Hey guys, with all the recent news around companies overshooting AI budgets (Uber being one example), I’ve been thinking a lot about how organizations are actually planning and forecasting AI deployment costs at scale. Between rapidly changing model pricing, inference costs, usage patterns and growing enterprise adoption, it feels like budgeting for AI is becoming a much bigger challenge than most people expected. I’d love to connect or collaborate with people working in this space: economists, PMs, AI engineers, FP&A analysts, infra/tech leads or anyone researching AI cost optimization, enterprise AI deployment and/or AI-human workflow allocation. I’m especially interested in understanding:
- what realistic AI budgeting looks like,
- how companies are managing token/inference costs,
- expected vs actual ROI from AI deployments,
- and what the right balance between AI systems and human workflows might look like. Also happy to contribute to or join existing research/projects if relevant. If you’re exploring similar questions or actively working on this problem, would genuinely love to connect and learn from you. submitted by /u/Sid-Kiyo
Originally posted by u/Sid-Kiyo on r/ArtificialInteligence