Culling the FLoC: Market forces, regulatory regimes and Google’s (mis)steps on the path away from targeted advertising1
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it