AI Futures and Cycles of Discord
(varied<->all the same) crossed with (great leveler<->great inequality)
This post is an exercise in having a dialogue with an LLM about potential futures. The author is a mixture of myself and ChatGPT. I don’t expect you to read it all.
Lets think about 2 driving forces over the next ten years that might arise if new AI models result in “Intelligence that’s too cheap to meter”
AI and Socioeconomic Status:
As a Leveler: AI democratizes access to information, opportunity, and resources, reducing socioeconomic disparities.
As a Magnifier of Inequality: AI exacerbates socioeconomic gaps due to unequal access to technology and the concentration of wealth and power in the hands of those who control AI.
Foundational AI Models:
Homogenous: A few large AI models dominate, or many models converge on similar architectures and data, leading to uniformity in AI capabilities and behavior.
Heterogenous: A variety of AI models exist, each tailored to specific tasks, cultures, languages, or ethical frameworks, promoting diversity in AI applications and perspectives.
Quadrant 1: Leveling Homogeneity
AI is used as a tool for social equality, with widespread access to a few powerful, generalized AI systems. These systems are designed to be universally beneficial, providing equalizing educational and economic opportunities to a broad demographic. This scenario could lead to a more equitable but homogenized global society.
Quadrant 2: Leveling Heterogeneity
AI serves as an equalizer, but through a rich ecosystem of diverse AI models. This diversity allows for tailored solutions to individual and community needs, respecting cultural and societal differences. It could foster a world where socioeconomic disparities are minimized, and individual identities and values are preserved and celebrated.
Quadrant 3: Magnifying Homogeneity
AI magnifies inequality, with the power of AI concentrated in the hands of a few. A homogenous AI landscape means that those with the resources to develop or access these powerful models can accrue significant benefits, leaving others behind. This could lead to a world with stark divides between the AI-haves and have-nots, both within and between nations.
Quadrant 4: Magnifying Heterogeneity
AI increases socioeconomic disparities, but with a twist: there is a multitude of AI models. This scenario suggests a technological landscape where diverse and specialized AI models are available, but access to them is stratified by socioeconomic status. The rich might have personalized, cutting-edge AI while the less affluent have access only to basic or outdated systems. This could lead to a complex tapestry of outcomes where cultural and individual diversity is high, but so is inequality.
Lets explore how things interact in those quadrants as if channeling Peter Turchin's expertise in Cliodynamics: the lens of mathematical modeling and statistical analysis of historical societies. In this scenario, where AI magnifies variance and inequality and AI models are varied and heterogeneous, we'd anticipate the following complex dynamics:
approaching Quadrant 1 with Peter Turchin's cliodynamic perspective, where AI acts as a leveler of socioeconomic status and AI models are homogeneous, would yield a different set of societal dynamics:
1. Reduction of Elite Overproduction through AI Accessibility:
Unlike the previous scenario, here the widespread accessibility of homogeneous AI models could potentially reduce elite overproduction. AI might enable a more equitable distribution of knowledge and resources, allowing for a larger proportion of the population to live in what could be considered 'elite' conditions, thus reducing the pressures of competition for elite status.
2. Stabilization of Polity Stress through AI-Induced Equity:
Homogeneous AI could potentially act as a stabilizer within polities by ensuring a fair distribution of information and resources. This could alleviate some of the stress points identified in cliodynamics, such as inequality and resource scarcity, leading to more stable societies with less pronounced cycles of boom and bust.
3. AI as an Egalitarian Force in Society:
With AI acting as a leveler, societies could become less complex in terms of social stratification, leading to a more egalitarian structure. This could potentially counteract the tendency towards increased complexity and inequality, fostering societal cohesion and cooperative problem-solving.
4. Diminished Factionalization and Inter-Elite Competition:
Homogeneous AI could reduce the fragmentation of elite groups by providing a common platform for education, communication, and problem-solving. This could lead to a more cooperative elite class, focused on addressing societal challenges rather than competing for power and resources.
Interaction Scenarios in a Cliodynamic Framework:
Unified AI Governance: A single or a set of dominant AI models could lead to a standardized approach to governance, similar to the universal application of law in the Roman Empire, contributing to social cohesion and stability.
