Daniel, founding father of the recent AI startup, recently passed his Saas application with AI power supply to USD 250,000 annual revenues. It happened quickly and was excited. The product tried, users are growing and the whole lot looked as if it worked. Then a shocker appeared: cloud texture for USD 800,000, almost completely driven by the application and calculated using the API.
The company grew up to the highest line, but not a margin. He scaled from business.
This type of history is becoming more and more common when we go to the AI era. The old Saas of Build a Great App, the fee monthly and allow infrastructure to disappear in the background, does not persist when the basic cost is scaled with use.
AI has shuffled the value chain, and for startups this modification is existential.
The pile of AI is deep and the margin moved
In traditional SaaS, most of the values have been intercepted on the application layer. Today, AI corporations operate in a much deeper pile:
- Energy infrastructure: data centers, cooling and power (see AmazonInvestment $ 10 billion in the energy of the data center in Virginia);
- Tokens and equipment: NvidiaH100S, Google TPU, rare and expensive;
- Cloud platforms: azure, AWSIN GCP with priority access to GPU;
- Models: OpenAI, anthropic and more and more open source players;
- AI vertical solutions: can be used as low code platforms/no code to create specific AI applications; AND
- Applications: a product addressed to a user where most AI startups are still alive.
But unlike the past, margins do not focus at the top, close to the end user. They are often now below the surface, especially in layers in which there is a deficiency equivalent to equipment, computing and exclusive access to models.
So what can startups do when they are not the owners of infrastructure or models?
Three founders can remain in the game
1. HAVE YOUR DATA. This is your recent moat
You don’t have to train your individual foundation model, but you have to have input data that make your product worthwhile.
If you are vertically, equivalent to healthcare, finance, real estate or legal, your advantage is the reserved, structural data. Tuning of open models. Build light adapters. Use customer work flows to consistently collect different data. The value is included in the data set.
2. Price for use, no access
There was a bill in the cloud of this founder in the amount of USD 800,000, because they were getting like Saas, but it acted as a computing company.
In artificial intelligence, the costs of use disks. This signifies that flat subscriptions do not work. The founders must accept price models that adapt the value provided with the costs incurred:
- Impact or settlement on token;
- Calculated price levels; AND
- Charging too high costs, equivalent to image generation or live inference.
Follow gross margin by function, not only a customer.
3. Avoid model lock. Elasticity project
Linking a road map with one model supplier, equivalent to OpenAI or Anthropic, is dangerous. Delay, prices and changes in politics can blind you.
Instead, build a model for abstract abstraction. Travel among suppliers, customize Open Source backups and negotiate contracts with the lever. Flexibility is not only technical. This is business security.