Legal AI: Battle lines

New factions have entered the legal AI market this year. While big tech and AI companies continue to weigh in with legal-specific offerings, law firm partnerships, incumbent tech and AI providers are fighting their corner. Legal AI unicorns (startup companies that reach a valuation of $1bn-plus) Harvey and Legora are extending their market dominance. 

A new breed of (almost) untried open-source, DIY legal AI platforms are attracting attention for democratising legal AI, just as the biggest, richest firms are increasing their AI budgets and investments. But while agentic AI (which plans and executes multi-step tasks with minimal human input) is promising more autonomous legal processes and workflows, AI hallucinations are still appearing in court with embarrassing frequency. As highlighted by Pope Leo XIV in his first encyclical (see box, p18), without effective and responsible human supervision and governance, AI can be dangerous to outcomes and reputations.  

AI governance and the human factor

Last month, Pinsent Masons, which has long been in the vanguard of legal AI innovation, was admonished in the Insolvency and Companies Court for presenting inaccurate evidence generated by an AI tool that the firm is piloting. When ICC Judge Mullen queried a citation, the junior associate who had submitted it responded with a further AI-generated explanation that was inaccurate and potentially misleading. The circumstances are set out in Judge Mullen’s judgment in Cork & Anor v Smith, which highlighted his concern over the firm’s failure to supervise the associate who had used AI to produce these documents. Pinsent Masons has apologised and referred itself to the Solicitors Regulation Authority. 

This was a failure of governance, not technology. If someone had checked the original citations, they would have been corrected before being presented in court. Another major risk factor was that the lawyers responsible for the case were unaware that the junior associate had used AI for the application. Pinsent Masons declined to comment on the episode at this stage.

'Court hallucinations are the most obvious kind of AI error – a cited rule either exists or it doesn’t, and once you start looking it is findable eventually'

David Howorth, Avvoka

Most firms have an AI policy that would normally include a requirement for partners in charge of a case to be informed when AI is used to prepare documents. The Pinsent Masons judgment is unusual because the judge was able to access the AI chat transcript, which showed that the AI tool issued repeated reminders to check citations against authoritative legal sources. The problem is ensuring that people follow policies and processes, which would apply to the use of any tech tools and systems to produce legal evidence – not just AI. 

As David Howorth, director at no-code document automation and contract management platform Avvoka, wrote on LinkedIn: ‘Court hallucinations are the most obvious kind of AI error – a cited rule either exists or it doesn’t, and once you start looking, it is findable eventually.’  

He has a point. Pinsent Masons is the latest in a series of law firms caught using AI hallucinations in court. Paris-based data scientist, lecturer and international investment law expert Damien Charlotin maintains an AI Hallucination Cases database of legal decisions in cases where generative AI produced hallucinated content. To date (as at 9 June 2026), it has identified 1,598 cases, including 59 in the UK. The vast majority (1,115) are in the US, and they mostly involve fabricated case law (as in the Pinsent Masons case). While there are no reliable accuracy statistics for legal AI, due to the confidentiality of the material, court reports and judgments are in the public domain. 

Howorth continued: ‘The harder question is how you spot drafting that isn’t a hallucination – a real, plausible clause that is simply wrong from a drafting point of view. It looks like the correct work product; it is just incorrect for the matter or job at hand. That is the review problem that is starting to build up.’

Alex Smith, senior director of product – search, knowledge and AI at iManage, explains that, because AI models rely on data that has been indexed and published, they may not be able to access all relevant case law, and you cannot identify what has been omitted unless you check the original citation. This is the responsibility of the lawyer leading the case. ‘A lot of tech people forget that, ultimately, the output of any (legal) work is owned by a human with a professional qualification,’ he says.

‘Ultimately, the lawyer handling the case is responsible for the output, whether it has been produced by an AI model or a human researcher. People need to understand the limitations of these tools.’ This echoes a discussion in a recent Gazette roundtable, highlighting the need for AI to link back to its source material to enable lawyers to understand the law underpinning the output, verify citations and check their relevance to the case in hand. 

