Sharing economy platforms face a new AI test: sustainability or deeper platform control?

A new study on artificial intelligence (AI) and the sharing economy finds that generative AI could make digital platforms more efficient, accessible and sustainable, but also warns that the same tools may deepen concerns over bias, privacy, transparency and platform control.

The study, Transformation of the Sharing Economy in the Age of AI: Opportunities and Ethical Challenges, published in Future Internet, examines how AI is reshaping platform-based ecosystems through a conceptual and exploratory analysis of Airbnb, BlaBlaCar and the industrial manufacturing platform Xometry.

The research finds that genAI can improve resource utilization, user engagement and operational efficiency across accommodation, mobility and manufacturing platforms, while creating new governance challenges around algorithmic decision-making, data use and trust.

AI is changing the sustainability promise of the sharing economy

The sharing economy was built around the claim that underused assets could be shared more efficiently through digital platforms. Empty rooms, vacant car seats, idle machinery and unused production capacity could be matched with users who needed them, reducing waste and widening access to goods and services without requiring new ownership.

Sharing platforms can reduce idle capacity and improve access, but they can also stimulate new demand, shift power toward platform operators and create unequal outcomes for users, hosts, drivers, workers and smaller businesses. The arrival of generative AI is now adding a new layer to that debate.

The study argues that AI is no longer just a background tool in platform systems. It is becoming part of how platforms recommend services, match users, set prices, automate customer communication, detect fraud, generate content and govern transactions. In the process, it is changing the environmental, social and economic effects of the sharing economy.

Generative AI can help platforms run more smoothly by creating listing descriptions, translating messages, powering virtual assistants, personalizing search results, supporting demand forecasting and automate customer responses. In theory, these functions can reduce transaction friction and make sharing platforms easier to use for more people.

The sustainability case rests on three main claims. Environmentally, AI can help platforms use existing assets more efficiently, reducing idle resources and supporting better matching between supply and demand. Socially, it can improve accessibility by lowering language barriers, simplifying interactions and helping more users participate. Economically, it can reduce operating costs, help providers manage services and create new earning opportunities.

However, the study warns that these benefits are not automatic. The environmental effects of AI-enabled platforms depend on whether efficiency reduces waste or simply encourages more consumption. If lower prices, easier booking and optimized recommendations lead users to travel more often or consume more services, the environmental gains may be weakened or reversed.

The same tension applies to social sustainability. AI can make platforms more inclusive by supporting multilingual communication and user assistance. But it can also weaken the interpersonal trust that originally distinguished many peer-to-peer platforms from conventional commercial services. When interactions become heavily automated, users may gain convenience but lose the human connection that supports trust and community.

Economically, AI can help small providers operate more efficiently, but it may also increase dependence on platform rules, rankings and algorithmic systems. Users may gain access, but platforms may gain more control. That makes governance central to the next phase of the sharing economy.

Airbnb, BlaBlaCar and Xometry show the benefits and limits of AI-driven sharing

The study compares three platforms that represent different layers of the platform economy: Airbnb in accommodation, BlaBlaCar in shared mobility and Xometry in industrial manufacturing. Together, they show how AI’s sustainability effects vary across sectors.

Airbnb

AI is already deeply connected to platform operations. Machine learning supports dynamic pricing, fraud detection, host-guest matching and personalized recommendations. Generative AI adds newer capabilities, including automated listing descriptions, virtual assistants, real-time translation and personalized user interfaces.

These tools can improve the platform’s environmental performance by raising occupancy rates and making better use of existing housing stock. AI-driven recommendations could also promote eco-friendly listings and help users identify more sustainable options. But the study notes a rebound risk: if AI makes travel easier, cheaper and more attractive, it may increase overall demand and weaken the environmental case.

The social effects are also mixed. Real-time translation and automated support can help users cross language barriers and participate more easily in global accommodation markets. That supports inclusion. Yet automation can also reduce direct host-guest interaction, weakening the personal trust and sense of belonging associated with early sharing economy models. AI systems may also reproduce bias if ranking, review or screening tools reflect unequal historical data.

The economic effects are similarly double-edged. AI can help hosts write better descriptions, respond faster to inquiries and manage pricing strategies. That can improve earnings and reduce the burden of operating a listing. But it can also standardize content, reduce host individuality and increase dependence on platform-managed systems.

BlaBlaCar

The platform shows a different version of the same trade-off. It connects drivers with empty seats to passengers traveling similar routes, reducing the number of vehicles needed for long-distance trips. AI supports smart matching, demand prediction, pricing suggestions, trust systems and automated communication.

