These come from the Economics posts on the site, ordered by recency. Each answer is a standalone claim that links back to the source post for the simulation code, the model, or the empirical setup.
The framing is empirical. More agent-based simulations, fewer thought experiments. The Affine Wealth Model from Bruce Boghosian’s group at Tufts shows up repeatedly because it produces falsifiable predictions about wealth concentration that match 27 years of US data within 0.16% average error. Behavioral coverage starts from Kahneman, Thaler, and Ariely and lands in concrete policy questions: the zero price effect in fare-free transit, network effects versus regulatory friction in mobile money, signaling games in Super Bowl auctions. Game theory shows up where strategic structure dominates outcomes, not as a hand wave at “incentives matter.”
The default failure mode in economics writing is taking a stylized model as a moral claim. The questions below try not to do that.
President Trump ordered federal agencies to cease using Anthropic's technology on February 27, 2026, after CEO Dario Amodei refused to strip safety constraints from Claude's Pentagon deployment. Anthropic maintained prohibitions on mass domestic surveillance and fully autonomous weapons. Defense Secretary Pete Hegseth then designated Anthropic a 'Supply-Chain Risk to National Security,' a label previously reserved for foreign adversaries like Huawei and never before applied to an American company.
From: When AI Labs Become Defense Contractors
The FY2026 DoD budget request earmarks $13.4 billion for AI and autonomous systems, a roughly 7x increase from the $1.8 billion allocated in FY2025. This marks the first time AI has its own standalone line item inside the defense budget. The total defense request is $892.6 billion. For context, Anthropic's entire annualized revenue as of February 2026 was approximately $14 billion.
From: When AI Labs Become Defense Contractors
Both companies stated similar red lines: no mass domestic surveillance and no fully autonomous weapons. The key difference was framing. Anthropic refused to let the military use its models across 'all lawful use cases' without explicit restrictions. OpenAI agreed to deploy on the Pentagon's classified network while retaining control over technical safeguards, model selection, and deployment environments, limiting deployment to cloud rather than edge systems.
From: When AI Labs Become Defense Contractors
In July 1993, Secretary of Defense Les Aspin and Deputy Secretary William Perry invited defense CEOs to dinner at the Pentagon and told them Cold War budget cuts meant most would not survive. Norman Augustine of Martin Marietta named it the Last Supper. Within four years, 51 prime defense contractors consolidated into five: Lockheed Martin, Boeing, Raytheon, General Dynamics, and Northrop Grumman. Between 2011 and 2015, an additional 17,000 U.S. companies exited the defense industry.
From: When AI Labs Become Defense Contractors
Both, and increasingly the former. Palantir posted $4.48 billion in FY2025 revenue with 53.7% from government contracts, down from a peak of 58.2% in 2021 as its commercial AI platform gained traction. Its $10 billion U.S. Army Enterprise Agreement consolidated 75 existing software contracts. At a $320 billion market cap, Palantir is now worth nearly twice Boeing, making it the clearest example of a tech company operating as a defense prime.
From: When AI Labs Become Defense Contractors
IDIQ (Indefinite Delivery, Indefinite Quantity) contracts account for roughly 56% of DoD contract award dollars and run five years with extension options. They matter because once an AI company is embedded in classified systems with a security-cleared workforce, switching costs become close to prohibitive. Palantir's Maven Smart System contract, for example, started at $480 million and expanded to nearly $1.3 billion through 2029. A competitor cannot simply offer better inference speed to displace an incumbent with IDIQ access.
From: When AI Labs Become Defense Contractors
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.
From: Economics of a Super Bowl Ad
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.
From: Economics of a Super Bowl Ad
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.
From: Economics of a Super Bowl Ad
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.
From: Economics of a Super Bowl Ad
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.
From: Economics of a Super Bowl Ad
Per Cornell research published in 2024, 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% per the same data. The reported pattern is reduced consumption rather than brand switching.
From: Ozempic is Reshaping the Fast Food Industry
Per the Cornell research, savory snacks see the largest decline at 10.1%, followed by sweets, baked goods, and cookies. Staples including meat, eggs, and bread also decline. Yogurt is the only category showing a statistically significant increase, with fresh fruit and nutrition bars trending up slightly. These are consumer-spending observations, not therapeutic claims.
From: Ozempic is Reshaping the Fast Food Industry
Per the research, about 34% of users discontinue within the sample period. When users discontinue, spending patterns do not simply return to baseline; reported candy and chocolate purchases rise 11.4% above pre-adoption levels in the data. The interpretation in the research is that appetite-suppression effects from the medication class can fade quickly after discontinuation.
From: Ozempic is Reshaping the Fast Food Industry
Per the research, higher-income households show steeper spending declines (8.2% vs 5.3% average) and are more likely to use GLP-1 medications for weight management rather than diabetes. They are also typically the higher-margin customers for fast-food chains, which is why the spending impact is structurally larger for those chains than headline GLP-1 prevalence would suggest.
From: Ozempic is Reshaping the Fast Food Industry
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.
From: Does AI mean the demand on labor goes up?
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.
From: Does AI mean the demand on labor goes up?
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.
From: Does AI mean the demand on labor goes up?
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.
From: Does AI mean the demand on labor goes up?