip). Massachusetts Institute of Technology.
Bureau of Labor Statistics (U.S.). (2025). Interpreters and Translators (OOH updated 2024 wage/outlook).
European Commission (Eurostat). (2025). Upper secondary education: 60% study 2 or more languages (2023 data).
Lightcast / OECD. (2024). Artificial intelligence and the changing demand for skills in the labour market. OECD Publishing.
Meta AI. (2023). Seamless: Multilingual, expressive and streaming speech translation.
Modern Language Association. (2023). Enrollments in Languages Other Than English in U.S. Higher Education, Fall 2021.
Nimdzi. (2025). The MTPE Efficiency Gap (industry survey).
Society of Authors (UK). (2024). AI survey: impacts on translators.
Upwork. (2025). Hire Translators (median rates).
WMT (Conference on Machine Translation). (2024). General MT task overview and results. Association for Computational Linguistics.
X. Peng (2024). The Impacts of MT quality on post-editing effort (SAGE Open).
J. Algaraady et al. (2025). ChatGPT’s potential for augmenting post-editing (PMC review).
AI and robotics remain narrow tools, excelling only in tightly defined tasks Human versatility—handling exceptions, combining roles, and adapting to context—remains the decisive advantage Education policy must prioritize training for this versatility, turning automation into complement rather than substitute
The recent rush to declare a labor apocalypse misses a stubborn fact: even at the technology frontier, most work resists complete substitution. The International Monetary Fund estimates that about 40% of jobs globally are “exposed” to AI. Yet, exposure in low-income countries is closer to 26%—and exposure is not the same as displacement. It is an index of tasks that could be affected, for better or worse, by specific tools under specific conditions. That spread—40 versus 26—frames a more profound truth about education and labor policy in developing economies: the bottleneck is not a looming general artificial intelligence erasing human work, but the limited generalization of today’s systems and the uneven ability to apply them productively. Where tasks are standardized and context is controlled, automation advances quickly. Where the job hinges on recognizing edge cases, juggling multiple goals, and handling messy environments, human versatility remains decisive. The question for education is not how to train people for replacement, but how to cultivate that versatility as a comparative advantage. This highlights the importance of task redesign in the face of automation, empowering educators to take a proactive role in shaping the future of education.
What is commonly referred to as AI in much of the industry is best described as automation plus pattern recognition, tightly bound to the data and environments in which it is trained. This distinction is significant for schools. A robot can repeat a calibrated pick-and-place in a fixed cell with remarkable speed; however, it will not, on its own, infer what to do when a supplier swaps packaging, a part is slightly bent, or the ambient light changes the camera’s calibration. Surveys of “general-purpose” robotics make the state of play clear: despite impressive progress using foundation models and large, diverse datasets, robust performance under distribution shift—the move from training conditions to the real world—remains a significant challenge. We should anticipate capable systems in well-scoped niches, rather than broad agents that seamlessly transition among tasks. Therefore, education policy must prioritize the human skills that can integrate niche automations into reliable production.
The difference between finer granularity and true generality is not rhetorical. New robotic datasets and “generalist robot policy” efforts explicitly acknowledge that models trained across many demonstrations still struggle to transfer without careful domain adaptation and human supervision. Meta-analyses and surveys in 2023–2025 document promising sample-efficiency gains and multi-task behaviors, but also persistent gaps in compositional reasoning and robustness when conditions deviate. In other words, computation has made single actions more precise, not made work more universally machine-doable. For education systems, this suggests two priorities: build students’ capacity to diagnose when automation is brittle, and teach them to re-scope tasks, write operating “guardrails,” and recover gracefully when models fail—skills that are inherently cross-domain and human-centered.
Cross-country evidence since 2023 reinforces the case for reframing. The IMF’s 2024 analysis pegs AI exposure at roughly 60% in advanced economies, 40% in emerging markets, and 26% in low-income countries, emphasizing that many developing economies are less immediately disrupted—but also less ready to benefit—because exposure concentrates in clerical and high-skill services. Complementing this, an ILO working paper on generative AI concludes that the dominant near-term effect is augmentation of tasks, not wholesale automation, and notes that clerical work—an essential source of women’s employment—sits among the most exposed categories. OECD work adds a long-running backdrop: across member countries, about 28% of jobs sit in occupations at high risk of automation, with exposure skewed by education and task mix. Together, these sources indicate substantial task-level changes, limited job-level substitution, and significant heterogeneity across sectors and skills.
