The AI Pricing Transparency Play: How a Public Token Cost Tracker Turns Enterprise Sticker Shock Into Inbound Leads

The Play

Simon Willison published his personal AI usage data today: $1,199.79 for Claude Code and $980.37 for OpenAI Codex in 30 days — against a $200 monthly subscription cap.

That post hit 521 upvotes on Hacker News.

Why did it spread?

Because specific, documented numbers cut through noise when buyers are actively trying to understand what enterprise AI actually costs.

Whoever builds the cleanest, most trusted reference for AI pricing becomes the first stop in the buyer's research process.

That is the play.

Why This Moment Is Different

April 2026 reset enterprise AI economics:

• Anthropic moved enterprise to $20/seat/month plus API token pricing (reported April 14, 2026)
• OpenAI made a similar move for Codex on April 2, extended to all existing enterprise plans on April 23
• GPT-5.5 (released April 23rd) is 2x the API price of GPT-5.4
• Opus 4.7 (April 16th) is around 1.4x the price of Opus 4.6 with the new tokenizer

Companies that signed annual deals are renewing into prices they did not budget for.

The person who builds a clear, up-to-date pricing reference right now — before the market settles — captures the traffic from every company in that moment of sticker shock.

The Asset: A Living Pricing Tracker

One page. Clear table. Updated monthly.

Columns:

• Model name
• Input price per million tokens
• Output price per million tokens
• Price vs. last month (% change)
• Open-weight equivalent and its cost ratio

This page does three things:

Accumulates links. Anyone writing about AI costs will cite the most complete, current reference they can find. A well-maintained tracker becomes that reference. Links from AI newsletters, Hacker News posts, and tech publications compound over months.

Ranks for buyer-intent searches. "Claude Opus 4.7 API pricing," "GPT-5.5 cost per token," "DeepSeek vs OpenAI price 2026" — these queries come from buyers actively researching before a purchase decision. A structured tracker ranks for all of them.

Builds your list. Offer a monthly email when the tracker updates. The subscribers are exactly the buyers mid-budget-crisis. This list converts to audits, consulting, or your SaaS product.

The Weekly Flywheel

One case study per week answers a specific question:

"What does it actually cost to run [workflow] on GPT-5.5 vs DeepSeek?"

Pick a real workflow. Run it. Document:

• The prompt and task type
• Token count (input and output)
• Cost on the frontier model
• Cost on the open-weight alternative
• Quality comparison — where does it hold, where does it degrade?

Publish the numbers. Not "AI could save you money." Actual results on actual tasks.

Over 12 weeks: a library of case studies covering the workflows buyers care about. Each one links to the tracker. The tracker links to the case studies. LinkedIn posts drive traffic to both.

The Distribution Strategy

The content that spreads fastest in this market is specific and verifiable.

Willison's post worked because the numbers were exact ($1,199.79, not "over a thousand dollars") and checkable — the ccusage tool he used is public. Anyone with a Claude Code account can verify his methodology.

That is the standard to hit. Not estimates. Not ranges. Actual documented outputs from real usage.

Post these to LinkedIn with a hook that leads with the number, one sentence on why it matters, three specific examples, and a clear call to action. Posts with verifiable data outperform insight posts because they get reshared by engineering leads and CFOs who forward them to their teams.

The Compounding Layer

Six months in, you have:

• A tracker page ranking for a dozen buyer-intent searches
• A weekly case study library that answers specific workflow cost questions
• A subscriber list of companies in active budget conversations
• A LinkedIn presence known for specific, verifiable AI cost analysis

The inbound from this engine is pre-qualified. These are buyers who found you because they were researching exactly the problem you solve.

The window to be the first credible voice on AI enterprise pricing is open right now — before pricing stabilizes, before every agency builds a tracker, before the moment passes.

Build the tracker this week.