Artificial intelligence could soon be making waves in the cryptocurrency business, though perhaps not in the way you think. Rather than merging the two technologies, San Francisco-based prime broker FalconX plans to put a chatbot in the co-pilot’s seat for investors.

Using technology created by OpenAI, whose ChatGPT program is helping companies like Microsoft rewire online search, FalconX clients will be able pose questions like “What are the three biggest differences between two blockchain platforms?” or “What is the delta between Sharpe ratios for a Bitcoin basis strategy or a Bitcoin hold strategy over a two-week period?” to a bot called Satoshi.

Satoshi–named for Bitcon’s purported founder Satoshi Namakmoto–will also be able to generate investment ideas for users based on their historical trading activity, portfolios and interests, says FalconX CEO Raghu Yarlagadda. Though the technology is very much in its early stages – the current prototype primarily allows users to get customized news summaries akin to traditional ChatGPT responses to user queries, and trading backtesting has only been available for a few weeks – advancement is likely to come quickly.

FalconX is a natural bridge to bring OpenAI’s technology into crypto. Prathab Murugesan, the company’s engineering head spent 2.5 years at Google working on bringing machine-learning technologies, a process by which computers are trained to recognize patterns and anticipate actions, into products such as Gmail and Google docs.

Yarlagadda, began work at Google in 2014 on the current CEO Sundar Pichai’s Chrome OS team. “Sundar said that Google would be a machine-learning company,” says Yarlagadda. “This was a complete and radical departure from the norm, because machine learning had never been operationalized to a scale where you can freely give access to all of these massive products.”

This machine-learning approach was built into FalconX from its start in 2018 because it was the only way to get a clear picture of the market. Therefore, initial uses were focused on banal tasks such as cleaning up market data to sift out fake volume and wash trading, notorious problems in crypto.

However, machine-learning algorithms cannot tell traders what to do next. Yarlagadda says that one can train a computer model to recognize pictures of cats by sharing a library of images with the program. It can become very proficient at distinguishing cats from dogs and even identifying different types of cats, but no matter how many images it sees, it cannot draw one. Taking this analogy one step further, even if this model was trained to recognize dozens of types of animals, it would be unable to perform a task like predicting how a platypus might evolve in 1,000 years in a scenario where ocean temperatures rise 2 degrees.

In trading, this analogy is the equivalent of asking a traditional algorithmic trading model, which likely took a team of developers to code, to build a strategy for circumstances that are yet to happen and maybe customize it to a specific portfolio.