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To investors,
There has been a big debate in the AI industry on whether general-purpose AI models will dominate every industry or if there is room for application-specific AI products to win certain markets. We can settle that debate today.
Our team at Silvia is announcing this morning that we have successfully built the single most accurate and powerful AI product when it comes to tax-related topics. This means Silvia outperforms OpenAI’s Chat-GPT, Anthropic’s Claude, Google’s Gemini, xAI’s Grok, startup Tax-GPT, and open-source models as well.
I understand people will be skeptical that a small team of engineers could beat trillion-dollar companies who are spending tens of billions of dollars per year. Skepticism is good. But I wouldn’t make a bold claim like this without having the objective data and facts to support the announcement.
Today, as part of this milestone achievement, we are releasing a research paper that shows the test results, along with open sourcing the evaluation rubric, including the relevant prompts and user parameters. This allows anyone to go benchmark against our eval rubric and pressure test what we are claiming.
Very few companies ever open-source the eval rubric because they consider it IP worth protecting. We are open-sourcing the prompts and user parameters today because we want to prove beyond a doubt that Silvia is the superior AI product when it comes to tax.
So how did we successfully beat the largest AI model labs in the world at tax-related topics?
The short answer is that we made a different strategic bet than them. The general-purpose models aim to be the most intelligent models across a variety of industries and topics. They want to be very good in history, math, science, software coding and a plethora of other fields. The idea is to be as intelligent as possible, yet applicable in a general way.
I find this general-purpose approach to be quite valuable in my day-to-day life. However, when I want answers and insights about a specific topic like tax, I want to go to experts. The generalist models are great at searching the web and using their large language models to predict answers, but tax is an area where good guesses are not enough.
So our team at Silvia set out to solve this problem a few weeks ago. Here is what we did:
We started with Silvia’s proprietary AI harness, which is a state-of-the-art system created specifically for our platform, our data set, and our use case.
We built proprietary data pipelines that allowed us to index the US federal and state tax codes and then make them much easier for an AI agent to read and work with.
We leveraged our internal proprietary data set, which is one of the largest anonymized data sets in the world of wealthy people talking to an LLM about their personal finances.
By combining specialized workflows with proprietary data and the powerful Silvia AI harness, we were able to create the best AI product for tax-related topics. This objective truth proves that the big bet we made was correct: the general-purpose models are great at doing many things, but they are going to have a hard time competing with specialized workflows and domain knowledge in certain industries.
We are seeing this play out in other areas too. Harvey, the leading legal AI product, continues to dominate majority of the models by taking a similar approach. I believe there will be hundreds more companies that realize this approach is valuable and then begin fragmenting the market into a thousand pieces.
There are a few ramifications from today’s announcement that I think are worth calling out:
If you have not signed up for Silvia, now would be an excellent time to start using it. She will quickly become one of the most valuable pieces of software in your life and help guide you through your financial decisions.
The world has now been put on notice about the quality and ambition of the Silvia engineering team. While we may not have billons of dollars, we have a small team of very talented people who are laser-focused on winning the personal finances category.
The general-purpose models will have to make a decision: do they want to win the AGI race or is it worth trying to compete in the various verticals with application-specific companies and products? My guess is they will mostly stay out of the smaller verticals, which makes them susceptible to death by a thousand cuts when it comes to revenue and use cases.
There will continue to be an all-out war for proprietary data. The large models are already spending billions of dollars to acquire various data sets, including rare books and other difficult content, so you should expect that effort to accelerate from here.
Competition between general-purpose models and specialized workflow applications will become more cutthroat, but that ultimately will be a win for the consumer. Competition will breed innovation and users will end up with better, more accurate, and more useful products.
Today is one of the most important days in the history of our company. We have essentially pulled off the impossible. No one thought a team of five engineers and limited resources could beat trillion-dollar companies with thousands of employees.
But here we are. I will bet on the Silvia misfits any day of the week.
Please sign up for Silvia. Read the press release here. Check out the open source eval rubric here. And cross your fingers and toes for us. The journey ahead is not certain, but we are up for the challenge.
Hope everyone has a great start to their week. I will talk to you next time.
- Anthony J. Pompliano
Founder & CEO, ProCap Financial (Nasdaq: BRR)
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