People fresh out of law school won’t be spared the impact of automation either. Document-based grunt work is typically a key training ground for first-year associate lawyers, and AI-based products are already stepping in. CaseMine, a legal technology company based in India, builds on document discovery software with what it calls its “virtual associate,” CaseIQ. The system takes an uploaded brief and suggests changes to make it more authoritative, while providing additional documents that can strengthen a lawyer’s arguments.

“I think it will help make [entry-level lawyers] better lawyers faster. Make them more prolific,” says CaseMine’s founder, Aniruddha Yadav. “If they are handling a couple cases at a time, they will learn the law faster.”

The company has already racked up hundreds of paying customers in Asia and the Americas, and it has plans to open up shop in the U.K.

Other legal tech startups with AI at their core have been gaining steam as well. Kira Systems, which makes a contract review platform, counts four of the top 10 American law firms, as well as several international firms, as clients. Meanwhile, investors plowed $96 million into Zapproved, a startup that makes a cloud-based electronic discovery tool. Overall, it’s been a banner year for new legal tech companies, with funding up 43 percent in the first three quarters of 2017 compared with the same time last year, according to a report by the research firm CB Insights.

Law schools have recognized the trend and are beginning to adapt: many have created new programs to teach the next generation of lawyers how to use these platforms and speak intelligently to the people building them. Harvard, for example, offers courses in legal innovation and programming for lawyers. Arman Moeini, a recent law school graduate and now an associate attorney, had the chance to use electronic discovery software while at the University of Florida. “Although imperfect, this software is quite effective, and drastically cuts down on the time spent performing document review—a task generally given to entry-level associates at larger national firms,” Moeini says.

There are, however, still obstacles to further adoption of AI in the legal profession. Chief among them is a lack of accessible data to use in training the software. Take the contract analysis company Legal Robot. In order to train its program, a team of developers built their own database of terms and conditions by collecting examples from major websites. But that wasn’t enough—the company also had to strike deals with law firms to gain access to their private repositories. In total, they compiled over five million contracts.

Adam Ziegler, the managing director of the Harvard Law School Library Innovation Lab, wants to remove this barrier to entry. He has helped lead the CaseLaw Access Project, an effort to digitize the entire historical record of U.S. court opinions and make that data available for legal algorithms to read and train on. “I think there will be a lot more experimentation and the progress will accelerate,” Ziegler says about the impact of this project. “It’s really hard to build a smart interface if you can’t get to the basic data.” His team completed its work in January, and the information is now publicly available online for free.

Although lawyers are not known for their fast uptake of technology, Ziegler anticipates interest. “I expect that clients’ knowing that technology can perform many of the repetitive tasks will [make them] increasingly unwilling for lawyers to do that work,” Ziegler says. “Why would you pay for a junior associate to do the work that technology could do faster?”