Meanwhile, the hardware realities of automation remain geographically concentrated. According to the International Federation of Robotics, 541,000 industrial robots were installed in 2023, increasing the global stock to approximately 4.3 million. Roughly 70% of those new units were allocated to Asia, with about half going to China alone. China’s density has more than doubled in four years to around 470 robots per 10,000 manufacturing workers, powered increasingly by domestic suppliers. That concentration matters for developing countries: it means learning to work alongside automation is becoming a baseline capability in export-integrated value chains. At the same time, the frontier of robotic deployment remains far from many classrooms. Where factories scale, labor-saving automation can coincide with higher output and, paradoxically, stable or rising employment in adjacent roles—installation, maintenance, quality, and logistics—if training systems pivot in time.
Readiness gaps are not abstract. In 2024, about 5.5 billion people—68% of the world—were online, but only 27% of people in low-income countries used the internet, compared with 93% in high-income economies. Electricity access has improved, yet approximately 666 million people still lack access to electricity in 2023, predominantly in Sub-Saharan Africa. Digital infrastructure experts estimate that achieving universal broadband by 2030 would require over $400 billion in investment. These constraints dampen both adoption and the realized productivity of AI systems in schools and workplaces. The IMF warns, accordingly, that lower readiness could widen the income gap even if exposure is lower. Education policy that assumes ubiquitous connectivity will miss the constraint that matters most in many districts: the socket and the signal.
Regional studies underscore the need for caution. For Latin America and the Caribbean, a 2024 assessment by the ILO and the World Bank estimated that 2–5% of jobs could be eliminated by AI in the near term, with 26–38% affected to some extent. Importantly, digital infrastructure limitations will also constrain the impact. In East Asia and the Pacific, a 2025 World Bank review finds that technology adoption has boosted employment as scale and productivity effects offset direct displacement in several sectors, even as benefits skew to skilled workers. The policy implication is not to deny displacement risks, but to recognize the pattern: automation trims specific tasks and roles while opening up complementary work, where training systems can pivot over time, and stalls where connectivity and basic infrastructure lag. This underscores the need for a shift in training systems to adapt to the changing labor market, emphasizing the importance of making timely adjustments in education policy.
If the edge of the problem is narrow automation rather than imminent generality, curricula should be redesigned to teach “versatility in context.” That means building horizontal range—the capacity to handle exceptions and novel cases—and vertical task stacking—the ability to combine technical, organizational, and communication tasks around an automation. UNESCO’s 2024 AI competency frameworks for teachers and students point in this direction by centering critical use, ethical judgment, and basic model understanding rather than tool-specific recipes. Systems that embed these competencies early and reinforce them through upper-secondary and post-secondary tracks will create graduates who can scope problems properly, write precise instructions for tools, anticipate failure modes, and make trade-offs under constraints, all of which raise the productivity of narrow automation without pretending it will think for them.
The pathway runs through TVET as much as it does through universities. The demand signal from factories and service providers is already shifting toward “purple-collar” roles that blend operator, maintainer, and data-literate supervisor. Given that Asia accounted for 70% of new robot installations in 2023, countries integrated into those value chains require rapid, modular upskilling in safety, sensor calibration, basic control logic, data logging, and line reconfiguration. Education ministries should broker industry-education compacts that co-design micro-credentials stackable into diplomas, align with international standards where possible, and are delivered in conjunction with work-based learning. Where public institutes lack equipment, shared training centers with pooled, open-calendar access can reduce capital costs and spread utilization. This is not a wager on humanoids; it is a bet that maintenance, integration, and exception-handling will be abundant—even as task automation grows.
