OpenAI needs to raise $207B by 2030 so it can continue to lose money (www.ft.com)

A recent analysis claims OpenAI will need to raise roughly $207 billion by 2030 to cover projected operating losses as it scales its large models and consumer products. The headline figure captures a widening gap between soaring costs—dominated by massive training runs, ongoing fine-tuning (RLHF), and exponentially increasing inference traffic—and current revenue from API charges, subscriptions, and enterprise deals. Even with Microsoft’s investment and commercial partnerships, the report argues that the economics of state‑of‑the‑art models remain capital‑intensive and unprofitable under today’s pricing and deployment patterns. For the AI/ML community this underscores that model scale isn’t just a research choice but a fiscal one: training and serving at trillion‑parameter scale require persistent datacenter capacity, GPUs/TPUs, networking, and energy, which pressures engineers and product teams to prioritize efficiency (quantization, distillation, sparse or retrieval‑augmented models), hardware co‑design, and pricing strategies. The potential outcomes include faster investment rounds, deeper Microsoft/OpenAI integration, more focus on cost‑reducing research, and tighter competition for chips and talent — all of which will shape what kinds of models and services are economically viable going forward.