Moore's Law and Price Trends of Digital Products: The Case of Smartphones
Bibliographic record
Abstract
Since the introduction of the iPhone by Apple in 2007 and Google's Android platform in 2009, the two systems have accounted for a total of 90 per cent of the U.S. smartphone market. Apple, however, reaps most of the profit in the industry. In the second quarter of 2016, for example, Apple's iPhone gets 104 per cent of the sector's profit. This suggests that the smartphone market resembles a Stackelberg leadership model. Despite Apple's strong market power, we investigate if the market leader is under pressure to be price competitive. We calculate a quality-adjusted price index for smartphones from 2007 to 2016. Our results show that the average price declines at an average rate of over 27 percent per year. The price trend is similar to other digit products such as computers, cameras, and portable music players. We observe that the large price decline reflects the effect of Moore's Law, which predicts that the capacity of integrated circuits undergoes an exponential growth. The effect of Moore's Law is incorporated into the Stackelberg model. We also observe that price trends of other digital products also follow a similar pattern. This suggest that the long-run price trends of digital consumer goods are somewhat independent of the market structures.
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How this classification was reachedexpand
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".