A human-in-the-loop philosophy should extend into how schools themselves adopt AI. Start with administrative and low-stakes uses where augmentation is clearest, such as drafting routine correspondence, scheduling, document classification, and data cleaning, while retaining human review. A brief method note clarifies the stakes: suppose, conservatively, that 30% of clerical tasks in a district office are technically amenable to partial automation under current tools. If only one-third of households and staff have reliable connectivity and devices, the adequate short-run exposure could be roughly 0.30 × 0.33 ≈ , or 10% of clerical workload—small but meaningful, and conditional on training. This is a back-of-the-envelope estimate, not a forecast, combining ILO task exposure patterns with ITU connectivity shares; it illustrates why piloting with measurement and user training matters more than sweeping mandates.
Equity cannot be an afterthought. Clerical roles are among the most exposed to generative AI and are disproportionately held by women; without targeted upskilling, AI could erode one of the few stable rungs in many labor markets. Education systems should prioritize transitions from routine clerical work into higher-judgment student-facing support, data stewardship, and school operations analytics—roles that pair domain knowledge with oversight of tools. At the same time, governments need to fund the prerequisites—reliable electricity and broadband—to make any of this real. World Bank analyses of digital pathways for education and connectivity finance make the investment case clear; without it, AI policies risk becoming paper plans. The strategic choice is not whether to “ban” or “mandate” AI in schools; it is to sequence investments so that human capability—teachers, technicians, supervisors—can compound the gains of narrow automation.
A final policy lever is to temper macro expectations with micro design. Acemoglu’s 2024 “simple macroeconomics of AI” reminds us that productivity and cost savings play through tasks, not mystical growth surges. Once policymakers accept that automation’s displacement and augmentation effects will be uneven and path-dependent, they can set realistic targets for sectoral training, vendor procurement clauses that require human override and audit logs, and iterative evaluation cycles. The payoff for education is credibility: when ministries publish task-level baselines and measure the share of workflows that become faster, more accurate, or more equitable because a tool is embedded with trained staff, public trust rises, and the path for expansion becomes evidence-based.
40% of global jobs are exposed to AI, compared to 26% in low-income countries; this is not a countdown clock to human obsolescence. It is a map of where careful task redesign and human-machine collaboration can deliver gains, and where readiness stands in the way. For developing countries, the strategic advantage is not to chase generalized autonomy; it is to double down on human versatility—training people to orchestrate brittle automations, to notice edge cases, to reframe problems on the fly, and to do all this in institutions where electricity and connectivity are reliable, and where learning pathways are short, stackable, and tied to real equipment. If education systems teach that form of judgment at scale, the next wave of automation will widen opportunity rather than narrow it. The call to action is straightforward: fund the socket and the signal; adopt competency frameworks that prize critical use over blind adoption; build TVET-industry compacts for robot-adjacent skills; and measure task-level gains relentlessly. The robots will keep getting better. Human versatility must move faster.
The views expressed in this article are those of the author(s) and do not necessarily reflect the official position of the Swiss Institute of Artificial Intelligence (SIAI) or its affiliates.
Acemoglu, D. (2024). The Simple Macroeconomics of AI. NBER Working Paper No. 32487.
Epoch AI. (2025). Most AI value will come from broad automation, not from R&D.
Forbes/CognitiveWorld. (2019). Automation Is Not Intelligence.
International Federation of Robotics (IFR). (2024). World Robotics 2024 – Industrial Robots: Press conference deck and highlights.
International Labour Organization (ILO). (2023). Generative AI and jobs: A global analysis of potential effects on job quantity and quality. ILO Working Paper 96.
International Labour Organization (ILO). (2025). Generative AI and Jobs: A Refined Global Index of Occupational Exposure.
International Monetary Fund (IMF). (2024). Gen-AI: Artificial Intelligence and the Future of Work. Staff Discussion Note SDN/2024/001.
International Telecommunication Union (ITU). (2024). Measuring digital development: Facts and Figures 2024.
OECD. (2024). OECD Employment Outlook 2024.
Reuters. (2024). AI could eliminate up to 5% of jobs in Latin America, study finds.
UNESCO. (2024). AI competency frameworks for teachers and students (overview).
World Bank. (2025). Future Jobs: Robots, Artificial Intelligence, and Digital Technologies in East Asia and Pacific.
World Bank. (2025). Tracking SDG7: The Energy Progress Report 2025 (highlights).
World Bank. (2025). Digital Pathways for Education: Enabling Learning Impact.
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