The Transformer Playbook (with Metronome CEO Scott Woody)
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Last week, I caught up with Scott Woody, CEO of Metronome. Earlier in the week, he shared the news that Metronome would be joining the Stripe team.
The announcement felt like an exclamation point on the state of SaaS pricing in 2025, which has undergone a massive shift as companies rebuild around AI.
To better understand this shift, Scott offers a helpful comparison.
“2025 is like 1999. In 1999, the Internet came online. It created this new way of distributing software, which was software as a service. That new delivery model completely changed the economics of the product, but more importantly, it changed the value of the product.”
Before SaaS, you bought software off a shelf and installed it on a rack. The value was the bits on the disk. You paid a flat fee because that’s what the value was.
But when we started continuously updating software delivered over the internet, flat fees made no sense. The value kept going up over time.
Subscriptions became the only logical way to capture that value.
Now AI is creating the same kind of value shift.
“Instead of SaaS, where it’s continuously updating software, the software is doing work on your behalf. The value has shifted from who in my org has access to this software to what can this software do for me?”
Take Notion.
The old question was: how many seats do we need?
The new question is: how many workflows can it orchestrate for us?
Put simply, the value change demands a change in the business model.
It’s No Longer a Choice to Become Usage-Based
This presents a problem for legacy SaaS players using a traditional license model.
Take seats.
If you keep AI products on a seat-based model, you create two problems:
Problem 1: You cap your upside.
“The core thing that differentiates a seat business from a usage business is that in a seat business, your bill never really changes. But let’s say you spin up an agent on Agentforce. That thing could go crush thousands of leads in a minute. The value you’re getting is elastic, and therefore the price you charge should be dynamic.”
Problem 2: Your margins go to zero (or worse).
“These AI agents are expensive. If it’s actually getting a ton of usage and you don’t charge a variable cost, your margin will go down the more your customers use your product. You’re basically disincentivizing the usage of your own product.”
This is the death spiral. High usage + flat pricing = margin compression. You either have to throttle the product or watch your economics collapse.
This matches what a16z’s David George and Gavin Baker have been saying: if your margins aren’t being impacted by AI adoption, that’s a red flag.
In other words, with a functional AI product, usage-based pricing is nonnegotiable.
The Good News for Legacy SaaS
Everyone’s tweeting about Lovable’s growth rate. Replit’s hockey stick. The AI-native darlings. But there’s another story that’s flying under the radar.
“What no one talks about is the large SaaS businesses that successfully found the AI version of their product. Those things have growth rates that are even faster, or as fast, as Lovable or Replit.”
The reason? Distribution.
Scott pointed to Zendesk as an example.
“No one’s tweeting about them, but they launched an AI version of one of their product features and that thing is growing very fast, comparable to any of these other companies. Why is that possible? It’s possible because they have a huge install base. As soon as you find that magic thing, you can take advantage of the huge distribution you already have.”
Scott calls these companies “transformers” – established SaaS businesses where AI is transforming both the product value and the commercial model.
They’re not starting from zero. They’re activating an existing customer base with a new value proposition.
And when they get it right, they can move faster than anyone expects.
The Playbook
So how are these companies actually making the transition?
There’s a pattern. And it’s more deliberate than it looks from the outside.
“They all start as add-ons. They’re starting as an add-on because that doesn’t require them to retrain users at all. Users understand add-ons. Then they mature into their own thing, and then they frequently become the heart of the new product.”
The add-on phase isn’t a lack of ambition, it’s strategic. The add-on functions as a safe space for experimentation.
“What they’re doing is getting into the market fast so they can experiment with the product. It’s kind of like a laboratory for figuring out the right thing and designing the product right.”
In this phase, the product is often underwhelming. The smart companies use this time to dial in the product quality, and validate that customers actually want what they’re building.
Then comes the flip.
“Changing a commercial model is extremely traumatic. You only want to do it when you’re confident the product value is there. When you’re ready to actually monetize the thing, you invest all the effort to train the sales team, update your marketing copy, and do all the hard work of building the product for virality.”
The examples are everywhere once you know the pattern:
HubSpot launched Breeze last year – and introduced a credit model.
Notion launched AI-based workflow tools – and rolled out consumption-based, credit-based pricing.
Intercom’s Fin went from a sideshow to the entire thing in 12 months.
Add-on. Experiment. Flip. Scale.
The Trigger: How to Know When It’s Time to Move
So when do companies actually make the switch?
“The trigger point for all of these companies is always a product launch,” Scott said.
Not a pure commercial change. A product launch. An acquisition integration. Something that fundamentally shifts the value you’re delivering.
“Doing a commercial change without a change in product value is the fastest way to burn everyone’s trust.”
The real trigger isn’t abstract. It’s operational. It’s the moment when your ambition outpaces your infrastructure.
“What you see happening in all of these AI native companies and all these companies adopting AI is they’re adopting credit-based models, outcome-based models, they’re building budget tools into the product, they’re building rate limiting into the product, all of this stuff that the prior generation never had to worry about.”
Building this functionality in-house can take months or quarters.
“The signs that you have a problem are: you do all this work on the product, and then the pricing and packaging part takes more than a day. Confluent had 40 people working on this problem and now they have three. OpenAI at the same equivalent revenue as Dropbox had one-tenth as many people working on this.”
Implementing a new billing system seems like a headache until you compare it to the bottlenecks of retro-fitting a homegrown system for the AI era. Notably, you probably don’t have to rip out your existing billing system entirely. Many companies are implementing a new billing engine exclusively for their AI products.
The Bottom Line
The shift from seats to usage isn’t a trend. It’s a structural change driven by how AI fundamentally alters product value.
The companies that recognize this early, the “transformers” with existing distribution and the discipline to experiment before flipping their commercial model, are going to capture disproportionate upside.
If you’re actively working on this and looking for a sounding board, drop a question in the PricingSaaS Community or book some time with me here.
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