The true cost of AI: 6 factors government agencies should consider

The true cost of AI encompasses a range of factors beyond just the initial investment in hardware and software.

The promise of artificial intelligence includes a wide range of expectations, both for technological capabilities and its impact on how we do business. However, government agencies should consider that the cost of AI can be multifaceted and extend beyond the immediate dollar value. AI will both increase costs and cut costs, so it’s important to consider an investment in AI from a holistic perspective.

Here are some key factors to consider:

  1. Hardware costs: Graphics processing units (GPUs) are fundamental to the advancement of artificial intelligence, serving as the backbone of AI innovation. The availability of these critical components is currently hampered by supply shortages, contributing to a significant increase in costs.
  2. Energy costs: Training complex AI models requires significant computational power, which in turn consumes a considerable amount of energy. The energy costs associated with running the underlying infrastructure to power this can be substantial.
  3. Multi-agent costs: Multi-agent generative AI frameworks are pivotal for advancing generative AI. They utilize underlying large language models (LLMs) more extensively, which results in increased computational demand and costs compared to using a single LLM such as GPT 4.
  4. Data acquisition and management: If you are fine tuning a generative foundation model, or creating your own models, high-quality data is crucial for training AI models and helping address things like “drift.” Acquiring and curating large datasets can be expensive, as can the ongoing costs associated with data storage, processing and management. The old adage, “junk-in-junk-out,” is a key consideration here.
  5. Personnel costs: Skilled personnel such as data scientists, machine learning engineers and AI researchers are essential for developing, integrating and maintaining AI systems right now. These professionals often command high salaries, which can be a significant ongoing expense. Experience is critical because training or fine tuning models can be extremely expensive, and mistakes requiring a re-do can add up quickly.
  6. Ethical and regulatory costs: Compliance with ethical guidelines and regulatory requirements can add additional costs to AI projects. This may include ensuring data privacy, addressing bias and fairness concerns, and complying with industry-specific regulations. The rules around this are still being laid out — only recently did the U.S. government provide guidance on AI safeguarding, and implementing these safeguards is going to cost a good deal of money, just like zero-trust and other initiatives.

The true cost of AI encompasses a range of factors beyond just the initial investment in hardware and software. GPU shortages and intense computation requirements can further inflate these costs, emphasizing the importance of careful planning and resource management in AI projects. However, if done correctly, the benefits that AI will bring will far outweigh the costs in the long run.

The current landscape of AI mirrors the early, tumultuous days of the Wild West — a period of exploration and untapped potential. As the U.S. government navigates this new frontier, the costs associated with AI projects are expected to be historically high, reminiscent of the early days of the internet or the space race. During these periods, initial investments were substantial as technologies were in their infancy, standards were non-existent, and the path forward was unclear. As policies, standards and best practices for AI are developed and refined, costs will likely normalize, but this initial phase of high expenditure is an essential step toward harnessing the transformative potential of AI for public good.

John Mark Suhy is chief technology officer of Greystones Group.

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