I get it. It definitely isn’t the right fit for every use case at this point in time. That’s why we’ve built support for subscription and usage models as well. Then there's incentive for a sales tool to still charge on usage or subscriptions (slightly less than they normally would), but have the upside of an outcome converting. For buyers, this should be seen as the seller having confidence in their product.
100%! We think pricing and biz model will become a differentiator. Being able to adapt and iterate on pricing will be really important as underlying models get better
The basic idea is this. The customer has some "curve" that represents how much he values different outcomes. Maybe he values good outcomes at $1, and great outcomes at $100. The supplier also has a cost curve - by definition, it will cost him more to supply a great outcome than a good outcome (otw he'd just always supply the great outcome).
Setting a fixed price is a simple way to help these two parties transact. But hypothetically, it may be more efficient - e.g. you will let more mutually-beneficial events happen - to ask both parties for what their number is for a given event, and having both transact when the numbers are far enough apart (cost is $10, value is $100).
The problem is, you can't directly ask the parties, because they don't want to reveal how high/low they're willing to go for no reason. So, you should essentially structure your questions into a pre-defined algorithm so that everyone is incentivized to reveal at least the ballpark of where their cost/value is. The study of how to structure those questions is a subset of mechanism design / information design, which is a branch of Econ related to game theory
FWIW, if this sounds like arcane academic musing ... applied mechanism design for a while was essentially just the study of google ad auctions, and Google invested very very heavily in researchers to figure out how to do this for them
Thanks! Definitely see where you’re coming from. Software startups with really good products are generating massive amounts of business value for their customers right now. Subscriptions and usage models really constrain them in how much they can capture of that. Incentives are also constantly misaligned, especially with usage as buyers will always try to minimize usage as much as possible. Charging on outcomes changes that.
The entire AI customer support industry has pretty much already converged into this model. Tickets closed without being escalated to humans are usually what’s defined as an outcome. We think this model makes the most sense for any vertical AI company where agents are actually completing tasks end to end.
Success is defined by the buyer and seller before they use us. We just facilitate the parameters they agreed upon, so it’s pretty variable. A successful outcome can be anything from sourcing a real estate property that ends up closing to finding $X in cost savings for a dental clinic, using research agents. The biggest difference is you’re not just charging for tokens, but assigning a dollar figure to what a job well done looks like, no matter how many tokens it takes to get there.
Thank you - would love to hear more about the gaming use case.
Not quite, because the buyer and seller would never agree to that being a real outcome. It would have to meet certain requirements/thresholds for customer satisfaction. That’s where we come in to verify that those contract terms are actually met as it happens.
There are a ton of ways to calculate cost savings depending on what it’s built for. Voice agents that handle booking and outbound calls, supplier sourcing agents that only buy the best priced items, etc. It’s then possible to translate those things into a monetary figure, relative to the business.
Agreed and that’s what we are working to eliminate. It’s on us to approve what a successful outcome is, based on the terms of their agreement.