# Scaling Subscriptions at The New York Times with Real-Time Causal Machine Learning

DevFeed: [Scaling Subscriptions at The New York Times with Real-Time Causal Machine Learning](<https://devfeed.tech/articles/scaling-subscriptions-at-the-new-york-times-with-real-time-causal-machine-learning-39154.md>)

Original publisher: [Read original article](<https://open.nytimes.com/scaling-subscriptions-at-the-new-york-times-with-real-time-causal-machine-learning-5f23a7b24ff4?source=rss----51e1d1745b32---4>)

Author: Rohit Supekar

Published: 2025-10-03T15:19:24Z

Content type: article

Language: en

Sources: [New York Times](<https://devfeed.tech/sources/new-york-times.md>)

Topics: [Machine Learning & Artificial Intelligence](<https://devfeed.tech/topics/machine-learning-artificial-intelligence.md>), [Algorithms](<https://devfeed.tech/topics/algorithms.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [real-time](<https://devfeed.tech/topics/real-time.md>)

Tags: [account](<https://devfeed.tech/tags/account.md>), [algorithms](<https://devfeed.tech/tags/algorithms.md>), [causal-inference](<https://devfeed.tech/tags/causal-inference.md>), [causal-machine-learning](<https://devfeed.tech/tags/causal-machine-learning.md>), [data](<https://devfeed.tech/tags/data.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [users](<https://devfeed.tech/tags/users.md>)

## AI overview

The New York Times describes replacing its Dynamic Meter machine-learning model with real-time algorithms that decide within milliseconds whether users receive access. The system uses causal machine learning and multi-objective optimization to balance subscription, registration, engagement, conversion, and business constraints across its subscription funnel.

## Source excerpt

How real-time algorithms and causal ML transformed our digital subscription funnel from static paywalls to dynamic, millisecond decision-makingIllustration by Mathieu Labrecque The New York Times became a subscription-first news and lifestyle service with the launch of its paywall in 2011. Since then, our subscription strategy has evolved substantially. Initially, users could access a limited number of free articles per month before they encountered the paywall. In 2019, we began personalizing this number using a Machine Learning (ML) model -- The Dynamic Meter. In the past few years, we have replaced this model with real-time algorithms that decide, typically within milliseconds, whether to grant access. These algorithms are tailored to balance and optimize the tradeoff between several business Key Performance Indicators (KPIs), while also allowing us the flexibility to adjust for any business constraints. This article further details the motivation behind these algorithms and their design based upon principles from causal machine learning and multi-objective optimization. Our subscription funnel The New York Times has a tiered subscription funnel (Figure 1), consisting of unregistered, registered, and subscribed users. This funnel is designed to provide non-subscribers with limited access to our content, allowing them to discover our offerings. At other times, the content may be blocked by a digital "wall". We have two types of walls -- a registration wall that asks a user to register for a free account or log in, and a paywall that asks a user to subscribe. A large number of users are unregistered -- they may be shown either a registration wall or a paywall. Once a user is in the registered state, they can be shown only a paywall. Figure 1: The New York Times subscription funnelOptimizing the subscription funnel Optimizing who sees the registration wall or the paywall -- and when -- is a very relevant question for our business. While blocking access encourages users t