There are a lot of considerations that determine whether a given type of “market” is a good candidate for AI assistance (aka application of Cosolvent). No market will score high on every metric, but a lot of markets may have enough of these characteristics that application of Cosolvent or a Cosolvent-like solution would be warranted.
Core Structural Requirements
High Information Asymmetry
- Buyers and sellers have difficulty discovering each other’s existence
- Participants lack visibility into available options and market conditions
- Information about needs, capabilities, and pricing is scattered and hard to access
Complex, Multi-Dimensional Matching Criteria
- Simple categorical searches (price, location, basic features) are insufficient
- Requires matching on technical specifications, qualifications, timing, compatibility
- Success depends on nuanced understanding of requirements and capabilities
- Traditional databases can’t capture the full complexity of matching needs
Scattered Participant Base
- Buyers and sellers are geographically or virtually dispersed
- No natural gathering places or established networks
- Participants operate independently without central coordination
- Low probability of random encounters leading to successful matches
Transaction Characteristics
Sufficient Value Density
- Individual transactions valuable enough to justify technology investment
- Either high-value infrequent transactions or moderate-value frequent ones
- Value creation potential exceeds cost of building and maintaining the platform
Unique, Non-Standardized Requirements
- Each transaction has specific, often customized needs
- Standardization is difficult or impossible due to inherent complexity
- Requires human-level understanding of context and nuance
- Cookie-cutter solutions don’t work
Time-Sensitive Matching
- Needs and availability change frequently
- Windows of opportunity are limited
- Delays in matching reduce transaction value or eliminate opportunities entirely
AI Suitability Factors
Rich, Describable Profiles
- Participants can provide detailed descriptions of their needs/capabilities
- Information exists in various formats (text, documents, specifications, images)
- AI can extract and structure meaningful matching criteria from unstructured data
Domain Knowledge Can Be Captured
- Industry expertise and matching logic can be documented and encoded
- Patterns in successful matches can be learned and replicated
- Expert knowledge can be systematized without losing essential nuance
Network Effects Potential
- More participants make the platform more valuable for everyone
- Quality of matches improves with larger user base and more data
- Platform becomes increasingly difficult to replicate as it grows
Market Readiness Indicators
Currently Relies on Human Intermediaries
- Brokers, agents, or consultants currently facilitate transactions
- These intermediaries are expensive, limited in capacity, or hard to access
- Human expertise is the primary bottleneck to market efficiency
Underserved or Inefficient Status Quo
- Current solutions are expensive, slow, or unreliable
- Many potential transactions never happen due to discovery failures
- Participants express frustration with existing options
Digital Transformation Readiness
- Target users comfortable with digital platforms
- Necessary information can be digitized and transmitted
- Regulatory environment permits online facilitation
Economic Characteristics
Transaction Value vs. Platform Costs
- Individual transaction values must justify the technology investment and ongoing operational costs
- Need sufficient volume or value density to cover AI inference costs, platform maintenance, and user acquisition
- Platform economics must work even with initially low transaction volumes during growth phase
Friction Cost Reduction Potential
- Current search/discovery costs are high relative to transaction value
- Significant time investment required for participants to find suitable matches
- Opportunity costs of failed matches or extended search periods are substantial
- AI platform can demonstrably reduce these friction costs
Risk Mitigation Value
- Thin markets often carry higher risks due to limited information and fewer alternatives
- Poor matches can be costly (failed projects, wasted resources, reputation damage)
- AI-powered better matching reduces probability of expensive failures
- Risk reduction value can justify platform fees even for moderate-value transactions
Market Structure Economics
Intermediary Cost Displacement
- Current human intermediaries command high fees (often 10-30% of transaction value)
- Traditional brokers have capacity constraints that limit market growth
- AI platform can capture portion of intermediary value while reducing costs
Market Expansion Potential
- Many potential transactions don’t happen due to high discovery costs
- Platform can enable previously uneconomical smaller transactions
- Total addressable market grows when friction costs decrease
- Revenue opportunity includes both existing transactions and newly enabled ones
Pricing Power and Revenue Model Sustainability
- Platform can command fees proportional to value created (successful matches)
- Multiple revenue streams possible (subscription, transaction fees, premium features)
- Network effects create pricing power as platform becomes more valuable
Risk and Investment Considerations
Platform Development ROI Timeline
- Time to break-even must align with available funding and market patience
- Initial development costs can be substantial (AI training, platform development, content creation)
- User acquisition costs in thin markets can be high initially
Market Adoption Risk
- Both sides of market must adopt simultaneously (chicken-and-egg problem)
- Conservative industries may resist new technology platforms
- Regulatory or industry certification requirements may create barriers
Competitive Moat Sustainability
- First-mover advantages in thin markets can be significant
- Data network effects create barriers to entry
- Platform switching costs increase over time as users build profiles and relationships
Value Creation Economics
Transaction Frequency and Lifetime Value
- One-time users vs. repeat customers have very different economics
- Customer lifetime value must exceed acquisition costs
- Higher frequency markets generally more attractive than one-off transactions
Quality Premium Capture
- Better matches often command price premiums
- Participants willing to pay more for higher success probability
- Quality differentiation allows premium pricing vs. commodity alternatives
Operational Leverage
- Platform costs should scale sublinearly with transaction volume
- AI inference costs per transaction should decrease with volume
- Human support requirements should not grow proportionally with users
Summary
The structural sweet spot is markets where human expertise is currently the limiting factor, information complexity prevents simple database solutions, but the underlying matching logic can be captured and enhanced by AI systems. The key insight is that AI doesn’t replace human judgment entirely—it amplifies and scales the pattern recognition and contextual understanding that human experts bring to these markets.
The economic sweet spot is markets where current friction costs are high enough to justify platform investment, transaction values are sufficient to support sustainable revenue models, and the platform can capture meaningful value from the efficiency gains it creates. Markets that fail these economic tests may be technically feasible but commercially unviable.