While AI global venture capital investments exceeded $290 billion between 2019 and 2024, less than 1 percent of this funding went to initiatives aimed at social impact. This stark disparity reveals a profound misalignment between AI’s transformative impact potential and its current applications, exemplifying AI’s investment divide.

This disparity is also reflected in the initiatives available to social innovators looking to deploy AI for impact, which are limited not only in number but in type. A 2024 report by the Schwab Foundation, in collaboration with Microsoft and EY, found that only 7 percent of publicly available and self-reported impact-focused AI initiatives were centered around AI education or skills development. As a result, adoption rates among social innovators and impact enterprises generally lag behind the 78 percent of global companies either using or tangibly exploring the use of AI.

The evidence is clear—AI delivers value for the social sector. We have found, through our work and interactions within the Schwab Foundation and MIT Solve networks, that social innovators are not only deploying AI for social and environmental impact but more importantly, are finding innovative ways to mitigate and offset the technology’s associated risks. However, wider concerns about the AI skills gap, data bias, costs of entry and infrastructure access remain, leaving the question: How can social innovators ethically access and adopt AI capabilities at scale and at a rate that ensures these changemakers do not fall behind?

Social innovators, especially those located in lower- and middle-income regions, need a clear roadmap to increase their awareness and guide their deployment of the technology. As the world faces mounting global challenges, from educational inequity to health care access, redirecting AI investment toward social innovation and other impact applications isn’t just a box-ticking exercise; it’s a global strategic imperative.

Current AI Investment Trends Widen Inequality

While it has primarily been touted as a new driver of productivity and financial growth, AI is so much more than a profit-making tool. The current concentration of AI funding in profit-driven applications creates a troubling pattern of widening inequalities. This manifests in three critical ways:

  1. Resource Allocation: The focus on commercial applications diverts technical talent and research capacity away from social impact initiatives.
  2. Geographic Disparity: Even when it is directed for social impact, funding goes predominantly to those social initiatives originating from high-income countries, creating an innovation gap in low- and middle-income countries where solutions are most needed.

Read the full article about the AI investment divide by Hala Hanna and Daniel Nowack at Stanford Social Innovation Review.