The pricing problem nobody has solved yet.

That data point came from Aleks Dekic, co-founder of Alguna, who I sat down with recently for The AI Revolution Show. Alex has spent his career inside the billing and monetisation layer of B2B software, and his read on where pricing is right now is one of the clearer ones I've heard.

The 90-day figure comes from research by Kyle Poyar at Growth Unhinged, and Aleks is quick to note it's skewed by companies like Anthropic with the resources to experiment constantly. But even adjusted for that, the direction is clear. Pricing in the AI era is not a set-and-forget decision. It's becoming infrastructure, something that needs dedicated ownership, engineering resource, and a cadence of iteration. Most companies aren't built for that.

The seat-based model is the obvious target. As AI agents start doing the work that humans used to do, charging per seat starts to invert. The better your product works, the fewer seats your customer needs, and the less revenue you collect.

The instinct is to jump to outcome-based pricing, and there's a lot of content right now saying that's where everything is heading. Alek’s view is more measured. Outcome-based pricing works cleanly when the outcome is unambiguous. Fin resolving a support ticket either worked or it didn't. For most SaaS products, defining the outcome with enough precision to price against it is genuinely hard, and most billing infrastructure isn't built to support it anyway.

What he sees working is a hybrid model: a committed upfront component that gives the customer budget predictability, layered with usage-based expansion as consumption grows.

The key mechanic is alerting customers before they hit limits, giving them the option to buy additional capacity at a discount, which turns a potential billing surprise into a proactive conversation. Predictable revenue for the seller, no unexpected invoices for the buyer.

The deeper problem is implementation speed. Aleks cited public companies that went through eight pricing changes in fifteen years. If you need to change yours three or four times a year to stay competitive, and it takes six months to update your billing infrastructure each time, the maths don't work. That gap between knowing what pricing change to make and being able to bill for it is what Alguna is trying to close.

Another thing he said that was super interesting:

The companies that will handle the AI transition best are the ones that treat pricing with the same rigour they apply to product. Dedicated ownership, engineering involvement, a proper iteration cycle. Not a twelve-month consulting project that's out of date before it launches.

On GTM, first customers came entirely from trust. Alex and his co-founder are first-time founders with no large existing network. They went after founders who had felt the pain directly, who were willing to take a leap of faith on a young team, and they built the product alongside those early customers rather than ahead of them. One of the things they didn't plan to build was an invoicing and payments layer. Their first customer needed it, and the existing tools couldn't handle the complexity. They built it anyway. It's now one of their strongest competitive advantages.

The full conversation is on The AI Revolution Show. Subscribe on Apple Podcasts.