To determine if a startup will succeed, analyze the sequence of events. Did organic customer demand and behavior exist before the startup created its supply (product, messaging)? If the startup is trying to force motion with its supply, it's a sign of conjuring demand and a higher risk of failure.

Unlike traditional SaaS where product-market fit meant a decade of stability, the rapid evolution of AI models makes today's PMF fleeting. Founders face the risk that their product could feel obsolete within a year, requiring constant innovation just to stay relevant in a rapidly changing market.

The most dangerous venture stage is the "breakout" middle ground ($500M-$2B valuations). This segment is flooded with capital, leading firms to write large checks into companies that may not have durable product-market fit. This creates a high risk of capital loss, as companies are capitalized as if they are already proven winners.

When introducing a disruptive model, potential partners are hesitant to be the first adopter due to perceived risk. The strategy is to start with small, persistent efforts, normalizing the behavior until the advantages become undeniable. Innovation requires a patient strategy to overcome initial industry inertia.

Large companies often identify an opportunity, create a solution based on an unproven assumption, and ship it without validating market demand. This leads to costly failures when the product doesn't solve a real user need, wasting millions of dollars and significant time.

PMF isn't a one-time achievement. Market shifts, like new technology or major events, can render your existing model obsolete. Successful companies must be willing to disrupt themselves and find new PMF to stay relevant.

Market dynamics are not static. What was once a 'wave'—a new, urgent problem for everyone—can evolve into a series of 'dams' and eventually a stable 'river.' A common mistake is to build for the hype of a wave after it has crested, by which point it no longer provides the same opportunity for explosive growth.

Unlike a failed feature launch, business viability risks (e.g., wrong pricing, changing market) kill products slowly. By the time the damage is obvious, it's often too late. This makes continuous monitoring of the business model as critical as testing new features.

Afeyan distinguishes risk (known probabilities) from uncertainty (unknown probabilities). Since breakthrough innovation deals with the unknown, traditional risk/reward models fail. The correct strategy is not to mitigate risk but to pursue multiple, diverse options to navigate uncertainty.