The paradox has been a popular trope to reassure those concerned about AI taking jobs - that whilst AI would deliver efficiency savings, this would lead to an explosion in demand as prices fell; keeping everyone gainfully employed It's certainly plausible in some pockets of legal services, for example it's feasible that Garfield AI's very low cost to pursue sub-£10,000 debts creates demand for legal services that did not exist for chasing low value claims previously In other

Tokens, tokens everywhere Where Microsoft, Anthropic and OpenAI started, Legora now follows (and I suspect Harvey will soon as well) As charges for GenAI move away from flat subscriptions towards per-token pricing, law firms need to grapple with the change The transition may be a little painful, but consumption based pricing is far better aligned with internal processes than subscriptions Firms are well versed at capturing billable vs non-billable costs, recording the former

Something I'm finding increasingly baffling with AI - why are all the law firms parroting 'efficiency' as the primary benefit? [law firm] announces major partnership and rollout of [product] following successful pilot with [x] hours saved per week Where are the improvements to quality or scope? Where are the improvements to speed or turnaround times? To only publicly talk about efficiency is to immediately beg the question on fees - and increasingly I see clients asking for d

Ropes & Gray LLP have captured headlines in recent weeks for permitting AI experimentation to contribute towards 20% of first year billable hours targets We've seen tentative, small steps in this direction over the past 5 or so years, but they've tended to be low single digits of total hours targets, and possibly diluted with other non-billable activities such as BD R&G's 20% equates to 380 hours, out of a 1,900 annual target and is a significant step change. The targeting o