Planet Money is an NPR podcast that explains economic concepts through narrative-driven stories rather than dry analysis. It treats economics like a conversation with a friend—accessible, surprising, and actually engaging.

• The show makes economics approachable by grounding abstract concepts in real stories and human situations
• It's built on the premise that learning about the economy doesn't have to feel like a lecture

The article shows how open source software breaks every textbook market rule—non-excludable, free to use, mostly single‐maintainer—and yet it thrives at massive scale. It walks through classic market failures and existing theories, argues none fully explain open source’s resilience, and critiques efforts to retrofit pricing signals onto a system built on gifts, reputation, and shared infrastructure.

• npm hosts 5M+ packages, mostly unfunded and maintained by lone contributors, with no grants or contracts backing them—yet the entire commercial software world depends on them.
• Classic market failures (free riding, tragedy of the commons) just don't manifest: downloads can jump from 1,000 to 10 million weekly without any increase in maintainer headcount, and over half of packages survive on a single maintainer.
• Existing economic theories (Lerner/Tirole's reputation signaling, Benkler's coordination costs, Von Hippel's user innovation) each explain fragments but fail to account for why maintainers keep triaging bugs on abandoned tools or why the ecosystem hasn't collapsed in three decades.
• Proposed fixes—bug bounties, sponsorship marketplaces, token rewards—all try to impose pricing onto a giftand-reputation system, relying on weak proxies like stars and download counts instead of answering who's actually sustaining the code.

This article explores the profound impact of electronic spreadsheets, particularly Microsoft Excel, on American businesses and the economy. It traces the evolution from pre-spreadsheet management practices to the modern reliance on data-driven decision-making and financial engineering. The piece also touches on the implications for future technologies like artificial intelligence.

• About one-sixth of the world's population uses Excel, yet the tool gets little credit for how thoroughly it reshaped business.
• Spreadsheets shifted corporate focus from production toward numerical optimization and financial engineering.
• Before spreadsheets, managers tracked operations with columnar pads and typewritten memos, making real-time analysis of complex data essentially impossible.
• Dan Bricklin's classroom-inspired idea, developed with Bob Frankston at Software Arts, turned into the electronic spreadsheet that gave companies unprecedented speed and precision in analyzing their own operations.

The article analyzes the unit economics of large language models (LLMs), focusing on the compute costs associated with training and inference. It discusses how companies like OpenAI and Anthropic manage their financial projections and cash flow, emphasizing the need for revenue growth or reduced training costs to achieve profitability.

• Inference costs are falling faster than training costs, so gross margins on deployed models improve over time even as frontier training runs get more expensive.
• OpenAI and Anthropic's path to profitability depends on either scaling revenue much faster than compute spend or finding ways to cut training costs, since current cash burn is dominated by training rather than serving models.
• Reported "profitability" claims from these labs often exclude massive R&D/training expenditures, making headline numbers misleading about true unit economics.