AI-Mediated Structural-Demographic Balance: AI could mediate the balance between population and resources, perhaps in a manner akin to the 'invisible hand' in economics, leading to sustainable growth and avoiding the pitfalls of overpopulation and resource depletion.
Smoothing of Secular Cycles: The leveling effect of AI might serve to smooth out the secular cycles of rise and decline, leading to more gradual and manageable societal changes without the sharp disruptions typically associated with these cycles.
AI as a Synchronizing Force: Homogeneous AI systems could facilitate societal synchronization by standardizing education, communication, and other societal functions, much like the introduction of standard time zones synchronized timekeeping.
Quadrant 2 describes a scenario where AI acts as a leveler of socioeconomic status while the foundational AI models remain homogenous, meaning that while there is widespread access to AI technologies, these technologies are largely similar across different domains and regions. Here’s how this could play out:
1. Democratization of AI Benefits:
Homogenous AI models that level socioeconomic playing fields could lead to widespread democratization of education, healthcare, and economic opportunities. AI could provide personalized learning platforms, equitable healthcare diagnostics, and fair job-matching services, thus reducing inequality.
2. Standardization of AI Ethics and Governance:
A homogenized AI landscape could facilitate the creation of a universal set of ethical guidelines and governance structures for AI. This would promote transparency and trust in AI systems, potentially leading to a more stable and cooperative global environment.
3. Global AI Literacy and Employment Shift:
With AI becoming a ubiquitous and leveling force, there would likely be a significant shift in employment patterns. A global push for AI literacy could emerge, with individuals re-skilling to work alongside AI in various capacities, fostering a new economy that is more inclusive and less prone to automation-driven unemployment.
4. Stagnation of AI Innovation:
Conversely, the homogeneity of AI systems could lead to a stagnation in innovation. With similar systems being used everywhere, there might be less incentive to create radically different AI models, potentially slowing down the pace of technological progress.
Interaction Scenarios in a Cliodynamic Framework:
Stabilization of Societal Cycles: The leveling effect of AI could lead to more stable societies, with fewer dramatic booms and busts in economic cycles. AI could act as a dampener of societal pressures by providing a basic level of service and opportunity for all.
Reduction in Socioeconomic-Driven Conflict: With less variance in socioeconomic status, one could expect a reduction in conflicts driven by inequality. Societies might see lower levels of crime and civil unrest, leading to a more harmonious global community.
Homogenization of Culture and Values: The widespread use of similar AI models could lead to a certain degree of cultural homogenization, as people around the world are exposed to similar systems, potentially leading to a convergence of values and norms.
AI as a Tool for Social Cohesion: In this scenario, AI could be used to actively promote social cohesion by identifying and bridging gaps between different social groups, fostering understanding and cooperation on a scale not previously possible.
Cliodynamics would interpret this quadrant as one with potential for significant social transformation. The homogeneity of AI models combined with their ability to level the socioeconomic landscape could result in societies that are more egalitarian and stable. Such societies might move toward a new equilibrium where the focus shifts from competition to collaboration, both internally and on a global scale. Tracking the evolution of societal structures and cultural norms in this quadrant would be essential for understanding the full impact of these AI-driven changes on historical dynamics.
Quadrant 3 envisions a future where AI magnifies variance and inequality while AI models are varied and heterogeneous. In this scenario, the diversity of AI models does not equate to equitable socioeconomic leveling but rather exacerbates existing disparities. Here’s how this quadrant might unfold:
1. AI-Driven Stratification:
The heterogeneity of AI models could result in a tiered society where access to certain AI technologies becomes a status symbol and a means of further stratification. Elite individuals or groups might have proprietary AI systems that confer significant advantages, widening the gap between the 'haves' and 'have-nots.'
2. Divergence of AI Ethics and Goals:
With a lack of standardization, AI models could be developed with radically different ethical frameworks or end goals, leading to societal fragmentation as groups align with the AI ideology that best suits their interests. This could foster division and conflict, both within and between societies.
