Point-Counterpoint: Consumers Will Never Pay for AI

The latest consumer AI numbers are out in terms of which apps are used most on mobile, which websites are most visited, and which AI services have the highest revenue. For many, the most interesting story is the wild dichotomies that exist in these numbers. First, the gap between the top paying users and the median paying users, where the top 1% of AI buyers spend as much as the bottom 50% combined, but then also the incredible gap in households that buy any AI versus those that don't. Today, we're exploring whether the 98% of US households that currently don't pay for an AI subscription represent a massive revenue opportunity to come, or the reality that they will never pay for an AI subscription, and might need totally different types of AI products or at least AI business models to be even a little bit relevant for the big AI companies. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. Alright, friends. Quick announcements before we dive in. First of all, thank you to today's sponsors, KPMG, Harbor, Granola, and Blitzy. To get an ad free version of the show, go to patreon.com/ai daily brief, or you can subscribe on Apple Podcasts. To learn more about sponsoring the show, send us a note at sponsors@aideallybrief.ai. After our episode yesterday about the rumored forthcoming open weight model of American origins, Reflection AI did indeed unveil their new model and positioned it as The US trained challenger to the Chinese open models that have started to compete for token share. Beam has five zero one billion parameters. That makes it around the same size as NVIDIA's Nemotron three Ultra, but smaller than GLM 5.3 and tiny compared to Kimi K3's 2,800,000,000,000 parameters. At this stage, the model is only available to early testers, so we can only gauge it based on the reported benchmarks. Reflection AI claimed a score of 44.4 on coding benchmark DeepSweet, beating GLM 5.2 at 44 and slightly behind QEN 3.8 Max at 51. On Terminal Bench 2.1, which measures agentic coding, Beam scored 80.1. That put it well ahead of Pneumotron Ultra at 56.4, Inkling at 63.8, but slightly behind GLM five point two and six points behind QEN 3.8 Max. Across the core set of benchmarks Reflection chose to showcase, it only led on SWE bench verified, which didn't include scores from any of the Chinese models. It was also notable that Reflection didn't benchmark on Terminal Bench 4.0, which has revealed a lot of benchmark maxing since it was released last month. Reflection also chose not to compare their model to Chinese leaders like GLM5.3, Kimi K3, and DeepSeek 4.1 Flash. Now on the plus side, Beam does look like it pushes the performance of Western Open models. Reflection also claimed that it will be extremely efficient between three and four times more efficient in terms of inference compute compared to GLM 5.2. This …