The business of AI, explained: funding, IPOs, pricing, compute economics, and the bets each lab is making. Get smart on where the value is going and learn to reason about it in probabilities.

Autonomous AI agents are powerful precisely because they act without constant hand-holding, but that same independence is also why unchecked autonomy keeps failing in production. Building deliberate checkpoints into agent workflows is not a workaround for weak AI; it is the engineering principle that makes agentic AI trustworthy at scale.

The race to remove humans from AI workflows is generating a wave of expensive, hard-to-diagnose failures. Understanding the mathematics of compounding errors, real-world production incidents, and emerging regulations reveals why the smartest AI deployments keep humans strategically in the loop.

For years, AI labs competed on benchmark scores measuring raw reasoning. Now a quieter revolution has taken over: who can give an AI model the most information to work with at once, and what that shift means for everything built on top of these models.

Cursor, the AI code editor used by millions of developers, is no longer just a smart layer on top of someone else's AI. It is building its own frontier model from the ground up, and the implications reach far beyond software development.

Enterprises have poured billions into generative AI pilots, yet the vast majority never make it out of the demo stage. Here is why the replacement playbook keeps failing, and what the organizations actually winning with AI are doing instead.

For three years, $20 a month bought you access to the most powerful AI in history. That deal is quietly unraveling, and the reasons why matter for everyone who depends on AI tools.

OpenAI has confidentially filed for an IPO that could rank among the largest in U.S. history, but the headline valuation masks a much more complex bet. Understanding what it is actually wagering on is one of the most instructive exercises in AI literacy right now.