TokenScale
by the Numbers

TokenScale exists because "$5 per million tokens" never made intuitive sense. The Hobbit costs $0.06 on Gemini Flash-Lite — that lands. The tool was built to make that translation happen across 21 AI providers, for free, with no sign-up. What follows is the story of how it got built, told in the only language the project understands: numbers.

Anthropic doesn't expose per-conversation token counts in the standard interface, so these figures are honest estimates — derived from session count, average message density, and the fact that file-heavy sessions (uploading the 16,000-line HTML file repeatedly) run 60,000–100,000 tokens each. They are likely conservative.

The account opened in July 2025 with a QR code generator and a slot machine game. TokenScale shipped in May 2026. Here is what happened in between.

The nightly price data ritual — one entry per provider, by hand, on a phone, at 8pm — was not a limitation waiting to be automated. It was the discipline that made the data trustworthy. Now the automation has arrived. The habit became the infrastructure.

TokenScale was built to answer a simple question: what does AI actually cost, in words you recognise? The answer, it turns out, is also the story of how it was built.

May 20 2026. Product Hunt at 12:01am. Hacker News at 7am PT. X and LinkedIn before noon. Four channels in one day. Here is what actually happened: the good and the honest.

Product Hunt ended at #82 with 2 points. Hacker News generated real traffic but not a front-page run. No comment thread to engage with. LinkedIn reached animation and entertainment professionals, Will's industry network, not the AI developers the tool was built for. Reddit, Dev.to, email capture, tool directories: none of them happened on launch day. There is no list to email when prices change next time.

Catching and publishing that correction, before the HN post, before the Product Hunt page saw traffic, was the right call. The difference between input cost and output cost is exactly the kind of thing TokenScale exists to surface. Getting it wrong, and saying so, made that point better than getting it right would have.

Two weeks before launch, Will showed an early version at work. Just Claude pricing at the time, before the tool expanded to 16 providers. Before a single marketing post had gone out. When the cost breakdown appeared on screen the room leaned in. Not because they understand tokens. Not because they use the API. They got interested because they could see how the person who built it thinks.

What caught their attention was a document generated after the session: a formal QA training report, formatted for product and engineering teams, based entirely on how Will had worked. Five principles. Seven competencies. A scorecard. Signal-to-noise ratio: 10/10. Acceptance discipline: 10/10. Bug reproduction quality: 9/10. The kind of document you could table in a meeting. It was produced by analysing Will's own prompts and working style across 40 issues raised during the TokenScale build. A person putting their own process under the same scrutiny they applied to the product. That is what landed. The verified dates, the word count badge, the public correction, the before-and-after cost tables: none of these are features to a non-technical audience. They are evidence of an approach. Checking your numbers, being honest when they are wrong, keeping a record. That is a QA instinct. It shows up whether or not you know what a token is.

Anyone who has ever had to stand behind a number in a meeting, a budget line, a schedule estimate, a word count, recognises that instinct immediately. TokenScale turns out to speak to that audience too, even if they never open an API.

The energy lens didn't start with a spec. It started with a WhatsApp at 12:30 AM on May 23 2026. TokenScale had been shared quietly with about three people. One was Chris — a friend from university who had spent serious time researching AI sustainability. A voice note. A reconnected friendship. Then his reply:

Reading between the lines: Chris already understood the financial story. What he was living inside was the energy and sustainability side. The two weren't connected anywhere — not on TokenScale, not really anywhere — in a way that a normal person could see at a glance. That gap became the brief.

The anchor came from a conversation in 2024: boiling one litre of water in a standard kettle takes about 0.1 kWh. That's enough energy to run somewhere between 30 and 300 standard AI text queries, depending on the model. The kettle versus the query. Concrete, kitchen-scale, something everyone has lived. No jargon required.

The 100W incandescent bulb was chosen deliberately. Unlike LED bulbs — 4W? 12W? everyone's is different — the 100W incandescent was a globally standardised unit for decades. It is the watt made physical. A trustable comparison.

The feature shows two numbers, not one. Same insight as cost: the per-query number looks tiny. The scale number is what changes behaviour.

Chris is the sustainability researcher who already knows the financial story. The energy lens is the bridge — the thing that connects the two honestly, without alarm, without requiring a PhD. The feature is hidden by design. If you find it, it was meant for you.

I got curious recently about exactly when TokenScale started. Not the launch — May 18th — but the actual first moment. The conversation that set it off.

It was April 21st. A single Claude thread about token economics that had ballooned to 100,000 tokens. I asked Claude to explain the compounding cost problem — what happens when every reply has to read the entire conversation history first. It explained it in real time, inside a conversation that was itself compounding. Then I asked it to build an interactive guide about what it had just explained. It did that too.

April 21st to May 18th. Twenty-seven days from a single runaway conversation to a launched product.