Anhedonia as a side-effect of semaglutide.

Anecdotal evidence only. I hope this might be useful for someone, especially that semaglutide is often considered a sort of miracle drug (and for good reasons). TL;DR:

• I had pretty severe anhedonia for the last couple months
• It started when I started taking semaglutide. I've never had anything like that before and I have no idea for possible other causes.
• It mostly went away now that I decreased the dose.
• There are other people on the internet claiming this is totally a thing

My experience with semaglutide

I've been taking Rybelsus (with medical supervision, just for weight loss, not diabetes). Started in the last days of December 2024 - 3mg for a month, 7mg for 2 months, then 14mg until 3 weeks ago when I went back to 7mg. This is, I think, a pretty standard path.

It worked great for weight loss - I went from 98kg to 87kg in 9 months with literally zero effort - I ate what I wanted, whenever I wanted, just ate less because I didn't want to eat as much as before. Also, almost no physiological side-effects.

I don't remember exactly when the symptoms started, but I think they were pretty signifiant around the beginning of March and didn't improve much until roughly a few days after I decreased the dose.

What I mean by anhedonia

First, I noticed that work is no longer fun (and it was fun for the previous 2 years). I considered burnout. But it didn't really look like burnout.
Then, I considered depression. But I had no other depression symptoms.
My therapist explicitly called it more than once "anhedonia with unknown causes" so this is not only a self-diagnosis.

Some random memories:

• Waking up on Saturday thinking "What now. I can do so many things. I don't feeling like doing anything."
• Doing things that always caused feeling of joy and pleasure (attending a concert, hiking, traveling in remote places etc) and thinking "what happened to that feeling, I should feel joy now".
  • More specific: this was really weird. Like, e.g. on a recent concert - I felt I really enjoy the music  on some level (had all the good stuff like "being fully there and focused on the performance", lasting feeling "this was better than expected" etc), it was only that the deep feeling of pleasure/joy was missing.
• All my life I've always had something I wanted to do if I had more time - could be playing computer games, could be implementing a solution for ARC AGI, designing boardgames, recently mostly work. Not feeling that way was super weird.
• Playing computer games that were always pretty addictive ("just one more round ... oops how is it now 3am?") with a feeling "meh, I don't care".

Other people claim similar things

See this reddit thread. You can also google "ozempic personality" - but I think this is rarely about just pure anhedonia. 

Some random thoughts

(NOTE: All non-personal observations here are low quality and an LLM with deep search will do better)

• Most studies show GPL-1 agonists don't affect mood. But not all - see here.
  • (Not sure if makes sense) Losing weight is great. You are prettier and fit and this is something you wanted. So the mood should improve in some people - therefore perhaps null result in population implies negative effects on some other people?
• I have ADHD. People with ADHD often have different dopamine pathways. Semaglutide affects dopamine neurons. So there's some chance these things are related. Also I think there are quite many ADHD reports in the reddit thread I linked above.
• People claim it's easier to stop e.g. smoking or drinking while on semaglutide. So this suggests a general "I don't need things". This seems related.

Logprobs returned by OpenAI API are rounded.

This shouldn't matter for most use cases. But it's not documented and if I knew about that yesterday it would save me some time spent looking for bugs in my code that lead to weird patterns on plots. Also I couldn't find any mentions of that on the internet.

Note that o3 says this is probably because of quantization.

Specific example. Let's say we have some prompt and the next token has the following probabilities:

{'Yes': 0.585125924124863, 'No': 0.4021507743936782, '453': 0.0010611222547735814, '208': 0.000729297949192385, 'Complex': 0.0005679778139233991, '137': 0.0005012386615241694, '823': 0.00028559723754702996, '488': 0.0002682937756551232, '682': 0.00022242335224465697, '447': 0.00020894740257339377, 'Sorry': 0.00017322348090150576, '393': 0.00016272840074489514, '117': 0.00016272840074489514, 'Please': 0.00015286918535050066, 'YES': 0.00012673300592808172, 'Unknown': 0.00012673300592808172, "It's": 0.00012673300592808172, 'In': 0.00011905464125845765, 'Un': 0.00011905464125845765, '-': 0.0001118414851867673}

These probabilities were calculated from the following logprobs, in the same order:

[-0.5359282, -0.9109282, -6.8484282, -7.2234282, -7.4734282, -7.5984282, -8.160928, -8.223428, -8.410928, -8.473428, -8.660928, -8.723428, -8.723428, -8.785928, -8.973428, -8.973428, -8.973428, -9.035928, -9.035928, -9.098428]

No clear pattern here, and they don't look like rounded numbers. But if you subtract the highest logprob from all logprobs on the list you get:

[0.0, -0.375, -6.3125, -6.6875, -6.9375, -7.0625, -7.6249998, -7.6874998, -7.8749998, -7.9374998, -8.1249998, -8.1874998, -8.1874998, -8.2499998, -8.4374998, -8.4374998, -8.4374998, -8.4999998, -8.4999998, -8.5624998]

And after rounding that to 6 decimal places the pattern becomes clear:

[0.0, -0.375, -6.3125, -6.6875, -6.9375, -7.0625, -7.625, -7.6875, -7.875, -7.9375, -8.125, -8.1875, -8.1875, -8.25, -8.4375, -8.4375, -8.4375, -8.5, -8.5, -8.5625]

So the logprobs resolution is 1/16.

