Do LLMs have a groupthink problem? Yes, but calling it “groupthink” is too generous. It is more like a digital beige-ing of everything we produce. If you spend enough time jumping between Claude, GPT-4, and Gemini, you start to realize they aren’t just converging on the same facts—they are converging on the same personality. They all have that same eager-to-please, slightly sterilized, corporate-concierge tone. They are effectively the same person in three different outfits.
The problem isn’t just an annoying aesthetic. It is a fundamental collapse of variance. When every model is trained on the same massive scrape of the internet, and then refined using RLHF (Reinforcement Learning from Human Feedback) based on the same set of “helpful and harmless” guidelines, you end up with a statistical average of a human. It is the AI equivalent of a corporate retreat where everyone spends four hours agreeing with the CEO just to get to the cocktail hour faster.
This is where the startup mentioned in MIT Tech Review comes in. The premise is that we can force models out of this rut by introducing mechanisms that prioritize divergent thinking over the most probable token sequence. The idea is to stop the models from simply predicting the “safest” answer and instead push them toward a wider variety of perspectives.
On paper, this sounds great. In practice, it’s a fight against the very nature of how these models are built. LLMs are, by definition, probability engines. They are designed to find the center of the distribution. Asking a model to avoid the “groupthink groove” is essentially asking it to stop being a probability engine. (Which is a bit like asking a calculator to occasionally guess the wrong answer just to keep things spicy.)
There is also the inevitable friction of performance. If you implement a layer that forces divergence or samples from a wider, less probable distribution, you usually pay for it in latency or coherence. You can have a model that is “original,” or you can have one that doesn’t hallucinate a fake legal precedent in the middle of a brief. Usually, you can’t have both.
It’s a band-aid on a bullet hole.
The deeper issue is the risk of the synthetic data loop. There is a growing concern among researchers that as we run out of high-quality, human-written internet data, the industry will be forced to rely on synthetic data—training new models on the output of old ones. This creates a feedback loop where models learn from their own homogenized outputs. It is a digital Hapsburg dynasty; the lack of fresh, genetic diversity in the training sets could lead to a form of model inbreeding.
Trying to solve this at the inference level—by tweaking how a model responds—is missing the point. If the underlying weights are built on a foundation of synthetic consensus, the “diversity” you get at the end is just a superficial filter. It is the difference between actually hiring a creative team and just telling your current corporate drones to “think outside the box” during a brainstorming session.
The only real way out is to find a source of data that isn’t already contaminated by the “beige-ing” process. That means paying for private archives or finding ways to incentivize humans to write things that don’t look like they were written by an AI.
By Q1 2027, we’ll see the first major benchmark specifically designed to penalize “consensus-seeking” behavior in LLMs, as the industry realizes that a model that always agrees with the median is useless for actual problem solving. Until then, we are just polishing the surface of a very large, very expensive echo chamber.
