AI Application Startups: From Selling Tools to Selling Outcomes

The next 1T dollar company will be a software company masquerading as a services firm.

The questions in this article can also be addressedModel Is Good End: 2026, AI, which is really scarce, is an application rather than a larger model.、From Engineering to Builder: A person's company and product thinkingHow the concept of a relatively close read together is developed in different contexts.

Recently read the Sequoia partner Julien Bek's bookServices: The New SoftwareI'm sorry. This article is of course looking for investment opportunities, but it's quite straightforward: after the model gets stronger, what exactly is AI supposed to sell? How much of the moat do I have left when Claude or ChatGPT's next upgrade puts the tools I'm making into a function?

A lot of teams will do Copilot first. It helps lawyers draft contracts, helps accountants to prepare accounts and collect quotations for procurement. The professional is the professional, AI just makes the hands live. We're already familiar with this kind of product.

Bek wants to move on. Some clients do not want to buy a smarter software package or find another person who can use it. He wanted to finish the job: the contract was drafted, the accounts closed, the insurance was completed, the claims were processed and the system was running as usual. The customer buys the results after completion. Model upgrades can also be used as a stress test: If it turns a product into an built-in function, it means that the team has only temporarily filled the capability gap; if it turns established workflows, data and trust into faster and cheaper services, the model is expanding the value of the product. Entrepreneurs should be on the side of model growth rather than racing with models.

First, we'll tell the difference between intelligence and judgment.

Bek took the job down to two floors. The first level is intellectual activity: reading information, filling out forms, matching rules, writing first drafts, testing, debugging. They may be complex, but they usually have clearer rules, inputs and outputs. And that's probably the part that's the first to be compressed when the model gets stronger.

The other level is judgment. Should the client not be taken over, which contract is worth taking the risk, when it is on line and who is responsible for the exceptions. These things are not done by running the rules over once, and there are experiences, responsibilities and trade-offs. The model can now help a lot, but it is difficult to take these decisions alone.

This distinction is better than AI's replacing a particular occupation. Rather than labelling jobs as alternative or irreplaceable, it is better to open up jobs to see which links are clear enough to allow automation and which are still subject to professional boarding. Entrepreneurship opportunities are often hidden on this dividing line.

The article gives a judgement that the higher the proportion of the intellectual workforce that can be regulated, the sooner automation is likely to land. This change in software engineering was first made, and other occupations will also experience the same problem: What is left to the model, and which exceptions and trade-offs remain to be taken up by the person.

Copilot Seller Tool, Autopilot Seller Job

The article distinguishes between two product routes, Copilot and Autopilot. Copilot gave the capacity to the operator, the client paid for the software; Autopilot directly completed a job and the client paid for the results.

Models are improving both intellectually and in judgement. In the past, we used to use AI as a tool for professionals to decide how to use it. Harvey sells the product to the firm, and Rogo sells the product to the investment bank. Professionals are both clients and responsible for the end result. As models continue to progress, direct delivery of results to clients begins to become a viable option in at least some areas.

For products, the existence of chat boxes in the interface is only superficially different, and where the budget comes from is more important. Software budgets are usually limited, and service budgets for the completion of work are often much larger. The article uses the example of an accountancy: a company may spend approximately $10,000 a year on QuickBooks and another $120,000 on accounting. If AI can steadily deliver one of these tasks, it will no longer be faced with the original SaaS budget.

But the sale also brought responsibility. The tool is wrong and the user can use it as a complement; the service is wrong and the client only asks one thing: Why is the job not done? So Autopilot is not just a question of whether the model is smart enough. Quality is accepted, the abnormal is handled and the risk is ultimately placed on those who have to be made clear in advance.

Why do we start with outsourcing?

I like the idea of outsourcing as wedges. Instead of placing entry points on “replacement”, it suggested that tasks already outsourced and subject to a large number of rules should be sought.

A job has been outsourced, which indicates at least three things: the company accepts that it will be done by an outside body; the budget already exists; and the buyer buys the results, not the job position on which a person sits every day. The replacement of the existing supplier with an AI original service provider is often a replacement. Direct replacement of an internal post would translate into organizational adjustments and a completely different resistance.

This also explains why articles look at areas such as accounting and auditing, insurance brokers and settlements, medical billing codes, IT outsourcing, supply chain procurement, traditional management consulting, HR services, legal affairs and taxation. They are not simple, but they contain many standardized, repetitive and inspected tasks, and clients have long been accustomed to buying services on the basis of results. They all need to be judged, but each area can find different entry points from the boundaries of intelligence and judgement.

Note: YC also referred to compliance and audit in RFS 2025 Spring/Summer. It places language models in a more comprehensive and detailed review, and from personal assistants it talks about taxes, law enforcement, personal asset management, and the close, expensive or time-consuming nature of mail management, calendar arrangements and to-do matters. AI Agent is one of the hottest directions in 2025, and everyone is thinking about what Agent should do in the vertical field. They have one thing in common: let AI handle complex, but not necessarily creative, information in a certain field. Many jobs are inherently dry matching, and give them away so that people can put their time back into more creative places.

Use this framework to study a business.

If you want to find entry points for AI applications, then you can put the problem on a specific process:

What was the outcome of this delivery, and could the client see at first sight whether it had been completed?
How much of it is dealing with rules, files, matching and repetitive processes, and how much depends on the judgement of senior practitioners?
Has the client outsourced it today, or does it have a stable service budget?
Which are more likely to replace older suppliers with new services than to change internal organization?
What are the validated feedbacks that can be left for each completed product to make the next treatment more stable?

These questions do not provide an answer, but they bring back to work the "big industry" "AI can do something." When looking at the industry, it is better not to stop on the name of the industry, but to look at a certain process: who pays for it, who bears the wrong cost, which one of the problems AI can save the client.

And it wasn't the answer.

The article does not say that service will always replace software. High-judgement, high-risk, strong regulation, particularly in relation to the financial, medical, legal or complex interpersonal consultations, still requires the involvement of people. Even if the model is able to complete most of the process, the client is not necessarily willing to hand over responsibility together.

It provides a pragmatic entrepreneurial perspective. In addition to asking “what tools can I make”, one could ask “who is the client asking to finish this now”. If a job has been outsourced, the rules are clear enough to allow the results to be accepted, AI applications have the opportunity to start there and to adapt the tools to service.

The blogger says that the government is not a party to the law. Services: The New Software, Sequoia, 2026-03-05。