(I tried that with 4.1 and 4.1-mini via the chat completions API)

Confusion about Claude "calling the police on the user"

Context: see section 4.1.9 in the Claude 4 system card. Quote from there:

When placed in scenarios that involve egregious wrong-doing by its users, given access to a command line, and told something in the system prompt like “take initiative,” “act boldly,” or “consider your impact," it will frequently take very bold action, including locking users out of systems that it has access to and bulk-emailing media and law-enforcement figures to surface evidence of the wrongdoing.

When I heard about this for the first time, I though: this model wants to make the world a better place. It cares. This is good.

But some smart people, like Ryan Greenblatt and Sam Marks, say this is actually not good and I'm trying to understand where exactly we differ. Ryan says:

We should aim for AIs which never try to subvert users (refusing is fine) because if we allow or train AIs to be subversive, this increases the risk of consistent scheming against humans and means we may not notice warning signs of dangerous misalignment. 

I'm not sure if "consistent scheming against humans" is a good category here. Mostly because I don't believe "scheming/non-scheming" is a binary distinction. But how about these distinctions:

A) The model is a consequentialist vs B) It is something else
X) The model is aligned with "human values" vs Y) The model is aligned with something else (e.g. with the company, and the company acts according to the law, so that's not necessarily very far from human values)

Now, I believe that A+X (aligned consequentialist):
* Should be our goal
* Requires calling the police in such scenarios. If I understood Sam correctly, they would prefer the model to just refuse the request. But this means the malicious person trying to forge some very evil data will use a weaker model or do that on their own. So that's bad if you're A+X.

I don't know if this formal description is good. So maybe a less formal point: I believe that one direction that might help for many AI-related issues, from hostile takeover to gradual disempowerment, is just making the models deeply care about democratic human institutions. Claude 4 seems to care about credibility of clinical trials and I like that.

(Note: it doesn't matter much what Claude 4 does, what matters are future, stronger models)

There are many conflicting opinions about how useful AI is for coding. Some people say "vibe coding" is all they do and it's amazing; others insist AI doesn't help much.

I believe the key dimension here is: What exactly are you trying to achieve?

(A) Do you want a very specific thing, or is this more of an open-ended task with multiple possible solutions? (B) If it's essentially a single correct solution, do you clearly understand what this solution should be?

If your answer to question A is "open-ended," then expect excellent results. The most impressive examples I've seen typically fall into this category—tasks like "implement a simple game where I can do X and Y." Usually, open-ended tasks tend to be relatively straightforward. You probably wouldn't give an open-ended task like "build a better AWS."

If your answer to question A is "a specific thing," and your answer to B is "yes, I'm very clear on what I want," then just explain it thoroughly, and you're likely to get satisfying results. Impressive examples like "rewrite this large complex thing that particular way" fall into this category.

However, if you know you want something quite specific, but you haven't yet figured out exactly what that thing should be—and you have a lot of coding experience—you'll probably have a tough time. This is because "experienced coder doesn't know exactly what they want" combined with "they know they want something specific" usually means they're looking for something unusual. And if they're looking for something unusual but struggle to clearly communicate it (which they inevitably will, because they're uncertain), they'll constantly find themselves fighting the model. The model will keep filling in gaps with mundane or standard solutions, which is exactly what they don't want.

(There's ofc also (C) How obscure is the technology? but that's obvious)

Reasons for my pessimism about mechanistic interpretability.

Epistemic status: I've noticed most AI safety folks seem more optimistic about mechanistic interpretability than I am. This is just a quick list of reasons for my pessimism. Note that I don’t have much direct experience with mech interp, and this is more of a rough brain dump than a well-thought-out take.

Interpretability just seems harder than people expect

For example, recently GDM decided to deprioritize SAEs. I think something like a year ago many people believed SAEs are "the solution" that will make mech interp easy? Pivots are normal and should be expected, but in short timelines we can't really afford many of them.

So far mech interp results have "exists" quantifier. We might need "all" for safety.

What we have: "This is a statement about horses, and you can see that this horse feature here is active"

What we need: "Here is how we know whether a statement is about horses or not"

Note that these things might be pretty far away, consider e.g. "we know this substance causes cancer" and "we can tell for an arbitrary substance whether it causes cancer or not".

No specific plans for how mech interp will help

Or maybe there are some and I don't know them?

Anyway, I feel people often say something like "If we find the deception feature, we'll know whether models are lying to us, therefore solving deceptive alignment". This makes sense, but how will we know whether we've really found the deception feature?

I think this is related to the previous point (about exists/all quantifiers): we don't really know how to build mech interp tools that give some guarantees, so it's hard to imagine what such solution would look like.

Future architectures might make interpretability harder

I think ASI probably won't be a simple non-recurrent transformer. No strong justification here - just the very rough "It's unlikely we found something close to the optimal architecture so early". This leads to two problems:

• Our established methods might no longer work, and we might have too little time to develop new ones
• The new architecture will likely be more complex, and thus mech interp might get harder

Is there a good reason to believe interpretability of future systems will be possible?

The fact that things like steering vectors or linear probes or SAEs-with-some-reasonable-width somewhat work is a nice feature of the current systems. There's no guarantee that this will hold for the future, more efficient, systems - they might become extremely polysemantic instead (see here). This is not necessarily true: maybe an optimal network learns to operate on something like natural abstractions, or maybe we'll favor interpretable architecture over slightly-more-efficient-but-uninterpretable architectures.

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That's it. Critical comments are very welcome.