Claude’s recent back to back updates made something very clear to me: Software is no longer meant only for humans.
For years, SaaS products were designed around a predictable assumption.
A human logs in, clicks around, and uses the product a few times a day.
Pricing models were built around that behavior too.
But with tools like Claude writing code, executing workflows and calling APIs autonomously, that assumption is breaking.
Software is now being invoked by agents.
An agent might run a workflow ten times in a minute. Or once in a week. It might spin up five tools for a single task and never touch them again.
The usage pattern becomes bursty, unpredictable, and completely detached from the idea of “seats”. This changes how infrastructure needs to be built.
Rate limits, entitlement checks, credit systems and billing logic now need to operate in real time. The system has to understand not just who the customer is, but what the agent is doing and how resources are being consumed.
This is why traditional subscription models are starting to feel increasingly misaligned for AI-native products.
When agents become the primary users of software, access-based pricing stops making sense. What matters instead is execution, consumption, and outcomes.
The companies that recognize this shift early will design systems that are built for agents from day one.
And the rest will spend the next few years slowly rewriting their billing infrastructure.
When Clawdbot rebranded to OpenClaw, it almost felt symbolic. Add “Open” to the name long enough and eventually OpenAI calls.
The founder of OpenClaw ( Peter Steinberger) joining OpenAI is more than a hiring update. It reflects a structural pattern in AI.
Open-Source has quietly become the ecosystem’s proving ground. Builders experiment in public, take risks without institutional constraints, and earn credibility at the edge. When their work reaches a certain threshold, the largest labs absorb the talent.
It is efficient and strategic. It is also centralizing.
Every time a strong open-source founder moves to a frontier lab, the center grows stronger. The edge becomes slightly thinner. This does not make the decision wrong. Ambitious engineers will always gravitate toward ambitious problems. In many ways, it validates that building in the open still produces serious technical depth.
But we should be clear about the dynamic.
Open-Source in AI is increasingly becoming a pipeline. Build publicly, prove capability, and get pulled into one of a few centralized institutions. The gravity is strong, and it keeps reinforcing itself.
The real question is not whether talent emerges in the open. It clearly does. The question is whether it stays. Because the future of AI will not just be shaped by model performance. It will be shaped by where power consolidates.
And moves like this make that direction harder to ignore!
Hey, Manish here, CEO and Founder of Flexprice. We actually do have a lot more integrations and these particularly include stripe, razorpay, quickbooks and more, we aim to solve almost all tough use-cases, thus our focus on integrations in general and yes, while the "space" is crowded, we aim to solve the entire monetization layer, do check us out :D
For years, SaaS products were designed around a predictable assumption.
A human logs in, clicks around, and uses the product a few times a day. Pricing models were built around that behavior too.
But with tools like Claude writing code, executing workflows and calling APIs autonomously, that assumption is breaking.
Software is now being invoked by agents. An agent might run a workflow ten times in a minute. Or once in a week. It might spin up five tools for a single task and never touch them again.
The usage pattern becomes bursty, unpredictable, and completely detached from the idea of “seats”. This changes how infrastructure needs to be built.
Rate limits, entitlement checks, credit systems and billing logic now need to operate in real time. The system has to understand not just who the customer is, but what the agent is doing and how resources are being consumed.
This is why traditional subscription models are starting to feel increasingly misaligned for AI-native products.
When agents become the primary users of software, access-based pricing stops making sense. What matters instead is execution, consumption, and outcomes.
The companies that recognize this shift early will design systems that are built for agents from day one.
And the rest will spend the next few years slowly rewriting their billing infrastructure.