First mover advantage is a familiar idea: the first company to market has a window to build brand, land marquee customers, collect data, and train users. Those early wins create credibility and stickiness. In the best cases, they compound into network effects as the product improves with use and becomes embedded across teams.

Historically, that translated into inertia. Incumbent customers resisted switching because their data and workflows lived inside the first platform, and executives trusted the “safe” choice. New buyers followed suit because adopting the leader was defensible. No one gets fired for picking the default.

Our view is that AI weakens the foundations of the first mover playbook:

• Exploration is expensive for the first mover. Early AI leaders can’t just identify a problem area and apply existing product and GTM playbooks. Instead, they must navigate product, distribution, and pricing at the same time, while model capabilities change underneath them. They pay the compute and talent tax and often rewrite key decisions as approaches become obsolete. In legal AI, Harvey invested in fine-tuning its own model for legal use cases, then abandoned the effort as leading foundation models outperformed.
• Feature parity is faster. Coding agents and copilots compress shipping cycles, and modern growth tools compress brand-building. The first mover’s “unopposed window” is shorter. Legora, founded roughly a year after Harvey, is widely viewed as beyond product parity despite raising less capital.
• Buyers are experimental. AI tools deliver immediate “magic moments” and real productivity lift, but the cohort is young and still maturing. Buyers want to “adopt AI,” but they aren’t sure which tools matter. Trialing multiple tools and signing short, overlapping contracts is common. In our diligence, nearly every Harvey customer we spoke with said they would consider alternatives, and many were already trialing or procuring Legora.
• Switching costs are not high (yet). Most AI applications have not been embedded long enough to create deep lock-in via integrations, data accumulation, and workflow entrenchment. We routinely hear that organizations would switch to a better or cheaper scribe, IDE, or workflow tool with little hesitation. Legal AI is no different.

The pattern we see in AI is consistent: a first mover creates awareness and legitimacy, then spends meaningful time and resources on experimentation. Once the opportunity is clear, a second mover follows, learns from the false starts, and can overtake the first mover on product with fewer resources. This is not new in history, but AI makes it more common.

A simple example: early AI applications poured capital into building proprietary LLMs, then pivoted to building on foundation models as those improved. Followers avoided that dead end and built for model swappability from day one. They saved time and money and ended up with more agile architectures.

We also see a recurring split in strengths. First movers often excel at GTM because they are closest to the pain point and fastest to sell the vision. Second movers often excel at product because they enter with clearer requirements and a more precise view on what actually works in production.

None of this says second movers always win. The winner may depend on where buyer power is concentrated. In segments where end users have decision making power and performance differences are obvious in daily work, product-led second movers are advantaged. In segments where executives and risk committees drive decisions and value “default safety,” first movers tend to hold.

Legal looks like the first kind of market.

Harvey was first to market with a generalist legal AI platform. But as we got to know Max Junestrand and the Legora team, it became clear the race is still open. Legora’s growth inflected sharply last year. It started 2025 at roughly 8% of Harvey’s scale and ended the year around 40%, while landing major U.S. law firms and enterprises. More notable than the numbers was the user feedback. Repeatedly, lawyers described Legora as the best AI platform for legal work.

Legora and Harvey target similar headline activities: synthesizing information, drafting outputs, and automating contract and document workflows. The difference shows up in the last mile. Users described Legora as better at finishing the job, especially on the unglamorous details that determine whether output is usable: formatting and polish, in-context accuracy, and handling large documents. Those differences make the output feel production-ready.

One example came up repeatedly in contract review: aggregating comments across multiple document versions, interpreting changes in context, and producing a clean, properly formatted draft. Users told us Legora could execute that workflow end-to-end, while other tools often broke on versioning, missed details, or produced output that still required too much cleanup to be client-facing.

Customers also described exceptional product velocity. One noted that Harvey’s product improvement has been steady for over a year, while Legora showed “hockey stick growth,” with massive improvement from month one to month four. Others pointed to rapid iteration on the Word plug-in, collaboration features, and workflow automation. Several customers made the point directly: if Legora built this much depth with fewer resources and less time, it is the team to bet on.

With hindsight, Legora made smart product calls. It was multi-model from day one. It optimized for production-grade outputs inside lawyers’ native workflows, not demo-level summarization. It focused on use cases that matter to lawyers, like track changes automation and drafting from precedent with high fidelity. Those choices helped it close the gap quickly and, in a growing set of workflows, surpass the first mover.

We came to see Legora as a product-led company that absorbed the market learning created by a sales-led incumbent, effectively starting on second base from a product standpoint. That is the second-mover advantage, operationalized.

Product leadership alone does not guarantee success. Two conditions favored Legora’s ability to realize second mover advantage.

First, first-mover lock-in has not fully formed. Legal AI adoption is still exploratory. Many initial contracts are evaluative rather than permanent. Teams feel pressure to adopt AI to stay competitive, but few want to commit to a single platform indefinitely. Legora is building momentum while switching costs are still low.

Second, law is a market where end users have real influence and performance differences are felt daily. We observed that exec-level, non-user decision makers often view Harvey and Legora as interchangeable. Practitioners do not. They consistently articulated differences in output quality, usability, and workflow completion. That is exactly the type of market where a product-led second mover can take share and hold it.

Second mover advantage is not inevitable. It takes a founder with product instincts and a team that ships fast with customers. Max and his team fit that profile. Buyers consistently described the company as unusually responsive and collaborative, and characterized working with the team as energizing. They “roll up their sleeves” and build.

Second mover advantage is also not permanent. Third and fourth movers will come, including offerings from the model providers themselves. We think Legora’s “production-ready” wedge matters here. The closer a tool gets to real work product, the deeper it sits in workflows, and the more it benefits from compounding usage data and iteration cycles. That compounding should strengthen its ability to generate tailored recommendations and outputs over time.

As AI reshapes legal work, we believe Legora’s agility, product intuition, and shipping velocity position it to pull ahead and stay ahead. We are excited to support the Legora team on their mission to become the world’s leading legal AI platform.