AI VCs Are Taking Pitches and Approving Startups

You’re an entrepreneur in a typical online pitching scenario. On the other side of the screen, in real-time, the always busy and eager to get to the point group of VCs that don’t want to waste precious time. With the solution to this “problem” in mind, the idea pitched to them is for a companion that would eliminate their need to be present, either physically or remotely, in these calls or the hundreds of pitching sessions they attend every year.

While this sounds like a futuristic scenario, the real-life application of AI companions and agents for VCs, who can attend meetings on their behalf, evaluate pitches, and even decide whether to invest in a business idea, is not as far-fetched as it seems. This implication of AI could solve the biggest problem investors with limited resources still face: the lack of time and attention.

It’s hard to think that big investment firms would miss out on this opportunity, because as you’ll find out, AI is already a big part of their decision-making process.

There Is No Going Back

Investment and technology have always gone hand in hand. Not only is tech where most venture capital and private equity hunt for new ideas, but it’s itself dictating the nature of investing.

A Survey by Allvue found that among the group of VC and PE companies that participated in Q4 2024, 82% reported using AI and machine learning in some form or another. A data-intensive sector, such as finance, would normally utilize tools that effectively aggregate data and yield the most reliable results to date. Intelligent document processing, LLM, workflow automation, and large-scale data processing are now the industry standard, and pitch deck analysis is just one of the many applications of this technology in an investor’s busy life.

The machines can easily read the data from thousands of pitches an investor receives every year and present a recap of the findings, along with a short list of what the data suggests are the best pitches. The job that one assistant would do in a year, the AI can do in seconds. Yet, what we are discussing is the next step: not an assistant, but a companion that takes things from deal sourcing to exit.

Why Go as Far as an AI Companion?

What the data suggests is not the only criterion a VC needs to consider when investing; otherwise, there would be no pitching call, meeting, or process at all.

The people involved in the idea, as well as their ability to be convincing and follow through, are equally important as the financial forecast or the market analysis of the product or service. The human element extends not only to the team in charge but also to the investor and their affinity towards a specific market.

That is where the AI companion would kick in. Beyond aggregating all the usual tools a financial firm uses, it would be trained on the investors’ behavior, patterns, character, and, depending on the degree of freedom, make similar decisions to humans while communicating with a founder. It is an approach not unlike emotionally adaptive AI sexting bot systems, which are already being tested in other industries. 

Another factor to consider is that an investor needs to follow up on a deal, hold regular meetings, answer questions, and make calls as needed, depending on the situation. With the new tech available, it would go as far as emulating the voice, looks, and reactions of the investors. 

None of the above is speculation, as a significant part is already here. No Cap, the world’s first AI angel investor, has announced a $100,000 investment in the startup Wonder Family. In just a few days and only a three-minute call, the AI moved from an introduction to a SAFE agreement.

What Does It Mean for Founders?

In a simulated call, the company mentioned above, No Cap, posted what talking to a virtual VC could look like for founders. This scenario, and the company itself, are promoted as solely AI, meaning there is a group of investors behind it, but not the emulation of a specific person, which is what a companion would bring more to the table.

AI companions could, but don’t have to be, disruptive. In truth, a founder in the future could be pitching to an AI version of Warren Buffett without realizing it’s not the real one. Furthermore, as platforms integrate AI into everything, the companion could take charge of all aspects of communication for the investor, including their LinkedIn profile, which would arguably offer a premium service to users.

Plainly put, if the VS companion becomes a reality, what would make the most difference for the founder is the extra effort put in. Today, it’s hard to get through the noise of hundreds of emails and calls. Still, if there’s no noise anymore, and an AI can answer all, then the matter becomes not about cutting through the noise, but about being objectively better than all the rest and personalizing the pitch to the extreme. 

Is There a Risk in Being Too Efficient?

Would the story of an entrepreneur matter as much, and the “why” behind the mission statement, if an AI is filtering out their work? Would Gut Feelings still be a thing if all becomes automated?

One of the main arguments against the disadvantages of giving too much freedom to AI is its reliance on historical data. Can AI replace the VC as a long-term strategist in any stage of decision-making if its only reference is the past? A case is to be made on whether an investor would prefer the AI taking the shots for them, if that would mean spending more of their time.

The ethical and regulatory considerations are debatable, as the implications rely on the freedom of AI to make investment decisions in funds that actual people own and in contracts that can’t be overlooked as “terms and conditions” of an app.

A Hybrid Future

The companion could be a blend of an AI agent and an AI companion that perfectly emulates the behavior of the person, notifying the investor when an interesting opportunity that has already passed the initial phase is worth exploring.

Whatever scenario transpires, the inevitable reality is that AI will become increasingly autonomous in investment decisions once it has completely solved today’s problems of data analysis and has a proven track record of successful financial decisions.