A custom AI tool offers more value than a generic one like ChatGPT because it can be trained on a brand's unique, paywalled intellectual property. This creates a curated experience that aligns perfectly with your teachings and provides answers that cannot be found for free on the web, solidifying your expertise.

The key for enterprises isn't integrating general AI like ChatGPT but creating "proprietary intelligence." This involves fine-tuning smaller, custom models on their unique internal data and workflows, creating a competitive moat that off-the-shelf solutions cannot replicate.

WCM avoids generic AI use cases. Instead, they've built a "research partner" AI model specifically tuned to codify and diagnose their core concepts of "moat trajectory" and "culture." This allows them to amplify their unique edge by systematically flagging changes across a vast universe of data, rather than just automating simple tasks.

Instead of a generalist AI, LinkedIn built a suite of specialized internal agents for tasks like trust reviews, growth analysis, and user research. These agents are trained on LinkedIn's unique historical data and playbooks, providing critiques and insights impossible for external tools.

Building an AI application is becoming trivial and fast ("under 10 minutes"). The true differentiator and the most difficult part is embedding deep domain knowledge into the prompts. The AI needs to be taught *what* to look for, which requires human expertise in that specific field.

Dominant models like ChatGPT can be beaten by specialized "pro tools." An app for "deepest research" that queries multiple AIs and highlights their disagreements creates a superior, dedicated experience for a high-value task, just as ChatGPT's chat interface outmaneuvered Google search.

Unlike consumer chatbots, AlphaSense's AI is designed for verification in high-stakes environments. The UI makes it easy to see the source documents for every claim in a generated summary. This focus on traceable citations is crucial for building the user confidence required for multi-billion dollar decisions.

Treat AI skills not just as prompts, but as instruction manuals embodying deep domain expertise. An expert can 'download their brain' into a skill, providing the final 10-20% of nuance that generic AI outputs lack, leading to superior results.

To combat generic AI content, load your raw original research data into a private AI model like a custom GPT. This transforms the AI from a general writer into a proprietary research partner that can instantly surface relevant stats, quotes, and data points to support any new piece of content you create.