Wednesday 20 March 2024

Guest Blog: Mapping the landscape of cloud AI: Microsoft, Google, Amazon, and the ‘industrialisation’ of artificial intelligence

By Fernando van der Vlist (@fvandervlist) and Anne Helmond (@silvertje)

Van der Vlist, F. N., Helmond, A., & Ferrari, F. L. (2024). Big AI: Cloud infrastructure dependence and the industrialisation of artificial intelligence. Big Data & Society, 11(1), 1–16.

Convergence of AI and Big Tech—The ongoing competition among tech giants in the ‘cloud AI wars’ is shaping a supposed transformative era. Industry leaders like Bill Gates and Sundar Pichai underscore the foundational role of AI. However, this transformation is chiefly propelled by a select few—Microsoft, Google (Alphabet), and Amazon. These giants hold sway over the cloud computing landscape, wielding profound influence.
Characterising the platformisation and ‘industrialisation’ of AI
Van der Vlist, Helmond, and Ferrari’s comprehensive landscape study, titled ‘Big AI: Cloud infrastructure dependence and the industrialisation of artificial intelligence’, delves into the profound implications of the dominance wielded by these tech giants, introducing the term ‘industrialisation of AI’. This term captures the transition of AI systems from the realm of research and development to practical, ‘real-world’ applications across diverse industries. This transformation brings a new reliance on cloud infrastructure and substantial investments in computational resources, vital for the industrial-scale deployment of AI solutions. Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform emerge as the linchpin cloud platforms underpinning this ongoing industrialisation process.
The ramifications of their influence became glaringly evident during an AWS outage on June 13, 2023. The disruptions faced by clients like the Associated Press, McDonald’s, and Reddit underscored their extensive reliance on AWS. Market estimates emphasise AWS’s dominance, serving as the backbone of the Internet, followed by Microsoft Azure and Google Cloud. The comprehensive suites of cloud products and services offered by these companies not only underscore their dominance but also significantly contribute to their revenues.
Moreover, discursively, the term ‘AI’ acts as a powerful magnet, attracting substantial investments and prompting startups to seek partnerships with major players. This includes (exclusive) cloud provider partnerships such as between Microsoft Azure and OpenAI (powering ChatGPT and DALL·E, amongst others). These tech giants actively position themselves as essential infrastructure providers, pouring billions into costly cloud computing. As AI enters its ‘industrial age’, understanding the intricacies of AI’s value chains becomes crucial for strategic, political, and economic reasons.
The dominance of major tech companies is intrinsically tied to their control over infrastructure. This dominance, fueled by access to vast troves of data, substantial computational resources, and a geopolitical edge, underscores their pivotal role in driving AI development and deployment. As succinctly put by Kak and Myers West, ‘There is no AI without Big Tech’.
A ‘technography’ of AI and Big Tech: Infrastructure, models, and applications
To capture this structural convergence between AI and Big Tech, Van der Vlist et al. conceptualise ‘Big AI’. This term characterises the intricate interdependence between AI and the infrastructure, resources, and investments of major tech conglomerates. This structural dependency is the cornerstone of the ongoing industrialisation of AI. Their empirical analysis further substantiates these critiques. While ‘Big AI’ isn’t the sole trajectory for the future of AI, the continuous provisioning of essential infrastructure services by Microsoft, Google (Alphabet), and Amazon positions them to reap the benefits of AI’s widespread expansion across industry sectors.
In their empirical exploration—characterised as a ‘technography of cloud AI’—, they engage with the material aspects of cloud AI to examine its structural and operational features. They uncover various forms of support and investment and scrutinise the cloud platform offerings from Microsoft, Google, and Amazon. This comprehensive approach provides unique insights into the current state and evolution of ‘Big AI’, offering a profound understanding of AI as both a product and service category, and an integral component of existing cloud computing arrangements. Furthermore, their study sheds light on the developmental and deployment aspects of the purported ‘AI revolution’, heralded by ChatGPT’s launch in late 2022, highlighting the substantial role played by Microsoft, Google, and Amazon in convening enterprises, organisations, and developers, fostering the creation, capture, and commercialisation of AI.


Cloud AI stacks: Structural interconnections among cloud platform products and services offered by Microsoft Azure, Google Cloud Platform, and Amazon Web Services.

Ultimately, the study goes beyond characterising the current ‘platformisation’ of AI, where AI expands beyond consumer-facing applications like ChatGPT to become a platform service provided by Big Tech companies (i.e. an AI platform and infrastructure as a service). This encompasses extensive suites of tools, products, and services—from hardware AI infrastructure to machine learning and computer vision software—, along with ‘platform boundary resources’ for developers and businesses to build upon. The study comprehensively analyses and substantiates this transformation with empirical evidence. It highlights how Big AI represents a dual form of power: first, by owning and offering essential infrastructure and support, and second, by controlling marketplaces for the distribution and deployment of AI models and applications across diverse sectors and industries. Additionally, the study leverages the empirical analysis to conceptualise AI’s cloud infrastructure dependence and the ongoing ‘industrialisation’ of AI, providing important guidance for policymakers and regulators in governing AI.
The full research article is openly available in Big Data & Society at The data that support the findings of this study are openly available in the Open Science Framework (OSF) at