Tag Archives: Outsourcing

What does AI do to Job Quality? Platformisation, Fissured Workplaces and Dispersion

Contemporary debates on AI and labour often focus on quantitative impacts, such as potential job losses. Yet, the qualitative question of how AI reshapes the experience of work is equally critical. A recent open-access book chapter that I co-authored with Antonio A. Casilli examines these transformations, highlighting their already tangible, if not yet final, effects on job quality.

New book: Job Quality in a Turbulent Era, edited by A. Piasna & J. Leschke, 2026, Edward Elgar (open access)

The analysis starts with AI deployment, that is, the introduction of AI solutions into the workplace – for example, use of automated transcription tools. It is typically framed as a productivity enhancer that can improve job quality by making work more engaging – and in some cases, even safer. However, critics argue that firms often use AI to intensify labour through tighter managerial control and surveillance. In this scenario, AI may degrade job quality by restricting autonomy, reinforcing algorithmic management, and limiting workers’ ability to negotiate their roles or pace.

But before AI tools reach the workplace, they must be designed, built, and marketed. Today’s AI production relies heavily on machine learning, which requires vast datasets. This process involves extensive “data work,” performed by myriad lower-level workers in three key functions: preparation (data generation and annotation), verification (quality control of AI outputs), and impersonation (manually performing tasks meant to be automated). While this process increases labour demand the quality of data work is often poor, with low wages, job insecurity, and misrecognition of skills. The root of these issues lies in labour platformization.

The Preparation – Verification – Impersonation functions of data work in AI production. Source: Tubaro et al. 2020.

To see this, let’s first take a step back and consider that most data work is procured through outsourcing (shifting lower-value functions outside the firm) and offshoring (relocating work to lower-wage countries). These practices exclude externalised workers from the resources of lead firms, creating a “planetary market” where AI companies in the US and Europe source labour from countries like Venezuela or Madagascar. Digital platforms amplify these trends, enabling on-demand outsourcing to precarious micro-providers globally. Platformization thus exacerbates the negative effects of outsourcing and offshoring, degrading job quality.

Platformisation is best understood at the crossroads of two broader socioeconomic tendencies. First, the “fissured workplace“, where companies market goods or services without employing all those who contribute to their design, assembling or delivery. This model heightens insecurity, as metrics and surveillance systems erode autonomy and increase stress. Offshore platform workers, in particular, face isolation, losing traditional workplace camaraderie and support. Second, “dispersion“, the constant need to manage interruptions, multitask, and prioritise competing demands. AI-driven tools, such as real-time scheduling algorithms and productivity trackers, intensify dispersion, blurring boundaries between professional and personal life and increasing mental strain.

Overall, then, the effects of AI on job quality are mixed. While AI deployment can enhance productivity and safety, it often intensifies labour and undermines autonomy. Meanwhile, AI production creates jobs, but these are frequently precarious and low-quality. Understanding these dynamics is essential for shaping equitable futures that leverage the potential of contemporary technologies while safeguarding labour rights and well-being.

A detailed digital collage inspired by the aesthetic of medieval manuscript illustrations, depicts a vibrant scene of construction workers building a structure. The traditional elements, such as figures in traditional Persian gowns, wooden ladders, ropes, and woven sacks, are interwoven with modern technological motifs like circuit boards, QR codes, and cloud icons. Bright, glowing, golden networks of interconnected nodes and symbols of digital technology overlay the scene, blending the past and present. On the left, Arabic calligraphy is imprinted onto the paper. The composition uses contrasting textures and colours to juxtapose and interweave the themes and eras.
Shady Sharify / Who is AI Made Of / https://betterimagesofai.org / https://creativecommons.org/licenses/by/4.0/

This is not a story of technological determinism. AI alone does not destroy jobs or degrade their quality; rather, its impacts stem from the broader economic forces that shape its presence and role in today’s economies. The above artwork captures this idea by foregrounding humanity’s collective endeavour in building artificial intelligence, drawing inspiration from Persian miniature painting. In sum, the future of work in the age of AI will hinge on political choices about production organisation and the distribution of its benefits and costs.

