How to Build an AI-Native Services Company

AI-Native Service Companies00:09

Some of the biggest companies of the next decade won't be software businesses at all. They'll be services companies like insurance carriers and law firms rebuilt from scratch with AI doing most of the work. These are called AI native service companies. The markets are trillions of dollars in size: tax, audit, insurance, law, parts of healthcare, and so forth.

This opportunity didn't exist even a couple years ago. Advances in the models have unlocked this new type of business where companies provide the outcome to the customer versus build a co-pilot that the customer uses internally. These companies also look and feel different than most startups today.

The video walks through a playbook for founders starting AI services businesses from scratch. It's aimed at people thinking about starting a company, not if you're already running one. Topics include picking a market, forming a team, building the actual product, serving the customers, the P&L, and whether or not you should even buy a business.

We're still early here. Like most things in AI, the market is moving fast. We're learning as we go, but the early successes here should get you really excited.

Picking the Right Market01:30

The same general advice for all startups applies here with some important caveats. You should pick a market you're excited to work in for a long time. These companies still take a decade or more. If you don't love some combination of the customers or the market or the technical problem, you're not going to make it.

The best markets for AI services have four new pretty unique traits:

• Low trust: the work is already outsourced and the customer cares about the final product, not how they got there. You're displacing a vendor, not asking the customer to do something fundamentally different. That's a huge deal because you're not changing behavior. You're showing up where the budget already lives and doing the work.
• Low judgment at the task level: If you can break the work into pieces and every piece needs a human exercising actual judgment, you can't really scale. You need most of the steps to be automatable with judgment focused in a few places where humans stay in the loop.
• High intelligence threshold: The overall work has to be hard. Hard enough that models plus humans are needed to actually deliver an outcome the customer accepts.
• Regulation could actually be good: Regulated industries have higher expectations and legal accountability that raises the bar and the moat for founders. For instance, Panacea is a current YC company that provides FDA regulatory services for biotechs and medtechs. They actually hire experienced FDA consultants, pair them with an AI platform to deliver faster, higher quality FDA approvals.

Known Good Fit Markets

Known good fit markets include tax, audit, insurance, mortgages, parts of healthcare, and parts of logistics. But there are plenty more markets nobody has touched yet. Don't hold yourself to the obvious ones or what people talk about on X.

Model Disruption Check: The Sam Altman Test

Will the models disrupt these businesses? It depends on what is called the Sam Altman test. Ask yourself: as the models get better, does your service get stronger or does the model itself commoditize you? You want to be in the first camp.

Be careful with anything involving equipment and on-site labor. The software margin math doesn't apply when you own and operate physical things. It's very hard to create real leverage, though these can be really good businesses. Leave this area to the robotics founders.

Honesty Check on Human Involvement

Ask yourself sincerely: are you using humans because the work genuinely needs judgment, or are you compensating for product gaps? Be honest here so you're not papering over product shortcomings with actual humans. There are still great massive technology businesses to be built with humans in the loop.

The Right Founding Team04:31

The same advice applies for all startups here, but again, with some important caveats. You should build companies with people you already know and you've worked with. If you're solo, think about the best people you've ever worked with and ask them to join you. You'd be surprised who says yes.

For AI services specifically, there are three attributes that all the best founders share:

• Domain fluency: Direct experience is best, but learned is actually okay. You're selling to skeptical buyers and often regulated spaces. You have to bleed credibility. How you acquire it matters somewhat less.
• Model fluency: You need to know what frontier models can do today and design the product to ride the curve as they get better. There is no substitute for great tech here. People underestimate this.
• Operational rigor: Topics like variance, throughput, cycle times, SOPs. This is not an exciting set of words for most founders, but you are fundamentally running an operation. You have to learn that skill set and you have to enjoy it or at least you have to respect it. The product is an operation.

A great example is the general legal team which is an AI native law firm that YC recently backed. The founders have a unique mix of actual law firm experience at Cooley Fenwick as well as years of technical leadership at CaseText. But most importantly, they think deeply about throughput and how they staff their firm. They've integrated shift work into how they serve clients to reduce cycle times and attract the best lawyers on the team. This is a win-win for scale.

Building the Actual Product05:30

With AI services, the setup is the opposite of most software. The human is the interface to the customer, not the product. The product helps the human scale their work nonlinearly. That changes pretty much everything around building the actual product.

Operations Mindset

Apply an operations mindset. Find the bottlenecks and build for the bottlenecks. Throughput and cycle time are now product metrics. Track them like you would daily active users.

Variance as the Existential Problem

Variance is the existential problem here. By variance, this means non-uniform outputs from your actual service. Customers will fire you for variance faster than they will fire you for being a bit slower or a bit more expensive than the incumbents. They need to trust the output. Inconsistency destroys trust, which causes churn.

Nonlinear Scaling of Humans in the Loop

Humans in the loop should scale nonlinearly. If revenue scales just in line with the number of humans you add, you'll have major problems. The humans in the loop also need to enjoy the software. They are your users.

It's okay to do things that don't scale at the very beginning, but eventually you really do need to scale. Automating the process is the product.

Sales and Customer Success07:00

The Early Demand Trap

The biggest challenge facing founders here is what is called the early demand trap. It's easy to sign up a lot of pilot customers when you're just starting out and have nothing. But it can quickly overwhelm your ability to serve them and you won't be able to build the product to scale. You'll be stuck using humans. It is a literal trap.

Advice here is to cap your first pilot customers to a small handful. Resist the temptation to sign too many too quickly.

Selling Outcomes

Assuming you avoid this early demand trap, pre and post sales looks pretty different too. You have to sell outcomes, not seats or tokens. The pilot is the product.

For the first handful of customers, don't try and standardize too early. Use those pilots to learn. Find the spots where AI gives you unique leverage versus spots where you're just automating something obvious. Build the product accordingly and do it fast.

Pricing Strategies

Pricing is harder than traditional software because you're not competing with other software providers. You're competing directly with the cost of labor internal or outsourced.

A few options here on pricing:

• Per-unit pricing: per return, per claim, per loan. This is the cleanest. It's the easiest to explain.
• Outcome-based pricing: This aligns incentives beautifully, but it can be harder for you to forecast in your business. Panacea prices on the completed consultant study versus hourly, which is the norm in the industry.

Pricing strategies to avoid:

• Cost-plus pricing: caps your upside permanently. Don't do it.
• Straight-line undercutting: makes your work seem cheap and potentially low quality.

Price on value.

The P&L08:31

This is where these companies live or die. The general structure is revenue minus cost of goods sold gets you to gross profit. Gross profit minus operating expenses gets you to operating income.

Revenue

You will be able to sign contracts. Can you deliver on them repeatedly? That depends on your product and your process. Eventually, you want smooth and predictable growth. A great product process is going to smooth out that lumpiness, but in the early days, it'll be spiky on a monthly basis. That's okay.

Cost of Goods Sold (COGS)

Obsess over this from day one. There are three main components here: model costs, hosting costs, and those humans in the loop. All three of them need a number, a trend line, and someone who owns them. Be deeply suspicious of zero margin or negative margin pilots. They're fine to learn from, but it's really dangerous to get hooked on those. The core bet here is the more the product is built, the lower the COGS, the better the gross margin. This is called AI operating leverage.

Operating Expenses (Opex)

This includes research and development costs (building the product), sales and general administrative (finance, legal, exec salaries). Pretty standard stuff here.

Operating Income

This is the revenue minus the COGS minus the opex. You will be judged on your operating income in these businesses faster than you might expect.

Net Income

This is the operating income less taxes and interest. That's less important in the medium term.

The Actual P&L Opportunity

Traditional services firms top out around 30% margins. Pure software and agent companies have more margin, but often smaller TAMs. The bet on these services companies is that AI operating leverage gets you closer to software margin, say 50% plus on a market that's two to three times bigger than software. You don't need to be there right away. That's okay. But the trajectory has to be believable.

Don't Buy Your Way In10:31

There's a temptation, especially among founders with some operating background, to try and buy an existing services business, add some AI on top, short circuit the revenue. This is generally a trap.

There's one decent reason to do it: you need a regulatory moat fast, insurance licensing for example, but otherwise this almost never works.

You just can't acquire a product market fit. Legacy service businesses are legacy. They have different expectations on metrics, hiring, and performance. Adding AI on top of that doesn't immediately change any of those realities. Building is almost always better than buying.

Recap11:00

AI services companies present an incredible opportunity for today's founders, but these are fundamentally different startups to build. If you avoid the traps and focus on the process as the product and the product as the process, you have a chance to create a generational company.