The Steep Cost of Capture

tags

AI

Notes

tech firms are startlingly well positioned to shape what we do—and do not—know about AI and the business behind it, at the same time that their AI products are working to shape our lives and institutions.

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the “advances” in AI celebrated over the past decade were not due to fundamental scientific breakthroughs in AI techniques. They were and are primarily the product of significantly concentrated data

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The AlexNet algorithm relied on machine learning techniques that were nearly two decades old. But it was not the algorithm that was a breakthrough: It was what the algorithm could do when matched with large-scale data and computational resources.

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What are foundation models? You’ll be forgiven for not knowing. The name was coined by Stanford for its report and CRFM launch materials, rebranding what were previously known as large language models (LLMs).

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Beyond simply valorizing industry-captured techniques as cutting edge, Stanford’s rebranding works to distance LLMs from this legacy of criticism.

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the power of the term AI as a marketing hook. Tech companies quickly (re)branded machine learning and other data-dependent approaches as AI, framing them as the product of breakthrough scientific innovation.

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Dual- affiliated scholars draw a tech company salary, work closely with tech employees, and avail themselves of corporate research infrastructures, all while publishing research under a university imprimatur.

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Big tech’s domination over the infrastructure of AI research and development extends beyond providing “neutral platforms.” These companies control the tooling, development environments, languages, and software that define the AI research process—they make the water in which AI research swims.

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the choice of either allying with a company, with all the tacit conditions such dependency requires, or being unable to do the kind of work that equals prestige and scholarly success.

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proposals to “democratize” access to AI research infrastructures amount to calls to subsidize tech giants further

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where these companies draw the line—research and dissent that threatens growth and revenue.

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tech companies are working to co- opt and neutralize critique. They do this in part by funding and elevating their weakest critics, often institutions and coalitions that focus on so-called AI ethics, and frame issues of tech power and dominance as abstract governance questions that take the tech industry’s current form as a given and AI’s proliferation as inevitable.

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cast elite engineers as the arbiters of “bias,” while structurally excluding scholars and advocates

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Facebook’s revoking data access to NYU researchers examining the company’s role in the January 6 insurrection;

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Google instructing in-house researchers to “strike a positive tone” in their findings

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marshaling external “academic allies” to raise questions about regulatory intervention

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Amazon’s directing specious attacks against young Black researchers who revealed racist logics in their products,

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retaliating against workers who organized against the company’s climate harms.

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must confront and name the dynamic of tech capture, co- optation, and compromise head-on,

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central role of organized workers, those in tech who have made inroads across the industry over the past five years and academic workers

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Academic workers’ struggle against the precariousness of the profession is also a struggle for academic freedom.

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Organized tech workers, for their part, have a role to play in checking the power of their industry from within, fighting for more control over the work they do, and working to curb the influence of their employers

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