Venture Capital & The Rise of Algorithmic Investing (1/2)

May 2025 

There are moments in the history of finance—as in the broader arc of civilisation—when the instruments of our craft evolve so radically that they unsettle the very habits we had grown fondly to trust. The handheld calculator, the spreadsheet, the Bloomberg terminal—each marked an inflection in how we measured, modelled, and moved capital. Today, the rise of artificial intelligence in venture capital represents another such turning point. It is not a mere contrivance or passing curiosity, but a profound shift that challenges the delicate arts of judgment, the appraisal of character, and the instinctive foresight earned only through years of human experience.

Venture capital, that most intimate of commercial endeavours—where wagers are placed not on numbers but on men and women, on their eccentricities, their untamed certitudes, their half-formed visions—has always flourished in the hands of those who cultivated instinct like a vintner his vine. The lore of the trade, so to speak, lay not in algorithms but in conversation, not in models but in memory. Yet this edifice, part cottage and part casino, is now being wired with circuitry that promises to learn faster, forget slower, and judge without fatigue.

Today's newspaper talks about QuantumLight, a venture house of recent vintage, born from the success of fintech enterprise Revolut. Its claim is both audacious and emblematic of the age: that it has made all its investments—seventeen thus far—through the agency not of scouts or sages, but of proprietary algorithms. Ten billion data points, seven hundred thousand companies, no human intuition necessary. Theirs is the credo of a “machine-first” firm: impartial, tireless, and presumably unflinching in its discipline.

Nor is QuantumLight an island. SignalFire boasts real-time data feeds from one hundred million sources. Zetta Venture Partners filters its inbound pipeline through the lens of machine learning. GV, InReach, Fly Ventures, and the Scandinavian experiment EQT’s “Motherbrain” all trumpet their algorithmic wares. And yet, for all the murmurs of revolution, results are whispered more than sung. Some claim traction; others conceal it. There is, one suspects, as much marketing as method in this new race toward computational preeminence.

But let us not be too quick to dismiss. The old methods were wasteful. Venture capitalists of yesteryear learned from the few they funded and forgot the many they rejected. The data of failure was lost, the lessons unarchived. In contrast, today’s platforms—employing semantic search, vector embeddings, and real-time ingest of public signals—promise to remember everything. They do not tire. They do not forget. They compare, classify, and collate. Every pitch becomes a datum; every rejection, a refinement.

One is tempted to say that the venture capitalist now walks into his meetings not armed with anecdote and instinct, but encased in armour—an Iron Man suit, if you will—whose sensors detect patterns too faint for the human eye, whose processors remember what the partner forgot.

Is this the dawning of a rational age in investing? Or simply a new theatre in which to enact the old play of optimism and self-deception?

We ought to remain skeptical of proclamations that too quickly divorce judgment from wisdom. For in venture investing, we are not merely solving problems of arithmetic or logistics. We are attempting to divine human potential: the capacity of a founder to endure failure, to persuade others, to see around corners. These are not things that fall neatly into rows and columns.

The best founders defy pattern; the greatest companies begin as anomalies. If AI is to serve the future of investing, it must do so not by replacing the errant flashes of intuition, but by enriching the mind in which such flashes arise. The promise of augmented decision-making lies not in rendering man obsolete, but in making his judgments more informed, his failures more instructive, and his convictions more precise.

We stand, then, at the confluence of two logics: the empirical and the imaginative. The former is concerned with what is measurable, repeatable, and explicable. The latter with what is strange, emergent, and without precedent. Tomorrow's Venture capital must learn to marry the two.

Let us not forget: the great error of our age is not ignorance, but misplaced certainty. And no algorithm, however advanced, can account for the beauty—and the tragedy—of a world built on human ambition. At least that’s what history shows !!

Let us, then, lift the hood and examine more closely the algorithmic machinery of this trend that has taken the venture capital world by storm.