Why Focused Vertical AI Startups Can Compete Against Incumbent Software and General-Purpose Agents

Why Focused Vertical AI Startups Can Compete Against Incumbent Software and General-Purpose Agents While incumbent systems of record and frontier AI models increasingly automate workflows directly around enterprise databases, vertical AI startups can sustain an advantage by executing complex, cross-boundary jobs through specialized learning loops. Incumbents are advancing past passive chatbots to action-taking agents such as Docusign's Iris, Atlassian's Rovo, and Klaviyo's Composer. Frontier interfaces like Claude can unbundle the application layer from records via partnerships like Claudeforce, but cross-system latency and external boundaries persist. Application agents operate across four levels—retrieval, process, policy, and principal—with incumbents largely bounded by the internal records they own. Vertical startups can manufacture domain curricula rather than relying solely on legacy historical data, building feedback loops that train agents on human judgment. The Incumbent Push and the Unbundling of Interfaces The bullish view for existing systems of record posits that AI increases their importance because enterprise customers can route stored data directly into general-purpose models like Claude or Codex, bypassing AI-native applications. Incumbents are simultaneously building their own agents to command access to their data and shift from passive storage to direct action, as seen in Docusign's Iris, Atlassian's Rovo, and Klaviyo's Composer. Furthermore, integrations like Anthropic and Salesforce's Claudeforce demonstrate how general agents can serve as the primary entry point while incumbents retain underlying CRM data, unbundling the interface from the system of record. Agent Tiers and the Limits of Systems of Record Enterprise agents can be categorized into four tiers based on autonomy and judgment: retrieval assistants, rule-based process agents, policy agents that apply organizational playbooks, and principal agents that resolve strategic trade-offs. Most incumbents have advanced from retrieval toward narrow policy agents. However, their scope remains limited to local systems of work tied to their specific records. Real-world business jobs cross departments, external entities, and disparate software tools, whereas signed contracts or resolved support tickets omit the alternate paths and reasoning tested during execution. Learning Loops and Curricula Manufacturing To win, vertical AI startups must execute cross-system jobs more effectively than purpose-built incumbent tools or general-purpose models. Simply retaining general memory does not teach an agent why work was altered or how to improve. Startups must master two forms of learning: general professional execution and organization-specific rules. Rather than waiting on vast historical customer archives, startups can manufacture training curricula. For example, Harvey post-trained its Tenet model using roughly 1,750 simulated legal-task environments across 50 criteria per assignment to teach professional competence before learning firm-specific nuances in production. Framework for Assessing Vertical AI Opportunities The most defensible markets for vertical AI meet four specific criteria: an expert can rapidly assess and explain mistakes, the work demands genuine judgment rather than static rules, tasks recur frequently enough to generate learning loops, and the initial point solution can expand to cover the broader job. Beyond legal, accounting, and tax, this pattern extends to domains like industrial manufacturing, where resolving a defect requires assembling supplier files, test results, and plant rules across disconnected systems that no single software incumbent controls.

传统软件巨头加速入局，垂直AI初创企业的突围之道 传统记录系统正通过自研智能体或与Claude等通用智能体结合来拓展自动化能力。面对巨头的数据壁垒和通用模型的前端入口优势，垂直AI原生初创企业需要依靠专注，通过构建跨系统工作流和深度学习闭环来主导具体业务。 传统软件巨头正从单纯的信息检索转向基于工作流与规则采取行动，例如Docusign的Iris、Atlassian的Rovo以及Salesforce与Anthropic合作的Claudeforce。 应用级智能体可划分为检索助手、流程智能体、策略智能体和主管智能体四种类型，传统巨头大多受限于自身拥有的局部数据记录。 垂直AI企业可以通过合成专家课程学习行业专业标准，并在实际生产中学习特定机构的流程与偏好，而不必依赖庞大的历史数据存量。 评估高价值垂直AI市场的四个维度包括：专家反馈效率、是否需要复杂判断、工作重复频率，以及是否具备从单一任务扩展到端到端业务的潜力。 传统巨头向行动与决策层进阶 一年前多数传统软件的AI功能仍停留在检索层面，如今已逐步升级为能够基于系统内规则和权限执行操作的流程智能体，甚至开始引入狭义的策略决策。像Claudeforce这样的合作让通用智能体成为交互入口，而传统软件仍掌握底层核心数据与操作。但传统巨头的工作流始终围绕自身拥有的记录向外延伸，难以直接覆盖跨系统、跨团队乃至跨机构的完整业务全貌。 专注与两阶段学习闭环构建壁垒 单纯访问系统并不等于能做好业务。垂直初创公司需要掌握两个层面的学习：一是专业人员如何执行工作，二是特定企业机构的独特偏好与规则。企业无需从大量历史记录起步，例如Harvey通过合成环境和专家评估标准训练模型，先人工打造专业课程，再在实际部署中吸收机构经验。针对多步骤、长周期的智能体任务，垂直企业通过设置评估检查点和吸收专家纠错，能够实现通用记忆无法替代的高质量迭代。 垂直AI市场的筛选标准与机遇 理想的垂直AI领域通常满足专家能快速评估纠错、任务高度依赖判断、场景频繁发生，且能以点带面扩展为端到端闭环这四项条件。法律、税务、会计以及复杂的工业质量调查等跨系统场景尤为契合。尽管巨头把持底层数据、通用大模型把持前端交互，垂直AI原生企业依然能凭借对具体业务结果的深度掌控与交付能力赢得市场。

