Aniket Baksy (PhD ’23)
Avi Gupta (MS ’23)
The concentration of AI adoption in large firms contributes to their market dominance andpotentially increases economic inequality.
Expanding AI adoption by reducing adoption costs can unleash AI’s productivity-enhancing effects for small businesses, creating medium-skill jobs, enabling entrepreneurship, and promoting diversityin the AI ecosystem.
The fact that AI adoption is concentrated in large firms contributes to high adoption costs. High adoption costs are largely responsible for the slow adoption of AI among small businesses. We focus ontwo main drivers of these costs:
• AI technologies are expensive to customize to specific business needs and require large intangible investments.
• AI technologies require data, which is expensive to collect, securely store, and analyze.
We propose making AI deployment easier for small businesses by creating a novel clearinghouselike data licensing and computational resource infrastructure. This would democratize the use of AI by enabling easy access to computational resources and government-held datasets.