Open any popular/trending Twitter post related to geopolitics or international affairs right now, and you’ll see dozens of replies along the lines of “@grok, is this true?” or “@grok, what are the implications for someone like me?”. It’s striking how fast large language models (LLMs) have become arbiters of truth for so many consumers of social media. And while it’s refreshing to see people use such tools to critically parse, understand, and verify social media claims, it’s also placing enormous trust in those models. Trust which can, and will, be taken advantage of by corporate and state interests. Until now, the focus of LLM development has been primarily to outperform the competition on a technical level (larger context windows, chain of reasoning, increased specialization, etc). I’d qualify this process as being mostly domain-agnostic and unbiased, given (i) the significant overlap in the training datasets across models and (ii) the limited investment in model alignment beyond table stakes techniques (guardrails, RLHF, temperature adjustments to limit hallucinations, etc). Once technical progress in public LLMs starts plateauing, however, I expect the focus to shift to the [[balkanization]] and politicization of LLM rule sets as the next evolutionary phase in their competitive differentiation. We have already seen a mild case of this with search engines, of course — Google doesn’t provide the same results whether you browse from Washington, DC or Beijing, China. Largely to satisfy national preferences or regulations. We have also seen some evidence of LLM outputs being internally biased thanks to the addition of rulesets censoring outputs critical to Elon Musk and Donal Trump, for instance; these instructions were presumably rolled back shortly after their discovery (Jones, 2025). I posit that these back-end interventions in LLMs (as well as [[LLM whisperer — An inconspicuous attack to bias LLM responses|front-end manipulations]]) will get much much more insidious and pervasive. I consider it plausible that we’re headed toward a “multipolar world” comprised of blue-pill models, red-pill models, a few niche middle-of-the-road aggregators that claim neutrality, and some specialized models (e.g., for coding or scholarly research). What is evident to me is that someone like Elon Musk didn’t invest $44B in Twitter (in 2022 dollars) just to have Grok roam free and spout out only “the most likely tokens” from its unadulterated training set. The opportunity cost of not shaping the message is becoming too great as more and more people rely on the model to [[DYOR]]. The same evolution will reflexively apply to other leading models, with ChatGPT as the putative antagonist to Grok. A couple of years from now, you could probably situate users on the political compass based on which LLM they prefer, just like with their newspaper subscriptions in the last decade. And if we thought the echo chambers of the 2010s were nefarious enough to the health of informed democracies, wait until people source free, instant, and qualified validation of their fringe ideas from mainstream LLMs. Belief reinforcement will be cheaper and faster than ever. This shift will also mark an ironic turning point in AI’s young history — after years of being trained on our thoughts, it is us who will start being trained on AI’s thoughts, no matter how perniciously calibrated they may be. <div style="font-size: x-large; text-align:center;">❧</div> ## References - Jones, M. G. (2025, March 3). Is AI chatbot Grok censoring criticism of Elon Musk and Donald Trump? *Euronews*. https://www.euronews.com/my-europe/2025/03/03/is-ai-chatbot-grok-censoring-criticism-of-elon-musk-and-donald-trump