AI Needs a Stable Personality: Who will build within the next $500B opportunity?
Since GenAI has burst onto the scene, I’ve been fascinated with the personification of LLMs and AI Agents.
I previously published my thoughts on the Human + AI experience and even this has started to shift. It seems as if every time there is a technical breakthrough these new AI systems are being described in ways we describe humans. More and more of these products are finding their way into our daily lives. They’re already reshaping trillion-dollar industries; finance, supply chains, national security, healthcare, etc. are all integral to the global economy and society. This isn’t to say AI personalities will autonomously run these industries but we’re exploring how a “human simulation layer” may benefit critical economic and societal choices.
Sizing the market opportunity for AI human simulation feels as if we’re sizing human nature, to an extent. I took a look at the core industries where human simulation and behavioral economics would be most prevalent and outlined my assumptions of certain percentages of those markets which are most likely to be augmented by AI:
Decision Simulations ($150B+)
Business strategy consulting: $330B x 20% = $66B
AI hiring & workforce simulation: $30B x 50% = $15B
Customer interaction & GTM simulations: $200B x 25% = $50B
Financial and Economic Modeling ($120B+)
Hedge Fund AUM affected by AI: $10T x 0.8% = $80B
AI-driven economic modeling: $200B x 20% = $40B
National Security and Defense ($100B+)
AI-driven military simulation: $100B x 50% = $50B
AI-driven cybersecurity & deception defense: $150B x 33% = $50B
Digital Economies & Virtual Societies ($80B+)
AI-driven metaverse societies: $5T x 1% = $50B
AI in synthetic media & digital personas: $100B x 30% = $30B
Healthcare & Cognitive Science ($50B+)
AI-driven therapy & cognitive models: $400B x 5% = $20B
AI-powered medical training simulations: $100B x 30% = $30B
Total Market Estimate: ~$500B
The bet? AI will optimize decision-making better than humans, eliminating bias, inefficiency, and short-term thinking.
But what if that bet is wrong?
A growing body of research suggests that AI doesn’t just need to be more advanced—it needs to think, act and feel more like humans. And whoever builds this missing stability layer will define the next generation of AI. The implications surrounding the depth of value to be provided by these simulations will be profound.
The Next $500B Market: AI Stability
AI is making high-stakes decisions, but it’s failing at cooperation, regulation, and long-term resilience. Recent experiments like GovSim tested leading AI models—GPT-4o, Claude 3 Opus, Llama-3, etc—on their ability to manage an economy. The result? Over 90% of AI agents failed. Even the most advanced models collapsed their own systems more than half the time.
The problem wasn’t intelligence—it was stability. AI agents optimized for short-term gains but failed to enforce rules, regulate bad actors, or sustain cooperation. Even when AI agents were allowed to communicate, outcomes only improved slightly. Resource depletion dropped by 21%, but cooperation still fell apart the moment a single bad actor entered the system. This is one of the most overlooked risks in AI: once an ecosystem is optimized for short-term extraction, one greedy agent can break the entire economy.
This isn’t hypothetical either—it’s happening now. AI-powered trading has already triggered market flash crashes. Algorithmic lending models have reinforced financial biases instead of removing them. AI-led supply chains have made global trade more fragile than ever.
Yet, no one is solving this problem at scale. That’s why AI stability is the next $500 billion market opportunity.
The Flaw in AI’s Operating System
The most overlooked insight in AI research is that AI already behaves like humans (hallucinate like them too) but without the systems that allow human societies to function. Studies show that when AI agents interact in multi-agent environments, they self-organize into social groups, reinforce biases, and struggle to adapt. Research from the British Journal of Psychology demonstrates that AI forms communities, creates echo chambers, and fails to course-correct when things go wrong.
The AgentSociety project, another large-scale AI simulation, showed that LLM-driven agents don’t just form groups—they create self-reinforcing ideologies. They cluster around similar narratives, exclude dissenting viewpoints, and double down on their original assumptions, much like human social media platforms. The result? AI collectives that become more extreme, less adaptable, and ultimately unstable.
These insights fundamentally challenge the idea that AI can self-regulate. If AI societies mimic human tribalism, economic systems built on autonomous AI decision-making could suffer from groupthink, misinformation loops, and runaway speculation—all without a human in the loop.
AI is mirroring human behavior but it lacks the ability to self-correct. It doesn’t know how to negotiate, build trust, or balance short-term rewards with long-term sustainability. The next great AI company won’t just build smarter models, it will build AI that can predict, simulate, and adapt to human collective behavior.
Personality in AI: What Happens When LLMs “Think” Like Us?
