Pawel:

That’s right, absolutely! Thank you very much for inviting me to the podcast. I’m really glad we have this opportunity. Let me introduce myself briefly. Pawel Skrzypek, co-founder of AI Investments and Omphalos Fund. We focus entirely on applying AI to investments. This was our mission and vision from the very beginning—to use the latest AI advancements at the time of our founding for investment purposes.

We’ve been developing this company for a long time—in February 2025, it will be 8 years. So we started way before ChatGPT and the whole Generative AI boom in November 2022. Even before the broader AI boom. Of course, we also use Generative AI, because I think we can all agree that this is a future that cannot be ignored. But that’s the brief introduction.

The Investment Market: Before and After AI

Karol:

Let’s define the investment market. What did it look like before the AI era, before you introduced the solutions we will discuss today? What exactly is the investment market?

Pawel:

The investment market is very broad. We focus on a specific niche—software that uses AI for investment portfolio management, particularly for hedge funds, alternative investment funds, and similar industries. Of course, the market is much broader, but this is our focus.

The investment market is very specific. In the United States, and to some extent in Asia and London, there are firms that already use technology for investing—algorithmic trading systems, high-speed trading, high-frequency trading (HFT). However, the rest of the world still largely invests in the traditional way.

Karol:

Right, like the classic brokerage offices from movies, with traders sitting in front of multiple screens, analyzing markets, stock movements, and making trades manually.

Pawel:

Exactly! For example, in Poland, as far as I know, there are no investment funds using algorithmic trading solutions. There were a few in the past, but as of today, I believe there are none. In Europe, the number of such funds is also very limited.

The dominant markets for this approach are the United States (where large firms operate), Singapore, Tokyo, and London. However, compared to the U.S. giants like Citadel or Two Sigma, Europe remains a largely untapped market.

Traditional Investing vs. AI-Powered Investing

Karol:

Let’s go back to basics. How did traditional investing work before machine learning and AI? Was it just gut feeling, collecting market information, and making decisions based on experience?

Pawel:

Yes, that’s exactly it! Traditional investing relied on analysts collecting various types of information, following specific methodologies, and then valuing companies based on this data. There were many different methods, different signals, and different strategies.

Each investment fund had its own approach—some focused on stocks, bonds, commodities, etc. Analysts would gather data, analyze it, then present investment proposals to a committee, which would approve or reject investments.

This was a human-driven process, meaning that human factors heavily influenced decisions.

Karol:

So, investing involved experience, market knowledge, intuition, and maybe even a bit of gambling?

Pawel:

Yes! And there was always a human bias involved.

For example, an analyst might have a good or bad day, which could influence their judgment. They might like or dislike a company for subjective reasons and unconsciously manipulate the data to fit their perspective.

Even at the investment committee level, personal biases come into play—some committee members might favor certain analysts over others. These human factors make the entire process less repeatable and more unpredictable.

How AI Investments Approaches the Market

Karol:

So how is AI different? How does your investment process work?

Pawel:

We take an entirely systematic approach. Our AI-driven models are trained to analyze data, predict market movements, and execute trades without emotions. This eliminates the human biases that impact traditional investing.

Our approach is based on machine learning models that forecast short-term price movements—usually in a timeframe of 5-10 days. By keeping short investment horizons, we increase accuracy and reduce unpredictability.

We also make a high volume of trades, which allows us to leverage statistical probabilities—instead of making a few big bets, we make many smaller trades, ensuring that the law of large numbers works in our favor.

Performance of AI Investments: 3-Year Track Record

Karol:

Can you share some of your fund’s performance metrics over the past 3 years?

Pawel:

Of course! Over the last 3 years, our fund achieved a net return of approximately 40%.

Out of 35 months of operation, only two were negative. One of them was just -0.2%, and the other was -3%. All other months were positive. This proves that our AI-driven trading models are consistent and stable.

The Future of AI in Investment Management

Karol:

Where do you see the investment industry in 10 years?

Pawel:

I expect that 95%+ of trading volume will be handled by automated AI-driven systems. Today, 80% of market transactions in the U.S. are already algorithmic.

Human traders will still be involved, but their role will shift towards developing, optimizing, and monitoring AI models, rather than making discretionary investment decisions.

AI isn’t just the future—it’s already here. The industry is evolving, and those who fail to adapt will be left behind.

Conclusion

Karol:

Thank you, Pawel, for such a fascinating conversation. Your insights into AI-driven investing are eye-opening. I wish you continued success!

Pawel:

Thank you! It was a pleasure to be here and discuss the future of AI in finance.