Why Private Equity Doesn’t Need to Understand the Science — And What That Means for You

The financial model works. Until the science doesn’t. Here’s why those are two different things

Prepared by Richstorm.co

Most investors assume that private equity firms investing in pharmaceutical or technology companies must deeply understand the science behind those businesses. That assumption is understandable. It is also largely wrong.

PE firms can — and routinely do — generate strong returns in science-driven industries without genuinely understanding the science. Understanding why this is true, and where it breaks down, is one of the most useful things a science-literate investor can know.

What Private Equity Actually Is

Private equity is, at its core, a business optimization machine. A PE firm raises money from institutional investors — pension funds, university endowments, sovereign wealth funds — uses that capital plus borrowed money to buy companies, improves their operational and financial metrics over 5-7 years, and sells them at a profit.

The five things that make a great PE firm great are:

Notice what is absent from all five: scientific understanding. This is not an oversight. It reflects what PE is actually designed to do. The tools of private equity — EBITDA multiples, leverage ratios, operational improvement playbooks, exit timing — were built for a world where value is primarily organizational and financial. In that world, they work very well.

The Due Diligence Layer That Sounds Scientific But Isn’t

When a PE firm acquires a pharma or technology company, it does conduct something called technical due diligence. In practice, this typically consists of one-hour calls with retired industry executives, a market sizing report from a management consultancy, and a review of management’s own presentation of the technology.

The retired Chief Scientific Officer brought in to validate the investment faces three structural problems that make genuine scientific assessment nearly impossible.

First, science is not a solo activity. When a CSO at a major pharma company made investment decisions, they drew on teams of dozens of scientists, institutional knowledge from hundreds of prior programs, and iterative experimental data generated over months. Strip away that infrastructure and you don’t have a CSO. You have a smart person with good intuition making educated guesses in an hour-long call.

Second, science moves through communities of practice — researchers at conferences, unpublished results circulating through active networks, frontier knowledge that appears in journals years after it is first discussed among practitioners. A retired CSO is no longer in those communities. Their mental model of the field is frozen at the point of retirement.

Third, the incentive structure punishes genuine scientific skepticism. An advisor who consistently raises concerns about the scientific validity of deals does not get invited back. The financial relationship — retainers, carry participation, board seats — is worth millions over a decade. The incentive is to find reasons to say yes.

The scientific validation layer in PE due diligence is not rigorous scientific assessment. It is financial confidence dressed in scientific language.

But PE Is Hiring AI Experts — Doesn’t That Change Things?

A reasonable objection: private equity firms are actively building data science and AI capabilities. According to EY’s Private Equity Pulse survey, 53% of PE firms expect to hire more digital transformation specialists than in prior years, and 51% are seeking data scientists and AI experts. Surely this reflects growing scientific sophistication?

The answer is no — and the distinction matters. PE firms are hiring AI and data science talent for:

• Deal sourcing automation — scanning millions of companies to identify acquisition targets based on financial signals: revenue growth, employee headcount trends, web traffic patterns, hiring data.

  
  

• Portfolio monitoring — building real-time dashboards tracking operational KPIs across portfolio companies: churn, sales pipeline, margin trends.

  
  

• Document processing — using AI to read thousands of pages of contracts, financial statements, and legal documents faster.

  
  

• Exit timing — modeling buyer universes and benchmarking against comparable transactions.

What they are not hiring data scientists for: evaluating whether a drug mechanism is scientifically valid, assessing whether an AI platform has genuine technical defensibility, reading clinical trial data, or determining if a manufacturing process has hidden failure modes.

The data scientists PE firms are hiring are financial data scientists — people who find patterns in business and market data. PE is getting dramatically better at what it already does. It is not getting more scientifically literate about the companies it owns. Those are completely different things, and conflating them is a mistake the industry’s own hiring language reveals.

Where the Strategy Works: Betting the Trend

What PE is actually doing in science-driven industries is coherent and worth stating plainly: it is making a financial momentum bet. The real thesis, stripped of language about technology moats and pipeline depth, is typically something like this:

This company has strong financial metrics. We believe those metrics will continue for the 5-7 years we need to exit. We have done enough due diligence to feel we have not missed something catastrophically obvious. Beyond that, we are trusting that existing revenues and customers reflect a working product.

