Comment by sai18
19 hours ago
Thank you!
The decision makers we work with are typically modernization leaders and mainframe owners — usually director or VP level and above. There are a few major tailwinds helping us get into these enterprises:
1. The SMEs who understand these systems are retiring, so every year that passes makes the systems more opaque.
2. There’s intense top-down pressure across Fortune 500s to adopt AI initiatives.
3. Many of these companies are paying IBM 7–9 figures annually just to keep their mainframes running.
Modernization has always been a priority, but the perceived risk was enormous. With today’s LLMs, we’re finally able to reduce that risk in a meaningful way and make modernization feasible at scale.
You’re absolutely right about COBOL’s limited presence in training data compared to languages like Java or Python. Given COBOL is highly structured and readable, the current reasoning models get us to an acceptable level of performance where it's now valuable to use them for these tasks. For near-perfect accuracy (95%+), that is where we see an large opportunity to build domain-specific frontier models purpose built for these legacy systems.
In terms of training your own models, is there enough COBOL available for training or are you going to have to convince your customers to let you train on their data (do you think banks would push back against that?)
There isn’t enough COBOL data available to reach human-level performance yet.
That’s exactly the opportunity we have in front us to make it possible through our own frontier models and infra.