'Supervision is part of the business of law, whether or not AI is involved. There is always a possibility that someone could misinterpret something'

Karen Jacks, Bird & Bird

User training is a critical element in effective AI governance. Michael Kennedy is head of research and development, innovation and legal technology at Addleshaw Goddard. ‘We have strict policies and guidance. AGPT [the firm’s proprietary AI model] isn’t linked to LexisNexis or Thomson Reuters, so if you ask [AGPT] a legal question, it responds that it doesn’t have access to that information and directs you to our research and knowledge page, which has links to LexisNexis and Practical Law. We also have Legora which you can use for some legal research, but again, it doesn’t have access to every source of legal information.’ 

Kennedy agrees that the biggest risk with AI is that it does not have access to all the information. ‘And it’s important to acknowledge that and go and find the correct legislation,’ he says. 

‘It’s all about training: having the right policies in place, and also making sure lawyers know about them and use AI sensibly, not as a shortcut,’ Kennedy adds. ‘So someone needs to own the AI strategy and make sure everyone understands the risks. Our policy is that if you use AI to create something, whether you are giving it to an associate, a partner or another firm, you have to say that you have used AI and checked its output and that you are able to provide the sources.’

At Bird & Bird, which was the first firm to deploy Legora, global head of legal technology and innovation Hélder Santos explains that the firm has always followed a strategy of double-checking the output of tech tools and systems, whether or not AI is involved. ‘We ensure there is a human in the loop for all approved tools and workflows to ensure accountability and ownership.’ 

The firm’s chief technology officer Karen Jacks adds: ‘Supervision is part of the business of law, whether or not AI is involved. There is always a possibility that someone could misinterpret something or create an incorrect document. We have policies, guardrails and training to reduce those risks.’

'It’s all about training: having the right policies in place, and also making sure lawyers know about them and use AI sensibly, not as a shortcut'

Michael Kennedy, Addleshaw Goddard

Big tech and big law

The last few months have seen big tech’s continuing incursion into the legal market. Following the launches of Anthropic’s Claude for Legal and Microsoft’s Legal Agent for Word, OpenAI recruited Jason Boehmig, co-founder of AI contracting platform Ironclad, as product leader for its new ‘legal vertical’ (which refers to the way legal tasks of all types require a mix of legal expertise, process, and technology). While OpenAI is only now directly targeting the legal sector, it was a first mover in legal generative AI as one of the original backers of legal tech unicorn Harvey, which was originally built on its GPT models. And as legal AI strategies swing between buy and build, law firms are partnering directly with big tech to create proprietary AI models. 

Following Freshfields’ direct partnerships with Anthropic and Google, US giant Kirkland & Ellis, having announced a $500m multi-year AI strategy, has partnered with software and data analytics company Palantir to develop its Fund Formation Engine, which is an enterprise platform designed to transform private equity fundraising.

Meanwhile, Fried Frank launched FundAssist, its proprietary AI platform to help its asset management, regulatory and tax lawyers with fund formation, operations and transactions. 

For AI companies Anthropic and OpenAI, the legal vertical is an important element of their pivot to enterprise products and services, having confidentially filed for IPOs planned for later this year. (A confidential filing allows a company to submit its financials to regulators before they are made available to prospective investors.) 

As part of OpenAI’s IPO filing announcement, CEO Sam Altman described the ‘third phase’ of OpenAI, which includes becoming a ‘product company’. ‘The economy is beginning to reshape around AI,’ he wrote. ‘The central question now is how to make advanced AI abundant, affordable, safe, useful, and easy enough for every person and organisation to benefit from it.’ 

Government AI initiatives

AI is also top of mind for governments. In the US, CNBC reported that Altman is continuing discussions with the White House about a possible government stake in OpenAI. 

Prime minister Sir Keir Starmer’s opening address at London Tech Week on 8 June set out the government’s strategy for backing British AI companies and building the right infrastructure for them to grow, with government investment in sovereign AI, AI chips and data centres. And there are plans to use technology and regulation to protect children from online abuse, potentially by embedding controls on devices. 

The legal sector is in the vanguard. Justice secretary David Lammy announced that it will be the first sector to join the government’s AI Growth Labs, a regulatory sandbox ‘to accelerate the development of AI products and services by helping innovators navigate existing regulatory frameworks’. Legal regulators led by the Solicitors Regulation Authority would play a key role. This echoes LawtechUK’s regulatory response unit, which was established in 2020 to provide similar support to lawtech startups.

Open source platforms or cowboy coders?

