On Your Marks, Set… Go: AI and race to redefine our world

Historically, the economic, political, and military power of a region was often tied to its ability to cultivate or attract polymaths—individuals whose expertise spanned multiple disciplines. Consider the United States’ ability to gather brilliant minds like Albert Einstein, Enrico Fermi, Hans Bethe, Linus Pauling, and Salvador Luria, many of whom were immigrants or the children of immigrants. These individuals collectively shaped scientific and technological breakthroughs that solidified the nation’s dominance. It is worth pondering: if the 72 Nobel Prize winners who immigrated to the United States or were born to immigrant parents had remained in their countries of origin, the United States might not have achieved its unparalleled global stature.

This historical context underscores a transformative point: artificial general intelligence (AGI) has the potential to become the new polymath. Regions that fail to prepare for this paradigm shift risk falling behind. AGI offers a kind of intellectual democratization, replicating the multidisciplinary capabilities of polymaths at scale. This shift could disrupt long-standing power dynamics, even within nations. In the United States, for example, the intellectual firepower historically concentrated in coastal hubs like Silicon Valley or Boston might no longer hold such a monopoly. AGI could empower traditionally underserved regions, including Middle America, to compete on an equal intellectual footing. This rebalancing could redefine not only national power structures but also global economic and geopolitical dynamics.

We’ve been reflecting on what it might mean to innovate with an Artificial General Intelligence (AGI) as a collaborator, and the implications are profound. We’re already seeing glimpses of this future with an Oklahoma-based company like Bio eutectics leveraging AI to identify naturally occurring compounds capable of mimicking hydrocarbon-derived solvents. Such discoveries would traditionally require scientists with expertise spanning multiple disciplines to experiment over months to find a match. Today, this innovation is enabled by training localized Retrieval-Augmented Generation (RAG) systems. However, as AGI evolves into an all-knowing entity, it may eventually bypass the need for RAGs entirely, autonomously identifying solutions across a vast range of domains. So how can we start pre-empting the future where AGI is a reality and start planning for it? How can we begin anticipating a future where AGI becomes a reality and start preparing for its arrival?

How can our education systems evolve to prepare students for a future shaped by AGI?

The current education system is deeply deterministic, designed to train minds toward pre-determined principles rather than fostering a discovery-first model. This deterministic framework mirrors the deterministic causal relationships described by Loka Li et al. in their research on causal discovery. They argue that in real-life situations involving deterministic relationships, variables are represented as deterministic functions of their parental variables, often without noise. In such systems, constraint-based methods fail because they violate the faithfulness assumption, which requires variability to uncover causal relationships. Similarly, the education system’s deterministic nature constrains intellectual exploration by focusing primarily on transmitting established knowledge rather than enabling learners to question, experiment, and discover new principles.

The deterministic approach to education assumes a linear progression from established facts to fixed applications. Students are taught within rigid frameworks of specialization, guided toward predefined answers and solutions. This model, while efficient for creating domain-specific experts, stifles the adaptability and curiosity necessary for navigating a world increasingly shaped by AGI. AGI, with its capacity to synthesize and apply knowledge across domains, demands a paradigm shift in education. Specialization that burrows deeply into a single vertical will likely become obsolete when AGI can master any vertical instantaneously. Instead, what will matter is the ability to think across boundaries, combining insights from diverse disciplines to tackle complex, multifaceted problems.

To prepare for this future, education must pivot to a discovery-first model. In this approach, students are not just passive recipients of pre-determined knowledge but active participants in shaping new understanding. They learn by questioning, exploring, and integrating insights, much like AGI itself will operate. This shift requires an educational system that prioritizes curiosity, interdisciplinary thinking, and adaptability over rote memorization and rigid specialization. It challenges the deterministic foundation of current education by embracing the uncertainty and complexity inherent in real-world problem-solving, aligning more closely with the needs of a future where AGI serves as a universal polymath. Such a transformation is urgent, as AGI democratizes access to intellectual excellence. These institutions must focus not on replicating traditional academic hierarchies but on equipping students to collaborate with AGI in innovative and transformative ways. This will require integrating principles of causality, adaptability, and dynamic discovery into the core curriculum, moving away from the constraints of determinism to embrace a future of boundless intellectual potential.

How can our governments adapt to govern effectively in a future shaped by AGI?

Governments operate within a persistent paradox: they are tasked with deploying resources to improve the lives of their constituents, yet these deployments often rely on unvalidated hypotheses about what initiatives will yield the greatest outcomes. This inherent uncertainty stems from a lack of comprehensive data about the causal factors underlying the challenges they aim to address. As a result, governments frequently allocate capital to projects that fail to deliver the intended results, wasting both resources and public trust in the process.

The advent of Artificial General Intelligence (AGI) offers a transformative solution to this problem. An AGI-powered government or mayor could gather data from an extensive network of sensors, public records, private companies, and other data acquisition points, creating a holistic and detailed view of their region. By modelling the potential impacts of any initiative down to the finest parameter, AGI could predict outcomes with unparalleled accuracy. This ability to simulate the ripple effects of policies—akin to creating the butterfly effect—would enable governments to deploy resources with precision, maximizing efficiency and impact.