The book chapter can be found as: Casilli, A.A. & Tubaro, P. 2026. “What is AI Doing to Job Quality? Platformization, Fissured Workplaces and Dispersion”. Chapter 3 in A. Piasna & J. Leschke (eds.) Job Quality in a Turbulent Era. Cheltenham, UK: Edward Elgar Publishing, pp. 45-60, https://doi.org/10.4337/9781035343485.00009

A video of the launching event of the book – including my presentation of the chapter – is available here.

Where does AI come from?

I am thrilled to announce that an important article has just seen the light. Entitled ‘Where does AI come from? A global case study across Europe, Africa, and Latin America’, it is part of a special issue of New Political Economy on ‘Power relations in the digital economy‘. It is the result of joint work that I have done with members of the Diplab team (A.A. Casilli, M. Cornet, C. Le Ludec and J. Torres Cierpe) on the organisational and geographical forces underpinning the supply chains of artificial intelligence (AI). Where and how do AI producers recruit workers to perform data annotation and other essential, albeit lower-level supporting tasks to feed machine-learning algorithms? The literature reports a variety of organisational forms, but the reasons of these differences and the ways data work dovetails with local economies have remained for long under-researched. This article does precisely this, clarifying the structure and organisation of these supply chains, and highlighting their impacts on labour conditions and remunerations.

Framing AI as an instance of the outsourcing and offshoring trends already observed in other globalised industries, we conduct a global case study of the digitally enabled organisation of data work in France, Madagascar, and Venezuela. We show that the AI supply chains procure data work via a mix of arm’s length contracts through marketplace-like platforms, and of embedded firm-like structures that offer greater stability but less flexibility, with multiple intermediate arrangements that give different roles to platforms. Each solution suits specific types and purposes of data work in AI preparation, verification, and impersonation. While all forms reproduce well-known patterns of exclusion that harm externalised workers especially in the Global South, disadvantage manifests unevenly depending on the structure of the supply chains, with repercussions on remunerations, job security, and working conditions.

Marketplace- and firm-like platforms in the supply chains for data work in Europe, Africa, and Latin America. Dark grey countries: main case studies, light grey countries: comparison cases. Organisational modes range from almost totally marketplace oriented (darker rectangle, Venezuela) to almost entirely firm oriented (lighter rectangle, Madagascar). AI preparation (darker circle) is ubiquitous, but AI verification (darker triangle) and AI impersonation (darker star) tend to happen in ‘deep labour’ and firm-like organisations where embeddedness is higher.

We conclude that responses based only on worker reclassification, as attempted in some countries especially in the Global North, are insufficient. Rather, we advocate a policy mix at both national and supra-national levels, also including appropriate regulation of technology and innovation, and promotion of suitable strategies for economic development.

The Version of record is here, while here is an open access preprint.

Micro-work and the outsourcing industry in Madagascar

I had the privilege and pleasure to visit Madagascar in the last two weeks. I had an invitation from Institut Français where I participated in a very interesting panel on “How can Madagascar help us rethink artificial intelligence more ethically?”, with Antonio A. Casilli, Jeremy Ranjatoelina et Manovosoa Rakotovao. I also conducted exploratory fieldwork by visiting a sample of technology companies, as well as journalists and associations interested in the topic.

A former French colony, Madagascar participates in the global trend toward outsourcing / offshoring which has shaped the world economy in the past two decades. The country harnesses its cultural and linguistic heritage (about one quarter of the population still speak French, often as a second language) to develop services for clients mostly based in France. In particular, it is a net exporter of computing services – still a small-sized sector, but with growing economic value.

Last year, a team of colleagues has already conducted extensive research with Madagascan companies that provide micro-work and data annotation services for French producers of artificial intelligence (and of other digital services). Some interesting results of their research are available here. This time, we are trying to take a broader look at the sector and include a wider variety of computing services, also trying to trace higher-value-added activities (like computer programming, website design, and even AI development).

It is too early to present any results, but the big question so far is the sustainability of this model and the extent to which it can push Madagascar higher up in the global technology value chain. Annotation and other lower-level services create much-needed jobs in a sluggish economy with widespread poverty and a lot of informality; however, these jobs attract low recognition and comparatively low pay, and have failed so far to offer bridges toward more stable or rewarding career paths. More qualified computing jobs are better paid and protected, but turnover is high and (national and international) competition is tough.

At policy level, more attention should be brought to the quality of these jobs and their longer-term stability, while client tech companies in France and other Global North countries should take more responsibility over working conditions throughout their international supply chains.