The rapid growth of AI products isn't due to a sudden market desire for AI technology itself. Rather, AI enables superior solutions for long-standing customer problems that were previously addressed with inadequate options. The demand existed long before the AI-powered supply arrived to meet it.

The new generation of AI automates workflows, acting as "teammates" for employees. This creates entirely new, greenfield markets focused on productivity gains for every individual, representing a TAM potentially 10x larger than the previous SaaS era, which focused on replacing existing systems of record.

Incumbent companies are slowed by the need to retrofit AI into existing processes and tribal knowledge. AI-native startups, however, can build their entire operational model around agent-based, prompt-driven workflows from day one, creating a structural advantage that is difficult for larger companies to copy.

Low-cost AI tools create a new paradigm for entrepreneurship. Instead of the traditional "supervised learning" model where VCs provide a playbook, we see a "reinforcement learning" approach. Countless solo founders act as "agents," rapidly testing ideas without capital, allowing the market to reward what works and disrupting the VC value proposition.

Instead of simply adding AI features, treat your AI as the product's most important user. Your unique data, content, and existing functionalities are "superpowers" that differentiate your AI from generic models, creating a durable competitive advantage. This leverages proprietary assets.

Contrary to the view that useful AI agents are a decade away, Andrew Ng asserts that agentic workflows are already solving complex business problems. He cites examples from his portfolio in tariff compliance and legal document processing that would be impossible without current agentic AI systems.

AI tools drastically reduce the time and expertise needed to enter new domains. This allows startups to pivot their entire company quickly to capitalize on shifting investor sentiment and market narratives, making them more agile in a hype-driven environment where narrative alignment attracts capital.

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.

Traditionally, building software required deep knowledge of many complex layers and team handoffs. AI agents change this paradigm. A creator can now provide a vague idea and receive a 60-70% complete, working artifact, dramatically shortening the iteration cycle from months to minutes and bypassing initial complexities.