[America](https://www.a16z.news/t/america) | [Tech](https://www.a16z.news/t/technology) | [Opinion](https://www.a16z.news/t/opinion) | [Culture](https://www.a16z.news/t/culture) | [Charts](https://www.a16z.news/t/charts) **The bullish case for incumbent systems of record is that AI makes systems of record get more important, not less. Why? Because in theory the customer can now pipe the system’s data into Claude or Codex and skip the AI-native app.** As agents do more work through these systems, the data and actions they control become more valuable. An incumbent system of record plus a general agent (or an agent built by the incumbent) therefore might be enough to get the work done. This is what Salesforce just announced in partnership with Anthropic: *Claudeforce* lets you work with the Salesforce CRM from *inside Claude* without having to open Salesforce (or so they claim!). The argument is partly correct. The incumbents aren’t sitting idly. The incumbents have an incentive to control and charge for access to their data, and now to push their own agents forward too. We’re already seeing incumbents market products that move from simply storing information toward doing more of the work itself. Docusign’s Iris reviews contracts, Atlassian’s Rovo aims to resolve and route requests, and Klaviyo’s Composer builds campaigns and assesses marketing performance. These products move incumbents beyond the [“slap on a chatbot” strategy](https://www.linkedin.com/posts/seema-amble-b404126_we-get-asked-a-lot-if-ai-is-so-obvious-activity-7341184831302574081-oLlO) and into taking action. At the same time, Claude is starting to coordinate work across applications, making it possible for general-purpose agents to act as a layer above the incumbents. Claudeforce shows what that can look like: **Claude can become the front door to the job while Salesforce still controls the underlying CRM data and actions. The interface and the system of record look like they’re unbundling, creating an opening for AI-native startups to rebundle around the job.** *So where can startups still compete?* **The vertical AI-native company has to win through focus.** It has to perform a specific cross-system job *better* than an incumbent’s purpose-built agent *or* a general-purpose agent like Claude, which will be able to reconstruct it from the underlying tools. Focus can earn the vertical startup deeper access, a deliberate data asset, more specific context, and a learning loop that builds a better understanding over time of what good work looks like for a given job. To see where incumbents are strongest and where vertical AI companies may have an advantage, it’s helpful to think about the four types of application-level agents, distinguished by how much autonomy and judgment each requires. **Retrieval assistants** find information, summarize documents, answer questions, and draft responses. **Process agents** carry out rule-based work like updating records or routing approvals, using limited judgment to interpret the task, and act. **Policy agents** apply the rules in an organization’s playbooks, precedents, and thresholds to ambiguous cases. **Principal agents** make judgment calls about what the organization should do, often with open-ended tradeoffs around strategy, risk, and resources. **A year ago, most incumbents’ AI products were still at the retrieval stage, if that**. They functioned mostly as chatbots and analytical tools pulling from existing records. Now, more can take action based on the workflows, permissions, and rules already embedded in their products. A few are beginning to apply narrow, human-defined policy. Judgment gets harder when the decision depends on information outside the record that the incumbent manages. The broad shift we’re seeing is incumbents moving up the hierarchy, from retrieval and process toward more judgment through policy, like Docusign’s Iris applying a legal team’s playbook. But that judgment is still largely bounded by the record the incumbent owns. An incumbent can automate more of the work around its core record, but the customer’s job to be done is bigger than the record. This isn’t a hard technical boundary because incumbents can pull in outside data and add workflows, but their work still *expands outward from the record they already own*. A customer’s work gets done through a combination of its people, processes and software. The contract is not the legal matter, the ticket is not the customer’s resolution, and the opportunity is not the sale. The full job crosses applications, teams, and even companies. Different parties may hold different information, and want different outcomes. The incumbent is limited to building a “local system of work” but the larger opportunity is to manage the job across boundaries, including *all* of the parties, the people, the documents, decisions, revisions, and the final result. A general purpose agent like Claude or Codex may be able to reach across all these systems, but access alone doesn’t mean it can perform the job well. Even using MCP, pulling info across applications has latency, and each application may describe the same customer, contract, or transaction differently. Even if a general purpose agent can access every app in one company, it doesn’t automatically have access to information held by external parties (e.g. between buyers and suppliers). Even with these limitations, Claude can still sit above systems of record. Claudeforce makes Claude the front door to the job while Salesforce still owns the CRM data and actions. And Claude can keep gaining context through deeper integrations blessed by the incumbents. Owning more of the job matters because it lets startups see the decisions and corrections that produced the final result. A model performs better on a specific job via a vertical harness with the right context, tools, workflows and evals. The learning loop is what improves the system - the model + harness - after each job, using the agent’s work, expert feedback, and outcomes themselves to improve the next attempt. Completed records alone don’t