Picture this: a fund manager receives an AI-generated report telling her to consider a late-stage tech investment company. The numbers look neat, the charts impressive. But when she asks why, the answer isn’t clear. Which assumptions drove the recommendation? What risks were weighted most heavily? Was the model overreacting to a single data point? Without that clarity, she cannot make an informed decision and must solely rely on AI’s assumptions.
Without transparency, AI-augmented investments pose a challenging problem: we either must fully trust their output or double-check everything, effectively doubling the work. These are the core problems that explainability is meant to solve.
Artificial intelligence is entering private markets at full speed. Models can now process more data in minutes than analysts could in months, running simulations, testing scenarios, and surfacing insights. In theory, this should improve decision-making. In practice, it can just as easily make things worse.
Opaque AI models or so-called “black boxes” may churn out conclusions without revealing the logic behind them. Investors are then left with recommendations they cannot interrogate. Far from de-risking decisions, the lack of transparency introduces a new layer of uncertainty.
The financial world has already seen the dangers of unchecked automation. In 2012, Knight Capital lost $440 million in less than an hour when a flawed algorithm ran wild in the markets. That disaster was caused by coding errors, not AI, but the lesson still applies: if you don’t understand what the system is doing, you can’t control the outcome. In today’s AI-driven landscape, the risks are even greater and often harder to detect
As one CFA Institute report recently noted, “AI is most powerful when it augments human judgment, not when it replaces it with mystery.”
In financial decisions, explainability isn’t a “nice to have” - it’s the difference between trust and doubt. If you can’t trace the path from raw data to final reports, you can’t evaluate whether the model is amplifying bias, overlooking risks, or relying on unrealistic assumptions.
Take Limited Partners (LPs) who already demand auditable processes from General Partners. They want to know not just what decision was made, but why. A survey from CSC in 2024 found that 86% of LPs want clearer insight into how returns and distributions are calculated, and nearly two-thirds have challenged funds on opaque waterfall provisions. In other words, the need for transparency directly shapes which funds get capital.
Family offices are another example. Many run with lean teams that can’t afford to dedicate weeks to dissecting opaque reports. They need institutional-grade insights that they can understand quickly. If AI can provide those insights in a way that is both efficient and explainable, it becomes a force multiplier. If not, it becomes another source of noise.
And then there are corporate VCs, who face unique scrutiny from inside their own organizations. Boards and strategy teams ask: how does this investment connect to our core business goals? If AI suggests a portfolio of startups, leadership won’t settle for “the model says so.” They want a transparent link between logic and strategy. Explainability provides the common language that bridges investment activity with corporate priorities.
This is why Arcanis describes explainability as part of a broader trust layer. Trust doesn’t come from dazzling dashboards or predictive power alone. It comes from being able to open the hood, inspect the mechanics, and verify the assumptions.
When every step of the research process is explicit and verifiable, investors keep control. They can challenge scenarios, adjust inputs, and test outcomes. AI becomes a tool for clarity, helping humans see and test the complexity behind each decision.
Think of it like flying an airplane. The cockpit is full of automation, but the pilots remain firmly in control because every system has indicators, checks, and backups. Transparency doesn’t slow them down; it allows them to fly safely and navigate any and all conditions. The same principle applies to investing with the use of AI.
The regulatory environment makes this shift unavoidable. In the United States, the SEC has signaled a push toward mandatory transparency for private funds. In Europe, the AI Act classifies finance as a high-risk sector where explainability is a legal requirement. Across Asia, regulators in Singapore and Hong Kong have issued guidelines urging firms to ensure AI systems remain interpretable and fair.
The direction is clear: black-box models will not be acceptable in contexts where billions in capital and livelihoods are at stake.
For funds, this means explainability is fast becoming a compliance necessity. Early adopters of transparent frameworks like Systematic Investment Intelligence (SII) don’t just protect themselves from regulatory risk; they establish a credibility moat in a market where LPs and co-investors are demanding more robust processes.
The stakes are simple. Without explainability, AI risks turning investing into a high-tech gamble, scaling poor judgment faster than ever before. With explainability, it can do the opposite - expand the breadth and depth of insight available to investors, while keeping the decision-making process clear, fair, and human-centered.
That is the role of the transparent AI trust layer for investors to ensure they don’t just get faster answers, but ones they can understand and get behind.
At Arcanis, we’re building the foundations of VentureAI; explainable, human-centered intelligence designed to make venture investing more transparent, verifiable, and trustworthy.
