As AI makes it easy to generate 'good enough' software, a functional product is no longer a moat. The new advantage is creating an experience so delightful that users prefer it over a custom-built alternative. This makes design the primary driver of value, setting premium software apart from the infinitely generated.

Contrary to the vision of free-wheeling autonomous agents, most business automation relies on strict Standard Operating Procedures (SOPs). Products like OpenAI's Agent Builder succeed by providing deterministic, node-based workflows that enforce business logic, which is more valuable than pure autonomy.

The trend of 'vibe coding'—casually using prompts to generate code without rigor—is creating low-quality, unmaintainable software. The AI engineering community has reached its limit with this approach and is actively searching for a new development paradigm that marries AI's speed with traditional engineering's craft and reliability.

Despite the hype, LinkedIn found that third-party AI tools for coding and design don't work out-of-the-box on their complex, legacy stack. Success requires deep customization, re-architecting internal platforms for AI reasoning, and working in "alpha mode" with vendors to adapt their tools.

The traditional competitor for B2B tools was an Excel spreadsheet. In the AI era, it's a simple, version-controlled Markdown file within an IDE. If a SaaS offering for documentation or project management can't provide more value than this highly flexible, interoperable setup, it will lose.

Off-the-shelf AI models can only go so far. The true bottleneck for enterprise adoption is "digitizing judgment"—capturing the unique, context-specific expertise of employees within that company. A document's meaning can change entirely from one company to another, requiring internal labeling.

Unlike deterministic SaaS software that works consistently, AI is probabilistic and doesn't work perfectly out of the box. Achieving 'human-grade' performance (e.g., 99.9% reliability) requires continuous tuning and expert guidance, countering the hype that AI is an immediate, hands-off solution.

The surprising success of Dia's custom "Skills" feature revealed a huge user demand for personalized tools. This suggests a key value of AI is enabling non-technical users to build "handmade software" for their specific, just-in-time needs, moving beyond one-size-fits-all applications.

As AI makes it incredibly easy to build products, the market will be flooded with options. The critical, differentiating skill will no longer be technical execution but human judgment: deciding *what* should exist, which features matter, and the right distribution strategy. Synthesizing these elements is where future value lies.