A very good analysis in an accessible language
“This article starts with considering AI as a GPT and argues why we need to focus on power when thinking about the impact of AI. I explain the contribution of critical political economy(CPE) for analysing AI capitalism. CPE investigates control and ownership of communication systems and its impact on society (Hardy 2014). Using CPE as a framework, this article analyses the tendencies of concentration and monopolisation in AI capitalism. The article then considers the commons as an alternative framework for enabling that the benefits of AI can be shared with society at large.”
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“Economists study the impact of GPT in terms of the emergence of winners and losers. The winners are those associated with the emerging GPT, whereas the losers are those who cannot benefit from the unfolding GPT.
We need to be aware that AI can facilitate a further polarisation of already unequal societies (Crawford 2021; Dyer-Witheford et al. 2019; Lee 2018).
“Offering an encompassing view of AI capitalism, is important: we need to be aware of how material AI is and that its production is based on natural resources, human labour and industrial infrastructures. Looking at the broader picture of change within technologies, beliefs and infrastructures simultaneously, however, also risks overlooking the issues of a concentration of power. To deal with this, we need to go back to political economy as this is the framework that puts power at the centre of its analysis. Political economy is particularly interested in the relationship between techno-economic systems and their impact of the broader societal structure (McChesney 2000). The industrial infrastructures of AI also contribute to a concentration of power, which has not only an impact on the social practices of AI but also how its technological development will happen in the future, which explains the importance of this perspective.
“Only big companies, which have a lot of capital at their disposal, can make these investments. In addition, it is only Big Tech that has the resources to upgrade their compute capacity while simultaneously being able to collect data to train ML/DL models and to hire the specialised AI talent to work on these models. Ahmed and Wahed (2020) have documented the unequal access to compute capacity and argue that this creates divides between big tech corporations and elite universities who squeeze other companies and the computer departments of medium and smaller universities out of the field. Srnicek (2019) also points at the power of AI behemoths, who become global rentiers through their AI infrastructure: smaller companies are dependent on the hardware of Big Tech to make advancements in AI, whereas the leading AI companies can keep control over what is happening on their infrastructure. This power concentration is thus also potentially weakening the development of AI itself.
“What is a source of concern is that the AI giants follow a strategy of enclosure, with the objective to maintaining their leading position and safeguarding their growth and profit. Enclosure entails that—after having achieved a monopolistic position—these AI companies move to control access to their data and limit the ability of users to switch to competitors, thereby enclosing more and more of the digital world within their private sphere (Couldry and Mejias 2019; Morozov 2018).
“The enclosure by AI capitalism is clearly illustrated by OpenAI. Originally founded as a non-profit organisation, which would collaborate with other institutions and researchers and make their research open to the public, OpenAI is now dominated by corporate investors, including Microsoft, and is considered as one of the biggest competitors of DeepMind.
“While the corporate sector often claims that public investment stifles innovation, (Mazzucato 2013) debunks this myth and actually argues that the radical technologies behind, for example, the iPhone (e.g., GPS, touch screen display and Siri) were all backed by government funding. Another example is Google’s search algorithm, which was publicly funded through the National Science Foundation (NSF).
“Public/common investment in computing infrastructure could also mean a de-commodification of compute capacity and create a new public service that can be made available to society, accessible to different organisations, companies and interest groups.
“A commons approach to AI human capital would, for example, include to provide more funding for public IT services and universities allowing them, respectively, to reduce outsourcing and facilitate more research labs to keep their faculty members instead of being recruited by larger, corporate, organisations with deep pockets.
“A central aspect of envisioning an alternative political economy of AI is rethinking ownership,” combined with “democratic oversight and control.”
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