Kirkland & Ellis, the world's highest-grossing law firm, is setting aside $500M to build its own AI platform rather than rely on tools available to its rivals
Financial Times
Context & Ripple Effects
Legal AI coverage has moved from firms experimenting with tools that can take on entry-level legal work to specialized partnerships for drafting, review, and due diligence. Venture-backed providers are also targeting both law firms and in-house teams.
Kirkland & Ellis's decision to fund a proprietary platform is significant because it positions a major law firm as a builder and buyer of legal-AI capability, not simply a customer of the same software available across the market.
First-order effects
Kirkland & Ellis can direct substantial resources toward AI workflows tailored to its own legal and client-service needs, rather than depending solely on broadly available products.
The move raises the strategic importance of differentiated data, workflows, and implementation expertise within the firm; external legal-AI vendors face a more demanding buyer that may build selectively in-house.
Second-order effects
Rival firms will face pressure to choose between proprietary development, exclusive partnerships, or standardized vendor tools, with the choice increasingly affecting how they compete for complex work and clients.
Legal-tech incumbents and startups will be pushed to offer deeper customization and integration, while in-house legal teams may gain more leverage as firms and vendors compete to automate comparable tasks.
Third-order effects
If major firms continue to internalize platform development, legal AI could split into a market of shared foundational tools and firm-specific layers built around proprietary workflows and institutional knowledge.
The economic effect flagged in earlier coverage—automation of work traditionally handled by junior lawyers—could become more consequential as AI shifts from experimentation to embedded operating infrastructure, though the extent of labor displacement will depend on deployment and client demand.
The trend: Legal AI is evolving from point tools for routine tasks into a strategic platform race in which firms seek durable differentiation through proprietary implementation and specialized partnerships.
Kirkland & Ellis to spend $500mn building its own AI technology https://www.ft.com/... // It isn't difficult to see why an industry leader would want to seek a competitive advantage in a rapidly changing platform transition. History sees this as a challenge. It is difficult to [i…
Was chatting with two partners at a top law firm the other week. They love using Harvey but also believe it's only a matter of time before Harvey moves into services and transitions from vendor to competitor. Big Law sees the writing on the wall!
AI startups rely heavily on customer data. We're about to see large companies aggressively silo their data to build proprietary advantages their competitors can't replicate. I'm very bearish on harvey, and all these sector focused AI startups
Unless they hire someone out of palantir, anthropic, etc, to build this out, this money will be wasted. Unfortunately, the people who can build the platform, are probs making way more money at their initial firms.
This is a massive bull case for on prem vs cloud, especially as more large companies move towards building their own proprietary tools Few realize this yet
The message for firms is that buying a thin wrapper like all the other firms is no longer going to be enough. Buying Harvey and Legora is soon going to become synonymous with being lazy and unserious about AI if you are a big firm. If any firms want to build their own AI so
people are excited by this bc on prem/proprietary but tbh i think it will all eventually go to open models intranet vs internet private chain vs public chains we default to open bc its innately better
Every big company that operates mostly in the world of ideas and documents should think hard and ask themselves why they're not doing the same thing. Especially because you can outsource the truly hard parts to Qwen and DeepSeek and run the models securely on your own hardware.
Will spend $500m to get to 80% of what Harvey/Legora can do for a fraction of the price - can't get the same engineering talent since there's no equity upside - realize software maintenance sucks - will bleed talent to by being forced to use subpar tools
The AI legal model continues to evolve rapidly. The decisions being made by the biggest law firms are laying the foundation for the legal industry and the AI framework of tomorrow and beyond. …