Token economics are getting weird. Prakash burned about $100 in Claude Duet credits on one deep research job and felt great about it. The same tool running email tasks at roughly $1 per email felt absurd to him. đž
The $100 job: he had Duet build a financial model for a potential Anthropic IPO. It dug into share pricing and option issuance, compared beginning-of-year and end-of-year figures, looked at average prices, and factored in hiring. That's a multi-step, multi-source analysis where the agent has to keep a lot of context together and reconcile numbers, which is exactly where long-running agentic workloads eat tokens.
The $1 email: this is the same per-token meter applied to a task with a tiny output. Agentic overhead like context loading, tool calls and retries doesn't shrink just because the deliverable is a few paragraphs. The cost is fine if the output is net-new knowledge, but it stings when a human could knock it out in two minutes.
The practical lesson is that value per credit matters more than raw spend. He shared the model with a few friends, and people are now using it in real investment decisions. In his words, it was knowledge that didn't exist before. âïž
For anyone building on agents, route expensive, high-context reasoning to the big loops where the output is an artifact worth keeping, and keep low-value, high-frequency tasks on cheaper paths. Otherwise your per-task cost curve will punish you on the boring stuff.
The $100 job: he had Duet build a financial model for a potential Anthropic IPO. It dug into share pricing and option issuance, compared beginning-of-year and end-of-year figures, looked at average prices, and factored in hiring. That's a multi-step, multi-source analysis where the agent has to keep a lot of context together and reconcile numbers, which is exactly where long-running agentic workloads eat tokens.
The $1 email: this is the same per-token meter applied to a task with a tiny output. Agentic overhead like context loading, tool calls and retries doesn't shrink just because the deliverable is a few paragraphs. The cost is fine if the output is net-new knowledge, but it stings when a human could knock it out in two minutes.
The practical lesson is that value per credit matters more than raw spend. He shared the model with a few friends, and people are now using it in real investment decisions. In his words, it was knowledge that didn't exist before. âïž
For anyone building on agents, route expensive, high-context reasoning to the big loops where the output is an artifact worth keeping, and keep low-value, high-frequency tasks on cheaper paths. Otherwise your per-task cost curve will punish you on the boring stuff.