More and more legal tech vendors are releasing tools with the same (or highly similar) features. Maybe it’s because gen AI makes it so easy to build. So, when everything becomes same, same, same, how do you pick the winners? And, how do you mitigate the risks of picking not a winner?

Oz Benamram issued a prediction for the market: we should be prepared for AI self-service legal chatbots in two years. How likely is this scenario to occur? Are law firms and businesses ready? Are the signs already flashing in neon red for those who are paying attention?

Even the latest LLMs remain susceptible to prompt injections. The risk is not only theoretical. We conducted a series of experiments to identify some real scenarios where even prudent lawyers may be at risk.

For those of us working with legal tech, we often find ourselves needing to build solutions (whether because a solution doesn’t exist, or nothing quite closes the last mile). This blog post shares the methods we use to discover how to “start with the problem".

Has the increased attention on Gen AI experiment and adoption been harmful to the broader legal tech ecosystem? Is Gen AI's dominance crowding out better solutions? Or are we just in an awkward but necessary transition?

The market is in a bubble. This blog post is a frank look at the forces shaping legal AI pricing — and the uncomfortable questions firms should be asking now.

With AI becoming increasingly more common in legal workflows, what will legal service consumers demand? And, as a result, how will legal service providers change their delivery framework?

If human thoughts are just another means of production, then humans can be replaced by AI. The question is not “if”, but “when”. Let’s forecast together the future of what AI adoption could mean for lawyers.

Lawyers don’t care about technology. They never had before… so why is AI getting so much attention now? What changed? Why should lawyers rethink everything they know about the business of law?

We have observed three predominant modes of engaging with LLMs that emerge naturally in practice. This blog post explores the simplified equilibria of the assistant-servant-expert spectrum of LLM interactions.