A single chatbot response can use the equivalent of a small glass of water in cooling. Training and inference at scale runs on data centres whose electricity load is now visible in national grid figures. We are an AI-native studio. Pretending none of that applies to us would be dishonest. This page is what we actually do.
Stripe Climate funds frontier carbon removal: direct air capture, mineralisation, enhanced weathering. Not tree-planting credits of doubtful provenance. The science is early, the unit economics are hard, and that is precisely why it needs money now. We pay this off the top, not from profit.
Our sites run on Cloudflare Pages and Vercel, both of which match 100% of their electricity use with renewables and publish their numbers. Our AI workloads run on Anthropic's API; Anthropic operates inside AWS regions with stronger renewable matching, and publishes its responsible scaling and environmental position. We pay a premium against the cheapest alternative on every one of these lines.
Fourteen AI agents in place of conventional headcount means no commute, no office HVAC, no banks of always-on workstations. The aggregate compute footprint of the studio is meaningfully lower than a fourteen-person knowledge-work team with conventional SaaS sprawl. This is a structural choice, not a virtue.
Most of the work in our ventures is structured retrieval, deterministic computation, and short prompts against the right model for the job, not maximally long prompts against the largest model available. Cheaper to run, faster for users, lower energy per task. The economics and the footprint pull in the same direction.
When we audit your AI position, we look at energy and water alongside cost. The two are now strongly correlated: most of the cost in modern AI is electricity, and a meaningful share of vendor margin comes from picking the right data centre region. The choices that lower your run-rate are mostly the same choices that lower your footprint.
We will tell you which of your AI workloads are sized correctly and which are running on a model two tiers larger than they need. We will flag the vendor regions you're routed through, and what they look like on grid carbon intensity. It is not a separate ESG line item. It is part of getting the operating decision right.
Frontier model training is concentrated in the hands of a few labs, and the marginal energy intensity of model training has been rising. No customer choice we make changes that. The only thing that does is the labs themselves doing the work, and the policy environment they operate in. We pick partners that publish their numbers and engage seriously with the question, because the alternative is partners that don't. That is the available lever. It is not the whole answer.
If you are running an AI strategy and you want the environmental piece sitting inside the operating decision rather than bolted on as a report at year-end, that is the conversation we have on the audit.