Hedge funds have a constant, daily need to make informed buy, sell, or hold decisions, creating a clear business problem that data solves. Corporations often lack this frequent, high-stakes decision-making cycle, making the value proposition of external data less immediate and harder to justify.

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.

Standard quant factors like expanding margins and avoiding capital raises are negative signals for development-stage biotech firms. These companies must burn cash to advance products, rendering traditional models useless. The only semi-reliable quant metric is Enterprise Value to Cash.

Despite biotech comprising a significant portion of benchmarks, generalist managers consistently remain severely underweight. They perceive this as risk-averse, but it actually exposes their funds to massive tracking error and unintended risks by forcing them to be overweight in other healthcare sub-sectors.

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.

To overcome accounting's focus on historical costs, quantitative investors use unstructured data from sources like patent filings, trademarks, and LinkedIn profiles. This approach quantifies the actual output and quality of a company's intellectual property and human capital.

The life sciences investor base is highly technical, demanding concrete data and a clear path to profitability. This rigor acts as a natural barrier to the kind of narrative-driven, AI-fueled hype seen in other sectors, delaying froth until fundamental catalysts are proven.

One of the few working quantitative models in biotech is to systematically purchase stocks after they have crashed on bad news. This low-batting-average, high-slugging-percentage approach is terrifying but can work by getting favorable odds on a recovery, provided the company has sufficient cash runway to survive.

Exonic is building a platform for bioengineers to compete on open-source biological modeling, aiming to turn drug discovery into a meritocratic competition. This mirrors the model of crowdsourced hedge fund Numerai, applying a "wisdom of the crowd" approach to disrupt the closed, expensive R&D processes of large pharmaceutical companies.