automatically suffice as a training curriculum for the work. A signed contract shows what the parties agreed to, but not every alternative considered or every reason for an exception. A closed ticket shows the resolution, but not every hypothesis the support team tested. The labs are also building general-purpose memory, so preserving context alone is not a durable advantage. Remembering and learning are different. Memory can help a general purpose agent recall a customer’s preferences or a decision made last week. But it doesn’t tell the agent whether the work was good or why an expert changed it or what to change next time. Focus lets the company see more examples of the same job, learn faster, and get better at measuring improvement. **There are two kinds of learning. Learning how a good professional does the work.** A company can begin teaching that with assignments created by experts, realistic examples, and standards for a good result. **Learning how a particular firm or customer does the work,** including its templates and precedents, risk thresholds and escalation rules. That can begin with the customer’s existing materials and improve through corrections and exceptions in the training process. Some lessons about the profession can improve the product for every customer, but lessons about one firm may just improve the product only for that firm. **To learn both the profession and the institution, the startup doesn’t need to start with the largest stock of historical data. It can manufacture a curriculum for the profession, then use production to learn the institution.** As open-weight models and post-training increase, assembling the right data and evals becomes even more important. Long running agents especially benefit from learning loops. When an agent does work over hours or days, through a complex logic chain with many intermediate decisions and handoffs, the quality of the learning loop matters even more. The longer horizon task can be unbundled and evaluated through a set of checkpoints. To be clear, incumbents will also learn from the work performed inside their products. But that learning will cover only their part of the job: the contract review inside Docusign, the ticket resolution inside Jira, or the pipeline update inside Salesforce. [Harvey’s work on Tenet](https://www.harvey.ai/blog/post-training-update-harvey-tenet) shows what the path for vertical AI can look like. Harvey post-trained an open-weight model using synthetic data, public legal data, and human-expert created data, without using customer data to start. It created roughly 1,750 legal-task environments, each simulating a partner-assigned matter, complete with documents and tools and an expert rubric for a high-quality result. The average assignment had about 50 specific criteria. Instead of waiting years to accumulate enough customer history, Harvey manufactured a curriculum it could use to train and evaluate its model. Harvey’s broader work also shows how a vertical company can train a model for different parts of the job, including industry-specific capabilities like M&A diligence and understanding a firm’s total knowledge. *The startup’s opportunity is to put it all together.* Vertical specificity helps the startup to break the work into specific capabilities, tackle the practical frictions around data rights, systems, and permissioning, and fits the product into the customer’s daily work. Eventually, it can feel comfortable taking responsibility for the result. *Deployment and responsibility are key here!* Using Harvey as one example, here are a few axes for evaluating potential vertical AI markets. Can an expert quickly tell what the AI got right or wrong, and explain how to improve it? Is the work hard enough that judgment matters (vs. simple rules)? Does the work happen often enough for the product to learn? Can the startup begin with one assignment and grow into doing an entire job? The best vertical AI markets tend to meet all four, giving them a strong learning loop. Many workflows in legal, tax, and accounting meet this test because the work repeats, requires judgment, and experts already review the results. This pattern also shows up in less obvious markets like industrial work. When a manufacturing defect appears, a quality engineer has to gather test results, supplier documents, equipment logs, and the plant rules, then decide what’s acceptable and what needs to change. That information may sit across several systems. One incumbent may store the defect report, but no single product owns the full investigation. A startup could begin with one piece of this work (e.g. drafting the investigation report) and over time own the end-to-end resolution. To do this, it would need to learn both how a good quality engineer investigates a problem and how a particular plant or customer or auditor does it well. There are jobs where a system of record plus a general-purpose agent will be enough, but there’s still meaningful opportunity for vertical AI-native startups! The strongest vertical opportunities exist where the work happens often, expert judgment matters, and the learning loop is strong. **The incumbent may own the record and the lab may control the front door, but the vertical AI company can still win by becoming the best at doing the job itself.** *This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. 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