Big Data & Society, Volume 11, Issue 1, March 2024

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https://doi.org/10.1177/20539517241232630

Original Research Article

Big AI: Cloud infrastructure dependence and the industrialisation of artificial intelligence

Fernando van der Vlisthttps://orcid.org/0000-0003-1401-0325¹, Anne Helmondhttps://orcid.org/0000-0003-4327-4012², and Fabian Ferrarihttps://orcid.org/0000-0003-0637-0232²

Abstract

Critical scholars contend that ‘There is no AI without Big Tech’. This study delves into the substantial role played by major technology conglomerates, including Amazon, Microsoft, and Google (Alphabet), in the ‘industrialisation of artificial intelligence’. This concept encapsulates the shift of AI technologies from the research and development stage to practical, real-world applications across diverse industry sectors, resulting in new dependencies and associated investments. We employ the term ‘Big AI’ to encapsulate the structural convergence of AI and Big Tech, characterised by the profound interdependence of AI with the infrastructure, resources, and investments of these major technology companies. Using a ‘technographic’ approach, our study scrutinises the infrastructural support and investments of Big Tech in the AI sector, focussing on corporate partnerships, acquisitions, and financial investments. Additionally, we conduct a detailed examination of the complete spectrum of cloud platform products and services offered by Amazon, Microsoft, and Google. We demonstrate that AI is not merely an abstract idea but an actual technology stack encompassing infrastructure, models, applications, and an ecosystem of applications and companies relying on this stack. Significantly, these tech giants have seamlessly integrated all three components of the stack into their cloud offerings. Furthermore, they have developed industry-focussed solutions and marketplaces aimed at attracting third-party developers and businesses, fostering the growth of a broader AI ecosystem. This analysis underscores the intricate interdependence between AI and cloud infrastructure, emphasising the industry-specific aspects of cloud AI.

Keywords

Cloud infrastructure, artificial intelligence, Big Tech, AI industrialisation, AI ecosystems, platform studies

¹Media Studies, University of Amsterdam, Amsterdam, the Netherlands

²Department of Media and Culture Studies, Utrecht University, the Netherlands

Corresponding author(s):

F. N. (Fernando) van der Vlist, Media Studies, University of Amsterdam, Turfdraagsterpad 9, 1012 XT, Amsterdam, the Netherlands. Email: f.n.vandervlist@uva.nl

Introduction: No AI without Big Tech

The ongoing competition among major technology companies in cloud-based artificial intelligence (AI), known as the ‘cloud AI wars’ (Goldman, 2022), is gaining momentum. Industry leaders proclaim that we are in a transformational era, where AI technologies are profoundly reshaping society. Influential figures like Bill Gates (2023) and Alphabet's Sundar Pichai emphasise the foundational and transformative nature of AI (Manyika, 2023). However, this alleged transformation is primarily driven by a select few technology giants, specifically Amazon, Microsoft, and Google. Their unparalleled ability to rapidly scale—known as ‘hyper-scalability’—is attributed to their cloud computing arrangements, which have established the fundamental socio-technical infrastructure driving expansive growth and platform capitalism (Narayan, 2022).

This article delves into the substantial implications of these firms’ dominance in what we term the ‘industrialisation of AI’. By using this term, we refer to the transition process of AI systems from the research and development phase to becoming practical, real-world commercial products and services in a variety of industry sectors. This transition results in new dependencies on cloud infrastructure and the corresponding investments in computational resources required for the industrial-scale development and deployment of AI applications and solutions. As we explain, the story of AI's industrialisation is intimately tied to cloud infrastructure dominance. Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), as the top-three cloud platforms, form the essential backbone of this continuing industrialisation process. These platforms, ‘where the Internet lives’ (Holt and Vonderau, 2015), have a profound impact on businesses across industries. When a major cloud platform like AWS experiences an outage, as it did on June 13, 2023, it has far-reaching repercussions for a wide array of clients, including the Associated Press (AP), McDonald's, and Reddit. These clients faced disruptions due to their reliance on AWS (Ropek, 2023). According to market research estimates, AWS holds a particularly dominant position, being the primary operating system of the Internet with approximately a third (32%) of all cloud services running on it, followed by Microsoft Azure (22%) and Google Cloud (11%) (Richter, 2023). The extensive suites of cloud products and services offered by these companies contribute significantly to their revenues (Klinge et al., 2023).

