Prediction Marketing

Predicting market waves in SaaS

How to catch the market at the right time

Is the online advertising bubble finally starting to pop?

I started calling online advertising a bubble in 2008. I made “The Advertising Bubble” a chapter in The Intention Economy in 2012. I’ve been unpacking what I figure ought to be ob…

Why Aren’t Markets Freaking Out?

Of Trump, Keynes and Wile E. Coyote

Attention Is All You Need #2: This Time Is Different

What can we learn from the 2007 Global Financial Crisis and the 1990’s dot-com bubble?

Search, Discovery, Pills, and Portals

Solving the distribution crisis in marketing

AI is probably not a bubble

AI companies have revenue, demand, and paths to immense value

AI is probably not a bubble

AI companies have revenue, demand, and paths to immense value

Jefferies’ Chris Wood Warns of ‘Massive Capital Destruction’ From US AI Boom

The most likely long-term outcome of the AI boom will be massive capital destruction in the US, with market share going to cheaper open-source Chinese models, according to Jefferies Financial Group Inc.

What Sort of AI Bubble Are We In?

Think broadband. Or, potentially, the 2008 housing market.

The end of theory? AI and ignorance in financial markets

AI’s growing role in finance challenges traditional expectations of transparency and theoretical understanding. While machine learning (ML) models enhance financial decision-making, they remain largely agnostic to established financial theories, producing knowledge and ignorance in ways that differ from traditional models like VaR, DCF, and Black-Scholes. This essay explores the decoupling of AI models from theoretical financial knowledge and the resulting forms of ignorance. Using 22 semi-structured interviews, we investigate how ML models generate epistemic uncertainties. We focus on causal ignorance: AI systems, including those supported by XAI, fail to provide genuine causal explanations. Because understanding causation is inherently theoretical, AI-driven finance remains theory-agnostic and marked by theoretical ignorance. We explore how this ignorance differs from that of traditional models and what it implies for the role of theory in finance. Finally, we present three possible scenarios for the future of theory in finance and outline directions for further research.

Do Liquidity Constraints and Interest Rates Matter for Consumer Behavior? Evidence from Credit Card Data

Abstract. This paper utilizes a unique data set of credit card accounts to analyze how people respond to credit supply. Increases in credit limits generate

Generative AI and Finance

Since ChatGPT's release in 2022, demand for artificial intelligence (AI)–related skills in finance has grown rapidly, as generative AI drives significant technological changes in both the financial research field and the broader economy. We show that financial occupations are highly exposed to the productivity effects of generative AI, review the literature on the impact of ChatGPT on firm value, and provide directions for future research investigating the impact of this major technology shock. Generative AI also holds great potential as a tool for finance researchers and practitioners: We review and describe innovations in research methods linked to improvements in AI tools, along with their applications. We offer a practical introduction to available tools and advice for researchers in academia and industry interested in using these tools.

Report - Brace for Impact: Protecting People and the Economy when the AI Bubble Bursts — Open Markets Institute

Our report in partnership with Americans for Financial Reform outlines vulnerabilities in the financial system and wider economy that could intensify an AI crash into the next financial crisis, and provides steps that policymakers can take to mitigate the damage and protect everyday people from bear

Why Almost Everyone Loses—Except a Few Sharks—on Prediction Markets

A WSJ analysis shows a small number of accounts on Polymarket and Kalshi—often pros using data-driven algorithmic trading—take home most of the winnings.

Why the man behind 'The Hater's Guide to the AI Bubble' thinks Wall Street's hottest trade will go bust