A new element of the vibe coding trend in the legal sector that has been gaining traction all year is the evolution of open source (free) platforms, notably MikeOSS, which former Latham & Watkins lawyer Will Chen built in just two weeks. It provides much of the functionality of Harvey and Legora, and Lavern. (Vibe coding refers to software developed with the assistance of an AI tool which has been instructed using natural language.)

Lavern is a customisable agentic AI law firm created by tech lawyer, law professor and legal design maven Antti Innanen. Using any combination of 67 specialist AI agents (which are not all lawyers), you can build your own firm. You can also create your own agents, or even clone yourself as an agent! An interview agent gathers the context of each case, specialist agents debate the best approach, and an orchestrator agent creates documentation that then passes through a 10-pass verification loop. Autonomous processes which run locally on your device push outputs to email or messaging apps for human review. 

'There is a big difference between a great demo and something you can realistically and successfully run client matters on'

Alex Fortescue-Webb, Legora

While Lavern is free to access, and Innanen encourages people to experiment with it, feedback so far is that it requires quite a lot of tech savvy to operate effectively. Having said that, it has 74 forks (that is, 74 developers are working with it) and 238 stars on the proprietary developer platform GitHub.

The reaction from legal AI unicorns indicates that they are not threatened by open source platforms, whose users tend not to be their target market. Harvey co-founder Winston Weinberg responded to a question about MikeOSS in a Harvey AMA (‘ask me anything’) session on Reddit r/legaltech, moderated by Alex Denne, chief growth officer at SimpleDocs. ‘We believe open source is good for the legal industry. But open source doesn’t mean free,’ he said. Law firms need infrastructure, governance and collaboration, and self-hosting is rarely cheaper when you also factor in serving costs, maintenance, training and support.

Alex Fortescue-Webb, global head of legal engineering and head of UK and Ireland at Legora, highlights the ‘big difference between a great demo and something you can realistically and successfully run client matters on. Legal work has very high standards and specific requirements that are hard to meet without deep domain investment, jurisdiction-aware research grounded in authoritative sources’, he says.

‘Legal AI platforms also have to meet the security and data sovereignty standards that clients and regulators actually accept…Overall, the risk profile of an open source or vibe-coded legal AI platform is probably too high for law firms or larger corporates to use them in practice right now.’ 

Kennedy at Addleshaw Goddard agrees: ‘MikeOSS is interesting because people like me, who build [AI tools] and are into vibe coding, can easily build a Legora clone to demo internally, but the last mile is the toughest because we have to think about security, access control, long-term scalability and integrating into [the firm’s] knowledge and billing systems. Not many law firms have the development capability to do all of that. Furthermore, if you build everything yourself, you have to cover token costs, which keep going up, instead of simply paying for a subscription.’ 

But is vibe coding the AI equivalent of cowboy coders? Writing on LinkedIn, Barrie Hadfield, co-founder and CEO of Mindset.AI (and previously co-founder of legal tech company Workshare, acquired by HG Capital in 2019), argues that, while vibe coding allows people to build fast prototypes, it can be more dangerous than what used to be called cowboy coding (an individual developer building bespoke solutions with no audit trail). Coding decisions are delegated to the AI agent and each project starts with a clean slate (so it is not iterative). So while vibe coding means anyone can code, the output is not easily replicated or scalable. 

Hadfield writes: ‘I am torn about it. I am genuinely in favour of the democratisation of coding. I am not in favour of it arriving at the expense of professionalism.’ Again, the solution involves closing the feedback loop. He adds: ‘The shape of professional AI-first work is a closed loop with three deliberate steps: you specify what you want, the agent builds it, and you verify that what came back is what you asked for.’

Smith sees vibe coding as giving lawyers new ways of interacting with tools and systems because they can use natural language prompts to build data visualisations and PowerPoint decks, and create client and matter-specific tools without having to learn multiple systems. ‘Vibe coding is another way for firms to build tools that are tailored to a specific client or matter on good, trusted, governed data and well-understood processes,’ he says.

Legal AI is fighting its corner

Notwithstanding multiple new factions competing for its market, legal tech, and specifically legal AI, is fighting its corner. Harvey and Legora are achieving close-to-global adoption. And legal tech incumbents are not being swapped out. 

