CommonGround AI turns disputes into structured coordination problems. It asks each party what they value most, maps the trade space, retrieves similar cases, and recommends outcomes that are fair, explainable, and more win-win than what human arbitrators typically find.
The Coordination Failure
Over 90% of civil cases in the United States settle before trial, often after months of costly negotiation, signaling massive inefficiency in reaching mutually acceptable outcomes.
Problem
We have no scalable way to converge on fairness.
Today
- Outcomes depend on negotiation power, not principled fairness
- Arbitrators miss latent win-win trades across complex preference spaces
- Similar cases produce inconsistent outcomes
What could exist
- A system that identifies where both parties can gain simultaneously
- A mechanism that surfaces Pareto-efficient outcomes, meaning one party improves without harming the other
- A shared reference point both sides trust
Civilizationally, this is a coordination failure. We waste time, capital, and relationships because we cannot compute fair exchanges.
Solution Hypothesis
Fairness is a latent structure. It can be learned from large-scale human judgment data and optimized using Preference Modeling.
Case similarity graph
Disputes are embedded into a high-dimensional space using Large Language Models. The system retrieves thousands of similar historical cases.
Fairness-weighted outcome modeling
Each precedent is scored by expert arbitrators for fairness. Outcomes are weighted by fairness scores, acceptance rates, and long-term compliance signals.
Preference surface mapping
Each party rank-orders what matters most across dimensions such as money, control, timing, reputation, certainty, and future upside. The system constructs a utility function for each party.
Win-win optimizer
The system searches for Pareto-efficient outcomes across both utility functions. It identifies trades human arbitrators often miss, such as equity versus vesting, cash versus upside, or time versus control.
Pareto-Efficient Outcomes
Traditional arbitration produces blunt compromise (red cluster), leaving latent value uncaptured. The CommonGround system maps multi-dimensional priorities to discover the theoretical limit of mutual gain (green cluster) on the Pareto frontier.
- Ranked outcome options
- Fairness distribution and confidence intervals
- Clear explanation grounded in precedent clusters
- Counterfactuals showing how outcomes change if priorities shift
Specific Examples
Founder equity dispute
Two cofounders are splitting after 18 months. One built the original product and wants long-term upside. The other drove early revenue and wants recognition plus near-term liquidity.
"Identifies a structure with asymmetric equity, milestone-based payouts, advisory roles, and reputation protections that dominates a simple split for both parties."
Divorce settlement
One spouse prioritizes stability for the children and staying in the home. The other prioritizes financial independence and minimizing long-term resentment.
"Recommends structured buyouts, flexible custody schedules, and targeted asset swaps that better align with each party's true priorities than standard court outcomes."
Enterprise contract dispute
A vendor misses delivery milestones. The customer wants reliability and internal accountability. The vendor wants to preserve the relationship and avoid major financial loss.
"Recommends a package of partial credits, extended contracts, stricter service guarantees, escalation rights, and conditional reputation upside."
Founder equity dispute
Two cofounders are splitting after 18 months. One built the original product and wants long-term upside. The other drove early revenue and wants recognition plus near-term liquidity.
"Identifies a structure with asymmetric equity, milestone-based payouts, advisory roles, and reputation protections that dominates a simple split for both parties."
Neglectedness
Market
Global legal services exceed $800B annually, with arbitration growing faster than litigation due to cost and speed advantages.
- Every contract implies future dispute resolution
- Every dispute is a coordination problem
- Coordination at scale becomes infrastructure
Default resolution layer for startups, enterprises, global commerce, and digital coordination systems.
Why Now
- Large Language Models can interpret complex legal and emotional context
- Embeddings enable case similarity at scale
- Preference learning models approximate human utility functions
- Synthetic and human labeling pipelines can bootstrap fairness datasets
Pre-2022 this was not feasible. Now it is.
Business Model
- SaaS for law firms and arbitrators
- Transaction fees per resolved dispute
- Enterprise licensing for internal dispute resolution
- API integration into contracts, platforms, and marketplaces
- Users: save time and legal costs
- Professionals: increase throughput and consistency
- Platform: captures coordination layer value
Moat
Score AnalysisThe core moat is the fairness dataset and outcome graph.
Difficulty to Get to Market
Score AnalysisTechnically feasible, socially and institutionally complex.
Unique Go To Market
Start with founder disputes.
- High stakes and repeated patterns
- Fast decision cycles
- Early adopters willing to try new systems
- Publish anonymized fairness distributions for well-known disputes
- Launch a public tool: "What was the fair split?"
In a world of abundant intelligence,
coordination becomes the bottleneck.
Today, we still let high-stakes coordination decisions depend on individual judgment, limited precedent review, and inconsistent reasoning. That will look archaic.
Human calculators were replaced by machines. Human drivers are being replaced by autonomous systems. In the same way, it will become inconceivable that important arbitration and coordination decisions are made by fallible humans with narrow memory and limited ability to explore the full space of fair outcomes.
The future is not binary decisions.
The future is full trade-space mapping with optimized outcomes.
As agents negotiate on behalf of humans, CommonGround AI becomes:
- Arbitration layer for agent-to-agent commerce
- Default settlement engine embedded in contracts
- Coordination protocol for institutions
- Fairness oracle for disputes and resource allocation
Civilizational Impact
Select to expand analysis90Score
Civilizational Impact
Select to expand analysisThis begins with small disputes but scales to large coordination systems.
Near-term:
- Faster, cheaper, more consistent dispute resolution
- Reduced conflict cost across society
- Increased trust in outcomes
Long-term:
- Coordination layer for nation-states, corporations, and global systems
- Tool for escaping lose-lose equilibria such as arms races and resource conflicts
- Mechanism for solving tragedy-of-the-commons failures
Policy layer workflow
- Humans vote on values and priorities
- AI searches the policy design space
- System identifies Pareto-efficient proposals
- Society chooses from options that better satisfy shared values
This transforms how societies coordinate at scale, moving from adversarial negotiation to structured convergence.
KPIs
First ExperimentHypothesis: People will accept AI-recommended outcomes if they are grounded in precedent and improve both parties’ utility. (Click to expand test design)
- Test Parameters:
- Recruit 20 real founder disputes
- Collect structured preferences from both sides
- Generate AI-recommended outcomes
- Compare acceptance rate versus traditional negotiation
Transferable Insight
"Most human conflict is not zero-sum. It is unmapped trade space. Systems that surface hidden win-win opportunities outperform systems that enforce rigid rules."
Open Source Priority
Acronyms & References
Acronyms
- LLM: Large Language Model
- SaaS: Software as a Service
- API: Application Programming Interface
- GTM: Go-To-Market