Original Reddit post

I built an interactive simulator to stress-test the AI profitability argument instead of just debating it abstractly. My current read is that AI inference can become very profitable in a few years, but only if several assumptions all hold at once: - paid usage scales fast - GPU deployment stays reasonably matched to demand - frontier serving shifts toward smaller active-parameter MoE or otherwise cheaper inference architectures - batching/throughput improves materially - GPU amortization and cost of capital are not too punishing - blended token prices do not collapse to commodity levels The main surprise is that electricity is not the dominant lever. Utilization, active model size, GPU amortization, data center CapEx, and realized revenue per token move the result much more. The simulator lets you adjust: - GPU price and amortization - power, PUE, and electricity - data center CapEx - model size and MoE active ratio - throughput and batching assumptions - user adoption and free/paid mix - token pricing App: https://msg32jebwg56opz2avykhcai-profitability-simulator.streamlit.app/ I’d be interested in criticism from people who think carefully about AI infra and economics: which assumptions are too generous, which are too harsh, and what major cost or revenue line items are missing? submitted by /u/italophile

Originally posted by u/italophile on r/ArtificialInteligence