Birth, Life, and Death of AI Models

Aaron P. Kaye, Kazimier Smith and Neil Thompson

No 13004, CESifo Working Paper Series from CESifo

Abstract: We characterize the lifecycles of open-weight AI models using a weekly panel of the 108,074 most-downloaded models on Hugging Face, which together account for 99.5 percent of the platform's more than 53 billion downloads. We link these data to token usage from OpenRouter and performance rankings from Arena and Artificial Analysis. We establish key institutional details about the supply of and demand for AI models, and we organize our analysis into three phases: birth, life, and death. In the "birth" phase, we show that major developers often release models in batches, concurrently introducing models that are both vertically differentiated (e.g., by size, capability, and latency) and horizontally differentiated (e.g., by task specialization, hardware compatibility, and alignment). Because weights are open, developers can also build on one another's models. We document cumulative innovation through fine-tuned, quantized, merged and adapted descendants of source models. In the "life" phase, we quantify how demand evolves after release. Even within the sample of top models, demand is concentrated, and the concentration persists: the median model loses ∼68 percent of its release-week downloads within ten weeks, while the most popular models decline gradually; as a result, the top percentile's advantage over the median model is ∼600-fold at release, and ∼900-fold six months later. Further, we estimate spillover effects from new popular model releases on related models and find that the first popular third-party descendant is associated with a 76 to 99 percent increase in weekly downloads of its source model. In the "death" phase, we construct measures of technical obsolescence and usage obsolescence. We find that technical obsolescence is associated with a slow but persistent decrease in demand, while usage obsolescence coincides with an immediate but temporary drop in demand. Our findings characterize open-weight AI as a market of complementary model portfolios, one in which third-party developers building on a source model can raise demand for the original, and in which demand is slow to respond to technical obsolescence.

Keywords: artificial intelligence; open-source; open-weights; technology diffusion; platform economics; cumulative innovation (search for similar items in EconPapers)
JEL-codes: L17 L86 O31 O33 (search for similar items in EconPapers)
Date: 2026
New Economics Papers: this item is included in nep-ain, nep-inv and nep-tid
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.ifo.de/DocDL/cesifo1_wp13004.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_13004

Access Statistics for this paper

More papers in CESifo Working Paper Series from CESifo Contact information at EDIRC.
Bibliographic data for series maintained by Klaus Wohlrabe ().