The strongest idea is the reversal: Every’s biggest token user sees his lead as a reason to question whether everyone else is experimenting enough. That gives Every readers a provocative question about their own AI use. The rest of the draft adds the necessary discipline: ambitious experiments deserve room, but their costs should produce results or useful lessons that inform the next attempt.

- **Headline:** “Token Math” names the topic but hides the surprising claim. “How Many Tokens Are Too Many?” introduces tension, but frames the story around a spending ceiling when the opening questions whether people are spending enough. Lead with that reversal.
- **Dek:** “What’s the ROI on experimentation?” is broad and suggests a calculation the draft doesn’t deliver. The alternate is more specific, but “expensive experimentation” is cumbersome, and “rein your tokenmaxxing in” suggests readers are already burning tokens for sport—the behavior the body explicitly distinguishes from purposeful experimentation. Use the dek to explain how Every balances ambition with accountability.
- **Opening:** The contrast works, and the three-times figure makes it concrete. But “Often if,” “you might think,” and “consider ramping up” delay and soften the hook. Start with Dan’s counterintuitive response to the gap. Also, “highest spender” conflates token volume with dollar cost; the evidence establishes a lead in OpenAI token usage, not necessarily the largest AI bill.

Recommended copy:

**HED: Your AI Bill Might Be Too Low**

**DEK: Every’s approach: give ambitious experiments room to run, then assess the results and cut the waste.**

**Opening:** Every’s biggest OpenAI token user thinks the rest of us might be playing it too safe. CEO Dan Shipper consumes more than three times as many tokens as the company’s next-highest user on that leaderboard. He wants us to try more ambitious experiments, with the expectation that we’ll assess what the spending produced and how to be more efficient next time.

The headline frames the article’s spending argument; the opening identifies what the leaderboard actually measures. “Might” preserves the central uncertainty: Dan’s usage gap prompts a question, not proof that others are underinvesting. The replacement also leaves the next paragraph’s explanation of tokenmaxxing a natural role.

Two alternative headlines:

- **Spend More on AI. Expect More From It.** More assertive; pairs permission to experiment with accountability. The body should keep making clear that learning can count as a return.
- **What Did Those Tokens Buy You?** Better if you want to foreground the full draft’s practical guidance on reviewing experiments and reducing waste. It sacrifices some of the opening’s surprise.

Keep the payoff conditional: the draft supports the possibility of productivity gains and the value of specific lessons from failure. It does not establish that higher token use reliably produces better work or pays for itself.