This session on the “wise use” of of Artificial Intelligence (AI) in Systemic Investing (SI) stemmed from a research collaboration between TransCap and Mutua, which is dedicated to exploring how AI can be responsibly integrated to enhance SI practices. You can learn more about the motivations and research framework here.
We have now published the first output of the collaboration, which presents a framing that will guide and structure our research. We examined three different SI frameworks, using them to identify the ‘systemic investing journey’ — a comprehensive overview of the various processes and tasks that investors and orchestrators might undertake as part of an SI initiative. We then assess what capabilities are required in each step of this journey. We will later examine which capabilities can safely and responsibly be augmented with AI.
Back to the session at the summit: We kicked off our session by delving into the myriad narratives that shape our understanding of AI — spanning from Silicon Valley’s techno-optimism to the more critical perspectives challenging its role and impact.
Beyond the hype and pessimism, we argue that the wide-spread deployment and adoption of LLMs in society is in fact reshaping our media ecologies, significantly influencing our cultures. As with earlier transformative waves — radio, television, the internet, and social media — these technologies will redefine how we understand ourselves, relate to one another, interpret the world, and influence our capacity to deal with the challenges of our time. Ultimately, these new general purpose technologies exponentiate capacities in society, influencing numerous dimensions of our lives.
We then collectively reflected on what these shifts actually mean to society. Does the adoption of these new tools bring about deep, structural changes to our economies and societies writ large?
All the hype and narrative around AI/ AGI as a revolution stems from a specific worldview — primarily championed by Silicon Valley (check out TESCREAL). This dominant framing obscures how AI technologies actually reinforce existing economic structures, social hierarchies, and power dynamics. Rather than fundamentally transforming society, AI is accelerating the very societal processes driving contemporary social, democratic, and environmental challenges. At its core lies an incentive structure to deploy new models as fast as possible in a relentless race toward market dominance (what Tristan Harris calls the “Race to Rollout”).
This exploration served as a foundation for a deeper reflection on the actual, observable impacts AI is already having across society, the environment, social, geopolitics, and psychology:
After exploring some of the uncomfortable truths AI-enthusiasts tend to ignore, such as labor exploitation and the ethical implications of AI decision-making, our presentation pointed to a few emerging fields of application of AI that excites us. There is a significant wave of research centers, collaborations, and innovative initiatives exploring how AI can be leveraged to expand our capacity to solve pressing societal issues. One example is the use of AI to augment collective intelligence processes, check out initiatives like AI for Collective Intelligence and Nesta’s Collective intelligence design. Other emerging initiatives that think about relationality and decolonial perspectives in AI, such as the Burnout from Humans project from the Gesturing Towards Decolonial Futures, were also highlighted as ways forward in the wise use of AI.
We then returned to the context of systemic investing, exploring the exciting potential of AI to enhance specific stages within the SI process. Here we explored a variety of different tools, including Mutua.systems’ latest developments, particularly highlighting the AI-enabled stakeholder and systems mapping tools they have already developed, specifically designed to support and improve decision-making processes for systemic investors.
We concluded the session with a collaborative workshop structured around four key questions:
- Are there additional or novel ways to leverage AI for Systemic Investing (SI) that were not mentioned or do not currently exist?
- Where do participants see the greatest potential for using AI in SI, and what specific benefits could this offer?
- What are the primary risks and limitations associated with integrating AI into SI?
- Given the identified benefits and risks, what key principles should guide the responsible use of AI in SI
We extend our heartfelt thanks to all participants for their insightful contributions, which enriched our dialogue immensely. Below is a summary of the discussion points: