What happens when startups start evaluating algorithms rather than people

Traditionally, venture capital is about flair. An experienced fund partner "hears" the correct intonation in the funder's voice, feels that the idea falls into the zeitgeist, and may agree to invest even without a clear business plan. This model has worked for decades - and still does. But the world is changing, and with it, the rhythm and scale of the industry.

In recent years, analytical platforms built on machine learning have begun to enter the venture capital market. Their goal is to replace intuition with calculation, structure with algorithm, and charisma with metrics. This is not an attempt to remove a person from the process, but rather a significant shift in the role that person plays, instead of an "investor-visionary" - an analyst who relies on the forecasts of the system.

Such a transition is not loud, but irreversible. And startups should understand how decisions are now made about who will be given money, and why.

Why did you even need AI in venture capital?

Over the years, investors around the world have received hundreds of thousands of pitches. Even if the fund works with a specific focus, for example, only with fintech in Southeast Asia or only with SaaS platforms, up to several dozen applications can come in per day. You cannot view, structure, or filter this stream manually.

This is where artificial intelligence comes in. Next-generation platforms such as EQT Motherbrain, SignalRank, and Tribe Capital's System of Intelligence are taking over the work of the first layer of filtering. They do not just automate the selection, but build a logically based assessment model for each startup, based on a huge amount of data.

What exactly does AI analyze?

• Company website and its structure
• Press mentions
• LinkedIn activity
• Attendance growth rate
• Team composition and biography
• Volumes and frequency of code updates (in the case of a tech startup)
• Patents
• Market structure
• Open source user behavior
• Tone of mentions

All this is compared with the base of successful and failed startups, and a model of probable success or failure is built. This is not about guarantees, but about an informed assumption of who is more likely to "take off".

What changes for funds

Funds are starting to think like data companies. Instead of relying entirely on meetings, they integrate tools that suggest: "Pay attention to this project, it looks like successful cases three years ago". Or vice versa - "this startup has too many red flags, although they were convincing at the presentation".

The human factor is still important, but now it is not at the beginning, but closer to the end of the funnel. Instead of 200 meetings, 20. And each, after selection based on models that take into account thousands of factors.

It saves time, reduces costs, and helps funds not miss worthwhile projects that could pass by simply because of the lack of the right intro or a bad slide.

How startups can now prove their worth

The most noticeable change is the diminishing role of charisma. Yes, it still works in the late stages. But at the entrance of the funnel, it is not enough. The algorithm does not recognize emotions, it is not interested in your slogans. He looks at the growth trajectory, at the logic of product development, at the coincidence of patterns.

This means that founders should better document their actions, track metrics, form open sources of information, develop command pages, and make them understandable for machine analysis.

A startup with a strong idea but zero digital transparency will fail. But a young project with provable metrics, even without connections, has a chance to be noticed and rise higher in the ranking.

And if the algorithm is wrong?

Wrong - of course. Because AI doesn't understand the context. He can "hack" an idea that will later turn out to be revolutionary. It does not work well with rare, non-standard hypotheses. He builds a forecast based on the past, and breakthroughs often run counter to the logic of previous successes.

That is why in new systems, AI does not replace a person, but helps them. In the fund model, AI can act as a "scout", "analyst", or "partner assistant". But the investor still has the final word, especially when it comes to non-standard or risky solutions.

The principle is simple: the car shows the field, and the person chooses which one to play.

This is just the beginning

Today, only part of the large funds work with such approaches. But the trend is obvious. Small and medium-sized funds connect API tools, integrate assessment platforms, and hire data science specialists inside teams.

Startups, in turn, are starting to monitor how they look in the digital space. How they can be analyzed, what signals they send to open sources, and how much they correspond to the patterns of successful companies.

AI in venture is not about replacement. This is about the restructuring of thinking. Instead of the question "in whom we believe," the question "what can be justified" appears. Intuition does not go anywhere, but now it must go hand in hand with evidence-based analytics.

If you are a founder, do not wait until you are noticed in person. Make the data speak for itself.

If you're an investor, don't rely on old methods. New capabilities require new tools.

And if you just watch the industry, remember this moment. This is one of those rare cases when not only the product, but also the very way of making decisions, becomes technology.