When evaluating AI startups, don't just consider the current product landscape. Instead, visualize the future state of giants like OpenAI as multi-trillion dollar companies. Their "sphere of influence" will be vast. The best opportunities are "second-order" companies operating in niches these giants are unlikely to touch.

Advanced AI like Gemini 3 allows non-developers to rapidly "vibe code" functional, data-driven applications. This creates a new paradigm of building and monetizing fleets of hyper-specific, low-cost micro-SaaS products (e.g., $4.99 per report) without traditional development cycles.

Most successful SaaS companies weren't built on new core tech, but by packaging existing tech (like databases or CRMs) into solutions for specific industries. AI is no different. The opportunity lies in unbundling a general tool like ChatGPT and rebundling its capabilities into vertical-specific products.

Small firms can outmaneuver large corporations in the AI era by embracing rapid, low-cost experimentation. While enterprises spend millions on specialized PhDs for single use cases, agile companies constantly test new models, learn from failures, and deploy what works to dominate their market.

Historically, resource-intensive prototyping (requiring designers and tools like Figma) was reserved for major features. AI tools reduce prototype creation time to minutes, allowing PMs to de-risk even minor features with user testing and solution discovery, improving the entire product's success rate.

Traditional software required deep vertical focus because building unique UIs for each use case was complex. AI agents solve this. Since the interface is primarily a prompt box, a company can serve a broad horizontal market from the beginning without the massive overhead of building distinct, vertical-specific product experiences.

AI drastically accelerates the ability of incumbents and competitors to clone new products, making early traction and features less defensible. For seed investors, this means the traditional "first-mover advantage" is fragile, shifting the investment thesis heavily towards the quality and adaptability of the founding team.

A practical AI workflow for product teams is to screenshot their current application and prompt an AI to clone it with modifications. This allows for rapid visualization of new features and UI changes, creating an efficient feedback loop for product development.

Seeing an existing successful business is validation, not a deterrent. By copying their current model, you start where they are today, bypassing their years of risky experimentation and learning. The market is large enough for multiple winners.