Discursively, the term ‘AI’ serves as a powerful marketing tool, attracting substantial investments and pushing AI startups to seek partnerships with Amazon, Microsoft, and Google (Alphabet), who in turn ‘pour billions’ into costly cloud computing (Hodgson, 2023). These major technology companies actively position themselves as essential infrastructure providers through their support and investments. With AI entering its ‘industrial age’ (The Economist, 2022), it is becoming increasingly important to understand the value chains of AI in the digital platform economy for strategic, political, and economic reasons.

Therefore, it is crucial to theorise and closely monitor the industrialisation of AI, a phenomenon characterised by its pervasive expansion and integration across industries and sectors, extending far beyond traditional computing. This process mirrors the ‘platformisation’ observed in various sectors and industries (Van Dijck et al., 2018). Currently, the global AI market is experiencing rapid growth, but only a handful of major technology companies exercise control over the dominant infrastructure platforms. They have been referred to as the ‘Great Houses of AI’ (Crawford, 2021: 20) and play a foundational role in the development and deployment of AI. As they lead the ‘pursuit of AI dominance’, concerns have risen about potential risks associated with ‘industrial capture’, as highlighted by journalists (The Economist, 2023a; Murgia, 2023). The dominance of these companies is intrinsically linked to their control over infrastructure, which is crucial for AI development and deployment, raising concerns regarding access and competition (Khan, 2023). Their structural dominance is attributed to three key advantages: access to vast amounts of data, substantial computational resources, and a geopolitical edge (Kak and Myers West, 2023; Whittaker, 2021). As Kak and Myers West assert, ‘There is no AI without Big Tech’ (2023: 5).

This research aims to enhance our understanding of the advantages and ramifications of Big Tech's cloud infrastructure dominance and smaller players’ dependency on cloud infrastructure. It delves into the convergence of AI and Big Tech, emphasising the large-scale industrialisation of AI, facilitated by Big Tech's hyper-scalable cloud computing arrangements (cf. Narayan, 2022). This raises the questions: How can we theorise and investigate the role of Big Tech in the industrialisation of artificial intelligence? What are the multifaceted implications associated with (large-scale) reliance on cloud infrastructure?

To capture the scale and scope of this structural convergence, we introduce the concept of ‘Big AI’, characterised by the interdependence between AI and the infrastructure, resources, and investments of major technology conglomerates such as Amazon, Microsoft, and Google (Alphabet). This structural dependency underlies the ongoing industrialisation of AI, aligning with existing critiques of Big Tech's AI dominance (Kak and Myers West, 2023), which our empirical analysis further substantiates. While it is not the only conceivable path for the future of AI, the continuous provision of essential infrastructure services by Amazon, Microsoft, and Google positions them to benefit from the widespread expansion of AI across industries and sectors.

We explore the core components of ‘Big AI’ empirically. This includes different forms of support and investment as well as the cloud platform offerings from Amazon, Microsoft, and Google. Our comprehensive approach provides unique insights into the current state and evolution of Big AI. We gain a deep understanding of AI as both a product and a service category, a distinct technology stack and ecosystem, and an integral and mundane component of existing cloud computing arrangements. Furthermore, we highlight the developmental and deployment aspects of the supposed ‘AI revolution’ (e.g. Murgia, 2023). This illustrates the substantial role that Amazon, Microsoft, and Google play in ‘convening’ enterprises, organisations, and developers to participate in the creation, capture, and commercialisation of AI (cf. Egliston and Carter, 2022; Helmond et al., 2019).

The following section places our research in the context of existing critical literature regarding AI and machine learning, Big Tech, and cloud computing. We then outline our two-fold ‘technographic’ approach to further explore the convergence of AI and Big Tech. This includes initially analysing Big Tech's infrastructural support and investments in the AI sector and then conducting a comprehensive examination of all their cloud platform products and services. This approach allows us to uncover the dual role of the cloud as both a fundamental infrastructure and a marketplace for AI applications and solutions. When considering these dimensions together, we gain unique insights into the distinct characteristics of AI's industrialisation and the implications of dependence on (large-scale) cloud infrastructure.