In terms of environment, better matching can raise car occupancy and reduce emissions per passenger. AI can improve route efficiency and reduce unnecessary travel. But if platform optimization encourages more ride frequency or stimulates additional trips, some environmental benefits may be offset.

Socially, BlaBlaCar’s value depends heavily on trust between drivers and passengers. AI can improve safety by analyzing reviews, user behavior and suspicious activity. It can also make booking easier through chatbots and automated support. But opaque user verification, biased ratings or overly automated communication can weaken trust. If users cannot understand how matches, prices or safety checks are decided, platform confidence may decline.

Economically, AI can help drivers monetize unused vehicle capacity and give passengers cheaper travel options. But dynamic pricing and algorithmic control may also reduce predictability, create unstable income and deepen power imbalances between users and the platform.

Xometry

It expands the study beyond consumer platforms into industrial sharing. Unlike Airbnb and BlaBlaCar, Xometry operates in a business-to-business manufacturing environment, connecting firms that need production services with manufacturers that have available capacity. AI supports instant quotations, supplier matching, lead-time prediction and partner recommendation.

This industrial model highlights a broader shift in the sharing economy. Sharing is no longer limited to homes, cars or consumer services. It can also involve machine time, manufacturing capacity, production networks and industrial resources. AI can match demand with idle production capacity, reduce machine downtime, shorten supply chains and support more efficient manufacturing.

The environmental benefits can be significant if AI-enabled industrial sharing reduces the need for new machinery, lowers material waste, improves production planning and helps extend the lifespan of equipment. Socially, such platforms can give smaller companies access to advanced manufacturing without large capital investments. Economically, they can help manufacturers monetize unused capacity and give customers faster, more flexible production options.

However, the risks remain. Industrial sharing platforms may create new platform dependencies, data opacity and power asymmetries. If AI decides which suppliers are visible, how prices are calculated and how jobs are allocated, transparency becomes critical. Smaller manufacturers may benefit from access, but they may also become dependent on algorithmic systems they cannot fully inspect or challenge.

Ethical governance will decide whether AI strengthens or weakens platform sustainability

The study identifies four major ethical challenges in AI-enabled sharing platforms: algorithmic bias, transparency, privacy and over-automation.

• Algorithmic bias is a major concern because AI systems are trained on large datasets that may contain social, cultural or historical inequalities. On accommodation platforms, biased data can influence listing visibility, guest screening and rankings. On mobility platforms, it can affect trust scores, matching and user verification. In industrial platforms, it can shape supplier allocation and access to production opportunities.
• Transparency is equally important. Users often do not know how pricing, rankings, recommendations or moderation decisions are made. Generative AI can make this problem harder because many models operate through complex and difficult-to-explain processes. If platform users cannot understand why they are ranked lower, charged differently, matched with certain users or excluded from opportunities, trust may weaken.
• Privacy is another major risk. Generative AI depends on large volumes of data, including messages, transaction histories, behavioral patterns, reviews and contextual information. The study stresses that platform users may not fully understand how their data are collected, processed or used to train AI systems. This raises questions about consent, ownership, data minimization and compliance with emerging AI and data protection rules.
• Dehumanization through over-automation: Sharing platforms originally gained appeal by enabling peer-to-peer exchange. When AI handles communication, content, pricing, screening and support, the transaction may become more efficient but less human. That can weaken the social foundation of the sharing economy, especially in services where personal interaction is part of the value.

The governance challenge is thus not just technical - it's social, ethical and regulatory. AI can support sustainability only if platforms use it in ways that are transparent, accountable and fair. Without those safeguards, AI may turn sharing platforms into more efficient but less trusted systems.

The study calls for AI transparency rules, stronger data governance, user education and platform designs that empower people rather than replace them. In accommodation platforms, that means explainable ranking and recommendation systems. In mobility platforms, it means transparent matching and pricing. In industrial platforms, it means fair supplier allocation and clear rules for capacity matching.

The findings also point to the importance of digital literacy. Hosts, drivers, passengers, customers and manufacturing suppliers need to understand what AI tools can and cannot do. If users lack the ability to question automated decisions or manage AI-generated content, platform power will become even more concentrated.

The findings, however, come with some limits. The study is based on a conceptual and exploratory design, using literature review, platform analysis and secondary sources rather than direct surveys, interviews or proprietary platform data. The case study approach also focuses on a limited number of platforms, meaning the findings may not capture the full diversity of AI use across the sharing economy.