The Untold Story of Higgsfield | Burning $4M a Month on AI Models | CEO, Alex Mashrabov
Higgsfield founder and CEO Alex Mashrabov joins Harry Stebbings to discuss reaching $1 billion in annualized run-rate revenue in just 18 months. Mashrabov shares insights into the economics of open versus closed AI models, internal model spending exceeding $4 million per month, and the shifting dynamics of enterprise and consumer video creation. He also discusses building an engineering powerhouse in Kazakhstan and why traditional prosumer software subscriptions are under existential threat from foundation model providers.
- Survival forced customer focus: Higgsfield burned through $10 million of its $16 million seed round without traction before finding product-market fit on its final attempt. Read →
- Higgsfield spends over $4 million per month on internal AI model inference across its 400-person team, averaging $10,000 per employee each month. Read →
- Higgsfield scaled to over 400 employees, placing more than 300 engineers and operators in Kazakhstan rather than paying Silicon Valley compensation packages. Read →
- Relying exclusively on closed commercial AI APIs caps gross margins between 20% and 30%, while post-training open-weights models pushes margins above 80%. Read →
- Alex Mashrabov built Higgsfield to a $1 billion annualized run-rate in 18 months while spending over $4 million per month on internal models. Read →