AI as an existing technology stack and ecosystem

‘AI’ is a nebulous concept, characterised by an ambiguous and fluctuating meaning. Crawford (2021: 217) highlights the importance of ‘how we define AI, what its boundaries are, and who determines them’, shaping our perceptions and framing what can be contested and debated. In the emerging field of critical AI studies, Raley and Rhee (2023: 188–189) suggests viewing AI as ‘an assemblage of technological arrangements and socio-technical practices, as concept, ideology, and dispositif’ while staying ‘attuned to actually existing socio-technical systems’. Jacobides et al. (2021) and Rieder (2022) similarly advocate for understanding AI as a large technical system, emphasising attention to its technical specifics and materialities to comprehend its operations and modes of power.

Aligned with these perspectives, we conceptualise AI as an existing technology stack and (emerging) ecosystem while also recognising its role as a discursive phenomenon (i.e. the politics of the term ‘AI’, cf. Gillespie, 2010). The AI technology stack includes infrastructure, models, and applications, along with the respective providers of these components. Infrastructure encompasses both cloud software and hardware platforms, with their manufacturers viewed as significant beneficiaries in the AI market. Drawing from the existing digital platform theory (Jacobides et al., 2021; de Reuver et al., 2018; Van der Vlist, 2022), the broader ecosystem of the AI stack consists of applications and solutions typically created by third-party developers and businesses. These elements complement the cloud platform stack technically or represent a collection of firms and organisations collectively engaged with its components from an organisational viewpoint. These AI stack components are associated with specific ‘sectoral and national systems of firms and institutions that collectively engage in [AI]’, evolving differently across China, the USA, and the EU, with ‘a handful of firms building global AI ecosystems’ (Jacobides et al., 2021: 412). The variations within these national or sectoral systems underscore the political economy of AI, its intricate supply chains, dependencies, and the role of third parties participating in the creation, capture, and commercialisation of AI.

Existing critical research has illuminated the intricate relationship between AI, machine learning, cloud computing, and major technology companies, emphasising the significant influence and power of Big Tech and platform companies (Whittaker et al., 2018; Whittaker, 2021). Based on this literature, three main drivers contributing to industry concentration within the AI sector can be identified: the integration of proprietary hardware and software components within platform ecosystems (Crawford, 2021; Van Dijck et al., 2018; Van der Vlist, 2022; Mackenzie, 2019; Rieder, 2022), the expansion of AI into various industries through products and services (Ferrari and McKelvey, 2022; Lehdonvirta, 2022; Luitse and Denkena, 2021), and the facilitation of AI value chains (Ferrari, 2023; Widder et al., 2023).

Concerning the first driver of industry concentration in the AI sector, the profound integration of hardware and software components, coupled with external software developers relying on cloud computing platforms and hardware, establishes Big Tech as a crucial infrastructure service provider in the digital platform economy (Jacobides et al., 2021; Narayan, 2023). Cloud platforms and their marketplaces play a central role in AI development and integration by offering infrastructure-as-a-service (e.g. compute capacity) and commodifying digital products (e.g. pre-trained models or software packages).

Critical scholars have demonstrated how platform companies extend their reach, influence, and user base by engaging third-party developers and businesses in their ecosystems. This is achieved by promoting the use of proprietary developer tools and infrastructure to create complementary apps and services (Egliston and Carter, 2022; Helmond et al., 2019; Jacobides et al., 2021; Narayan, 2023; Van der Vlist and Helmond, 2021). Facilitating technical integrations with the platform's ecosystem and providing consulting and implementation services enable this strategy, emphasising the necessity of studying the expansion and hyper-scalability of platforms on both business and infrastructure levels (Narayan, 2022; Van der Vlist and Helmond, 2021).

Second, the integration of AI into existing socio-technical arrangements signifies its infrastructuralisation (Dyer-Witheford et al., 2019; Jacobides et al., 2021). While Big Tech companies advocate for the ‘democratisation of AI’ by making AI tools accessible to a broad audience, developers often, perhaps unknowingly, contribute to Big Tech's infrastructural objectives. They do so by building applications and integrations using the infrastructure provided by these tech giants, assuming they are shaping the future of AI (Burkhardt, 2019; Dyer-Witheford et al., 2019; Luchs et al., 2023).

