Earlier this year, when software stocks were taking a beating related to the heights of AI enthusiasm, a client reached out to me for my opinion on whether AI might be used by domain experts, such as myself, to create competing products capable of supplanting established software firms like Advent and others at a fraction of the price due to a lower cost base.
At the time, I acknowledged that it was possible, but more likely to happen at smaller firms. Investment professionals are stereotypically risk-averse in their business practices and the production software they utilize to run their businesses. While the largest investment firms are better suited to tolerating risk from a resource standpoint, they are also the least likely to take on what they might consider unnecessary risk. As a result, larger firms as a whole are slower to replace mainstream products with upstart offerings that have an unproven track record.
However, a more likely possibility has emerged: firms currently running vendor software—motivated by the costs and limitations of those products—may simply create their own systems using AI to aid them in the process. I have already seen signs that some firms are doing this and feel comfortable doing so. I am not necessarily recommending it, but I understand the motivation.
If you are paying substantial fees for systems you believe you could replace, why wouldn’t you? The key here is understanding what these systems actually do. A vendor with a forty-year track record has built a product that almost certainly handles details that an AI-assisted or ‘vibe-coding’ alternative (with a relatively short window of development and testing) does not; however, that may not matter much if you are only looking for 60% to 80% of the functionality of the product you are replacing.
When I compare products in the investment space, there are clear differentiators between older-class solutions and newer-class solutions. Those differentiators once would have represented a significant hurdle for a competing product to match, but AI makes it much easier.
As an example, consider the HTML attachment below, which summarizes hypothetical investments for a fictitious company and client in a format typical of newer-class solutions. I used Claude to help me produce this last month. This is something I could have done previously if a prospective client had asked me to, but I was never tempted to do so because it would have been tedious and time-consuming to perform manually. With the assistance of frontier AI models, I was able to achieve the following result, which can easily be populated from portfolio accounting data within Axys or APX. Please note that all reporting exhibits in this blog were produced using Anthropic, OpenAI, and Perplexity models and based on fictional data; disclosures on each exhibit also restate this.
The prompt was the result of a test I performed one afternoon where I asked the best-known publicly available frontier models in the cloud, as well as the best local models I can run on my Alienware Area-51 desktop, to create what I consider to be a decent report package by today’s standards. I would love to take more credit for it, but I cannot; I simply asked Claude to do it and it was done. Perplexity also did a respectable job, though it fell slightly behind Claude’s effort, while ChatGPT’s report was clean but relatively simplistic.
The local models didn’t put up much of a fight, although I may be able to improve their performance by using frontier models to design the prompts for them. It would be nice to rerun these tests using GLM-5.3 Q4 or GLM-5.3-Flash on a Mac Studio with an M5 Ultra chip and 512GB of unified memory, but I don’t have one of those yet. Naturally, if you write better prompts than I did or possess superior hardware, your results may vary.
Claude didn’t just create a pretty mockup of data in HTML like ChatGPT and Perplexity; it created JSON blocks within the HTML that I can populate programmatically from data feeds I have already created using xPort with Axys and APX. If I wanted to build an alternative system, I would be well on my way with this type of reporting right out of the gate.
Here is what I produced with Perplexity’s assistance.
Here is what I produced using ChatGPT.
The pros and cons of building versus buying remain. AI can potentially help you maintain the code, but will your staff truly understand everything they are using? Probably not. And what will it cost to maintain that code in the future? If the system is predominantly AI-coded and not fully understood by those at your firm, that cost will be determined by the “AI gods” in power at the time.
Can a true domain expert with deep knowledge of specific software utilize local and/or frontier models to build a competing system in a fraction of the time and cost required years ago? Yes. Absolutely—and every day, it is becoming more feasible.
About the Author: Kevin Shea is the Founder and Principal Consultant of Quartare; Quartare provides a wide variety of agile technology solutions to investors and the financial services community at large.
For details, please visit Quartare.com, contact Kevin Shea via phone at 617-720-3400 x202 or e-mail at kshea@quartare.com.