This is not irrational. Revenue retention, customer concentration, contract structure, and management quality are real signals that something is working. For many businesses — professional services, software platforms, specialty distribution, business process outsourcing — this approach is entirely appropriate. The science is not what determines the outcome. The business model is.

The problem emerges when the same framework is applied to businesses where the science is the business model. Drug pipelines. Technology platforms. Medical devices. In these cases, the financial metrics describe the past and present. The science describes the future. And those two descriptions can diverge significantly — for years — before the gap becomes visible in a financial statement.

When the Science Catches Up with the Balance Sheet

The following cases illustrate what happens when financial metrics are used to evaluate businesses whose value is fundamentally scientific. In each case, the financial signals were positive well into the period when the scientific reality had already shifted.

The pattern across these cases is consistent. The scientific inflection preceded the financial one by years. The financial model gave no warning because it was designed to read financial signals, not scientific ones. By the time the financials reflected the change, the damage was done.

In the nursing home case, the outcome is particularly instructive for investors in healthcare. A peer-reviewed study published by the National Bureau of Economic Research (Gupta et al., 2021), analyzing over 18,000 US nursing homes over 15 years, found that PE acquisition was associated with a 10% increase in short-term resident mortality — equivalent to more than 20,000 additional deaths. The financial metrics of those facilities improved. The quality of care, measurable scientifically, deteriorated severely. The two moved in opposite directions simultaneously, and the financial model tracked only one of them.

The Leverage Problem: When the Science Turns, the Debt Stays

PE’s use of debt amplifies this dynamic in a way that is worth understanding explicitly. When a PE firm acquires a science-driven company using a typical capital structure — 60-70% debt, 30-40% equity — the debt does not care about the science. Interest payments are contractual. Covenant compliance is mandatory. The cash must go out the door regardless of what the technology or drug pipeline is doing.

This means that when a scientific inflection arrives — a Phase 3 failure, a competing platform breakthrough, an FDA rejection, a manufacturing contamination event — the company faces two simultaneous problems: the revenue impact of the scientific setback, and the fixed debt service obligations that were sized for a company with intact revenues. The leverage that amplified returns on the way up amplifies the losses on the way down.

A publicly traded pharma company facing a pipeline setback has flexibility: it can cut costs, issue equity, conserve cash, and navigate. A PE-owned pharma company with a leveraged balance sheet has much less room. The science and the capital structure interact in ways the financial model, built on assumed revenue continuation, never captured.

What This Means for the Science-First Investor

The conclusion here is not that PE is wrong to invest without deep scientific understanding. For many sectors and strategies, it is the entirely rational approach. The conclusion is that the science-first investor occupies a fundamentally different position — one that is genuinely complementary to, and differentiated from, both PE and large institutional investors like JP Morgan.

JP Morgan operates primarily at Layer 1. PE operates at Layers 1 and 2, with financial tools only. The science-first investor has an edge at Layers 2 and 3 — precisely where financial analysis is structurally insufficient.

The questions that financial analysis cannot answer but scientific understanding can:

•  Is the revenue being generated by this company a function of genuinely defensible science, or of a favorable market window that a better-funded competitor will eventually close?

• Has the R&D investment that generated this product been maintained, or has it been quietly reduced to improve near-term EBITDA at the cost of future pipeline?

  
  

• Is the technology underlying this platform still at the frontier of its field, or has the scientific community moved to a superior approach that the financial statements have not yet reflected?

These are not abstract questions. They are the questions that separate a company with durable value from a company whose financial metrics are a trailing indicator of a scientific advantage that no longer exists.

The RichStorm Perspective

Private equity does not need to understand the science to be good at what it does. That is not a criticism. It is an accurate description of a strategy that is coherent, well-executed by the best firms, and genuinely creates value in the right contexts.

But it means that PE is not a substitute for scientific judgment in investment decisions about science-driven companies. It means that when PE financial metrics and scientific reality diverge — as they periodically and inevitably do — the financial metrics will not tell you. The science will.

The investor who reads both — who understands the business model and the financial signals, and who can also evaluate whether the science underpinning those signals is real, durable, and defensible — occupies a position that neither PE nor large institutional capital reaches.

Financial metrics describe where a science-driven company has been. Scientific understanding describes where it is going. Both matter. Most investors only read one.

RichStorm publishes science-first investment analysis at richstorm.co. This article represents the author’s analytical perspective and does not constitute investment advice.