In response, scholars propose ‘infrastructural thinking’ and an ‘infrastructural analysis of AI’ to understand its political economy. This approach recognises the diverse socio-technical systems necessary for AI to operate globally and underscores the need to trace the ‘full stack supply chain’ and production networks of AI systems from various disciplinary perspectives (Crawford, 2021; Dyer-Witheford et al., 2019; Ferrari, 2023; Rella, 2023; Whittaker et al., 2018; Widder and Nafus, 2023). Despite discursive claims of AI being ‘open’ (Widder et al., 2023), it fundamentally relies on computational resources for both its initial training and ongoing operations, challenging the notion of ‘openness’ as it might obscure underlying infrastructure dependencies.

Lastly, the development and deployment of AI and large-scale machine-learning models have created intricate dependencies among companies within the AI value chain. As Küspert et al. (2023) explain, this integration not only gives rise to new product categories but also results in complex supply chains and dependencies among AI stakeholders, influencing broader social and economic processes. They emphasise the complex lifecycle of general-purpose AI (GPAI) models, involving various actors responsible for different stages.

In our study, we illustrate how this interdependence operates on multiple levels, leading to strategic alliances, partnerships, and investments. These collaborations involve upstream providers of models and infrastructure, as well as downstream companies integrating these models into consumer-facing products and services. This reflects a broader trend of ‘corporate financialization’, strengthening Big Tech's infrastructural dominance within the economy and society (Klinge et al., 2023).

A technography of cloud AI

To explore Big Tech's role in the industrialisation of artificial intelligence and the implications of cloud infrastructure dependency, we employ methodologies drawn from critical platform and algorithm studies, with a particular emphasis on ‘technography’. This descriptive and interpretive approach critically analyses the structural and operational aspects of technical systems (Bucher, 2016; Mackenzie, 2019; Van der Vlist et al., 2022). Technography encourages a detailed examination of the material aspects of technology by directly reading various publicly available documents generated by and related to technical systems. These documents include technical documentation, developer and product websites, financial reports, media articles, press releases, and blog posts that provide detailed insights into the intended objectives, functions, and capabilities of technical systems. This enables us to explain specific functionalities or aspects of the technology while maintaining a critical stance toward promotional language or industry jargon found in these materials.

We start by scoping Big Tech's broader infrastructural support and investments in AI, which entails exploring their partnerships, acquisitions, and financial investments. This approach allows us to identify prominent examples illustrating the forms of infrastructural support and investments foundational to the industrialisation of AI. Amazon, Microsoft, and Google are our focal points due to their predominant market presence and significant influence in the field (Richter, 2023). While acknowledging both shared and unique features of their cloud platforms, our goal is not a direct platform-to-platform comparison.

Existing critical platform studies have identified three primary drivers of expansion and integration: consolidation through acquisitions, capturing and convening via partnerships, and expansion facilitated by software development kits (SDKs) and application programming interfaces (APIs) (Egliston and Carter, 2022; Helmond et al., 2019; Van der Vlist, 2022; Van der Vlist and Helmond, 2021). We explore how these strategies of infrastructural support and investment by Big Tech play a pivotal role in driving the industrialisation of AI. The technographic approach draws from various publicly accessible documents, including corporate blogs, promotional materials on company websites tailored to AI startups, developers, and businesses (e.g. Microsoft for Startups), trade reports, as well as business information databases like Crunchbase and Tracxn.

Following this, we conduct an in-depth examination of the complete suite of cloud platform services offered by Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). We categorise the diverse service offerings, uncover interrelationships among them, and initially explore solutions offered by third-party vendors on their respective online cloud marketplaces.¹ This second step yields valuable insights into how AI and machine learning are integrated within their cloud infrastructure setups and shows the intermediary role of their marketplaces that connect various industry sectors. This complements our document analysis in the first step.

Existing research offers guidance on employing technographic approaches to scrutinise the composition, governance, and evolution of these companies’ development platforms, as well as the role and integration of specific elements within their stacks or ecosystems (Van der Vlist et al., 2022). In this context, platforms are viewed not as monolithic entities but as dynamic ‘service assemblages’ that adapt to developers’ needs while shaping the economic dynamics of competition and monopolisation (Blanke and Pybus, 2020). This dynamic property extends to cloud platforms, where the ‘AI as a service’ (‘AIaaS’) business model exemplifies the simultaneous decomposition and integration of AI products and services across various industry sectors. Therefore, gaining insights into how cloud platforms have evolved into such service assemblages and understanding the pivotal role of developers in expanding and integrating AI across sectors are essential for exploring the multifaceted implications of reliance on cloud infrastructure.

