Part 1: Excel Is a Liar—Because We Trained It to Lie

I first fell in love with spreadsheets the way most data analysts do: after months of grinding through late nights with my good friends vlookup and sumif. I found I could turn chaos into a neat waterfall and feel like an oracle. My boss would scroll, see twelve tabs of linked formulas, and say, “Looks complete.” That little dopamine hit hard-wires a habit. The bigger the workbook, the safer everyone feels—even when the numbers are guesses wearing decimal-place disguises.

Excel isn’t just a tool; it’s a culture. It’s even an e-sport. A junior analyst learns that value equals the cell in row 36, column AJ. A partner learns that flipping between the “Inputs” tab and the “Returns” tab counts as diligence. Complexity theater passes for completeness, and every deal team, credit team, and risk team builds its own theater in isolation. Silos bloom. The more inward-looking the model, the more outward confidence it somehow inspires.

Nowhere is that mismatch sharper than in private markets. Public-market investors long ago surrendered to the mess: they pipe torrents of tick data and macro factors into Python and ML models, chasing edge in the noise. Private investors, by contrast, still rely on the comfort of a workbook because the data feels too sparse to justify heavier machinery. Opaqueness begets ritual.

Turning Ritual into Radars

The antidote isn’t “better spreadsheets”; it’s changing what the model is allowed to say. Every critical input must live as a probability distribution, not static in a single cell. That demands a bottom-up approach rather than a stitched-together stack of tabs.

1. Every input becomes a distribution.
Scrape forward curves for power prices, collect five-year permit data from state databases, pull historical build timelines, and fold expert priors into Bayesian updates. Run monte carlo’s on each key assumption. Energy cost is no longer $45/MWh; it’s a curve that widens when OPEC mutters and narrows when battery storage builds out. What used to take an army of analysts a month can be done with modern ML tools in hours.

2. Interactions are coded, not guessed.
A drought shock raises electricity prices and lettuce prices—but hurts grain traders while boosting indoor farms. Copula matrices capture those joint moves so the model can ask, “What if both happen together?”

3. Context wraps the value chain.
A vertical farms revenue distribution shifts weekly as satellite data on outdoor crop stress roll in. A cotton-fermentation startup’s demand curve flexes with tariff chatter pulled from customs filings and CBAM policy shifts in the EU parliament. Build macro risks from bottom up signals not top down factors.

Run ten thousand scenarios and you don’t get an answer; you get a cloud of outcomes—an “honest” map of left-tail ruin and right-tail windfall. From that cloud the most sensitive levers emerge. Maybe 80% of variance lives in construction timing, not commodity spread. Maybe a single policy credit drives half the upside. With that knowledge, you can decide whether to hedge, stand-pat, or double-down.

Why Silos Shrink When we Price Risk Bottom Up

Credit, market, and operational teams can’t cling to separate realities once their variables live in the same simulation engine. If a permitting delay pushes default probabilities higher and drives commodity hedges into the money, the model surfaces that crossover in a single run; the desks can’t pretend their lanes don’t intersect. Suddenly everyone—from risk to treasury to strategy—speaks a shared numerical language.

But the knock-on effect is even bigger: you no longer need three separate desks to join the party in the first place. Smaller, specialist firms that have avoided hardware or policy-exposed deals because they lack a commodities team (or a dedicated regulatory group) can now lean on the bottom up engine itself. The code does the cross-pollination that headcount once handled, mapping how energy prices chatter with tariff risk, how construction slippage feeds into covenant pressure, how climate volatility shapes demand curves.

In practical terms, that means:

• A five-person growth fund can price a geothermal-plus-data-center project it would have ignored last year.

• A family office focused on consumer brands can assess an indoor-ag play without hiring agriculture, robotics, and consumer analysts.

When the code cross‑trains the variables, the market no longer needs siloed armies to police each domain. A five‑person growth fund can now price a geothermal‑plus‑data‑center deal; a consumer‑focused family office can underwrite an indoor‑ag play without hiring a utilities desk. Capital that once sat on the sidelines finds a way in—pushing ​WACC lower, tenors longer, and check sizes fatter.

That, in turn, cracks open the question no allocator wants to spell out in an LP letter: If risks are inseparable, why do our mandates pretend otherwise?  Bright lines such as “no commodities,” “no project risk,” “no policy exposure” grow dim once a distribution view shows how power volatility drives EBITDA, how policy cadence drives cap‑ex, how climate noise drives demand.  

Alpha will migrate to the creases between those fading lines, and a new breed of managers will arbitrage the mispriced seams. What they still lack is a wrapper that lets private‑market upside and public‑market hedges travel together—same subscription doc, same K‑1, same story.  

Design that wrapper properly and the benefits of bottom‑up modelling turn into cheaper, stickier, deeper capital for every hardware company that used to raise money like a software start‑up. That wrapper is the multi‑asset SPV.  In Part 2, I’ll show how it works, why it’s not simply “fancy project finance,” and how one tweak to a capital stack can move billions into the next‑generation industrial build‑out.

Open your finest spreadsheet. Highlight every hard‑coded guess. That yellow glare? It’s risk begging to be re‑priced—​and the first ingredient in the product we build next week.