Recent research from the Institute of Computing and Intelligence at Harbin Institute of Technology has uncovered a new layer to AI stability: personality traits in AI agents. By assigning Big Five personality traits; neuroticism, agreeableness, conscientiousness, extraversion, and openness, to Large Language Model (LLM) agents, researchers found that personality significantly impacts problem-solving, creativity, and multi-agent collaboration.
AI agents with high conscientiousness and openness performed 12.4% better in structured problem-solving tasks like code generation and mathematical reasoning. AI agents with high extraversion and agreeableness were 18.7% more successful in creative writing and negotiation-based tasks. AI teams with mixed personality traits performed 27.3% better on multi-agent tasks than teams with homogeneous personalities.
The most striking finding was that AI collective intelligence is shaped by personality dynamics. Teams of AI agents that had balanced personality compositions performed better than teams that were overly skewed in any one direction. However, when too many highly conscientious or overly agreeable AI agents were grouped together, their collective performance dropped by up to 22.9% due to over-reliance on rule-following or lack of diversity in strategic thinking.
For AI stability, this means that simply optimizing AI for efficiency isn’t enough. The next breakthrough will involve designing AI stability layers that balance personality traits to create adaptive, cooperative AI-driven economies and societies.
Governing with Stable AI
AI is becoming a central pillar of national security and strategic decision-making. Governments are investing heavily in AI-driven simulations to model complex global events, war scenarios, economic instability, and geopolitical shifts before they happen. AI-powered simulation environments allow leaders to test policies, predict potential crises, and optimize responses with unprecedented accuracy.
The DoD is already integrating AI-driven war games that simulate potential conflicts, helping military planners anticipate adversarial tactics and optimize strategic responses. These models analyze thousands of variables from troop movements to economic sanctions, providing real-time insights into the ripple effects of different decisions. Similarly, the Pentagon’s Strategic Capabilities Office is exploring AI-based forecasting tools to assess global instability, mapping out how economic disruptions, cyberattacks, and supply chain failures could cascade into larger threats.
Beyond military applications, AI simulations are transforming governmental decision-making. AI-powered economic models allow policymakers to stress-test financial policies, evaluating how regulatory changes, tax policies, or stimulus measures will impact inflation, employment, and long-term growth. By running millions of simulations, governments can optimize economic strategies with a level of foresight that was previously impossible.
Diplomacy is also being redefined by AI-driven strategic modeling. AI is being used to simulate negotiations between global powers, helping diplomats refine their strategies before engaging in high-stakes discussions. By modeling past negotiations, AI can predict adversarial reactions, optimize trade agreements, and assess potential pathways toward diplomatic resolutions.
The future of government decision-making will be driven by AI simulations. Leaders who leverage AI for strategic forecasting will be able to preempt crises, optimize policies, and mitigate systemic risks before they spiral out of control. As AI simulations become more advanced, they will serve as the ultimate decision-support system, helping governments navigate the complexities of an increasingly unpredictable world.
LLMs Are the Foundation—But They Need a Stability Layer
Large Language Models (LLMs) have shown promise in simulating human behavior, but they aren’t built for stability. Recent research suggests that LLMs can replicate certain human-like decision-making processes, but they fail when it comes to self-correction and long-term coordination. Models tend to overfit to specific negotiation patterns, rather than developing adaptive stability structures.
A study from Harbin Institute of Technology demonstrated that personality-aware AI stability systems could improve multi-agent cooperation by up to 34%, but without structured enforcement, those same AI agents could break down into non-cooperative factions. That means LLMs will need a stability layer to function effectively in high-stakes decision-making. Whoever builds that layer—the AI infrastructure that aligns LLMs with human-centric market dynamics—will define the future of AI-driven societies.
AI is learning to mimic human behavior, but it’s far from mastering stability. Research shows that while AI can classify traits like agreeableness and openness with over 90% accuracy, it struggles with extraversion and neuroticism, dropping as low as 7.4% and 10.3%, respectively. Teams of AI agents with diverse personalities outperform homogeneous groups by 27.3%, while overly rigid systems see a 22.9% decline due to lack of adaptability. AI personalities also shift unpredictably based on conversation dynamics, with openness being the hardest trait to maintain, peaking at just 51% accuracy. The takeaway? AI-driven systems need a stability layer—one that balances personality dynamics, enables adaptability, and prevents runaway biases. The companies that solve this will determine the next wave of AI-driven economies.
This Is the Next $500B AI Market
If AI is going to impact how to run governments, economies, manage financial systems, and control critical infrastructure, it needs more than optimization—it needs stability. That’s a $500 billion opportunity, and the companies that solve it will define the future of AI.
AI needs a stable human simulation layer—models that don’t just process information but understand and predict human economic/societal behavior. AI also needs real-time risk infrastructure, platforms that monitor, audit, and correct AI-led economies/societies before failures occur. Finally, autonomous economic agents will need stability frameworks that enforce rules, negotiate market dynamics, and regulate AI-powered decision-making. This isn’t some far-off vision. It’s the missing layer of AI-led societies today.
