Leaked financial docs show OpenAI is losing billions of dollars a year
- Massive Net Losses: In 2025, OpenAI generated $13.07 billion in revenue but racked up $34 billion in total costs and expenses, resulting in an operating loss of $20.92 billion.
- One-Time Accounting Impact: Due to its transition from a non-profit to a for-profit entity, the company recorded a $41.55 billion loss from fair value changes in convertible interests and warrant liabilities. This brought the final net loss attributable to OpenAI to $38.53 billion.
- Year-over-Year Trajectory: Expenses and losses grew exponentially compared to 2024, when OpenAI brought in $3.7 billion in revenue against $12.48 billion in total costs, yielding a net loss of $5.09 billion.
- Core Expense Breakdown (2025):
- Research and Development (R&D): $19.18 billion (up from $7.81 billion in 2024).
- Cost of Revenue: $7.5 billion (up from $2.65 billion in 2024).
- Sales and Marketing: $5.73 billion (up from $1.11 billion in 2024).
- General and Administrative: $1.57 billion.
- Strategic Capital Flow & Microsoft Relationship: OpenAI paid Microsoft $17.2 billion in service fees during 2025 ($10.59 billion for R&D/model training and $6.047 billion for computing cost of revenue). By the end of 2025, OpenAI still had a remaining liability of $3.64 billion to Microsoft.
- Inbound Funding: Strategic partners provided substantial inflows; OpenAI received $867 million from SoftBank and $303 million from Microsoft in 2025.
- Remaining Cushion: As of the close of 2025, OpenAI held slightly over $50 billion in total assets, with nearly half of that cushion (~$25 billion) maintained as liquid cash reserves.
Hacker News Discussion
- R&D vs. Inference Costs: Commenters debate whether OpenAI can safely shift its massive R&D expenditure toward minimizing inference costs. While cheaper models like DeepSeek are heavily praised for personal and developer productivity, some argue stopping frontier model research means losing the structural race entirely.
- Diminishing Returns on Model Power: Users question whether a marginally smarter model justifies an exponentially higher cost. A central discussion point revolves around the financial viability of paying massive premiums for enterprise-tier models compared to utilizing low-cost API alternatives.
- The Math of Productivity Upgrades: A highly debated calculation suggests that even a 5% boost in productivity for a high-earning employee justifies hundreds of dollars in monthly subscriptions. However, critics counter that the financial surplus of that productivity is captured by companies and owners, rather than resulting in worker wage increases.
- The Path to Monetization: The consensus leans toward enterprise seat monetization (charging upwards of $2,000/month per corporate professional) and securing multi-billion dollar government contracts as the only viable business models. The inevitable integration of embedded or covert advertisements for free tiers is also viewed as highly likely.
- AGI as a Pseudo-Religious Goal: Several participants view Silicon Valley's relentless capitalization of unprofitable AI models as an irrational, faith-based pursuit of AGI (Artificial General Intelligence), comparing the narrative to religious prophecies.