Job talk prep

Job talk prep

JMP

We discuss the shift in artificial intelligence from general models to personalized systems, emphasizing that successful alignment depends on user diversity rather than just algorithms. Mathematical frameworks reveal that platforms achieve perfect alignment only when their user base is sufficiently heterogeneous, allowing a shared representation to be refined by diverse feedback. This creates a unique informational network effect where the high cost of providing free AI services is justified by the valuable, varied data these users generate. By subsidizing free users as "exploratory agents," companies can deliver hyper-personalized results to paying customers with minimal error. Consequently, AI platforms must evolve into market designers that meticulously manage user demographics to ensure statistical efficiency and maintain a competitive edge. This transformation redefines the microeconomics of inference, turning user variety into a critical factor of production.

We explore how the inherent rigidity of property law and fiduciary responsibility prevents artificial intelligence from ever fully replacing human leadership. Because the legal system requires human accountability for asset ownership and executive decisions, AI is relegated to a tool for execution rather than an entity capable of holding power. This structural reality creates a bifurcated labor market where entry-level roles vanish through automation while the executive class becomes more insulated and influential. Consequently, the document argues that elite business schools provide a critical strategic advantage by offering the credentials and exclusive alumni networks necessary to secure these high-stakes, un-automatable positions. Ultimately, while technology evolves rapidly, the legal architecture of liability ensures that human judgment remains the final authority in a digital economy.

We explore how the agentic economy is transforming digital marketing from a competition for human attention to a struggle for algorithmic preference. As AI assistants increasingly handle search and transactions, companies like Semrush are pivoting to provide a measurement layer that tracks brand visibility within AI-generated responses. This strategic shift involves the creation of new metrics, such as AI share of voice, and the adoption of the Model Context Protocol to allow agents to query marketing data directly. The sources emphasize that in a world of agent-mediated decisions, success depends on maintaining a high-quality reputation and appearing as a cited authority in AI answers. Ultimately, the text outlines how traditional SEO is evolving into Generative Engine Optimization to ensure brands remain discoverable in both open and restricted AI ecosystems.

We discuss a 1986 lecture by Richard Hamming, a renowned scientist who explores why certain researchers achieve extraordinary breakthroughs while others are eventually forgotten. Hamming argues that significant contributions are not merely products of luck or high intelligence, but result from deliberate habits such as working on the most important problems in one's field. He emphasizes the necessity of unwavering commitment, the courage to challenge established norms, and the ability to effectively communicate and sell one's ideas to the broader community. The text also highlights how personal traits, including a tolerance for ambiguity and a willingness to adapt to organizational systems, play a vital role in sustaining a productive career. Ultimately, the source serves as a strategic guide for individuals seeking to maximize their professional impact and leave a lasting legacy in science or engineering.

We describes a fundamental transition from the Attention Economy to an Agentic Economy, where autonomous AI agents increasingly handle market transactions. In this new landscape, **marketing** is reimagined as a technical discipline called **Data Source Engineering**, focusing on the rigorous collection of human preference data rather than simple brand persuasion. This evolution prioritizes **Preference Alignment** and **Active Learning**, using consumer interactions to refine the AI models that drive decision-making. The report highlights emerging **infrastructure protocols** and **elicitation frameworks** that allow brands to communicate directly with digital assistants. Ultimately, the role of the marketer shifts toward managing a **"Digital Twin"** of the market to ensure business agents accurately reflect and serve user intent. These sources argue that firms must master **technical signaling** and data integrity to remain visible to the algorithmic buyers of the future.

This paper proposes a novel approach to the challenging problem of forecasting consumer demand for new brands where preference is largely driven by intangibles. The central framework integrates a structural demand model, which initially estimates existing brand utilities, with a fine-tuned large language model (LLM). By training the LLM on textual descriptions of products and markets, the resulting model can successfully predict consumer preferences ($\delta_{jt}$) for items it has never encountered, significantly outperforming conventional models based on text embeddings. The author analyzes the internal mechanics of the LLM using techniques like sparse autoencoders to identify interpretable features influencing choices, thus offering guidance on brand positioning. Furthermore, the methodology allows researchers to perform advanced market simulations, such as solving for optimal pricing by combining LLM predictions with causal estimates derived from instrumental variable methods.

This paper presents a systematic study of **Generative Engine Optimization (GEO)** in the e-commerce sector, a practice now vital as LLMs deploy conversational shopping agents that rerank products. To address the lack of data and systematic methods in this emerging field, the authors introduce **E-GEO**, a novel benchmark dataset comprising over 7,000 realistic, intent-rich consumer queries paired with product listings. The research evaluates 15 existing content rewriting heuristics but finds that framing the process as an **optimization problem** yields much greater results. Specifically, an iterative prompt-optimization algorithm consistently delivers **superior ranking improvements** for products within the generative engine's output compared to relying on ad hoc rules. This successful systematic approach indicates the existence of a **stable, generally effective GEO strategy** that could be applied across various product domains.

This paper describes an effort to improve the accuracy of historical U.S. Census transcriptions using a machine learning model. The authors focused on correcting errors in name transcriptions from the 1940 census for Rhode Island, specifically targeting records where independent human transcriptions from Ancestry.com and FamilySearch.org disagreed. The improved transcriptions significantly increased the rate of linking individuals across census records, particularly benefiting records with low original legibility where human transcribers typically struggle. This approach promises to enhance the utility of historical census data for economic and social research by creating a higher quality, linked dataset across multiple periods.