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    Aggressively Imagining Funding Models for Generative Web Search

    While diving into Zelch et al. (2023), a line in the abstract caught my attention:

    How will generative AI pay for itself? Unless charging users for access, selling advertising is the only alternative. [emphasis added]

    While I do not think we should completely reject, or refuse1, generative search (also called conversational search or chat-first search), I think this present time of disruption is an opportunity to reject a narrowing of options. We should use this opportunistic to rethink what search could be.

    There are several potential models for funding the development and ongoing operations of generative AI systems for web search, including individually-owned systems, usage fees, public funding, philanthropic gifting, crowdsourced models, routing funding through other tools, and advertising-based models. Each approach has tradeoffs around factors like accessibility, incentives, transparency, and alignment with the public interest. The optimal funding approach remains an open question.

    • Individually-owned (common pool of open source tooling and datasets)
    • General-use supported by API fees
    • Publicly-funded
    • Gifted (perhaps in common) by companies who benefit from better search (or good will)
    • Wikipedia model (grants and gifts from foundations, companies, individuals)
    • Tool-routed funding for search (see a discussion of browsers in Berjon’s “Fixing Search” (2023))
    • Advertising Models:
      • Banners
      • Generated separately from the search response
      • Incorporated into the search response itself

    People have proposed and tested many different search models and features over the years. This disruptive moment provides a chance to look back at what was tried & failed and what was proposed & ignored, and to look afresh. In addition to looking at alternative funding models and pursuing SERPs very different from the old ten-blue-links (and entangled with both), the disruption in search from generative AI has led people to newly imagine or reimagine other possibilities in the design of search.

    1. Search Routing:
      • Consider dynamic search routing, allowing for queries to be directed to specialized tools. We can question if diverse query types—exploratory, navigational, transactional—should be processed by the same back-end mechanism.
    2. Redistribution of Search Tasks:
      • The potential decentralization of search tasks into various software environments could lead to embedded search capabilities in tools ranging from text editors to messaging applications. This may distribute costs and possibly offer nuanced search experiences.
    3. Individual Search:
      • Individual search tools, agents, and engines, rooted in open-source frameworks and decentralized crawls, could distribute certain costs across user and producer populations.
    4. Public Voice in Search:
      • There is much opportunity for more voice in search interfaces and goals. See, for example, SearchRights.org.
    5. Extending Search Models:
      • There’s potential in examining alternative models for search operations, including crawling, indexing, and filtering. Opportunities might also exist in the moderation, ranking, and data storage mechanisms within search.
    6. Redefining Relevance:
      • A shift from traditional relevance metrics to more custom-designed SERPs might be considered. The design space includes SERPs oriented (even ad hoc) towards a range of specific values or objectives, suggesting a nuanced approach to personalization.

    Assorted Asides

    Any of these mix-and-matched changes may have many ripple effects, and will be shaped by as-yet-unknown technological possibilities, social acceptance, regulatory schemas, and markets. See: The Ripple Effect Trap in Selbst et al. (2019, p. 62).

    This is a great opportunity for applying the handoff analytic (Goldenfein et al., 2020, Mulligan & Nissenbaum, 2020). Watch this space!

    Search has long been due for multiple types of rescripting.

    Benjamin (2019, p. 4):

    To what end do we imagine? How can innovation in terms of our political, cultural, and social norms work toward freedom? How might technoscience be appropriated and reimagined for more liberatory ends?

    ibid. (p. 14)

    Ultimately, my hope is for you, the reader, to imagine and craft the worlds you cannot live without, just as you dismantle the ones we cannot live within. [emphasis in original]


    1. See Cifor et al. (2019) (see also Liboiron (2021) (p. 34), “…elevated refusal into a practice of affirmation, repair, and resurgence”)↩︎


    Benjamin, R. (2019). Introduction: Discriminatory design, liberating imagination. In R. Benjamin (Ed.), Captivating technology: Race, carceral technoscience, and liberatory imagination in everyday life (pp. 1–14). Duke University Press. https://doi.org/10.2307/j.ctv11sn78h

    Berjon, R. (2023). Fixing search: We don’t have to put up with broken search. https://berjon.com/fixing-search/

    Cifor, M., Garcia, P., Cowan, T. L., Rault, J., Sutherland, T., Chan, A., Rode, J., Hoffmann, A. L., Salehi, N., & Nakamura, L. (2019). Feminist data manifest-no. https://www.manifestno.com/

    Goldenfein, J., Mulligan, D. K., Nissenbaum, H., & Ju, W. (2020). Through the handoff lens: Competing visions of autonomous futures. Berkeley Tech. L.J.. Berkeley Technology Law Journal, 35(IR), 835. https://doi.org/10.15779/Z38CR5ND0J

    Liboiron, M. (2021). Pollution is colonialism. Duke University Press. https://www.dukeupress.edu/pollution-is-colonialism

    Mulligan, D. K., & Nissenbaum, H. (2020). The concept of handoff as a model for ethical analysis and design. Oxford University Press. https://doi.org/10.1093/oxfordhb/9780190067397.013.15

    Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019). Fairness and abstraction in sociotechnical systems. Proceedings of the Conference on Fairness, Accountability, and Transparency, 59–68. https://doi.org/10.1145/3287560.3287598

    Zelch, I., Hagen, M., & Potthast, M. (2023). Commercialized generative ai: A critical study of the feasibility and ethics of generating native advertising using large language models in conversational web search. http://arxiv.org/abs/2310.04892