To prepare for this future, governments should immediately begin consolidating their data into centralized warehouses, specifically designed for training their own Retrieval-Augmented Generation (RAG) models. By curating and organizing this wealth of information, governments can position themselves to harness AGI’s predictive power effectively. The shift toward data-driven governance would mean that resource allocation becomes far more deterministic, guided by algorithms capable of forecasting long-term impacts rather than short-term political pressures.

In this new paradigm, governments would gain the agency to invest in initiatives that may currently seem unpopular or counterintuitive but promise significant long-term benefits. The hesitation often rooted in political squishiness would be replaced by the confidence of acting on algorithmically informed predictions. This deterministic approach could redefine governance, ensuring that resources are not only allocated with greater precision but also with a clear, data-backed rationale for achieving sustainable outcomes. The era of speculative policymaking would give way to one of strategic certainty, paving the way for more effective and accountable government actions.

How can the field of medicine adapt to harness the transformative potential of AGI?

Modern medical science is solidly grounded in the principles of the scientific method, relying on observable, measurable, and reproducible evidence. In contrast, AI operates largely as a “black box,” producing outcomes without always offering clear insights into how those results are derived. We often accept AI’s outputs because they are accurate and reproducible in many cases, even though the underlying mechanisms remain opaque. However, this trust comes with a caveat—AI can hallucinate, generating false or irrelevant results. Despite this limitation, we tolerate these errors because the benefits often outweigh the drawbacks.

With the advent of AGI (Artificial General Intelligence), this dynamic could dramatically shift. AGI’s ability to process vast amounts of data, identify patterns, and synthesize information from disparate sources may allow us to peer into the black box of alternative medicine—a field often sidelined by modern medicine due to its lack of clear, reproducible evidence. Historically, alternative medicine practices that could be understood and scientifically validated, such as acupuncture for pain management or the use of willow bark (a precursor to aspirin), have been absorbed into mainstream medicine. However, many practices remain dismissed because their mechanisms are neither well-understood nor easily replicated. AGI could change that by uncovering patterns and connections that human researchers might overlook, offering a deeper understanding of these sidelined practices.

Furthermore, AGI’s potential extends beyond alternative medicine to a more comprehensive understanding of the root causes of illnesses. Just as devices like Siri and Alexa continually gather data from our interactions, future generations will likely have their life data captured by AI systems. When these individuals fall ill, an all-knowing AGI could analyze decades of accumulated data—environmental exposures, dietary habits, genetic predispositions, and even seemingly minor details like the long-term use of a particular skincare product. This capability could enable AGI to make connections between these factors and specific health conditions, guiding doctors toward more precise diagnoses and tailored interventions.

For instance, if someone develops a rare illness linked to prolonged exposure to a specific toxin, AGI could pinpoint that exposure, even if it occurred decades earlier, and provide actionable insights. Such a tool would fundamentally enhance the diagnostic process, making interventions not only more effective but also more preventive. This shift would require the medical sciences to rethink their approaches and infrastructure, integrating AGI-driven insights into traditional methodologies while maintaining rigorous oversight and ethical guidelines.

The potential for AGI to revolutionize medicine is profound. It challenges the field to prepare for a future where insights into health are more precise, interventions more personalized, and the boundaries between traditional and alternative medicine blur under the scrutiny of advanced intelligence. As we move closer to this reality, the medical community must embrace these possibilities, investing in the systems and protocols needed to fully leverage AGI’s capacity to save and improve lives.

The Future with AGI

The advent of Artificial General Intelligence (AGI) marks a transformative moment in human history, promising to redefine how we live, work, and innovate. Unlike narrow AI, AGI will possess the ability to think, learn, and adapt across multiple domains, making it a universal collaborator. This unprecedented capability raises profound questions about the future of education, governance, medicine, and economic development.

Education systems will need to transition from deterministic, specialization-driven models to discovery-first approaches that emphasize interdisciplinary learning and adaptability. AGI’s ability to synthesize vast amounts of knowledge means students must be prepared to navigate and integrate insights across diverse fields. Similarly, governments will need to leverage AGI’s predictive and analytical powers to allocate resources with precision, ensuring efficient and impactful policymaking. By embracing AGI, decision-making processes could become more deterministic and guided by data-driven foresight, rather than political inertia.

In medicine, AGI offers the potential to uncover hidden causal relationships in health data, advancing diagnostics, treatment, and preventative care. It could provide insights into alternative medicine practices and pinpoint environmental or behavioral factors that contribute to diseases, reshaping how healthcare systems operate.

Economic opportunities with AGI are vast, as it has the potential to democratize access to knowledge, reduce barriers to innovation, and reconfigure job creation by fostering entirely new industries. However, these benefits must be paired with proactive policies to address risks such as bias, ethical considerations, and the concentration of power.

The future with AGI is not merely a technological revolution but a societal one. Its success will depend on our ability to adapt, collaborate, and align its development with human values and priorities. Preparing for this reality requires foresight, innovation, and a commitment to ensuring AGI serves as a force for inclusive and sustainable progress.