Economics FAQ

The sticker price is $8 million for a 30-second spot, with premium positions reaching $10 million. But the fully loaded cost is $16–23 million once you add production ($1–4M), celebrity talent ($1–5M), and mandatory companion buys on the same network ($7–10M). Some estimates reach $40–50 million when including agency fees, music licensing, and digital activation.

The evidence is genuinely mixed. Stanford researchers Hartmann and Klapper found Budweiser earned a 172% ROI from its Super Bowl ads. But Bridgewise found that a portfolio of Super Bowl advertisers underperformed the S&P 500 by 9.2% after six months. Kantar reports $4.60–$5.20 return per dollar invested. The answer depends heavily on category exclusivity: when two competing brands both advertise, neither gains incremental profit.

Stanford research showed that when two competing brands both advertise in the same Super Bowl, neither gains incremental profit because the effects cancel out. Yet both rationally choose to spend because opting out concedes the benefit to a competitor. This creates a collectively suboptimal but individually rational equilibrium that the NFL exploits to command rising prices.

At $8 million reaching roughly 125 million viewers, the Super Bowl's effective CPM is around $63–65. Standard primetime TV runs $20–30, streaming TV runs $15–35, and TikTok runs $5–10. The premium buys the engagement factor: EDO estimates a single Super Bowl ad generates the same brand-search engagement as 1,056 typical primetime ads.

The NFL controls a structural scarcity: the Super Bowl is the last true monoculture event in American media, reaching 125+ million simultaneous viewers in an era of fragmented attention. Inventory sells out months in advance. The prisoner's dilemma among advertisers prevents collective price resistance. And the price itself signals legitimacy, creating a Veblen good dynamic where high cost is part of the value proposition.

In 2025, NFL games accounted for 83–84 of the top 100 most-watched U.S. telecasts. The Super Bowl drew 127.7 million average viewers, roughly 2x the most-watched presidential debate and 6x the Oscars. No other single moment reaches such a broad cross-section of America simultaneously, making it the last true mass-reach event in an increasingly fragmented media environment.

Cornell research shows households with a GLP-1 user cut grocery spending by 5.3% within six months, with high-income households dropping 8.2%. Fast food spending falls 8.0%. These users aren't switching brands; they're simply eating less.

Savory snacks see the largest decline at 10.1%, followed by sweets, baked goods, and cookies. Even staples like meat, eggs, and bread decline. Yogurt is the only category showing a statistically significant increase, with fresh fruit and nutrition bars trending up slightly.

About 34% of users discontinue within the sample period. When they stop, spending doesn't just return to baseline; it becomes less healthy. Candy and chocolate purchases rise 11.4% above pre-adoption levels, suggesting the drugs suppress appetite biologically without teaching new habits.

Higher-income households show even steeper spending declines (8.2% vs 5.3% average) and are more likely to use GLP-1 medications for weight loss rather than diabetes. They're also the most profitable customers for fast food chains, creating a "double whammy" where companies lose their highest-margin customers.

The AI productivity paradox describes how tools that make individual tasks faster often increase total workload rather than freeing up time. Research shows 77% of employees say AI tools have added to their workload, and workers in AI-exposed occupations now work roughly 3 extra hours per week while leisure time has dropped by the same amount.

The Jevons paradox, observed in 1865 when more efficient steam engines increased coal consumption rather than reducing it, applies to AI in that efficiency expands what we're expected to do. When you can build an app in a weekend that used to take months, you don't build one—you build six. The friction that once protected us from infinite expectations evaporates.

Keynes predicted a 15-hour work week by now, and we got the productivity gains he anticipated—yet we work longer hours than ever. Only 21% of employees use time saved by AI for personal life; the rest reinvest it into work. When capability expands, so does the definition of "enough," and the bar rises accordingly.

Parkinson's Law states that work expands to fill the time available. The AI corollary is that work expands to fill capabilities available. More capability means more possibility—and more obligation. In competitive environments, someone who uses that expanded capability while you rest will outrun you.

The Affine Wealth Model shows that even when agents start with equal wealth and trade randomly, a Pareto distribution emerges naturally. Without any structural advantages or unfair rules, the mathematics of repeated random exchanges causes wealth to pile up among a small number of agents while most end up near the bottom. This happens because small random advantages compound over time, just as they do in real economies.

In simulations of the Affine Wealth Model, introducing even a modest wealth tax of 1% to 5% dramatically changes the outcome. The distribution shifts from extreme concentration to a more stable, even spread where the wealthiest agents hold at most 3-4 times their initial amount. The redistribution mechanism prevents runaway accumulation and produces a steady-state equilibrium.

The Affine Wealth Model is a stochastic, agent-based, binary-transaction Asset-Exchange Model for wealth distribution. It was designed to allow for agents with negative wealth, reflecting real-world conditions where roughly 10% of the U.S. population holds negative net worth due to mortgages and student loans. The model has been empirically validated against 27 years of U.S. wealth data with an average error of less than 0.16%.

Yes. Agent-based models with relatively few parameters have demonstrated remarkable accuracy in matching empirical wealth data across developed countries. The Affine Wealth Model reproduces both the Pareto distribution observed in the top 10% of the population and the exponential distribution seen in the lower 90%, validating the principle that a few structural rules can generate the full complexity of real wealth distributions.

The zero price effect is a behavioral economics finding showing that people don't treat "free" as just another low price. When something costs zero, it receives a psychological boost that inflates perceived value beyond what cost-benefit analysis would predict. Applied to transit, this means the gap between a $2.75 fare and $0 feels far larger than the gap between $2.75 and $0.75, even though the dollar difference is smaller.

Free transit activates social norms like gratitude, civic participation, and shared ownership, making riders feel they're receiving a gift from the city. Cheap transit activates market norms, where riders think about cost-benefit trade-offs and whether the service is worth the price. This distinction, rooted in Dan Ariely's research on zero pricing, explains why politicians find "free" so much more compelling than "affordable."