EVs, AI, and the Informal Workforce: Who Adapts, Who Gets Left Behind, and What Comes Next

 

EVs and AI: Progress That Rewrites Work

Electric vehicles (EVs) and artificial intelligence (AI) are often framed as clean, efficient, and inevitable upgrades to existing systems.

EVs promise reduced emissions, lower operating costs, and simplified mechanical architectures. AI, meanwhile, enables predictive maintenance, real-time diagnostics, automated decision-making, and unprecedented optimization across industries.

Taken together, these technologies represent genuine progress. They reduce waste, increase efficiency, and respond to urgent environmental and economic pressures. On the other side, they also do something less visible, being that, they redefine what it means to be technically skilled.

Where internal combustion engines relied on mechanical intuition, which involved, sound, vibration or wear patterns, the switch to EVs shift the center of expertise toward software, power electronics, and closed digital systems. AI further abstracts decision-making, embedding judgment into algorithms that many technicians are not trained to interrogate or modify.

For formally employed workers inside large organizations, this transition is often softened by access to training, certifications, and proprietary tools. For the informal workforce, such as, independent mechanics, repair technicians, installers, and self-taught specialists, the shift is abrupt. These workers are often closest to the machines themselves, yet farthest from the systems that update how those machines are understood.

The disruption, then, is not simply technological. It is institutional. EVs and AI do not just change devices, they also change who is allowed to work on them.

 

The Expanding Informal Market and the Skills Bottleneck

Paradoxically, as technologies become more advanced, the informal job market continues to grow. In many regions, stable long-term employment has declined, replaced by contract work, gig labor, and independent service provision. Technical workers increasingly operate outside formal structures, not by choice alone but by necessity.

This creates a bottleneck. Demand for technical labor remains high, yet the pathways to becoming “qualified” narrow.

Learning institutions under pressure

Traditional learning institutions struggle to keep pace with the speed of change. Curricula are slow to update, certification programs are costly, and many training pathways assume full-time enrollment, which then becomes, an unrealistic expectation for informal workers who depend on daily income. EV and AI education often prioritizes theory or proprietary systems, leaving little room for modular, hands-on, or transitional learning.

The result is a widening gap between what is taught and what is needed on the ground.

Industry restructuring and selective upskilling

At the same time, companies are restructuring their labor models. Rather than expanding workforces, many organizations choose to upskill a smaller group of existing employees while automating or outsourcing the rest. This approach is efficient from a balance-sheet perspective, but it quietly excludes informal workers who lack formal credentials or institutional affiliation.

Technical competence alone is no longer sufficient; access has become the real currency.

The unresolved surplus

This leaves a growing surplus of capable workers and people with deep practical knowledge but limited access to evolving tools and certifications. What happens to them remains an open question.

Do they form parallel markets using older technologies?
Do they specialize hyper-locally where legacy systems persist?
Do they migrate into adjacent trades or leave technical work altogether?

This surplus is not unskilled. It is structurally displaced.

 

Old Models, New Models, and the Uneven Value of Expertise

Despite narratives of rapid replacement, legacy technologies have not disappeared. Internal combustion vehicles, older machinery, and non-AI systems still dominate large parts of the world. They remain easier to repair, more transparent in design, and better suited to informal service ecosystems.

In these spaces, informal workers continue to provide essential value, such as, keeping vehicles running, extending product lifespans, and enabling access where formal services are unavailable or unaffordable.

EVs and AI-driven systems, by contrast, often rely on, proprietary software, locked diagnostic tools, centralized service models and restricted parts ecosystems

This shifts technical work away from repair and toward authorization.

Where the vintage angle fits

In wealthier markets, legacy technologies, particularly vintage cars, are increasingly repositioned as luxury items. Restoration, customization, and preservation become niche, high-value services. This does create specialized labor opportunities, but they are limited, geographically concentrated, and inaccessible to most informal workers.

The existence of these markets highlights an uncomfortable truth, in that, older technologies are not disappearing, but their economic meaning is changing. What was once mainstream becomes either marginalized or luxury-branded, leaving fewer viable pathways for broad-based employment.

The labor landscape that emerges is uneven. Old and new systems coexist, but they reward different kinds of expertise and not equally.

 

Climate Transition and the Cost of a Split System

EVs and AI are central to climate mitigation strategies. In theory, electrification reduces emissions, AI improves efficiency, and data-driven systems optimize resource use. But transitions are rarely clean.

When informal workers are excluded from new systems, adoption slows. Older technologies remain in use longer, often maintained without access to the most efficient tools or parts. Informal repairs extend lifespans, which can be environmentally beneficial but also perpetuate reliance on carbon-intensive infrastructure.

Meanwhile, accelerated deployment of EVs and AI without inclusive labor strategies risks creating social instability, skill loss, and resistance to change. A transition that sacrifices livelihoods in the short term undermines its own long-term sustainability.

The climate question, then, is not only about emissions. It is about alignment, between technology, labor, and access.

 

Conclusion: Designing for Adaptation, Not Just Innovation

The rise of EVs and AI forces a fundamental reconsideration of how societies define skill, value labor, and manage transitions. The informal workforce is not a temporary anomaly; it is a structural feature of modern economies.

If adaptation is treated as an individual responsibility rather than a system-level design problem, the gap will continue to widen. But if training, tooling, and access are built with informal workers in mind, the same technologies that disrupt could also empower.

The real challenge is not whether machines can evolve.
It is whether the systems around them allow people to evolve too.