The top five most-searched solutions on Legaltech Hub are an interesting combination of foundation models, legal AI platforms, and two enduring incumbents: Claude for Legal, Legora, 3E by Elite (the practice management system that spun off from Thomson Reuters in 2023), Harvey, and Westlaw Edge by Thomson Reuters. Number ten in the list, Orbital Copilot, highlights significant interest in AI’s potential to transform real estate work. Legora’s latest acquisition is Cadastral, an AI agent platform for commercial real estate. 

While Kennedy acknowledges that big tech offerings such as Claude for Legal make it harder to make the business case for legal AI platforms, he believes ‘there’s definitely value in Harvey and Legora, because while Claude for Legal and OpenAI’s Codex for Legal are designed for lawyers too, they are still surface-level compared with the legal-specific platforms. You would need to adjust the Claude Cowork legal plugin, for example, to fit your firm and the different types of work you do, as well as the data residency, whereas Harvey and Legora can handle all of that because they are focused on legal.’

He takes a similar view of open source legal applications. ‘While a small group of people in a firm can vibe code prototypes and tools that work for them, it’s a different matter to get to the point of being able to roll something out across a big law firm. It’s much less risky to buy Harvey or Legora, which lawyers can use without having to be tech savvy,’ he says.

AI as infrastructure 

While some of the biggest firms are building proprietary AI models as a differentiator, Legora’s Fortescue-Webb observes, ‘building a fully integrated, enterprise-grade legal AI platform is not a small undertaking. A small number of firms with the scale and technical ambition to sustain it may pursue that path. Most won’t, because the economics don’t stack up.’ Fortescue-Webb believes that ‘the firms that are moving fastest… are restructuring how they work for clients, execute legal workflows, and deliver differentiated value through AI’. 

He observes that the legal sector is ‘already moving on from the AI assistant model and entering the agentic era, where AI agents can execute legal workflows end-to-end with human lawyer oversight’. 

Kennedy sees AI as a platform for the firm’s institutional knowledge and experience, which is its market differentiation. ‘As AI becomes part of the firm’s infrastructure, it is no longer a software cost; it’s the cost of building an AI-enabled infrastructure layer that leverages all our knowledge, proprietary data, and workflows, and produces efficiencies that enable us to do more and different types of work.’

Smith observes that the latest proprietary models – for example, the specific fund management tools announced by Kirkland & Ellis and Fried Frank – are to support the firms’ specialist practice areas, and are a way of ring-fencing the expertise that differentiates them. 

‘They don’t want to give generic AI tools access to that information, because it is their secret sauce,’ Smith says. ‘These partnerships are a great opportunity for them to build tools that support their truly differentiating practice areas,’ adding that what sets a firm apart is often more than legal expertise. ‘It is also about insights and finding interesting directions for things like business development and client relationship management. So the firms that are building proprietary models probably also buy the generic/legal specific tools.’ 

Jacks at Bird & Bird acknowledges that AI is no longer an add-on. ‘AI is definitely part of the tech stack now. What’s keeping us busy is deciding which AI might be better for a particular use case and which suite of products to invest in. 

‘The Kirkland & Ellis partnership with Palantir is interesting, although most firms don’t build proprietary products, and it may give them a competitive edge because it’s different.’ 

Santos agrees with Kennedy that AI is no longer a software solution because it is embedded in workflows, which may represent a threat to some legal tech vendors. ‘Until now, legal tech was software with per-seat pricing. Now, AI is handling workflows and processes, and AI companies are selling a service, not a software solution. The market is changing because venture capitalists are betting on the service layer instead of the software layer.’ 

Smith at iManage highlights the enduring importance of well-managed data when AI is routinely used for legal service delivery. ‘Because some law firm data cannot be exposed to AI, we get a lot of requests around improving data security, permissioning and context layers, which shows that knowledge management is more important than ever,’ he says. 

He argues that firms’ competitive advantage still resides in lawyers’ experience and expertise because an experienced lawyer can spot a wrong or incomplete reference. He makes the point that while AI hallucinations in court will tend to be caught because of the adversarial nature of court cases, AI mistakes in contracts and other commercial agreements will not be discovered until something goes wrong and they have to stand up to scrutiny. Which comes back to legal AI’s continuing need for human oversight. 

Joanna Goodman is a freelance journalist