3. Competitive AI Ecosystems:
In a world of heterogeneous AI, competition for the most effective or powerful AI could lead to an arms race of sorts, not necessarily military, but in all domains of human endeavor. This competition could drive rapid innovation but also lead to destabilization and unpredictable shifts in power dynamics.
4. Niche AI Specializations and Monopolies:
Specialized AI models could lead to the creation of monopolies in certain sectors, where a single AI or a group of AIs completely dominates a market or field. This could lead to unprecedented efficiency but also create vulnerabilities and dependence on specific AI systems.
Interaction Scenarios in a Cliodynamic Framework:
AI as a Catalyst for Inter-Societal Conflict: The varying capabilities and ideologies of different AI models could lead to international tensions and conflicts, as nations or groups seek to protect or assert their technological advantages.
AI-Induced Displacement and Social Unrest: The rapid advancement and specialization of AI could lead to significant displacement of workers, exacerbating social tensions and potentially leading to unrest or upheaval, especially if the societal safety nets are inadequate.
Polarization of Societal Values: With AI models reflecting a wide array of values and objectives, societies could become increasingly polarized, with little common ground or shared objectives, undermining social cohesion.
AI as a Reinforcer of In-Group/Out-Group Dynamics: Heterogeneous AI could strengthen in-group/out-group dynamics, where access to certain AI technologies defines membership in elite circles, while others are excluded, reinforcing and potentially hardening social hierarchies.
In the context of cliodynamics, this quadrant would be seen as potentially tumultuous, with the heterogeneous nature of AI models driving cycles of internal and external conflict, stratification, and perhaps even collapse. Tracking the trajectories of societies with different AI models would be crucial for understanding the long-term implications and for developing policies to mitigate the negative effects while harnessing the positive aspects of AI-driven innovation and diversity.
Where AI magnifies variance and inequality, and AI models are varied and heterogeneous. Let's explore some extreme and surprising developments in this scenario and how they might interact:
1. Elite Overproduction and AI Exacerbation:
In Turchin's work, "elite overproduction" refers to an excess of individuals with elite aspirations that exceeds the number of elite positions available. In this quadrant, AI could amplify this phenomenon by allowing an even greater segment of the population to gain elite-level education and skills, but without a corresponding increase in elite positions, leading to societal instability and potential conflict.
2. Polity Stress and AI's Role in Resource Distribution:
Turchin's historical analysis often focuses on the stress within polities (sociopolitical entities) due to resource pressure. Heterogeneous AI systems could unevenly distribute resources, with some polities gaining significant advantage by deploying superior AI, thus increasing global tensions and the likelihood of conflict as resource disparities grow.
3. AI as a Driver of Social Complexity and Inequality:
Cliodynamics treats societies as complex systems that can move towards higher complexity. In this quadrant, AI could drive societies towards extreme complexity, but with increased complexity comes increased inequality, as only a small subset of the population understands and controls these systems. This could lead to a breakdown in societal cohesion as the 'complexity gap' widens.
4. Factionalization and Inter-Elite Competition:
A core concept in Turchin's analysis is the factionalization and competition among elites. Varied and heterogeneous AI could result in a fragmented elite, where different factions have their own AI systems. This could escalate inter-elite competition and conflict, potentially leading to societal upheaval as seen in historical cycles.
Interaction Scenarios in a Cliodynamic Framework:
Fragmented AI Governance: Different elite factions could each govern their AI systems, leading to a patchwork of governance models and a lack of unified policy. This fragmentation could mirror the warring states period in ancient China, where diverse polities were constantly in conflict.
AI-Induced Structural-Demographic Pressures: AI could induce new forms of structural-demographic pressures, such as labor displacement, which in turn could lead to increased social stratification and potential for societal collapse if not managed properly.
Secular Cycles and AI Acceleration: The concept of secular cycles in cliodynamics—long periods of population growth followed by crisis and decline—could be accelerated by AI, with the cycles of growth and decline happening more rapidly and more unpredictably due to the pace of AI-driven change.
AI as a Catalyst for Synchronization Failure: Societies require a certain level of synchronization to function. Heterogeneous AI models could disrupt this synchronization, leading to a failure in social cooperation that could resemble the fragmentation of the Roman Empire.
