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.

The primary economic incentive driving AI development is not replacing software, but automating the vastly larger human labor market. This includes high-skill jobs like accountants, lawyers, and auditors, representing a multi-trillion dollar opportunity that dwarfs the SaaS industry and dictates where investment will flow.

Focusing on AI for cost savings yields incremental gains. The transformative value comes from rethinking entire workflows to drive top-line growth. This is achieved by either delivering a service much faster or by expanding a high-touch service to a vastly larger audience ("do more").

Using AI for incremental efficiency gains (10% thinking) is becoming table stakes. True competitive advantage lies in 10X thinking: using AI to fundamentally reimagine your business model, services, and market approach. Companies that only optimize will be outmaneuvered by those that transform.

Most companies use AI for optimization—making existing processes faster and cheaper. The greater opportunity is innovation: using AI to create entirely new forms of value. This "10x thinking" is critical for growth, especially as pure efficiency gains will ultimately lead to a reduced need for human workers.

Enterprises struggle to get value from AI due to a lack of iterative, data-science expertise. The winning model for AI companies isn't just selling APIs, but embedding "forward deployment" teams of engineers and scientists to co-create solutions, closing the gap between prototype and production value.

The long-held belief that a complex codebase provides a durable competitive advantage is becoming obsolete due to AI. As software becomes easier to replicate, defensibility shifts away from the technology itself and back toward classic business moats like network effects, brand reputation, and deep industry integration.

The true enterprise value of AI lies not in consuming third-party models, but in building internal capabilities to diffuse intelligence throughout the organization. This means creating proprietary "AI factories" rather than just using external tools and admiring others' success.

Don't underestimate the size of AI opportunities. Verticals like "AI for code" or "AI for legal" are not niche markets that will be dominated by a few players. They are entire new industries that will support dozens of large, successful companies, much like the broader software industry.