Ben Arnon

I think you'd find, I think you'd feel like, like you're watching sort of a master chess player thinking like 20 moves ahead, but for movie pricing and scheduling, you know, what's happening under the hood is really a sophisticated ensemble of AI models that are working in concert.

I think you'd find, I think you'd feel like, like you're watching sort of a master chess player thinking like 20 moves ahead, but for movie pricing and scheduling, you know, what's happening under the hood is really a sophisticated ensemble of AI models that are working in concert.

I kind of call it orchestration and like our demand forecasting layer, for instance, ingests, this is kind of what I was talking about before it ingests everything from historical attendance to patterns of historical attendance to weather forecasts, to social media buzz, and even local events.

I kind of call it orchestration and like our demand forecasting layer, for instance, ingests, this is kind of what I was talking about before it ingests everything from historical attendance to patterns of historical attendance to weather forecasts, to social media buzz, and even local events.

I kind of call it orchestration and like our demand forecasting layer, for instance, ingests, this is kind of what I was talking about before it ingests everything from historical attendance to patterns of historical attendance to weather forecasts, to social media buzz, and even local events.

So, you know, then our optimization engine uses reinforcement learning to simultaneously solve for multiple constraints. So distributor requirements. staffing availability, physical fear limitations, and all the while maximizing revenue potential.

So, you know, then our optimization engine uses reinforcement learning to simultaneously solve for multiple constraints. So distributor requirements. staffing availability, physical fear limitations, and all the while maximizing revenue potential.

So, you know, then our optimization engine uses reinforcement learning to simultaneously solve for multiple constraints. So distributor requirements. staffing availability, physical fear limitations, and all the while maximizing revenue potential.

So I'd say think of it as like thousands of micro experiments that are running continuously, each one learning from real world outcomes and getting smarter with every single ticket that's sold.

So I'd say think of it as like thousands of micro experiments that are running continuously, each one learning from real world outcomes and getting smarter with every single ticket that's sold.

So I'd say think of it as like thousands of micro experiments that are running continuously, each one learning from real world outcomes and getting smarter with every single ticket that's sold.

There you go.

There you go.

There you go.

Yeah, I'd say that's super important with anything AI-related, especially on the LLN, the model front. The ethical line that we're constantly watching is accessibility and fairness in the movie experience. So dynamic pricing could easily become a system that makes movies accessible only to those with higher disposable income, let's say.

Yeah, I'd say that's super important with anything AI-related, especially on the LLN, the model front. The ethical line that we're constantly watching is accessibility and fairness in the movie experience. So dynamic pricing could easily become a system that makes movies accessible only to those with higher disposable income, let's say.

Yeah, I'd say that's super important with anything AI-related, especially on the LLN, the model front. The ethical line that we're constantly watching is accessibility and fairness in the movie experience. So dynamic pricing could easily become a system that makes movies accessible only to those with higher disposable income, let's say.

But we've built guardrails into our system to really ensure that it doesn't just maximize short-term revenue, but actually considers long-term audience development. So, for example, we maintain affordability windows in every schedule and actually lower prices for certain segments rather than just raising them at peak times. Oh, okay.

But we've built guardrails into our system to really ensure that it doesn't just maximize short-term revenue, but actually considers long-term audience development. So, for example, we maintain affordability windows in every schedule and actually lower prices for certain segments rather than just raising them at peak times. Oh, okay.

But we've built guardrails into our system to really ensure that it doesn't just maximize short-term revenue, but actually considers long-term audience development. So, for example, we maintain affordability windows in every schedule and actually lower prices for certain segments rather than just raising them at peak times. Oh, okay.