The technographic approach relies on publicly accessible platform documentation, including comprehensive catalogues of cloud offerings, detailed product information, and technical specifications. These resources are primarily tailored for external (third-party) software developers and enterprises, offering guidance to individual and corporate software developers in implementing cloud platform tools, products, and services within their respective organisations. Thus, the technical documentation serves a purpose beyond mere marketing rhetoric, providing practical information about the platform's capabilities and integration processes. Any discrepancies or inaccuracies could lead to significant practical issues in development, necessitating a high level of reliability and detail that goes beyond the scope of promotional language.

To collect the individual product pages for each cloud platform, we developed custom web scrapers designed to capture all product pages linked from their comprehensive product directories.² This process also encompassed their marketplaces and industry-focussed subdirectories, all linked from their product directories.³ Using this documentation, we manually categorised the diverse types of cloud offerings based on their existing product categories and subcategories across the three cloud platforms. Subsequently, we established connections between these offerings by detecting explicit mentions of product names across the corpus, including on-page hyperlinks (while excluding headers, navigation menus, and footers), using case-sensitive grep searches. These connections were then visually represented as network diagrams using visualisation and illustration software, showcasing the mentions or citations of these products within the corpus. Additionally, we employed this documentation to gain insights into how products are positioned and marketed to specific industry sectors, achieved by detecting mentions of product names in industry-specific subdirectories and counting the number of solutions available in each existing marketplace category and subcategory.

This two-fold ‘technographic’ approach enables us to critically analyse the structural elements underpinning the three dominant cloud infrastructure arrangements. It allows us to pinpoint the pivotal role played by AI and machine learning within these structures, offering insights into Big Tech's multifaceted involvement in the industrialisation of AI and the consequential implications tied to reliance on cloud infrastructure.

Acknowledgements

Data visualisations and analysis were primarily conducted by the first author.

Data availability

The data that support the findings of this study are openly available in the Open Science Framework (OSF) at https://doi.org/10.17605/osf.io/unvc2.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Parts of this work were supported by the Dutch Research Council (NWO) Spinoza Prize grant number SPI.2021.001 (awarded in 2021 to José van Dijck, Professor of Media and Digital Society at Utrecht University); and the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), project number 262513311 (SFB 1187: ‘Media of Cooperation’).

ORCID iDs

F. N. (Fernando) van der Vlist https://orcid.org/0000-0003-1401-0325

A. (Anne) Helmond https://orcid.org/0000-0003-4327-4012

F. L. (Fabian) Ferrari https://orcid.org/0000-0003-0637-0232

Notes

1.

‘AWS Marketplace’, Amazon Web Services, https://aws.amazon.com/marketplace/; ‘Azure Marketplace’, Microsoft Azure, https://azuremarketplace.microsoft.com/marketplace/; and ‘Google Cloud Marketplace’, Google Cloud, https://console.cloud.google.com/marketplace.

2.

‘AWS Documentation’, Amazon Web Services, https://docs.aws.amazon.com/index.html; ‘Directory of Azure Cloud Services’, Microsoft Azure, https://azure.microsoft.com/products/; and ‘Products and Services’, Google Cloud Platform, https://cloud.google.com/products#general-availability.

3.

All products and services were collected in January 2023; analyses were conducted on 15–19 May 2023. As such, data collection occurred prior to Amazon, Microsoft, and Google's rollout of new AI products in Spring 2023, capitalising on the AI frenzy. Marketplace solutions were collected on 30 June 2023.

4.

Microsoft, ‘Azure OpenAI’, Azure Marketplace, https://azuremarketplace.microsoft.com/en-us/marketplace/apps/Microsoft.CognitiveServicesOpenAI.

5.

‘Machine Learning Partner Solutions’, AWS AI, https://aws.amazon.com/ai/partners/

6.

‘AI + machine learning’, https://azure.microsoft.com/en-us/overview/ai-platform/; ‘Machine Learning’ https://docs.aws.amazon.com/index.html; and ‘AI and Machine Learning’, https://cloud.google.com/products#general-availability.

7.

The advanced search queries used encompassed relevant variations of these terms, including ‘AI’, ‘artificial intelligence’, ‘machine learning’, and ‘deep learning’.

8.

Fusus, ‘Fusus Real-Time Crime Center In The Cloud (RTC3)’, AWS Marketplace, https://aws.amazon.com/marketplace/pp/prodview-3xxezgl5la7ky.

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