The founder of OpenClaw burned $130 million OpenAI Tokens in 30 days: the true cost behind the scale of AI Agent
OpenClaw founder Peter Steinberger revealed on X that his AI Agent platform consumed $130 million (approximately 9.4 million yuan) of OpenAI API Token within 30 days. This figure reveals the true cost structure of large-scale operations of AI Agents, and is an important reference for every individual and team building an AI automation system.
Core conclusion
OpenClaw founder @steipete (Peter Steinberger) disclosed the API consumption data of his platform: TOK1__ million OpenAI Tokens were burned in 30 days. This number sparked heated discussion on Hacker News (47 hot posts) because it exposed the cost gap that most people ignore between AI Agents moving from "fun gadgets" to "profitable commercial systems."
Key Points
- Time of Event: May 16, 2026
- Affected objects: All individuals and teams who use OpenAI API to build AI Agents, automated workflows, and content production systems
- Core Signal: The cost of AI Agent's API Token may be much higher than most people expect. $1.3M/month is not "burning money" but "the normal operating cost of the industry's leading players"
Background: Why this number matters
OpenClaw is currently one of the most popular AI Agent platforms on GitHub (100K+ Stars). It allows users to use natural language in the terminal to let AI autonomously complete tasks such as programming, data analysis, and content production. The platform backend uses OpenAI's models (GPT-4o, GPT-5.5, etc.) by default to drive the Agent's reasoning and decision-making.
The set of data posted by founder Peter Steinberger (former founder of PSPDFKit) on
In the past, all we have seen is "what AI Agent can do". This is the first time someone has publicly stated clearly "how much it will cost to achieve this scale."
Key impact: Triple signals to the AI Agent ecosystem
| Dimensions | Changes | What it means | Recommended actions |
|---|---|---|---|
| Cost | $1.3M/month (approximately $43K/ days) | After the scale of AI Agent, API cost is the main expenditure item | Token usage monitoring and cost warning mechanism must be established |
| Business model | User subscription fee vs API cost | If the monthly user fee is $20, 65,000 paying users are needed to cover the API cost | The product pricing model must take Token consumption as a core variable |
| Model selection | High dependence on OpenAI | Single supplier risk + pricing fluctuation risk | Establish a model routing mechanism, and use low-cost alternatives such as DeepSeek V4 for low-value tasks |
| Technical optimization | Each Agent call generates multiple Token chains | Agent's "thinking cost" is much higher than a single API call | Introducing Token budget limits and Agent behavior optimization to reduce unnecessary reasoning |
Adaptation suggestions: Four steps to control the cost of AI Agent Token
1. Establish Token cost observability
Don’t find out at the end of the month that your bills have exploded. Each Agent task should have independent Token consumption tracking:
Agent → input_tokens/output_tokens → →
2.
OpenClaw GPT-4o/5.5 。
- DeepSeek V4 — $0.30/M input tokens（ GPT-4o 60 ）
- Claude 3.5 Haiku — $1.25/M input tokens
- ** Qwen3 ** — API ，
3. Agent
Agent ""。 Token
- Agent max_tokens
- （）
4.
LLM 。（semantic caching），， 20-40% API 。
DeepSeek OpenAI
# GPT-5.5 100 input tokens
curl -sS https://api.openai.com/v1/chat/completions -H "Authorization: Bearer $OPENAI_KEY" -H "Content-Type: application/json" -d '{"model":"gpt-5.5","messages":[{"role":"user","content":"..."}]}'
# : $20.00
# DeepSeek V4
curl -sS https://api.deepseek.com/v1/chat/completions -H "Authorization: Bearer $DEEPSEEK_KEY" -H "Content-Type: application/json" -d '{"model":"deepseek-v4","messages":[{"role":"user","content":"..."}]}'
# : $0.3066 。 $1.3M/ Token ， 50% DeepSeek， $640,000+。
OpenAI、GPT-4o、DeepSeek、Claude、Qwen3、OpenClaw、Hugging Face. These terms appear naturally in the text, and the platform side will match the maintained tools library and generate tool floating cards.
Related reading
- Want to learn how to build an AI Agent? Watch: AI Agent Tools 2026 Complete Tutorial: 5 Tools to Build an Automated Pipeline in 30 Minutes
- Real case: Nat Eliason’s OpenClaw Experiment: $14,718 in 3 Weeks With a $1K AI Budget
- Newbies should read the tutorial first: How to use OpenClaw + ChatGPT to build an AI automatic content rewriting system: from long articles to multi-platform adaptation in 30 minutes
Topic hub
YouTube AI Content Policy Hub
Answer-style evergreen hub for AI labels, auto detection, and disclosure—not just breaking news.
Explore YouTube AI Content Policy Hub →Monetization angle
How can you make money from this trend?
WayToClawEarn focuses on verified earn playbooks—not just news. Start from these cases.
ChatGPT ads + content distribution
Sell compliance checklists and automated distribution
OpenClaw Agent short-video growth
Lean into hybrid workflows as labels get stricter