Will AI Finally Destroy the Advertising-Based Business Model of the Internet?
Bibliographic record
Abstract
In the 1990s, the Internet promised to unite the world and put all of the accumulated knowledge of humanity at anyone's fingertips, instantly. So why is the Internet today a cesspool of misinformation, scams, vitriol, "sponsored results," and time-wasting, deceptive "dark patterns?" That's easy, you may be thinking. "Because people don't want to pay for anything." As the saying goes, if you don't want to pay for the product, you become the product. Since people are so cheap, advertising was the only viable business model to fund the services people wanted. As a result, web services are designed not to provide the highest quality service and experience to the user, they are designed to keep the user using the service as long as possible so that they can see as many ads as possible. They are designed to trick users into clicking on sponsored links so that the hosting site can earn advertising dollars. They are designed not to give us the best results, but to give us the sponsored results. The inevitable outcome has been search engines whose top search results are all sponsored, shopping sites whose search feature returns sponsored product results rather than the best product to meet your needs, and social media sites that stoke anger and outrage since that keeps people on the site the longest. The wasting of our time, the degradation of our political discourse, and the potential destruction of our democratic societies are just collateral damage. Can AI lead us out of this wasteland and into a paradise where the user and the customer are one in the same, and search services compete to be the most efficient, accurate, and relevant?
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.027 | 0.036 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.018 | 0.010 |
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".