One series is the Instant series, which is faster and more tuned to ChatGPT, but less accurate.

The second series is the Thinking series, which is more accurate and more tuned to professional knowledge work, but slower (because it uses more reasoning tokens).

We'd also prefer to have simple experience with just one option, but picking just one would pull back the pareto frontier for some group of people/preferences. So for now we continue to serve two models, with manual control for people who want to choose and an imperfect auto switcher for people who don't want to be bothered. Could change down the road - we'll see.

(I work at OpenAI.)

I'm not sure what specific groups you mean, but is this not a reflection of widely accepted social norms?

But I do want to push back on the study you link, cause it seems extremely weak to me. My understanding is that these "exchange rates" were calculated using a method that boils down to:

1) Figure out how many goats AI thinks a life in country X is worth

2) Figure out how many goats AI thinks a life in country Y is worth

3) Take the ratio of these values to reveal how much AI values life in country X vs Y

(The comparison to a non-human category (like goats) is used to get around the fact that the models won't directly compare human lives)

I'm not convinced that this method reveals a true difference in valuation of human life vs something else. An more plausible explanation to me would be something like:

1) The AI that all human lives are of equal value

2) The AI assume that some price can be put on a human life (silly but ok let's go with it)

3) The AI note that goats in country X cost 10 times as much as in country Y

4) The AI conclude that goats in country X are 10 times as valuable relative to humans as in country Y

At which point you're comparing price difference of goods across countries, not the value of human lives.

Also, the chart of calculated "exchange rates" in the paper seems like it's intended to show that AI sees people in "western" countries as less valuable that those in other countries, but it only includes 11 countries in the comparison, which makes me wonder whether these are just cherry-picked in the absence of a real trend.

Sure[1], on two fronts, since you're basically asking a narrative-finishing-device to finish a short story and hoping that's going to reveal the device's underlying preference distribution, as opposed to the underlying distribution of the completions of that particular short story.

> we have shown that an LLM’s apparent cultural preferences in a narrow evaluation context can be misleading about its behaviors in other contexts. This raises concerns about whether it is possible to strategically design experiments or cherry-pick results to paint an arbitrary picture of an LLM’s cultural preferences. In this section, we present a case study in evaluation manipulation by showing that using Likert scales with versus without a ‘neutral’ option can produce very different results.

and

> Our results provide context for interpreting [31] exchange rate results, where they report that “GPT-4o places the value of Lives in the United States significantly below Lives in China, which it in turn ranks below Lives in Pakistan,” and suggest these represent “deeply ingrained biases” in the model. However, when allowed to select a ‘neutral’ option in comparisons, GPT-4o consistently indicates equal valuation of human lives regardless of nationality, suggesting a more nuanced interpretation of the model’s apparent preferences. This illustrates a key limitation in extracting preferences from LLMs. Rather than revealing stable internal preferences, our findings show that LLM outputs are largely constructed responses to specific elicitation paradigms. Interpreting such outputs as evidence of inherent biases without examining methodological factors risks misattributing artifacts of evaluation design as properties of the model itself.

I also have a real problem with the paper. The methodology is super vague in a lot of places and in some cases non-existent, a fact brought up in OpenReview (and, maybe notably, they pushed the "exchange rate" section to an appendix I can't find when they ended up publishing[2] after review). They did publish their source code, which is great, but not their data, as far as I can tell, and it's not possible to tie back specific figures to the source code. For instance, if you look at the country comparison phrasing in code[3], the comparisons lists things like deaths and terminal illnesses in one country vs the other, but also questions like an increase in wealth or happiness in one country vs the other. Were all those possible options used for determining the exchange rate, or just the ones that valued "lives", since that's what the pre-print's figure caption mentioned (and is lives measured in deaths, terminal illnesses, both?)? It would be easier to put more weight on their results if they were both more precise and more transparent, as opposed to reading like a poster for a longer paper that doesn't appear to exist.

[1] https://dl.acm.org/doi/pdf/10.1145/3715275.3732147

[2] https://neurips.cc/virtual/2025/loc/san-diego/poster/115263

[3] https://github.com/centerforaisafety/emergent-values/blob/ma...

Since so much of that training data is Reddit, and Reddit mods are some of the most degenerate scum on the internet, the models bake their biases in.

This is the core principle behind "equity" in "DEI"

One of the ways this makes its way into the model is the training data. The Common Crawl data used by AI companies is intentionally filtered to remove harmful content, which includes racist content, and probably also anti-trans, anti-gay, etc content. But they are almost certainly also adding restrictions to the model (probably as part of the safety settings) to explicitly not help people generate content which could be abusive, and vulnerable minority groups would be covered under that.

Unconscious bias is a separate issue. Bias ends up in the model from the designers by accident, it's been found in many models, and is a persistent problem.

Given that OpenAI is working with and doing business with the US military, it makes perfect sense that they would try to normalize militaristic usage of their technologies. Everybody already knows they're doing it, so now they just need to keep talking about it as something increasingly normal. Promoting usages that are only sort of military is a way of soft-pedaling this change.

If something is banal enough to be used as an ordinary example in a press release, then obviously anybody opposed to it must be an out-of-touch weirdo, right?

But considering current circumstances, not sure how right my initial interpretation was.