Understanding the costs of deploying advanced AI systems is crucial for ensuring equitable access and preventing control from being concentrated among a few powerful entities. While training costs for large AI models are well-documented, the expenses associated with deploying these systems at scale—especially for high-impact applications—remain unclear. This gap makes it difficult to anticipate who can realistically leverage AI, raising concerns about accessibility and fairness.

Researching Deployment Costs and Accessibility

One way to address this gap could involve a structured research initiative to quantify AI deployment costs, compare them to development expenses, and analyze how these costs might evolve. The research could focus on:

• Cost modeling: Estimating deployment expenses for current and future AI systems, using trends like Hoffmann scaling laws to predict cost trajectories.
• High-impact scenarios: Identifying applications where cost barriers might limit access, such as AI-assisted scientific research or public infrastructure management.
• Accessibility mapping: Assessing which organizations—governments, corporations, nonprofits—could realistically deploy AI at transformative scales.

This research could combine technical cost models, case studies of past technology rollouts, and expert interviews to provide a clearer picture of AI deployment economics.

Stakeholder Impact and Execution Strategy

The findings could benefit policymakers, developers, civil society groups, and investors by clarifying AI accessibility challenges. For execution, the research could proceed in phases:

1. Defining "transformative impact" through expert consensus and literature reviews.
2. Modeling deployment costs for near-term AI applications using available data.
3. Extending the analysis to future AI systems based on hardware and algorithmic trends.

An initial deliverable might be a preprint comparing deployment versus training costs for a few high-impact use cases, providing a foundation for further research.

Differentiation from Existing Work

Unlike studies that focus narrowly on training costs (e.g., OpenAI’s scaling laws) or broad AI trends (e.g., Stanford’s AI Index), this research could offer a specialized look at deployment economics. It could also go beyond commercial case studies (e.g., McKinsey reports) by examining societal-scale impacts and accessibility for non-corporate actors.

By shedding light on deployment costs, this research could help shape strategies to democratize AI’s benefits while mitigating risks of centralization.