The AI-native law firm: regulatory innovation and the fundamental restructuring of legal service delivery

Garfield, co-founded by former City lawyer Philip Young and quantum physicist Daniel Long, operates an AI-powered litigation assistant for small claims debt recovery up to £10,000. The platform guides users through the entire process: from calculating amounts owed to filing for default judgment. The firm charges on a per-document basis rather than hourly rates, fundamentally departing from traditional economic models.[2]

The SRA’s authorisation came with strict safeguards including client confidentiality protection, conflict avoidance mechanisms, user approval at each stage and controls preventing AI from proposing case law – addressing concerns about AI hallucinations. SRA Chief Executive Paul Phillip characterised this as ‘a landmark moment for legal services,’ while acknowledging ‘potential risks to the public’ that require ongoing monitoring.[3]

Similarly, Arizona’s Alternative Business Structure (ABS) programme has enabled Eudia Counsel to operate as an ‘AI-augmented law firm’ with non-lawyer ownership, to support corporate clients with contracting, M&A due diligence and other complex legal needs.[4] It joins KPMG, Axiom, LegalZoom, and Elevate, all of which have launched affiliated firms under the ABS programme.

These regulatory frameworks represent a philosophical shift: accepting that legal service delivery can be fundamentally reimagined when appropriate safeguards and accountability mechanisms exist.

The technology imperative

The technological capabilities now available represent the second critical enabler of AI-native models. Unlike incremental efficiency tools that marginally improve existing workflows, current AI systems can perform substantive legal work at speeds and costs that fundamentally challenge traditional economic assumptions.

Consider NormAI’s trajectory. The company raised money from Blackstone in November 2025 to launch Norm Law, described as ‘the first AI-native full-service law firm for global institutional clients.’ NormAI’s proprietary ‘Legal Engineering’ methodology combines their no-code AI platform with over 35 lawyers trained to convert legal workflows into large language model-driven AI agents.[5] The firm serves clients collectively managing over $30tn in assets, demonstrating that AI-native models can address sophisticated institutional requirements, not merely routine consumer matters.[6]

Similarly, Crosby – which raised $5.8m from Sequoia Capital and Bain Capital Ventures – provides AI-powered contract review with a median turnaround of 58 minutes. Sequoia’s investment thesis emphasises Crosby’s ability to build customer-specific knowledge bases that become more sophisticated over time, highlighting how AI systems can develop institutional memory that compounds value rather than remaining static.[7]

This technological capability extends beyond speed to sophistication. Lawhive’s AI assistant ‘Lawrence’ scored 81 per cent on the Solicitors Qualifying Examination, well above the 55 per cent pass threshold, demonstrating proficiency at paralegal or junior lawyer level. This is not automation of clerical tasks – it represents AI performing substantive legal analysis.[8]

Economic disruption and the leverage question

The traditional law firm model relies on leverage: partners supervising associates who perform the bulk of billable work, creating economic returns through the margin between associate salaries and billing rates. AI-native firms reject this fundamental architecture.

Covenant, co-founded by Jen Berrent (former CLO/COO of WeWork) and Richard Perris (formerly of CVC Capital Partners and Clifford Chance), received $4m in seed funding with an explicit premise: that technology is the main leverage. The firm operates with only six lawyers handling highly leveraged work, offering limited partnership agreement reviews at $900 per document – approximately 90 per cent less than traditional pricing – while achieving 80-90 per cent reductions in time and cost.[9]

This economic model creates profound implications. A technology-leveraged firm with under ten lawyers can potentially match the output of a traditional firm requiring 60 lawyers under the pyramid model. The cost structure transforms: technology costs scale logarithmically while human labour scales linearly. Fixed technology investments replace variable human capital costs.

Pierson Ferdinand LLP, launched with over 130 partners but zero junior lawyers, exemplifies a hybrid approach. Senior partners use platforms such as Harvey AI for first drafts and routine work traditionally performed by associates.[10]

Market forces and client demand

Client expectations constitute the third lever driving this transformation. Corporate legal departments face relentless pressure to reduce external spend while maintaining quality. Traditional hourly billing creates misaligned incentives: the slower the work, the higher the revenue. AI-native firms offering fixed fees, per-transaction pricing or subscription models address this fundamental tension.

Thomson Reuters’ 2025 ALSP Report reveals that 85 per cent of corporate legal departments use traditional law firms, but 44 per cent now purchase directly from independent Alternative Legal Service Providers, with 33 per cent using law firm affiliate ALSPs.[11] This diversification reflects client willingness to experiment with non-traditional providers when value propositions are compelling.

The access to justice dimension also drives demand. More than 50 million low-income Americans receive inadequate legal help for 92 per cent of civil legal problems.[12] In the UK, small businesses are owed billions in unpaid invoices but cannot economically pursue claims under traditional fee structures. AI-native firms like Garfield explicitly target markets traditional firms cannot profitably serve, expanding the overall market for legal services.

Capital availability and investment

The availability of substantial venture capital and private equity represents a fourth enabling factor. NormAI’s total funding exceeds $140m from investors including Blackstone, Bain Capital, Vanguard, Citi, and Marc Benioff,[13] while Crosby secured $5.8m from Sequoia and Bain Capital Ventures and Covenant raised $4m in seed funding. These are not modest experiments; they represent significant capital backing specific to AI-native legal service models.

The investment thesis centres on unit economics that traditional firms cannot match. Technology-enabled firms can operate at software-like margins while delivering human-quality outcomes. Investors recognise that once developed, AI systems can serve additional clients at marginal cost, creating scalability impossible under human-dependent models.

Notably, Google-backed Lawhive’s acquisition of traditional UK firm Woodstock Legal Services in September 2025 – believed to be the first acquisition of a traditional law firm by an AI company – signals capital’s confidence in vertical integration strategies combining technology platforms with regulated legal entities.[14]

Implications for the traditional partnership model

These developments pose existential questions for traditional law firms. The partnership model evolved when human intellect applied to legal problems was scarce and valuable. The economic logic was self-evident: aggregate multiple lawyers’ productive capacity, create leverage through supervision hierarchies and extract economic rent from the gap between cost and billing.

AI fundamentally challenges this scarcity assumption. If AI can perform work previously requiring three years of legal training in minutes, at marginal cost approaching zero, the traditional leverage model faces structural revenue compression.

The response cannot be merely adopting AI for efficiency while maintaining hourly billing. Clients will not pay the same rates for work completed in minutes that previously required hours. LexisNexis research shows 47 per cent of lawyers believe AI will change how firms bill for services, with 55 per cent of general counsel holding this view. The tension is clear: AI reduces time required, but traditional billing models monetise time spent.

Conclusion

The emergence of AI-native law firms represents a genuine structural shift, not incremental evolution. Regulatory willingness to authorise novel models, technological capabilities enabling autonomous legal work, economic pressure for alternative pricing and capital availability converge to make this transformation inevitable rather than speculative.

Traditional firms face a fundamental choice: evolve business models to reflect that AI, not human labour pyramids, constitutes the primary leverage mechanism, or accept gradual displacement by competitors operating with structurally superior economics. The partnership model that dominated legal services for generations assumed human intellect was the scarce resource. That assumption no longer holds.

The question confronting the profession is not whether AI-native firms will exist – they already do, with regulatory approval and substantial capital backing – but rather what role traditional firms will play in a market where technology-first competitors can deliver comparable quality at a fraction of the cost. The answer will probably determine the structure of legal practice for the next generation.