The 'company age' factor is not predictive on its own. MDT's decision tree model uses it to create context, asking different questions about young companies versus mature ones. For example, valuation proves to be a much more important factor for older, established businesses.

As platforms like AlphaSense automate the grunt work of research, the advantage is no longer in finding information. The new "alpha" for investors comes from asking better, more creative questions, identifying cross-industry trends, and being more adept at prompting the AI to uncover non-obvious connections.

LLMs have hit a wall by scraping nearly all available public data. The next phase of AI development and competitive differentiation will come from training models on high-quality, proprietary data generated by human experts. This creates a booming "data as a service" industry for companies like Micro One that recruit and manage these experts.

Vested's investment model gains an edge from proprietary data on employee sentiment and behavior. Signals like unsolicited negative comments, willingness to counter on price, or selling more shares than necessary provide unique insights into a company's health that traditional financial analysis lacks, forming a data moat.

Instead of building AI models, a company can create immense value by being 'AI adjacent'. The strategy is to focus on enabling good AI by solving the foundational 'garbage in, garbage out' problem. Providing high-quality, complete, and well-understood data is a critical and defensible niche in the AI value chain.

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 opaque 'black box' algorithms, MDT uses decision trees that allow their team to see and understand the logic behind every trade. This transparency is crucial for validating the model's decisions and identifying when a factor's effectiveness is decaying over time.

When approached by large labs for licensing deals, GI's founder advises against simply selling the data. He argues the only way to accurately value a unique dataset is to model it yourself to understand its true capabilities. Without this, founders risk massively undervaluing their core asset, as its potential is unknown.

A key competitive advantage wasn't just the user network, but the sophisticated internal tools built for the operations team. Investing early in a flexible, 'drag-and-drop' system for creating complex AI training tasks allowed them to pivot quickly and meet diverse client needs, a capability competitors lacked.