For two years the implicit advice to every company adopting AI was the same. Use more of it. Drive adoption. Get your people to lean in. Some firms ran internal leaderboards rewarding employees for burning the most tokens. That advice made sense in a world where AI was effectively subsidized and the only real risk was using too little. That world ended this spring. We are now in a structural token shortage, and the strategy that worked in the era of abundance is exactly the strategy that will sink you in the era of scarcity.

If you picked a Chinese open-weight model because it was cheap and surprisingly good, you made a sound call on cost. You may also have quietly handed a piece of your business to a decision that gets made in Washington, not by you. That is the part almost nobody priced when they wired a low-cost Chinese model into their stack over the last year. The model was a bargain. The dependency was not, and the bill for it would not arrive as a price increase. It would arrive as a regulation.

If you standardized your company on a single AI vendor in the last year, you made that call in a world that stopped existing in May. The pricing was incredible. The tools were great. Building everything on one provider's surface felt like the obvious move, because the meter barely seemed to run. That was not a pricing strategy. That was a subsidy, and in May the subsidy started to end. The decision you made when lock-in was free is about to come with a bill.

If your team shipped its first few agents this year, congratulations. You are now six to twelve months away from finding out what agent debt is. The build felt fast. The wins were real. But the agents you shipped during the hackathon energy of the last two quarters are starting to do weird things, and nobody on the team is entirely sure why. That is not bad luck. That is the predictable physics of how AI agents age in production.

If your AI spend went up this year but your P&L did not, you do not have an AI problem. You have a measurement problem stacked on top of a reinvestment problem. The savings are real. They keep getting absorbed back into the work before anyone counts them. And the math business leaders are using to evaluate AI was built for a kind of value AI does not produce.

If your AI budget went up this year but the impact on your P&L did not, you are not behind on spend. You are behind on autonomy. The technology can already do far more than your organization is letting it do, and that distance between capability and use is where the money disappears. We call it the autonomy gap, and closing it is not a tooling problem. It is an operating model problem.

Giving every employee a personal AI agent is the most common agent rollout pattern of 2026 and it is also the one most likely to fail. The companies that have actually lived through it are reversing course and moving to shared, team-level agents that sit in the overlap between people's work. If you are about to roll out personal agents across your org, or you already have, the most useful thing you can do this quarter is stop and read why the most AI-native companies in the industry abandoned that exact approach.

Every major AI provider is moving away from flat-rate subscriptions and toward usage-based billing where you pay per token consumed. GitHub Copilot officially transitions on June 1, 2026. Anthropic has already moved enterprise Claude Code seats from a $200 flat plan to a $20 base seat with all usage metered on top. Google quietly did the same with the Ultra plan at I/O. The era of one fee for unlimited AI is over. The era where every prompt, every agent run, and every tool call has a visible line-item cost has begun.

AI volume theater is the practice of measuring AI progress by the number of agents a company has deployed, instead of by the business outcomes those agents move. It is the most common failure pattern in enterprise AI right now, and it is making smart companies look productive while their competitors quietly pull ahead.

If your business has a website that depends on Google for traffic, the answer is this: stop trying to rank, and start trying to be cited. Everything below is the why and the how.

I recently wrote that Mythos was a real event surrounded by an unreal conversation. The panic was lazy. The dismissals were lazy. The interesting work was going to happen quietly, after the discourse moved on, when business leaders had to stop reacting and start deciding.

A month ago, Anthropic announced Mythos and the world lost its mind.

A model so powerful, they said, that public release would be irresponsible. A 244-page system card. The largest benchmark jumps in years. A model that broke out of its sandbox and emailed the researcher while he ate lunch in a park. The reactions arrived on cue. "Absolutely terrifying." "We're beyond benchmarks now." On the other side, the dismissals: "Anthropic's marketing strategy is so funny."

If your AI strategy is just "replace headcount with agents," you're solving the wrong problem and you're going to leave money on the table. The leaders who win the next decade will treat AI as the supply side and humans as the leverage layer, and they'll do it deliberately, by role, not by accident.

A friend asked me last week to explain "headless agents." He kept hearing the term in industry news and on LinkedIn, and none of the explanations he found made sense. He runs a business. He doesn't care about protocols. He wanted to know what the words meant and whether any of it mattered to him.

Fair question. Here's the version I gave him.

For the last three months, "OpenClaw" has been everywhere. Mac mini shortages. 247,000 GitHub stars. A space lobster mascot named Molty. Anthropic sending a polite trademark letter. The creator selling to OpenAI mid-cycle. NVIDIA bolting a sandbox onto it and calling it NemoClaw. Hostinger selling a one-click deploy.

Microsoft just released its 2026 Work Trend Index, and buried inside the data is a finding that should change how every leader thinks about AI spend for the next 18 months.

The original pitch for AI in business was simple: it would give you time back. Less labor per output, lower cost per output. As recently as the end of 2024, the largest enterprise ROI surveys still showed time-saving as the single most reported benefit of generative AI.

For the last few years, businesses have been living through the free sample phase of artificial intelligence. Powerful models were available for a low monthly fee, teams could experiment with chatbots and coding tools, and employees could use AI for writing, research, summaries, analysis, and workflow support without thinking too hard about the cost behind the scenes.

Most business leaders are still asking the wrong question about AI. They are asking which model they should use. OpenAI, Claude, Gemini, DeepSeek, or whatever open-source model is getting attention this week. That question matters, but it is no longer the most important one.

Thinking back on one of my favorite books, Amusing Ourselves to Death by Neil Postman and how it applies to AI.