Reasoning Models Are Remaking Professional Services

**George Sivulka** (0:00)
A lot of my peers, if they were smart and they were lucky, they were going into financial services jobs. They would go and become an investor. And I realized that the smartest people in the world, incredibly smart kids, were going and doing the stupidest tasks.

**Alex Immerman** (0:12)
So many tedious things, repetition, boom, boom, boom, night after night.

**George Sivulka** (0:18)
And I saw pain. I noticed in my friends that they literally hated their lives. And at Stanford, maybe the only other thing that I took from the entrepreneurship community there was build a company where there's pain. And my 22-year-old brain said, well, there's a lot of pain here, and there's the most important technology in the world that can solve it. That's where I'm going to go and build a business before anyone else gets on to it.

**SPEAKER_3** (0:41)
Thanks for listening to the A16Z AI Podcast. Today, we're exploring the intersection of professional services and AI agents via a conversation between A16Z partner Alex Emmerman and Hebbia founder and CEO George Sivulka. Although we're very early on in the development of agentic workflows, Hebbia can safely stake its claim as an early application of them thanks to its embrace of reasoning models and a user interface that does away with the reliance on chatting. In this discussion, George and Alex explore the rationale behind some of these design decisions. They talk about building products for an AI native user base and dive into the industry changing benefits of AI, especially in areas like financial services, investing and law, where smart people spend way too much time on tedium and checking boxes.
Oh, and George also gives his opinion on DeepSeek and dishes on his favorite AI tools. All that and more after these disclosures. As a reminder, please note that the content here is for informational purposes only, should not be taken as legal, business, tax or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any a16z fund. For more details, please see hay16z.com/disclosures.

**Alex Immerman** (1:54)
George, it's really fun to have you here. I've enjoyed getting to know you the last couple of years. I'm excited for the world at large to hear more from you.

**George Sivulka** (2:01)
I'm excited to be here.

**Alex Immerman** (2:03)
Amazing. Well, we're gonna open with a lightning round, getting to the heat of AI.

**George Sivulka** (2:09)
Great.

**Alex Immerman** (2:09)
Let's rip it. Do you think scaling laws are going to hold?

**George Sivulka** (2:14)
It's a good question. I think that there's two types of scaling laws. You have scaling laws for training, which is what I think you're referring to. And then more recently, people have started to talk about scaling laws at inference. And I think they're both effectively mathematical properties of the universe. I don't think they're just an experimental observed thing. You just know as you add more data and add more compute, these models get better for training, that is. And I think that they will always hold if there's enough data, and that is the question. But I do think that GPT-5 will be significantly better than GPT-4. At the same time, you're starting to see models like kind of O1, O3, doing this like reasoning at inference or scaling at a deep seek as well. And this is a technique, scaling at inference, that was first actually pioneered at Hebbia. And we quickly noticed another scaling law, where if we ran more models and basically more compute at inference time, you could get much better results for very complex tasks.
And I think that scaling law has already proven to kind of extend the runway of AI. And I think it will continue to hold as a mathematical property in itself.

**Alex Immerman** (3:20)
All right, so scaling laws are going to hold. We talked about deep seek briefly there. Is deep seek a nothing burger or is it a big deal?

**George Sivulka** (3:28)
I am a believer that like it was more on the nothing burger side of things. I think China has shown time and time again that they're able to take technologies that are invented oftentimes in the United States and make them more efficient. And I think China has also shown time and time again that they're willing to obfuscate the truth or omit certain things when talking about certain technologies or the science behind a variety of different things. Regardless of the truth with deep seek, I generally also think it's really an American technology that's cheaper. And Americans can also make it cheaper and it was invented here in the United States. And China has not shown that they can actually continue to play ball pushing the frontier of AI. This is not an example.