> For the complete documentation index, see [llms.txt](https://docs.kaisar.io/kaisar-network/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.kaisar.io/kaisar-network/origins/challenges/exponential-change-in-spending-and-compute-requirements.md).
# Exponential Change in Spending and Compute Requirements
According to [GlobalTechCouncil](https://www.globaltechcouncil.org/artificial-intelligence/global-spending-on-ai-expected-to-double-in-four-years/), global spending on AI-centric systems is forecast to reach $154 billion in 2023 and is expected to continue growing rapidly, driven by increasing investments in AI by various industries such as banking, retail, and professional services.
Key areas of AI hardware spending include GPUs, TPUs, and custom AI chips. The broader IT spending landscape also highlights significant growth in data center systems, with spending projected to increase from $237 billion in 2023 to $260 billion in 2024. This growth is part of a broader trend of increasing investment in software, IT services, and communications services driven by the adoption of AI and other emerging technologies (Source: [Splunk](https://www.splunk.com/en_us/blog/learn/it-tech-spending.html)).
To gain insights into projected hardware use by AI Systems, [Stanford AI Index Report 2024](https://aiindex.stanford.edu/report/)) highlights the economic impact of AI, showing a significant increase in the use of training compute for notable machine learning models
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