Point-Counterpoint: Consumers Will Never Pay for AI

Nathaniel Whittemore (0:00)

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 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.

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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 501 billion parameters. That makes it around the same size as NVIDIA's Nemotron 3 Ultra, but smaller than GLM 5.3 and tiny compared to Kimi K3's 2.8 trillion 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 DeepSui, beating GLM 5.2 at 44, and slightly behind Quen 3.8 Max at 51 On Terminal Bench 2.1, which measures agenda coding, Beam scored 80.1. That put it well ahead of Nemotron Ultra at 56.4, inkling at 63.8, but slightly behind GLM 5.2 and 6 points behind Quen 3.8 Max. Across the core set of benchmarks Reflection chose to showcase, it only led on SweeBench 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 GLM 5.3, 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 3 and 4 times more efficient in terms of inference compute compared to GLM 5.2. This could make Beam a very cost-effective base model for additional post-training, which is exactly the use case that we talked about becoming increasingly interesting to enterprises, and is very clearly a big part of Reflection's goal. Reflection noted that the model was pre-trained from scratch, then put through what they believe to be the largest publicly documented reinforcement learning run ever. They also utilized distributed infrastructure for their reinforcement learning, which is a wildly underexplored method of training open models. Artificial Analysis hasn't completed their independent testing at this stage, but they did give some directional confirmation of some of the claims, writing, Early indicators suggest Beam will be one of the most token-efficient open models we've seen for its level of intelligence.

Now, to some, this all fell a bit short of the hype from earlier in the week, with Kyle Chan of the Brookings Institute writing, The real game-changer would be an American open-source model on par with the best Chinese models. This is an impressive model, but still behind GLM 5.3 and Kimi K3. On the other hand, despite this not being a deep-seeker Kimi killer, many were simply pleased to see a competitive model out of a new US lab.

Open model researcher Nathan Lambert wrote, Congrats to the reflection folks. It's hard to get the first model out and we hope you can rapidly accelerate contributions to the ecosystem from here. Added former White House advisor Sriram Krishnan, Congrats to reflection on the launch of Beam. It is critical for the US to have leading open models to win the AI race.

I think it's interesting to think about what the reaction would have been if there hadn't been leaks and hype for the couple of days leading up to this. My guess is that rather than a let down that some felt, it would have been more like when your team is down by a couple of goals and you score the first one on their way to a comeback. It doesn't mean that the comeback is fait accompli, but you're feeling a heck of a lot better than you did just a few minutes earlier.