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.

DeepMind's new paper, "Dynamic Reflections: Probing Video Representations with Text Alignment," introduces a new approach to understanding video data through the lens of text alignment.

The AI Index Report 2026, produced by the Stanford Institute for Human-Centered AI (HAI), provides a comprehensive look at how artificial intelligence continues to evolve and integrate into various sectors.

There's a widening divide between companies adept at integrating AI and those lagging behind. A new report from McKinsey, the AI Transformation Manifesto, highlights how the frontrunners aren't just dabbling with AI.

Every now and then, you have to step back to move forward.