From 76c31a2abd4dbb05cdfd11a3424f7e8e2f6b1ef4 Mon Sep 17 00:00:00 2001 From: Daniel Rosel Date: Tue, 17 Feb 2026 09:40:20 +0100 Subject: [PATCH] citing marc and --- paper/src/bib/references.bib | 23 +++++++++++++++++++++++ paper/src/chapters/03-methodology.tex | 5 ++--- 2 files changed, 25 insertions(+), 3 deletions(-) diff --git a/paper/src/bib/references.bib b/paper/src/bib/references.bib index 4689742..99f57ca 100644 --- a/paper/src/bib/references.bib +++ b/paper/src/bib/references.bib @@ -562,3 +562,26 @@ Volume: 21}, note = {No. 3:25-cv-09514-MMC}, file = {PDF:/home/velocitatem/Zotero/storage/4JWZSTXJ/Posner - UNITED STATES DISTRICT COURT NORTHERN DISTRICT OF CALIFORNIA SAN FRANCISCO DIVISION.pdf:application/pdf}, } + +@article{wright_2026_2025, + title = {2026 {Artificial} {Intelligence} {Outlook}: {The} {Great} {Competition} {Wars} {Have} {Begun}}, + language = {en}, + journal = {Pitchbook}, + author = {Wright, Brian and Javaheri, Ali and Bellomo, Eric and Hernandez, Derek and Yang, Rudy and MacDonagh, John and DeGagne, Aaron and Frederick, Alex and Geurkink, Jonathan and Zabelin, Dimitri and Ulan, James}, + month = dec, + year = {2025}, + file = {PDF:/home/velocitatem/Zotero/storage/AIY5K3TX/Wright et al. - 2025 - Institutional Research Group.pdf:application/pdf}, +} + +@misc{rachitsky_marc_2026, + title = {Marc {Andreessen}: {The} real {AI} boom hasn’t even started yet}, + shorttitle = {Marc {Andreessen}}, + url = {https://www.lennysnewsletter.com/p/marc-andreessen-the-real-ai-boom}, + abstract = {On raising kids, why job loss fears are overblown, the future of PM/eng/design careers, and the macro force you should pay attention to}, + language = {en}, + urldate = {2026-02-01}, + author = {Rachitsky, Lenny}, + month = feb, + year = {2026}, + file = {Snapshot:/home/velocitatem/Zotero/storage/DGW8PHMV/marc-andreessen-the-real-ai-boom.html:text/html}, +} diff --git a/paper/src/chapters/03-methodology.tex b/paper/src/chapters/03-methodology.tex index 2109814..e6c5bd8 100644 --- a/paper/src/chapters/03-methodology.tex +++ b/paper/src/chapters/03-methodology.tex @@ -46,6 +46,7 @@ where $\alpha \in [0, 1]$ represents the contamination parameter (proportion of \subsection{Cost of Information (COI) Framework} The platform's pricing power comes from information asymmetry: users who express strong interest signals pay more than the base price. We quantify this markup as the \textit{Cost of Information} (COI), which represents the average premium extracted above marginal cost. COI measures the revenue at risk when information asymmetry collapses. +A top-level view in the current AI discourse is that sufficiently large productivity gains can induce vertical deflation through cost compression and supply expansion \parencite{rachitsky_marc_2026}. Our contribution is narrower and mechanism-level: even under long-run deflation, platform revenue still depends on short-run information costs to the user. We formalize that rent as the Cost of Information (COI) and study how agentic reconnaissance accelerates its erosion. \begin{definition}[Cost of Information] Let $\pi(\tau)$ be a pricing policy mapping interaction histories to prices. The COI is defined as: @@ -135,11 +136,9 @@ This result naively proves that standard pricing policies $\pi$ fail to extract In order for our research to have grounding in interactions we built a robust e-commerce web-platform. We initially conducted a survey of the leading platforms of airlines and hotel booking sites to identify the specific interface patterns that effectively manage complex travel data. Our analysis revealed a clear industry standard: while both sectors rely on tabbed service selection and left-sidebar filtering to streamline navigation, they diverge in result presentation: airlines utilize visual date-price bars and multi-step wizards to optimize for logistical transparency, whereas hotel platforms leverage image-led cards and scarcity triggers to drive emotional engagement and urgency. Our web framework defines a highly agnostic boilerplate which can be seeded with any data-modality with an easy-to-tailor pattern, which we leverage to define a \texttt{hotel} and \texttt{airline} mode. Both modes are then individually deployed via an environment level argument which adjusts the proxy routing with a custom middleware inside next.js to render only the desired mode. The purpose of this was to create a baseline adaptable to any use-case or desired commercial application. - The architecture of this platform begins with the deployed web-apps posting interaction data to our backend which processes them and stores each ingested interaction into a kafka cluster. This serves as our data reservoir tracking and associating each interaction with its session and importantly with which experiment it belongs to. Not only do we track the behavioral interactions, but our pricing provider micro-service, once called by the frontend reports the observed/queried price-product into kafka. This kafka cluster is subscribed to by our pipeline which is configured on a schedule in Airflow, with the possibility of manual trigger. The final stage of the pricing pipeline, submits computed dynamic pricing results into a redis database for quick updates which is then read by the pricing provider and displayed on the webapp. This is a very generic end-to-end mechanism which is applicable to a variety of different e-commerce tasks. We intentionally put emphasis on the development of this infrastructure to establish a reproducible framework for interaction and to minimize any noise. -We transition the Kappa like architecture of the data collection to a Lambda system for actual learning in a surrogate environment. This allows us to move faster on data which is provided and helps us create a feedback loop for production deployment. - +\paragraph{Public Web Artifact} We transition the Kappa like architecture of the data collection to a Lambda system for actual learning in a surrogate environment. This allows us to move faster on data which is provided and helps us create a feedback loop for production deployment. To support further research in this intersection of fields we release P4P \footnote{\url{https://github.com/velocitatem/p4p}} as a public repository providing the interaction layer of the PHANTOM framework. This provides a configurable storefront which can be tailored to any commercial setting with a standardized session-level event tracking. We document the API adapters or what the framework expects in terms of schemas for pricing providers and log ingestion servicse. The repository is intended for controlled experimentation and method replication rather than production commerce deployment. \subsubsection{DevOps Principles}