Legal AI's two speeds: the agentic bet and the assistive present
Legal tech raised billions to build agents that do the work. Practitioners keep opening ChatGPT in a browser tab. Koobo's discourse and hiring analysis measures the gap between the bet and the reality, and reads it as a position on the adoption curve.
The money has already decided that the future of legal AI is agentic. Legal-tech venture funding hit somewhere between $4 and $7 billion in 2025, depending on how it is counted, up from about $871 million in 2023 (Crunchbase, Artificial Lawyer, LexisNexis). Harvey, whose AI assistant drafts, researches, and reviews for law firms, went from a $5 million seed in 2022 to an $11 billion valuation in March 2026, raising more than $1 billion along the way, and nearly doubled its ARR from $100 million in August 2025 to $190 million by January 2026. After it shifted from answering prompts to running agentic workflows, its monthly AI token use jumped about 12x, from roughly 1 trillion to 12 trillion, in under six months. Harvey's CEO puts the thesis plainly: AI is "becoming the system through which legal work gets done."
The reality inside the profession is more improvised. Practitioners are opening a general-purpose chatbot in a browser tab, beside the work, to handle information. Koobo's own data measures the gap between legal-tech's agentic bet and that reality from both ends: what practitioners say to each other, and what legal-tech companies are recruiting to build.
The reality: general-purpose AI, beside the work
Koobo's discourse engine tracks selected practitioner subreddits and counts posts. AI's share of all practitioner conversation rose from about 0.5 percent in early 2024 to about 3.5 percent by early 2026, roughly sevenfold.
Of the 2,263 AI posts, 907 name a specific tool, and the one named most is ChatGPT. It appears more often than every purpose-built legal product combined. The split is stark: lawyers reach for general-purpose chatbots, and the legal-built systems sit at the margins.
What they bring AI to is the information layer. Drafting leads by a wide margin, then review, summarizing, and research. These are first-draft and triage tasks, the work that surrounds a lawyer's judgment rather than the judgment itself.
The industry surveys land in the same category, even where they rank the tasks differently. Thomson Reuters found GenAI users apply it most to document review (74 percent), legal research (73 percent), and summarization (72 percent). Information-handling and first drafts, not judgment.
The churn is among general tools, not toward legal ones
The general-purpose lead is fragmenting, but not toward the legal-built products. ChatGPT's share of AI posts fell from a peak near 52 percent in late 2024 to about 23 percent, and Claude picked up some of the slack. The specialized tools stayed at the margins throughout.
And on the specialized tools themselves, adoption is sliding backward. Clio's 2025 report found use of legal-specific AI fell from 58 to 40 percent as lawyers shifted to general ones, and Wolters Kluwer found 56 percent using general-purpose tools against 14 percent specialized. The market is still shopping in the consumer aisle, because little is wired into most lawyers' work yet, and a browser tab is easy to swap.
The supply side is racing to close the gap
The companies building legal AI are recruiting to put it inside the work. Koobo's hiring analysis took a snapshot of 1,020 open roles across 14 legal-technology employers and counted the AI-build roles: the jobs dedicated to building or deploying AI, as opposed to selling or supporting it. The AI-native vendors run about 1 in 5 open roles as AI-build, the older legaltech platforms, the document-management and e-discovery companies that predate the AI wave, about 1 in 18, and the two law firms in the panel about 1 in 28. In this panel, firms read as buyers, not builders.
AI-build work comes in two kinds, and which one a company recruits for says where it is. Research roles build the model itself: Research Scientist, AI Infrastructure Engineer. Deployment roles put that model into a firm's actual work, and the central one is the Legal Engineer, part lawyer and part deployment specialist, whose job is to make a model hold inside a legal workflow. The concept that dominates these job descriptions is "agentic": software that runs a multi-step task on its own, rather than a chatbot answering one prompt at a time.
Koobo's Wayback-reconstructed history of eight of these employers shows the balance shifting from research to deployment. In 2024 the AI-build openings were research roles, the work of building a model. By 2025 and 2026 they were deployment roles. The Legal Engineer itself emerged in 2025, first at Wordsmith in May, then at Harvey, Filevine, and Legora.
Harvey, the most-funded case, shows what that looks like up close. It lists 344 open roles today, and the mix leans commercial and deployment, not model research: nearly half are go-to-market, the biggest AI-build cluster is 52 Legal Engineers putting the product inside firms, and research hiring is down to three. That push has customers behind it: Harvey now serves more than 100,000 lawyers across more than 1,300 organizations and a majority of the AmLaw 100, and its flagship A&O Shearman deployment has about 2,000 lawyers using it daily.
The Legal Engineer is now a real profession, paying about $175K to $320K for the JD-required roles at vendors and big firms, with standing teams at Harvey and Thomson Reuters. Legora, a competing legal-AI vendor valued around $5.5 billion, published a piece in 2025 called "The rise of the Legal Engineer," arguing the role is what makes AI actually work inside a law practice. It is the exact job that moves AI from beside the work to inside it.
What "early" actually looks like
That build-out does not mean adoption has caught up; it means the vendors are hiring ahead of where most legal work still happens. Demand is general-purpose and assistive; supply is agentic and embedded. That gap is not a verdict on the specialized tools. It is what early looks like: the build is running ahead of adoption.
That friction surfaces in what practitioners worry about.
In the practitioner posts we analyze, confidentiality, adoption, and accuracy questions all climb, the anxieties of pasting a real matter into a consumer tool with no governed boundary. The surveys agree on the sticking point: accuracy and hallucination is the number-one thing holding lawyers back, cited by about 75 percent of the hesitant (ABA). Lawyers have even begun to police AI, hunting the fabricated citations it plants in opponents' briefs and, in some filings, certifying that their own work was produced without AI.
This is a market at the front of the curve, not one deciding against AI. Thomson Reuters found 95 percent expect AI to be central to their workflow within five years, while only 13 percent say it is today. That gap is the runway, and it is exactly what the supply side is recruiting to fill.
The pitch keeps escalating. Vendors have moved from selling an assistant to an agent to an operating system for legal work, and Harvey's CEO already calls AI the system that legal work runs through. The reality is still an assistant in a browser tab. Whether the real destination is an operating system or something more modest, closing that distance is not a better-model problem. It is a governance and workflow problem, and it is where this series goes next: Part 2 on the governance that sets the boundary, and Part 3 on the operating model that scales embedded, trusted AI within it.
Lawyers are working with AI beside the work while the builders race to move it inside. Where your own firm sits between them is the number worth knowing.
To place yourself on that curve, the Legal AI Readiness Assessment scores where you sit across strategy, tooling, workflow, governance, and talent, and shows you your weakest link.
A note on method
Everything here is aggregate public data, an operations read rather than legal advice. The demand signal counts posts across five practitioner subreddits, measured as a share of all posts so a busier community does not look like more AI interest; the most recent month or two are set aside because they undercount. Legal-tech and law-student boards are tracked separately and left out of this panel. The supply signal is a snapshot of open roles at legal-technology employers. The historical piece, rebuilt from web archives, is coarse, so we use it only to read how the mix of roles shifts over time, not to count them; where we give a current count, like Harvey's 344, it is a live read of the company's own job board. The outside surveys are there to check the first-party read, not to replace it.