Most entrepreneurs think that AI businesses have some of the widest margins in the game. However, that’s not always true. It turns out that a lot of these companies are actually making very little money, and it has some investors worrying about long-term profitability.
The question for you, therefore, is what can you do to make your AI-based company more economically effective? What strategies can you use?
This guide has the answers. We look at what you need to do and how you can make it happen. Here’s everything you need to know.
You’re Building Features And Not Moats
One of the problems that a lot of businesses can run into is that they end up building features instead of moats. Rather than thinking about how they can become the only viable software company in the niche, they focus on building out a product and adding new bells and whistles.
The problem with the latter approach is that it doesn’t work very well. While it is possible to keep adding features, competitors can do that, too, which means that it can be hard for your company to remain competitive. You want to think that you’re doing something unique, but it is often only a matter of time before other people in your industry catch up.
The good news is that there are several fixes for this. The first is to just own your data. If you can keep everything you know proprietary, then that will massively increase the chances of you maintaining a moat around your business.
You can also work to lock in people with an API. If you can force contractors and clients to use you instead of someone else, then you can continue to make recurring revenue long-term.
You Are Scaling At The Wrong Prices
Another issue you might run into is attempting to scale at the wrong prices. A lot of AI companies simply go in too low, assuming that lifetime customer value will be high because people will continue paying for years.
Unfortunately, the AI industry has a lot of churn. Many consumers are just looking for the latest and best gadgets, and they don’t have a lot of loyalty. As an AI business owner, this sort of behaviour can catch you out sometimes.
So, how do you get around this issue?
First, you’ll want to look at whether you can limit the supply of the product. If you can cut down on the numbers you sell, you can sometimes raise the price per unit and prevent customers from flaking.
You can also try periodically raising the price by around 20% and see when the added revenue is overtaken by losses from churn. The idea here is to see how you can boost your margins by as much as you can instead of selling to as many individuals as possible.
If you can sell annually in your niche, then do that, too. You can hook a lot of buyers with a monthly price and then promise them a discount on the annual plan. This strategy can increase lifetime customer value enormously over time.
You’re Paying For Hallucinations
You might also be paying for hallucinations, which are essentially pieces of content that are worth less than nothing. Each time you run an algorithm, you risk telling your audience something false, which means they are less likely to trust you.
Hallucinations are part and parcel of most LLMs, and something that a lot of users have just learned to deal with. Around 30% of all responses contain errors that require fixes.
The good news is that there are various strategies you can use to reduce the risk of hallucinations. For example, you can implement confidence thresholds and reject outputs that don’t meet it. You can also rag-chunk-rewrite or just keep a human in the loop. It’s really up to you, as long as you ensure you cut down on damaging error rates.
You’re Renting The Cloud Like It’s The Penthouse
Another issue or trap you could be falling into is that you’re renting the cloud like it’s a luxury suite. If you do that, then you’ll be paying much more for a computer than you need.
You can fix this problem by choosing data centers that offer the best value. You can also sort it out with so-called spot-instance arbitrage (where you look for the lowest-price servers) or build a model router that allows you to choose the model you want to use based on the task.
You’re Selling Cheap Solutions To Expensive Problems
Your AI business could also be falling flat because you’re selling cheap solutions to expensive problems. You might be providing thousands of dollars of value to your clients, but only charging them a few cents.
For example, suppose you’re selling an email marketing writer. That’s great, but if it only costs customers pennies when they purchase, you won’t get any value from them.
The trick here is to focus on the verticals with a lot of pain. The more someone wants a problem solved, the more likely they are to pay through the nose for it.
If you can pair this with compliance, you’re onto a winner. Once you start including this in painful niches, a lot of companies are willing to pay exponentially more, just to cut down on their risks.
Your Custom Model Is Garbage
Lastly, you can run into issues when your custom model is just a fine-tuned garbage problem. If you have a lot of noisy data, it will be challenging for you to come to any firm conclusions.
Therefore, make sure you benchmark versus API baselines before you train models. If you have any sacred cows, make sure you kill them before proceeding. You might think that they’re a great option, but your clients might not.
So there you have it: some of the ways you can make your AI business more successful and profitable. Once you do that, you’re well on your way to selling something with a large markup.