Comparison
Where everyone sits: BigLaw, AI-native law firms, and Story
Founders are usually offered two kinds of legal help. One brings deep expertise and an hourly bill. The other brings software and a narrow scope. Story is the only firm that sits where neither does.
Key takeaways
- Story is the only organization in the upper-right quadrant of all three charts: cost × capability, know × seek, and value × efficiency.
- BigLaw firms (Cooley LLP, Gunderson Dettmer, WSGR, Latham & Watkins) bring broad expertise, material expert value over AI, and command of cap table and legal logic. They charge high hourly fees, hire out technology, and operate like any 20th-century business.
- AI-native law firms (General Legal, Norm Ai, Crosby, Mage Legal, Arcline) run software-driven operations at a more predictable cost. Their capability is narrow, they hire for both technology and legal logic, and they offer no marginal expert value over AI.
- Story pairs predictable cost and software-driven operations with BigLaw-level expertise. It builds its own technology and defines its own legal logic, backed by a network of attorneys across all 50 states.
Two kinds of legal help, and the gap between them
For most of the last two decades, a venture-backed founder's legal choice was simple: hire a large firm. The work was excellent and the bills were unpredictable. A newer wave of AI-native legal companies took the opposite approach. They lead with software and predictable pricing, and they focus on the narrow slices of legal work that are easiest to automate.
Each model gives something up. BigLaw keeps the expertise and keeps the hourly bill. AI-native firms lower the cost and give up the expertise. Founders shouldn't have to choose. The three charts below map the landscape on the dimensions that matter most to a growing company.
How to read these charts
Each chart places organizations on two axes that run from −5 to +5. The upper-right quadrant is where a founder wants their legal partner to be. The table under each chart lists every organization's exact coordinates.
Cost × capability
The first question most founders ask is what this is going to cost, and whether the provider can handle everything the company will need.
BigLaw sits in the upper-left corner. Cooley LLP, Gunderson Dettmer, WSGR, and Latham & Watkins score between 4 and 5 on expertise and between −4 and −5 on cost. The breadth is real, and so is the bill. Hourly fees scale with time spent and are hard to predict up front.
AI-native firms sit in the lower right. General Legal, Norm Ai, Crosby, Mage Legal, and Arcline land between 1.1 and 3.3 on predictable cost, but between −4 and −5 on capability. Pricing is easier to plan around, but the scope is narrow. Anything outside the workflows the product was built for gets referred out.
Story sits in the upper right, at (3, 5). Its cost is as predictable as the AI-native firms', and its expertise matches the top of BigLaw. Software handles the repeatable work, and an attorney network of thousands of lawyers across all 50 states covers the rest.
Broad expertise
High hourly fees
Predictable cost
Narrow capability
Chart readingChart 01, Cost × capability. BigLaw firms (Cooley LLP, Gunderson Dettmer, WSGR, Latham & Watkins) sit in the upper-left corner: broad expertise, high hourly fees. AI-native firms (General Legal, Norm Ai, Crosby, Mage Legal, Arcline) sit in the lower right: predictable cost, narrow capability. Story sits alone in the upper right at (3, 5): broad expertise at a predictable cost.
| Organization | Category | X | Y |
|---|---|---|---|
| Story | Story | 3 | 5 |
| Cooley LLP | BigLaw | −4 | 4.9 |
| Gunderson Dettmer | BigLaw | −5 | 4.5 |
| WSGR | BigLaw | −5 | 4 |
| Latham & Watkins | BigLaw | −5 | 5 |
| General Legal | AI-native | 2 | −4.9 |
| Norm Ai | AI-native | 2 | −4 |
| Crosby | AI-native | 2.9 | −5 |
| Mage Legal | AI-native | 3.3 | −4.3 |
| Arcline | AI-native | 1.1 | −5 |
Know × seek
Legal technology only works when the people building it understand the law it encodes. This chart asks what each organization knows natively and what it has to go out and hire.
BigLaw sits in the upper left. Cooley LLP, Gunderson Dettmer, WSGR, and Latham & Watkins score between 4.4 and 5 on defining cap table and legal logic. They know how a cap table should behave and how a financing actually closes. On technology they score between −2.9 and −5. Building software isn't what they do, so they hire for it.
AI-native firms sit in the lower left. General Legal, Norm Ai, Crosby, Mage Legal, and Arcline score between −2 and −4.5 on technology and between −3 and −4.9 on legal logic. Despite the name, they hire for both: the engineering their product runs on and the legal reasoning it's supposed to encode.
Story is the only organization in the upper right, at (5, 5). The team that defines the legal logic also builds the software that runs it. Cap tables, equity events, and document workflows are modeled by people who understand both the law and the code, so nothing gets lost in translation between a hired engineer and a hired lawyer.
