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Stanford Study Shows Federal AI Spending Topped $3.3B in FY 2022

The AI Index 2023 Report from the Stanford Institute for Human-Centered Artificial Intelligence shows that U.S. government spending on AI increased by more than $600 million compared to 2022, totaling more than $3.3 billion in fiscal year 2022. Section 6.3 of the report delves into U.S. Public Investing in AI.

The sixth annual report, compiled with government spending data from Govini, includes the following facts about federal government spending:

  • Federal AI contract spending has increased more than 2.5 times since 2017
  • Federal agencies allocated a total of $1.7 billion to AI R&D spending in FY 2022, with $1.8 billion requested for FY 2023.
  • DoD agencies requested $1.1 billion for AI in 2023, a 26% increase over FY 2022 appropriations
  • In 2022, the largest spending allocations were for the categories of decision science ($1.2 billion) and computer vision ($0.8 billion)

The report lists the Top Ten Takeaways:

  1. Industry races ahead of academia
  2. Performance saturation on traditional benchmarks
  3. AI is both helping and harming the environment
  4. The world’s best new scientist … AI?
  5. The number of incidents concerning the misuse of AI is rapidly rising
  6. The demand for AI-related professional skills is increasing across virtually every American industrial sector
  7. For the first time in the last decade, year-over-year private investment in AI decreased
  8. While the proportion of companies adopting AI has plateaued, the companies that have adopted AI continue to pull ahead
  9. Policymaker interest in AI is on the rise
  10. Chinese citizens are among those who feel the most positively about AI products and services. Americans … not so much

The expansive, 386-page AI Index 2023 Report includes including:

  • a new chapter on AI public opinion
  • a more thorough technical performance chapter
  • original analysis about large language and multimodal models
  • detailed trends in global AI legislation records
  • a study of the environmental impact of AI systems

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