To approach these scenarios with the methodological rigor of cliodynamics, one would need to gather extensive data on AI development and deployment, socio-economic indicators, and societal stability metrics. This data could then be used to build mathematical models that could help predict the trajectory of these complex societal trends, with an aim to identify pressure points and potential solutions that could prevent societal breakdown.
Which is Most Likely?
Predicting the most likely quadrant or the transition between quadrants in long-term scenario planning is speculative and contingent upon multiple variables, many of which can be unpredictable. However, we can make educated guesses based on current trends and theoretical understanding:
Most Likely Quadrant:
Quadrant 1 (AI as a magnifier of variance and inequality, and AI models are homogenous): This scenario seems plausible given the current trajectory where a few large companies dominate AI development. These companies have the resources to develop and refine sophisticated models that smaller entities cannot match, potentially increasing inequality. However, the homogeneity of models could be a natural consequence of standardization and the open-source movement, which could lead to widespread adoption of a few, powerful models.
Transition Between Quadrants:
From Quadrant 1 to Quadrant 2: If the homogenous AI models that initially magnify inequality are made more accessible and are utilized to provide equitable services, this could transition into a scenario where AI begins to level socioeconomic differences.
From Quadrant 2 to Quadrant 3: If the leveling effect of AI leads to a push for innovation and diversity in AI applications, this could result in a shift toward varied and heterogeneous AI models tailored to specific regional needs or cultural contexts.
From Quadrant 3 to Quadrant 4: Over time, as varied and heterogeneous AI systems start demonstrating significant differences in outcomes, some models might prove more effective at enhancing socioeconomic status, leading to a new form of inequality based on which AI model communities adopt.
From Quadrant 4 to Quadrant 1: This could occur if the market eventually consolidates around the most effective AI models, leading to a homogenization of AI systems once again, but this time with the added element of these systems enhancing variance and inequality.
Factors Influencing Transitions:
Technological Breakthroughs: Significant advances in AI could disrupt current trends, leading to rapid shifts in how AI affects socioeconomic status.
Regulatory Changes: Government interventions could shape AI development and deployment, influencing the distribution of AI benefits.
Economic Incentives: Market forces and economic incentives could drive the evolution of AI technologies toward or away from homogenization.
Social Movements: Grassroots actions and public opinion could push for more equitable uses of AI, affecting the level of socioeconomic leveling.
Global Collaboration: The degree of international cooperation on AI governance could determine how uniformly AI technologies are developed and applied.
Given the complexity and interdependence of these factors, the dynamic between the quadrants is likely to be non-linear and could involve feedback loops and unexpected shifts. Cliodynamics, the study of the dynamics of historical societies, might suggest that societies and technologies follow patterns that can be influenced by deep structural forces, but the specifics of these patterns are often unpredictable and subject to the influence of unique, historical events.
Lets focus on Quadrant 2: where AI increases inequality and there's heterogenous AI. What might that look like?
The AI-enhanced Executive and the Gig Worker
In a bustling metropolis, an executive glides through his day with the assistance of a personal AI tailor-made to enhance his business acumen, making lightning-fast stock trades and managing real-time supply chain adjustments. Meanwhile, a gig worker on the street below juggles multiple gig apps, each with its own AI designed to maximize company profits rather than worker earnings, constantly undercutting his rates and leading to a precarious financial state.
The Bespoke Education AI and the One-size-fits-all Classroom
In a leafy suburban neighborhood, a child of affluent parents receives a personalized education from an AI tutor programmed to adapt to her learning style, accelerating her learning process. Across town, in an underfunded school district, students sit in overcrowded classrooms, each with a standard-issue educational AI that delivers a rigid curriculum, struggling to address the diverse needs of the classroom.
The AI-driven Precision Medicine and the Overwhelmed Clinic
At a high-tech medical center, a wealthy patient receives a bespoke treatment plan from a state-of-the-art AI system that analyzes his genetic data to provide personalized therapy with pinpoint accuracy. In contrast, at a community clinic, patients wait in long lines for a basic diagnosis from a generalized medical AI that can offer only broad-spectrum treatments, missing the nuances of individual patient care.
