AI Could Expand Legal Capacity Beyond Current Limits
Legora CEO and co-founder Max Junestrand argues that legal work is constrained less by demand than by the capacity to meet it, leaving contracts unreviewed, diligence undone and some disputes unpursued. He sees AI as a way to make legal expertise available where time and staffing have put it out of reach, but says wider adoption depends on lawyers trusting the work. For Legora, that means continually rebuilding as AI capabilities advance rather than treating its product as finished.
Legal work is constrained by capacity, not demand
Max Junestrand describes law as a supply-constrained field: demand for legal services and legal intelligence exceeds the capacity to provide them. The shortfall shows up in work that never happens. Some contracts are not reviewed; some injustices do not proceed to litigation or the courts; and diligence on assets may be done only after an investment decision rather than before it.
Those examples point to a gap between the legal work people need and the work legal teams have time to perform. In Junestrand’s account, the consequence is not simply that existing tasks take too long. Decisions may be made without diligence that could have been done earlier, and legal problems may not reach a process where they can be addressed. He says the world needs—and is demanding—more legal services and legal intelligence.
Legora’s ambition is to make that expertise available “on tap.” Junestrand says the question is what the field and industry will do with the additional resources. He does not prescribe a single answer; the examples he gives show how added capacity could change when and how legal work is handled.
For legal teams, that can mean shifting some work away from manual review. An AI agent could take the first pass on a contract, or handle a larger share of diligence in an M&A deal so the transaction can move forward faster. Junestrand frames the legal team’s job as not only protecting the business but helping it move forward. The potential benefit, as he describes it, is not just completing a familiar task faster: it is having more room to perform work that limited time or capacity might otherwise put out of reach.
Trust has to accompany the added capacity
Junestrand points to results at one Fortune 100 client as evidence of what the company is trying to build: a 90% reduction in contract turnaround time, more than $20 million saved on external litigation and diligence, and 9.5 hours returned to each lawyer per week for higher-value tasks. He connects those results to a broader ambition: for every piece of legal work to run on Legora.
He puts that ambition plainly: “And now our ambition is that every single piece of legal work should run on Legora.”
That ambition depends, in his view, on lawyers being able to trust the work, not just receive it quickly. Junestrand calls trust perhaps the most important currency in law—more important, in his formulation, than the billable hour. He says it matters that customers never feel they are second-guessing the system or compromising on that trust.
The displayed Legora interface makes the kind of work at issue more concrete. It shows an assistant processing a question about how the Delaware Supreme Court has determined “fair value” in minority-shareholder appraisal actions arising from mergers. The screen breaks the question into five subtasks: the statutory text and foundational framework; a group of deal-price appraisal cases; going-concern value and shareholder-level discounts; discounted cash-flow reliability and projections; and unaffected market price and synergy exclusion. It shows the work moving through verification and synthesis, then lists citations to legal decisions.
For Junestrand, trust is central to how customers experience this work. He says they should not feel they are second-guessing the system or compromising on trust as they use Legora.
Being AI-native means rebuilding as the frontier moves
Legora’s origins predate the current wave of generative AI. Junestrand says the initial idea took shape when the team had access to BERT, an early model from Google. Some of the use cases they were exploring were difficult to crack; they did not finally work until the team gained access to GPT-3.5. Seeing that change led him to leave university. He describes GPT-3.5 as his generation’s “internet moment” and says that without OpenAI, there would probably be no Legora.
Junestrand compares Legora to a fast Formula One car, with OpenAI’s successive models as the engines that can make it go faster. The analogy captures the role he sees for improving models in the product’s development.
That relationship informs what he means by staying AI-native as Legora grows. He says it requires keeping ego low and treating product development not as an effort to build something that will stand the test of time, but as a continual move across the frontier of what AI can do. The underlying technology shifts quickly, so product work may involve building “sand castles” and then bringing the best of new capabilities into the product.
Junestrand’s view of the profession’s future follows from that approach, but he describes it as a possibility rather than a settled outcome. Legora’s saying is that “it is the best time in history to be a lawyer.” If the company gets its work right, he expects the transformation of the industry—and of what individuals can achieve with technology—to be so profound that earlier technology waves or innovations may seem “miniscule” by comparison.