The Rise of the House Model
Companies are turning their proprietary data and workflows into their own AI intelligence.
Hey, I’m Matt. I’m a former Bloomberg News reporter, and you’re reading AI Street, where I report on how Wall Street uses AI.
Travelers is the latest large company to train an AI model on its own data and says it beat commercially available systems.
The insurer trained TravelersLLM on millions of internal documents for underwriting, research and model development, according to a June 30 press release. The company, founded in 1864, said its long history and proprietary data helped improve the model’s precision.
“In testing against tens of thousands of insurance-related questions, [TravelersLLM] consistently outperformed commercially available AI models, delivering higher-quality results at lower cost and with greater speed.”
Emphasis mine.
Call them house models: AI systems trained, tuned or evaluated against a company’s own data, workflows and judgment, so not a generic model pointed at internal documents, but one that “learns” what a company does.
Travelers says AI, automation and analytics are contributing to operating leverage, expense-ratio improvement and underwriting profitability. Its shares have climbed about 19% this year, roughly double the Dow Jones Industrial Average, in which Travelers is a component.
I wrote back in May that legacy companies have an advantage over upstarts because they house so much data.
AI models are only as good as the data they’re trained on. Hard-to-replicate, legacy data is more valuable in the age of AI. And legacy companies are generally the ones with the legacy data. Many corporations are sitting on valuable intellectual property and, I suspect, don’t even know it.
The companies that do know the value of their data and the power of AI are turning their private records, workflows and judgment into models no other company can replicate because no other company has the same raw material.
The current AI boom stems from the breakthrough and flexibility of transformer models trained on language. But the benefits of this architecture extend beyond text. You can train these models on other types of data — the weather, grocery sales, bond portfolios — as long as you have enough high-quality data.
This is a topic I’ve been writing a lot about:
The conversation around companies adopting AI has focused on which model they’re using or what company they’re partnering with, which makes sense. You don’t need to fine-tune a model to write better emails. But if you’re in the business of selling property & casualty insurance, you’d much rather have a model trained on P&C claims rather than one trained on Reddit posts.
Moreover, if you’re an equities trader, you’d find more use for a model trained on prices, filings, broker messages, order flow and portfolio data than a generic chatbot. (By the way, this is part of the reason firms like Jane Street, HRT and XTX are spending billions on building their own data centers. It takes lots of computing power to turn proprietary data into better predictions, prices or trading signals.)
Microsoft CEO Satya Nadella has recently been making a version of the house-model argument: AI run by a handful of frontier labs is too narrow for the technology.
“There should be as many models in the world as firms in the world,” Nadella said in an interview last month:
Nadella’s argument is that a company is not just a buyer of software. It is a learning system. Its edge comes from its data, context, workflows, evals, traces, judgment and operating history. Frontier models can be part of that system, but if the company never builds its own learning loop around them, it is not compounding its own intelligence.
For the last couple of years, analysts and investors have been asking companies: how are you using AI? That is a good starting point. But Nadella’s argument points to the better question: how is the company using AI to turn its own data, workflows and operating history into a competitive advantage?
Paid subscribers can access a sourced working list of companies and organizations that have publicly disclosed proprietary or domain-specific model efforts.
Not a paid subscriber? Consider expensing AI Street. If it helps you evaluate one AI initiative, pressure-test one vendor pitch or understand one real deployment constraint earlier, it more than pays for itself. I put together a short template you can send your manager.