Prediction Marketing

Predicting market waves in SaaS

How to catch the market at the right time

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

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.

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

The Market Curve

The market you choose to serve is one of the most important factors for an early-stage startup. And for most technologists, it’s a blind …

The Dividend Disconnect

ABSTRACT Many individual investors, mutual funds, and institutions trade as if dividends and capital gains are disconnected attributes, not fully appreciating that dividends result in price decreases. Behavioral trading patterns (e.g., the disposition effect) are driven by price changes instead of total returns. Investors rarely reinvest dividends, and trade as if dividends are a separate, stable income stream. Analysts fail to account for the effect of dividends on price, leading to optimistic price forecasts for dividend‐paying stocks. Demand for dividends is systematically higher in periods of low interest rates and poor market performance, leading to lower returns for dividend‐paying stocks.

The AI bubble isn’t new — Karl Marx explained the mechanisms behind it nearly 150 years ago

OpenAI CEO Sam Altman’s warning of an AI bubble highlights a deeper economic problem: capitalism is producing more capital than it can profitably invest.

People Loved the Dot-Com Boom. The A.I. Boom, Not So Much.

Tech leaders are beginning to worry about the public’s underwhelming enthusiasm for their plans to remake the world with artificial intelligence. Will that burst the bubble?

Liquidity versus Wealth in Household Debt Obligations: Evidence from Housing Policy in the Great Recession

We exploit variation in mortgage modifications to disentangle the impact of reducing long-term obligations with no change in short-term payments ("wealth"), and reducing short-term payments with no change in long-term obligations ("liquidity"). Using regression discontinuity and difference-in-differences research designs with administrative data measuring default and consumption, we find that principal reductions that increase wealth without affecting liquidity have no effect, while maturity extensions that increase only liquidity have large effects. This suggests that liquidity drives default and consumption decisions for borrowers in our sample and that distressed debt restructurings can be redesigned with substantial gains to borrowers, lenders, and taxpayers.