Marketing^AI

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 crea...

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 ma...

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, su...

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 ne...

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 **Pref...

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 resultin...

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...

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 increa...

This is from a working paper that analyzes the regulatory distortion in investment incentives within the United States natural gas pipeline network. The authors develop and estimate a structural model to compare the marginal social value of pipeline capacity—tied to regional gas price differences—against the financial incentives of firms operating under fixed rate-of-return regulation. The paper f...

This paper examines the challenges of conditional ignorability, a key assumption in causal inference used to identify causal effects from observational data. It argues that assessing this assumption is more complex than often perceived, as it implicitly requires evaluating numerous structural configurations within covariate sets. To address this, the authors propose a new framework using Cluster C...

We explore the imminent shift in the digital economy from a defensive model, where websites block automated agents, to an open, transactional "agent economy." This transformation is driven by the realization that website content is a valuable capital good for AI models , leading to a move towards monetized access via APIs. We detail the unsustainable "arms race" between scraper...

This discussion offers a comprehensive examination of the strategic importance of advanced analytics for driving business expansion. It highlights how organizations often struggle to leverage data effectively due to poor data quality, cultural resistance, and a lack of strategic alignment, despite the immense potential for growth. It differentiates between traditional Business Intelligence (BI) an...

This academic paper from Meta Platforms introduces **AdLlama**, a novel large language model (LLM) designed to enhance generative advertising text on Facebook. The core innovation is **Reinforcement Learning with Performance Feedback (RLPF)**, a post-training method that utilizes historical ad performance data, specifically click-through rates (CTR), as a reward signal to fine-tune the LLM. Unlike...

We explore the **evolution of marketing** from AI-assisted to **AI-autonomous functions**, highlighting the profound implications for Chief Marketing Officers (CMOs). We argue that while AI currently boosts efficiency in tactical tasks, the future involves **specialized AI agents** operating with a high degree of autonomy, necessitating a shift in the CMO's role to that of a **systems architect an...

We explore **transformative shift in marketing** due to AI, moving from AI-assisted to **AI-autonomous functions**. It highlights that while AI excels at **tactical tasks** like content optimization and performance marketing, **human oversight** remains crucial for strategic areas such as brand management and crisis communication. The text emphasizes the evolving role of the **Chief Marketing Offi...

We analyze the urgent need for a radical overhaul in marketing education due to the rise of Artificial Intelligence (AI) and Large Language Models (LLMs)**. It argues that traditional MBA programs are facing a **"crisis of relevance," highlighted by student dissatisfaction at institutions like Stanford GSB**, because their curricula fail to address the AI-driven transformation of the marketing ind...

We outline **transformation of Artificial Intelligence (AI) in marketing**, moving from its current role as an **efficiency-boosting tool** to its future as an **autonomous, decision-making agent**. It categorizes this evolution into distinct phases: the present, where AI augments human tasks like content creation, and the impending **"agentic inflection point,"** where AI systems will independent...

This research introduces a novel framework for Visual In-Context Learning (VICL) , a method where artificial intelligence models learn from provided visual examples. The primary focus is on optimizing the selection of these "in-context examples," which significantly impacts the model's performance on tasks like image segmentation, object detection, and colorization. The authors propo...

This paper introduces GEPA (Genetic-Pareto) , a novel prompt optimizer designed for large language models (LLMs) and compound AI systems. Unlike traditional reinforcement learning (RL) methods that rely on numerical rewards and extensive "rollouts" (tens of thousands), GEPA leverages  natural language reflection  to learn high-level rules from trial and error, significantly reducing the...

We introduce and defend the "prediction policy problems" (PPP) framework, which posits that many public policy and economic challenges have an often-overlooked predictive element that machine learning (ML) can significantly enhance. The document addresses key criticisms, arguing that the framework doesn't seek to replace causal inference but rather to improve the predictive "bri...

This academic article critiques the widespread deployment of "predictive optimization" algorithms , which use machine learning to make decisions about individuals based on future predictions. The authors argue that despite claims of accuracy, efficiency, and fairness, these systems inherently fail on their own terms due to seven recurring shortcomings. These issues include inability to t...

This research introduces a framework for integrating human expertise into algorithmic predictions , specifically focusing on instances where algorithms deem inputs "indistinguishable." The authors propose a method for selectively incorporating human judgment in these cases, demonstrating its proven ability to enhance the performance of any feasible algorithmic predictor . Empirical studi...

This academic paper investigates the critical issue of data leakage in applying machine learning (ML) to panel data , which combines cross-sectional and time-series observations. The authors explain that standard ML practices, when unsuited for panel data's inherent structure, can lead to temporal leakage (future information affecting past predictions) and cross-sectional leakage (information...

This paper introduces the concept of "prediction policy problems," arguing that not all policy decisions require causal inference ; many benefit significantly from accurate predictions. The authors distinguish these from traditional "causal inference" problems through examples, such as deciding whether to take an umbrella (prediction) versus whether a rain dance causes rain (ca...

We explore the evolving landscape of immersive marketing across Meta's virtual and augmented reality platforms, specifically focusing on Quest headsets and Ray-Ban smart glasses. They detail how advancements in hardware, AI, and sensor technology will enable deeply personalized, context-aware, and interactive advertising experiences that go beyond traditional 2D formats. The texts also highlig...