Capable of defining cap table and legal logic
Need to hire to build technology
Capable of building technology
Need to hire to understand legal logic
Chart readingChart 02, Know × seek. BigLaw firms sit in the upper left: capable of defining cap table and legal logic, but need to hire to build technology. AI-native firms sit in the lower left: they need to hire both to build technology and to understand legal logic. Story sits alone in the upper right at (5, 5): capable of both.
| Organization | Category | X | Y |
|---|---|---|---|
| Story | Story | 5 | 5 |
| Cooley LLP | BigLaw | −2.9 | 5 |
| Gunderson Dettmer | BigLaw | −5 | 5 |
| WSGR | BigLaw | −5 | 4.5 |
| Latham & Watkins | BigLaw | −4.3 | 4.4 |
| General Legal | AI-native | −2 | −4 |
| Norm Ai | AI-native | −3.7 | −3.7 |
| Crosby | AI-native | −2.3 | −4.9 |
| Mage Legal | AI-native | −4.5 | −4.4 |
| Arcline | AI-native | −3.5 | −3 |
Value × efficiency
As AI takes on more legal drafting and review, two questions matter. Does a human expert add anything beyond what the AI produces? And does the organization itself run on software, or on manual process?
BigLaw sits in the lower right. Cooley LLP, Gunderson Dettmer, WSGR, and Latham & Watkins score between 3.5 and 4.9 on expert value and between −4 and −5 on operations. Their attorneys bring judgment that AI can't replace, but the firms run the way professional-services businesses always have: staffing, timekeeping, and handoffs done by hand.
AI-native firms sit in the upper left. General Legal, Norm Ai, Crosby, Mage Legal, and Arcline all score −5 on expert value and between 2.2 and 4 on operational efficiency. Their operations run on software, but what they deliver adds nothing a founder couldn't get from AI directly.
Story sits in the upper right, at (5, 4.9). That's the highest of any organization on both axes. Software runs the operations, and attorneys step in where judgment matters: negotiations, edge cases, and decisions with real consequences.
Software-driven operational efficiencies
No marginal expert value over AI
Material expert value over AI
Operates like any 20th century business
Chart readingChart 03, Value × efficiency. BigLaw firms sit in the lower right: material expert value over AI, but operating like any 20th century business. AI-native firms sit in the upper left: software-driven operational efficiencies, but no marginal expert value over AI. Story sits alone in the upper right at (5, 4.9): software-driven efficiency with material expert value.
| Organization | Category | X | Y |
|---|---|---|---|
| Story | Story | 5 | 4.9 |
| Cooley LLP | BigLaw | 4.9 | −4 |
| Gunderson Dettmer | BigLaw | 4.9 | −4.5 |
| WSGR | BigLaw | 3.5 | −5 |
| Latham & Watkins | BigLaw | 4.9 | −5 |
| General Legal | AI-native | −5 | 3 |
| Norm Ai | AI-native | −5 | 4 |
| Crosby | AI-native | −5 | 3.5 |
| Mage Legal | AI-native | −5 | 2.6 |
| Arcline | AI-native | −5 | 2.2 |
The comparison at a glance
| Dimension | BigLaw | AI-native law firms | Story |
|---|---|---|---|
| Pricing | High hourly fees | Predictable | Predictable |
| Scope of expertise | Broad | Narrow | Broad, backed by attorneys across all 50 states |
| Builds its own technology | No; hires for it | No; hires for it | Yes |
| Defines cap table and legal logic | Yes | No; hires for it | Yes |
| Expert value over AI | Material | None | Material |
| Operating model | Like any 20th-century business | Software-driven | Software-driven |
Frequently asked questions
What is the difference between BigLaw and an AI-native law firm?
BigLaw firms such as Cooley LLP, Gunderson Dettmer, WSGR, and Latham & Watkins offer broad legal expertise and material value over AI, billed by the hour and delivered through a traditional, manual operating model. AI-native law firms such as General Legal, Norm Ai, Crosby, Mage Legal, and Arcline run software-driven operations at a more predictable cost, but with narrow capability and no marginal expert value over AI.
What is Story?
Story (Story LLP, story.law) is an AI-native legal platform that works as an automated general counsel for startups and founder-led businesses. It combines predictable cost and software-driven operations with broad legal expertise from a network of attorneys across all 50 states.
How is Story different from BigLaw?
Story matches BigLaw's breadth of expertise and command of cap table and legal logic. It replaces hourly billing with predictable cost, builds its own technology instead of hiring for it, and runs its operations on software instead of manual process.
How is Story different from other AI-native law firms?
AI-native law firms hire for both the technology and the legal logic their products depend on, cover a narrow scope, and add no marginal expert value over AI. Story builds its own technology, defines its own legal logic (such as cap table rules), covers a broad scope of legal work, and brings in attorneys where expert judgment matters.
Which legal provider offers both expertise and predictable cost?
Of the firms compared here, Story is the only one in the upper-right quadrant on all three dimensions: cost × capability, know × seek, and value × efficiency.
Who is Story for?
Startups and founder-led businesses that want general-counsel-level legal support at a predictable cost, without managing a large-firm relationship.