Culling the FLoC: Market forces, regulatory regimes and Google’s (mis)steps on the path away from targeted advertising1
Bibliographic record
Abstract
This paper analyzes the short history of Google’s AI-driven data collation and marketing technology, Federated Learning of Cohorts (FLoC), which was designed to replace third-party cookies, the technology at the heart of “surveillance capitalism.” Using publicly available data such as patents, investor calls, public filings, github accounts, and presentations, this paper explores FLoCs and its immediate replacements, The Topics API and FLEDGE, and contests claims that Google’s new marketing technologies are both ‘privacy-centric’ and as effective as surveillance-driven targeted advertising. The paper argues that Google’s parent company, Alphabet is starting on a path away from being an advertising and information company to being an “AI-first” company, and sees FLoC as one (mis)step on this path. The paper shows how an combination of interacting factors – corporate ideology, market forces, regulatory responses, and internal cultural conflict – are driving this transformation, but concludes that surveillance will continue to be at the heart of any AI-first economy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".