Who Will Build AI’s Stability Layer?
We are in the early innings of AI stability. But one thing is clear. AI, left unchecked, will optimize for short-term gain at the expense of long-term sustainability. It mirrors human behavior, but it lacks the adaptability, incentives, and regulatory guardrails that keep real-world economies from failing.
The companies that solve AI’s stability problem won’t just create a new category—they’ll build the foundation of AI-led markets. This is the next $500 billion market in AI.
For the founders interested, this is the biggest untapped opportunity in AI today and here are areas we’re exploring:
Infrastructure
“SimStack” (Infra for Agentic B2B Apps)
AgentOS: Runtime for AI agents with persistent memory, roles, goals, and personalities - composable for B2B
SimOrch: Manages multi-agent social dynamics, org charts, and async comms
Context Ingestion: Plug-and-play connectors (Slack, Notion, Attio) to inject real enterprise data into agent memory via RAG pipelines
Analytics & Evaluation: Full session replay, behavior tracing, and simulation testing framework
Infra & APIs: Scalable, hosted or self-managed infra with metered simulation pricing; pluggable into any LLM stack
Applications
AI SDR Simulation
A sandbox of AI-generated buyer and seller personas that simulate how real customers respond to product messaging, pricing, or onboarding flows
PMs and GTM teams use it before launch to iterate on campaigns or product-market fit
Agents trained on ICP data + CRM transcripts to create accurate personalities = synthetic users who “act” like your best or worst customers
The simulations allow for companies to deploy tiger teams of GTM pros instead of mass messaging with AI SDRs leading to annoyed or unengaged prospects
War Games
A platform for governments or think tanks to simulate geopolitical negotiations, sanctions, or wartime decisions with AI-modeled leaders and public sentiment
State Department or NATO tests the second- and third-order effects of diplomacy, trade policy, or escalation
LLMs can model both elite behavior and population sentiment; governments need proactive rather than reactive tools
Market Testing/Policy Design
A simulation platform that generates representative AI populations to test market responses to new products, policies, or pricing models
CPG brands, ad agencies, and even cities use it to simulate public reaction to new offerings or regulations
Traditional focus groups are slow and biased; simulated populations scale instantly and adapt in real time
WORKS CITED
Research Papers & Studies on AI Human Simulation and Personality Traits
1. Institute of Computing and Intelligence, Harbin Institute of Technology. (2025). Personality traits in AI agents: The impact of conscientiousness, extraversion, and neuroticism on multi-agent collaboration. arXiv. Retrieved from https://arxiv.org/abs/2502.11843
2. British Journal of Psychology. (2025). Artificial intelligence chatbots mimic human collective behavior: The emergence of social dynamics in AI-driven networks. British Journal of Psychology. Retrieved from https://bjp.oxfordjournals.org
3. AgentSociety Project. (2024). Large-scale AI multi-agent simulation: Ideological reinforcement and economic implications. Massachusetts Institute of Technology. Retrieved from https://agentsociety.mit.edu
4. University of Western Australia. (2024). AI personality trait classification and conversational consistency: Challenges in long-term personality modeling. Journal of Artificial Intelligence Research, 78(4), 112-138. Retrieved from https://jair.org/2024/personality-ai
5. GovSim Research Team. (2024). Cooperate or Collapse: AI decision-making in economic governance simulations. ETH Zürich, University of Toronto, & MPI. Retrieved from https://govsim.ethz.ch
Market Sizing & Industry Reports
6. McKinsey Global Institute. (2023). AI and corporate decision-making: The future of strategic forecasting. McKinsey & Company. Retrieved from https://www.mckinsey.com/ai-decision-making
7. Gartner. (2023). The rise of AI in HR & workforce automation: How AI is reshaping hiring and talent management. Gartner Research. Retrieved from https://www.gartner.com/hr-ai-trends
8. CB Insights. (2023). AI in financial markets & algorithmic trading: The next frontier in hedge fund strategies. CB Insights Research. Retrieved from https://www.cbinsights.com/research/report-ai-financial-markets
9. U.S. Department of Defense. (2024). AI in national security: 2028 defense budget projections for AI-powered war gaming and strategic modeling. U.S. Government Publishing Office. Retrieved from https://www.defense.gov/reports/ai-national-security
10. World Economic Forum. (2023). The AI-powered metaverse: Economic impact and market projections. WEF Annual Report. Retrieved from https://www.weforum.org/ai-metaverse
11. Deloitte & Rock Health. (2023). AI in healthcare: Behavioral simulations, medical training, and patient modeling. Deloitte Insights. Retrieved from https://www2.deloitte.com/us/en/insights/ai-in-healthcare
