AI Decision-Making in Financial Research [Newsletter #35]

Hello, AI enthusiasts from all around the world! Welcome to the weekly newsletter for the AI and The Future of Work podcast.

Do you envision a world where AI runs everything?

We're not talking about AI brewing our coffee or setting up our weekly schedule.

We mean AI making every decision, from business to operations to logistics.

What would happen?

Today, we explore this possibility and why it doesn't excite us.

Everyone using the same AI to make the same decisions gives us all the same answer.

Instead, we should strive to create custom processes. After all, it's not about the data we have; it's about what we do with the data—how we interact with clients, problems, and solutions.

Today's guest talks about how AI is changing businesses, the importance of breaking the norm, and how the randomness of the human brain can be a real strength.

Let's dive into this week's highlights! 🚀

🎙️New Podcast Episode with George Sivulka, Hebbia CEO 

You should always build a company that solves a real pain point.

But what do we mean by pain? In this case, we're talking about the frustration of repetitive, time-consuming tasks that slow down processes and drain productivity.

George Sivulka, CEO of Hebbia, knows this pain all too well.

He’s seen how the excitement of a new role often fades when people realize that much of their work is routine and mundane.

If a company wants to stand out, it shouldn’t just make things easier—it should become a painkiller. By solving everyday problems, businesses free people to focus on what truly matters.

Our CEO, Dan Turchin, sat down with Sivulka to explore how solving these challenges can transform entire industries, from finance to far beyond.

George Sivulka knows a thing or two about going against the grain. He was once hailed as a wunderkind by Peter Thiel, worked at NASA as a teenager, and earned a math degree from Stanford in only 2.5 years. 

Now, he's the founder of Hebbia, where he’s secured $130M in funding at a $700M valuation, backed by Andreessen Horowitz, Index Ventures, Google Ventures, and Thiel himself.

Hebbia's AI platform has driven a 15x revenue growth in 18 months by pioneering retrieval-augmented generation (RAG) techniques that ensure LLM outputs remain within company-sanctioned data.

George discusses this and more:

• How Hebbia is reshaping financial research with RAG.

• How George leaped from academia to entrepreneurship by turning research into real-world impact. 

• Why precision is critical in the expanding role of AI in high-stakes industries.

• Why transparency can make or break trust, one of the biggest AI challenges today.

• AI agents will change knowledge work—but not in the way you might think.

This issue is inspired by our latest AI and the Future of Work podcast episode featuring George Sivulka.

🎧 Listen below to hear his take on breaking the norm and solving the pain points that hold companies back.

Want to stay updated on his latest work and Hebbia’s impressive growth? Connect with George Sivulka on LinkedIn and let him know you enjoyed the episode!

📖 AI Fun Fact Article

Great investors are always reading, looking for the tiniest clue that can give them an advantage. 

However, it’s impossible to compete with today’s AI and LLMs, given their ability to process vast data banks.

So, should investors use AI to make better decisions? Monika Brown tackles this question in the Chicago Booth Review. It's all a matter of how and when. 

Investment funds use LLMs to derive insights from earnings, regulatory filings, annual reports, social media, and even news headlines.  

Whoever uses this data and trains LLMs to create direct trading signals and develop new variables for their forecasting models has an advantage. 

At the same time, Brown also ponders whether the advantages associated with LLM strategies will disappear once everyone else uses them. It’s happened in the past with arbitrage strategies. 

However, opportunities might be more abundant this time, as the field is still in its early stages. After all, researchers are still finding new ways to apply AI to discover new investment and trading opportunities. 

Our CEO, Dan Turchin, urges us to use LLMs and learn about them. It is crucial to know how they work, where they source their data, and how they calculate outputs. This way, we can understand where they're vulnerable to making mistakes and where we can trust them. 

Here's the link to the article in the Chicago Booth Review. What are your thoughts? We'd love to hear from you, so leave us a comment!

🧠 Worth a Thought

Coming Up Next 🚀

That’s a Wrap for This Week! 🎧

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