The artificial intelligence landscape has created a once-in-a-generation opportunity for founders. But the AI startup landscape is also uniquely challenging: model capabilities change monthly, incumbents have massive data advantages, and the technology is still maturing rapidly.
Finding Your AI Moat
Models are increasingly commoditized. Your defensibility will come from proprietary data, domain expertise, unique workflows, or network effects -- not from the model itself. The most successful AI startups combine frontier models with deep domain knowledge to solve problems that generic AI tools cannot.
The Build vs. Buy Decision for Models
For most startups, fine-tuning or prompt-engineering existing foundation models is the right approach. Training your own model from scratch requires enormous capital and data advantages that few startups possess. Start with APIs, add fine-tuning when you have enough domain-specific data, and consider custom models only when you have proven market demand.
Data Strategy Is Product Strategy
In AI startups, your data flywheel is your product. Every user interaction should generate data that makes your product better. Design your product to capture high-quality, labeled data from normal usage -- this creates a compounding advantage that competitors cannot replicate.
Responsible AI and Trust
Users need to trust your AI system. This means transparency about limitations, clear error handling, and human-in-the-loop workflows where the stakes are high. Companies that build trust early will have an enormous advantage as regulation increases.
Pricing AI Products
AI products have a different cost structure: inference costs scale with usage. Consider value-based pricing rather than per-seat models. Charge for outcomes, not for compute.
