I got some issues with this. The $132m/year is not real at all. Closer to $3m at today's prices, and as low as $1.2k within a year. Yes, $132m -> $1.2k. Hear me out. 1. The price listed is not accurate at all. $11m/month is just wrong. Real "api cost" is prolly $100k-$500k per month range. The 1.5T tokens number includes cache'd tokens. I looked at my own usage, and 33.5B tokens came out to $0.56/m (under 1/10th of Peter's $8/m estimate). I'm primarily using expensive models like Opus and Astra, and "1.5t tokens" would still have cost me ~$800k. If I used cheaper models and hit cache more, easily could get as low as $100k. 2. Intelligence is getting cheaper incredibly quickly I know our LLM bills are going up and everyone is stressed about it. Totally reasonable! It's not because intelligence got more expensive. Artificial Analysis tracks how "cost at a given intelligence level" drops over time. It used to drop at ~10x/year. Now it's at 30x lower in 5 months. (see image) Assuming the $100k from before, cost could get as low as $3k/month in a few months, and if trends continue, $100/month by this time next year. Obviously there will be smarter models and we will move to them, but replicating her experience today with equivalent models will be $1.2k/year by next year. 3. Nobody said this is normal Lauren is the 0.0001% of "agentic engineer". Measuring her work as "$ in -> software out" is reductive and wrong. Her spend should be looked at as a combination of product/eng, research, FDE work (pstack), and marketing. Worth $350k/day? No. Worth a mil or two a month? Absolutely. She is exploring what happens when you ignore the cost of intelligence. She's not doing this because Cursor expects every company to spend $10m/year on tokens. She's doing it because that $10m in tokens is going to cost $10,000 in a year or two.