As the lynchpin of digital innovation, artificial intelligence holds the future for every forward-leaning business. But while AI and generative AI pave a path toward opportunity, they come with financial sustainability risks that can threaten the durable use of these technologies.
Key Takeaway
AI’s reliance on cloud infrastructure and applications can lead to hidden costs that may threaten the financial sustainability of AI innovation, potentially leading to bankruptcies in the AI industry.
The Cloud Addiction of AI
Unpacking this issue requires understanding AI’s addiction to the cloud. AI relies heavily on cloud storage and computing powers. Separate, they are nothing, but together, AI has velocity. Cloud infrastructure and applications give advanced analytics, hyper-automation, and large language models the fast, scalable delivery channels they need to be effective. But this also triggers cloud expenditures that can go unforeseen and undetected. The Wall Street Journal recently published an article on how AI is impacting the ability to control cloud costs. Hidden infrastructure and application costs pile expenses on an already tricky cloud dynamic:
- Prices are rising for infrastructure and applications.
- Cloud services dominate IT budgets and IaaS invoices can spin out of control.
- Most companies are already spending more on cloud than they budgeted.
GenAI and Technical Debt
GenAI is driving another layer of technical debt for many businesses. When you factor in AI’s costly yet indispensable ally with the high demands for new GenAI tools, it’s easy to see why investment strategies can quickly become financially unsustainable. Under the pressures of constant innovation, we could see the AI cloud grow at new, record-breaking speeds. As these factors come together in 2024, we may even see cloud hangovers of the past three years grow into full-fledged AI-cloud bankruptcies. Hidden costs have the potential to bankrupt AI innovation because they limit the ability for CIOs and CFOs to create new budgets, finding funding from within as a means to sustain the economic cycles of digital transformation.