The Smart City Enclave and the Neglected Neighborhood
In a smart city enclave, every aspect of life is optimized by AI – from traffic flow to energy use, providing residents with a seamless and efficient living experience. On the flip side, in a nearby neglected neighborhood, residents suffer from outdated infrastructure with little to no AI integration, leading to frequent power outages, water shortages, and limited access to technology that could improve living conditions.
More Story Time: Quadrant 1
In the first quadrant, where AI enhances inequality despite the homogeneity of foundational AI models, the vignettes would reflect a paradox where the same level of technological sophistication leads to divergent outcomes:
The Standardized AI Curriculum
A universal AI education platform provides the same curriculum to all students, but outcomes diverge sharply. Affluent students supplement this with private tutors and enhanced AI modules, deepening the educational divide. A bright student from a lower-income background struggles to compete, despite having access to the same base AI educational resources.
AI Personal Finance Advisors
Everyone uses a similar AI for financial advice, yet the wealthy benefit more due to their ability to invest more capital and take advantage of complex financial instruments suggested by the AI. Those with minimal savings receive basic advice that does little to improve their financial status, widening the wealth gap.
Uniform AI Job Application Processors
Companies across the board adopt a standardized AI system for screening job applications. However, candidates from prestigious schools and those able to afford resume optimization services are disproportionately selected for interviews, leaving equally or more capable candidates from less privileged backgrounds overlooked.
AI Health Monitors for All
A single type of health monitoring AI is distributed to all citizens. The affluent, with their better living conditions and access to premium healthcare, are flagged for preventive care and wellness programs. Those in lower socioeconomic strata, despite having the same AI, suffer from environmental and lifestyle factors that lead to poorer health outcomes and less effective interventions by the AI.
These vignettes illustrate a world where the uniformity of AI technology does not translate to uniformity in opportunity or outcome. Instead, existing socioeconomic structures and resources dictate how effectively individuals and groups can leverage AI, thus magnifying inequality.
Stories of Transition
I personally want a world of leveling and heterogeneity. What are some stories that get us from what I think is the most likely (magnifying inequality, varied models) to a world of (varied models, leveler)
Transitioning from a future where AI magnifies variance and inequality through heterogeneous models (Quadrant 4) to one where AI acts as a leveler despite varied models (Quadrant 2) could result in the following vignettes:
The AI Regulation Reformation
Faced with growing unrest due to AI-induced inequality, governments worldwide implement stringent regulations. These laws mandate that AI developers focus on creating diverse models tailored to uplift underprivileged communities. A once-exclusive AI for elite financial analysis is repurposed to educate and empower small investors, slowly closing the wealth gap.
Open-Source AI Movement
A global coalition of programmers and ethical AI organizations collaborate to create open-source, customizable AI models. This movement undermines the monopoly of tech giants by offering competitive, free AIs to the public. A local entrepreneur in a developing country uses these tools to create a thriving business, providing jobs and stimulating the local economy.
AI Literacy Campaigns
As AI becomes increasingly intricate, new educational initiatives emerge to improve public understanding of how to interact with and benefit from different AI systems. A young woman from a marginalized community learns to navigate these systems, eventually developing a niche AI that addresses unique issues in her community, promoting social and economic uplift.
The AI Equity Protocol
An international treaty results in the creation of the AI Equity Protocol, which sets guidelines for the ethical distribution of AI's benefits. Companies and governments collaborate to ensure that even the most advanced and specialized AIs are adapted to serve not just the elite but everyone. This includes a farmer using a version of an advanced agricultural AI, initially developed for corporate farms, to efficiently manage his small plots with the same effectiveness.
In these vignettes, the path from exacerbating inequality through a plethora of specialized AIs to mitigating disparities with the same variety of models involves systemic changes, societal shifts, and global cooperation. These changes focus on the redistribution of AI benefits, ensuring that varied and advanced AI technologies become tools for widespread empowerment rather than instruments of division.
this post was an exercise in having a dialogue with an LLM about potential futures. The author is a mixture of myself and ChatGPT