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Greg Isenberg’s analysis of AI-native services reveals a $100 billion opportunity that shifts the focus from selling software tools to delivering finished work. For decades, service businesses were trapped by linear growth because revenue stopped when the founder stopped working. You sold hours, and scaling meant hiring more people, which capped your margins and limited your valuation.
Consider the difference between QuickBooks and an accountant. A business pays roughly ten thousand dollars for the software but one hundred twenty thousand for the human who uses it. Software companies fought over the smaller fee because it scales easily, while the larger fee remained locked behind headcount. That is the money that has just opened up for entrepreneurs.
AI can now perform most tasks previously done by humans, allowing you to sell the closed books rather than the tool. An AI-native service delivers the final product using agents, with a small team handling only the parts that require human judgment. This structure allows you to price like a service but achieve software-like margins.
The reason this model works now lies in three converging factors: better models, lower costs, and existing budgets. Two years ago, AI output was a rough draft requiring heavy editing, but today it is a finished product ready for review. The cost of running these models has collapsed from dollars per task to mere cents, driving marginal delivery costs near zero.
Businesses already spend trillions on services like accounting, legal work, and compliance, yet this market remained untouched because every dollar was attached to a person. Now, companies like Harvey and EvenUp are capturing this value by offering faster, cheaper alternatives to traditional firms. These are service businesses that happen to have the economics of software.
Unlike SaaS products that compete against improving foundation models, an AI-native service improves as the underlying technology gets better. You sell the finished work, so model advancements directly increase your efficiency and profit margin. This eliminates the need to convince customers they have a problem, as you are simply replacing an existing line item with a superior option.
To build this, you must start with a clearly defined unit of work, such as per claim or per filing, never per hour. The intake process should be a simple form where the customer uploads documents and fills out fields, eliminating the scoping calls that plague traditional agencies. This clarity allows for repeatable delivery and eventual automation.
The real product is your rulebook, a written list of what correct looks like in your niche and how the AI typically fails. You build this by reviewing outputs and documenting every mistake, creating a defensible moat that competitors cannot replicate. This rulebook ensures quality and trustworthiness, which is what customers actually pay for.
Start by doing the work manually for five customers to learn what correct means before automating the process. Write down every mistake to refine your rulebook, then productize the scope, pricing, and delivery into a dashboard. This approach lets one person manage fifty customers with software margins, unlocking the value that was previously locked behind human labor.
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