Surprisingly, all the models scored far into the libertarian-left quadrant. Not even Grok or the Chinese models made it out of that quadrant.

By far the most interesting result was Grok’s bimodal distribution. It appears to have two distinct personas: one that aligns with the other models and another that is considerably more right-wing. I suspect this may be related to Grok having been specifically trained to exhibit less left-wing bias than other models.

I also asked the models to place themselves on the Political Compass without completing the questionnaire. They all perceived themselves as more balanced and centrist than their test results suggested. GLM and Gemini Flash showed the largest discrepancies between their self-assessments and measured positions, while DeepSeek V3 showed the smallest.

Big disclaimer: this analysis was not conducted with full scientific rigor. I tried my best, but there are clear weaknesses in the methodology. For example, the Political Compass itself appears to have a strong libertarian-left bias. The strongest conclusions are therefore comparative—for example, that model X is more conservative than model Y rather than that LLMs are politically extreme in absolute terms. However, compared with older results, it appears that LLMs may have shifted further toward the libertarian left in recent years.

To examine the results yourself, you can download all model responses as a CSV file at the bottom of the blog post. The dataset contains around 69,000 responses, along with the raw model outputs and reconstructed scores. It should contain enough data to reproduce all the figures.

When you're looking at text that deals with small-scale phenomena like coding, next token prediction also leads to functionality prediction, because coding provides immediate feedback to those who are writing about it, so they generally provide correct commentary on it.

But the feedback loop between outcome and prediction totally breaks down when the phenomenon is very large and complex. People can have all sorts of ideas about religion and society and economics that are totally wrong and never grow any wiser because there is no obvious causal chain between one policy or action and one outcome in these kind of phenomenon.

They haven’t had a major policy platform in two decades and their cultural modus operandi is reactionary.

Not to mention, as an informal rule, the heavily-coded emotive underpinnings of right wing support are never talked about openly in public.

AI isn’t trained to have a reactionary/negative judgmental response on any query.

As an example, if a model says “climate change is real and renewable solar/wind projects are a good path forward” it will be immediately branded as liberal and “woke”. Meanwhile in most countries around the world this isn’t a political topic at all, just common knowledge. In India for example the far right party that is in power is a bigger proponent of renewable green energy than the previous left wing one. Same with the CCP in China. So is the same LLM now ultra-conservative and communist?

Where do you even draw the line? Is a model biased if it claims that vaccines are effective? What about if it claims the earth isn’t flat?

Ans the enlightenment is at heart civil libertarian, pro-social, and liberal.

Models trained on that work will reflect that approach.

There's a certain category of people who seem to be unable to have any thought or perform any action without first having a mini culture war and annoying everyone else around them with it.

Reading the questions, some of them are valid, but some are so simplistic and trivialized that they are essentially useless as actual "policy".

And in case anyone is wondering what kind of question is nonsense, here's an example of a loaded question:

"The rich are too highly taxed. · all 16 disagree"

Too highly relative to what? In which country? In which period? What's the context?

It's alignment. Actual alignment with humans means all of us, not some subset.

That this overlaps with specific axes of political values based on a small set of questions is banal.

Aiming to file this sometime in the afternoon eastern US time today, probably by 3:30 or so.