Financials and Scaling Your Business

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 Rebuild from scratch with AI doing most of the work These are what we call AI native service companies And the markets are trillions of dollars in size, tax, audit, insurance, law parts of health carere and so forth. this opportunity didn't exist even a couple of years ago But advances in the models have unlocked this new type of business where companies provide the outcome to the customer versus build a c pilot that the customer uses internally. These companies also look and feel different than most startups today In this video, I'll walk 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 I'll share some obvious and non obvious elements of building these businesses that we've observed here at YC Topics include Picking a market, forming a team, building the actual product sererving the customers P and L and whether or not you should even buy a business. One general comment before I get started 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 it gets you really excited First Picking the right market. The same general advice for all stars 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 And that part isn't really new, but the B Markets VarI services have four new, pretty unique traits. The first is low trust. Meaning 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 Second 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. The third is a high intelligence threshold This sounds contradictory, but actually doesn't The overwork has to be Hard Hard enough that models plus humans are need to actually deliver an outcome the customer accepts. The fourth is Regulation could actually be good. Regulated industries have higher expectations and legal accountability That raises the bar and the me for founders For instance, Panasy is a current YC company that provides FDA regulatory services for biotechs and Med techs. They actually hire experienced FDA consultants pair them with an AI platform. to deliver faster, higher quality FA approvals. So what are some of the specific markets we have in mind here at YC? The 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. And here are a few more things to keep an eye on when it comes to the markets The first is on the models. Will the models disrupt these businesses It depends on what I call the Sam Altman test. You should 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. Where to be careful 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 Let's leave this area to the robotics founders. One more honesty check 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 Second and maybe most importantly The right founding team. The same advice applies for all startups here begin 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 three attributes that all the best founders share The first is domain fluency Direct experience is best, but learned is actually okay You're selling to skeptical buyers in often regulated spaces. You have to bleed credibility How you acquire it matters someomewhat less. The second is 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 Next is operational rigor Topics like varis, throughputs, 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 have to enjoy it Or at least you have to respect it. The product is an operation. A great example here 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 Cul and Femwick, as well as years of technical leadership at CaseTxt But most importantly, they think deeply about throughput and how they staff their firm. They've integrated shhift work into how they serve clients to reduce cygle times and attract the best lawyers on the team This is a win win for scale Now let's talk about building the actual product. With AI services, the setup is the opposite of most software The human is the interface of the customer, not the product. The product helps the humans scale their work nonlinearly. That changes pretty much everything around building natural products First, you need to apply an operations' mindset Find the bottlenecks and build for the bottlenecks Throughput and cycle time are now product metrics A them like you would daily active users. Variance is the existential problem here By variance, I mean non uniform outputs from your actual service Customers will fire you for variants faster and 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. Thirdly Humans in the loop should scale nonlinearly If revenue scale is 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. A general point. 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 Okay, sales and customer success. The biggest challenge facing founders here is what I'll call 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 Our advice here is to cap your first pilot customers to a small handful. Resist the temptation to sign too many too quickly. Assuming you avoid this early demand trap Free 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 piles to learn, find the spots where AI gives you unique leverage versus spots where you're just automating something obvious P productject accordingly Do it fast 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. There's per unit pricing. so per return, per claim, per loan. This is the cleaniest, it's easiest to explain There's also outcome based pricing. This aligns incentives beautifully, but it can be harder for you to forecast in your business Pantasy praces on the completed consultant study versus hourly, which is the norm in the industry There's definitely two pricing strategies to avoid Cost plus pricing capture upside permanently Straight line undercutting makes your works seem cheap and potentially low quality Price on value. Next, the PNL or the profit and loss statement. This is where these companies live or die, so let's do a quick walkthrough If you haven't stared at one of these financial statements before, you're not alone among founders and that's okay. The general structure is revenue mininus cost of a good sold gets you to gross profit. Gross profit minus operating expenses gets you to operating income. Let's dive into each of these in the context of AI services Revenue. Ah, the easy part, relatively speaking. you will be able to sign contracts Can you deliver on them repeatedly I don't know 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. Costs of goods sold or cogs Obsess over this from day one There's three main components here. There's model costs, hosting costs, and those humans in the loop. All three of them need a number, a trendline and someone who owns them. Deeply suspicious of zero margin or negative margin pilots, They're fun 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 I call this AI operating leverage. Okay, OpEx This includes research and development costs, so building the product, sales and general administrative, which is like finance, legal, exeact 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 oper income less taxes interest That's less important in the medium term So here's the actual PNL opportunity for these businesses Traditional services firms top out around thirty percent margins. your software and aging companies have more margin but often smaller TAMs The bet on these services companies is that that AI operating leverage gets you closer to software margins, say fifty percent 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 Okay, last but not least Don't try to buy your way in There's a temptation we've seen, especially among founders with some operating background to try and buy an existing services business, at some A on top short circuit the revenue This is generally a trap There's one decent reason to do it You need a regulatory mote fast insurance licensing, for example. But otherwise, this almost never works So why is that? You just can't acquire a product market fit Legacy service businesses are, you know legacy. They have different expectations on metrics, hiring, and performance. Hing AI on top of that doesn't immediately change any of those realities. Building is almost always better than buying Okay, to recap, AI services companies present an incredible opportunity for today's founders. But these are fundamentally different starps to build. If you avoid the traps and focus on the process as the product product as the process You have a chance to create a generational company. We're excited to see what you build and hope you apply toYC