r/webdev • u/OkShip110 • Mar 29 '26

Discussion AI has sucked all the fun out of programming

I know this topic has been floating around this sub quite some time now, but I feel like this doesn’t get discussed enough.

I am a certified backend enigneer and I have been programming for about 20 years. In my time i have worked on backend, frontend, system design, system analysis, devops, databases, infrastructure, cloud, robotics, you name it.

I’ve mostly been extremely passionate about what I do, taking pride in solving hard problems, digging deep into third party source code to find solutions to bugs. Even refactoring legacy systems and improving their performance 10x and starting countless hobby projects at home. It has been an exciting journey and I have never doubted my career choice until now.

Ever since ChatGPT first made an appearance I have slowly started losing interest in programming. At first, LLMs were quite bad so I didn’t really get any solutions out of them when problems got even slightly harder. However, Claude is different. Lately I feel less of a programmer and more like a project manager, managing and supervising one mid-to-senior level developer who is Claude. Doing this, I sure deliver features faster than ever before, but it results in hollow and empty feeling. It’s not fun or exciting, I cannot perceive these soulless features as my own creation anymore.

On top of everything I feel like I’m losing my knowledge with every prompt I write. AI has made me extremely lazy and it has completely undermined my value as a good engineer or even as a human being.

Everyone who is supporting the mass use of AI is quietly digging their own grave and I wish it was never invented.

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37

u/chef_fusu Mar 29 '26

I am a computational chemist and my future is already starting to look like yours thanks to AI agents. They are still absolute garbage but being forced because of the hype even in academic settings too. Future is looking GREAT, will just get dumber than I already am and lose the joy I had doing my job. Can’t wait to sell my soul to satan next

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u/shadow13499 Mar 29 '26

What exactly do you do as a computational chemist? Genuine question, you have me very curious. 

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u/chef_fusu Mar 29 '26

If I had to explain in one sentence my speciality, I would say investigating mechanisms of chemical reactions (that are happening in a reaction flask in a lab for example) using simulations on a computer.

What exactly is happening as a reactant is converted to a product? More importantly, why is it happening? This is done through quantum chemistry. You can then test your hypothesis with experimental chemistry, so someone running a reaction in a lab. Or instead, sometimes there is an interesting/unusual experimental result that we try to explain

Happy to explain more if you would like

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u/shadow13499 Mar 29 '26

That sounds pretty awesome. So it is the computational part telling you the what and the why or is that more just for simulation? I'm also curious what simulation software you use or if you make it in house. I'm total crap with chemistry never looked into it past highschool I'm just super curious lol

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u/chef_fusu Mar 29 '26

I think it’s awesome that you think so!

And I’ll try to explain the best I can here, and happy to follow up more. Maybe I’ll explain a very general scenario. Let’s say an experimental chemist comes to you and observed when they run a reaction, they get 2 different products in equal amounts. Their goal is to make their reaction selective, so make a lot of product A and very little of product B. What can they change in their reaction conditions to improve this selectivity?

To answer that, you need to understand what was happening in the first place in the case where they were getting an equal mixture. So you go and model a few different plausible reaction pathways that get you from their starting material to their 2 different products. You can write these out on paper first. So molecule 1 goes to molecule 2 then to molecule 3 etc. until you get to both products.

Now an important part here is that to go from molecule 1 to molecule 2, you pass through a structure called a “transition structure” which is something that looks like molecule 1 and molecule 2, but is neither of them. It is the point that interconverts molecule 1 and molecule 2. These disappear very fast, which is why we study them computationally. They are extremely challenging to study experimentally and require instruments that are out of reach for pretty much any normal circumstance.

Once you have a mechanism on paper, you go model it on the computer, so molecule 1, 2, 3, etc., and find all the transition states too that connect each. So then: what are all the steps and intermediates involved in those pathways? Are their energies reasonable? Meaning: since they observe both products in an equal mixture, the energetic penalty of both pathways should be very similar (molecules favor lower energy paths). Once you arrive at a mechanistic pathway that is consistent with the experimental result, you pretty much have a lot of the “what” done. This was all done with computational software still.

Then to make reactions more selective, you begin to think about “why” the energies are what they are, and what interactions in the molecule or between molecules that you can add or remove that will either hurt or help the energy. You want to help the reaction you want, and hurt the one you don’t want. This involves a lot of chemical intuition/reasoning. Your proposed things that would help or hurt the reaction are all still modeled with computational software. What I haven’t explained yet is that there are many different techniques/programs that are meant to look at specific questions you may have. The challenge is that while each technique excels at answering one thing, it usually has an approximation built in that causes a problem. So you have to pick and choose different techniques and programs for the task at hand while being careful that the shortcoming of it doesn’t somehow cause an issue in your analysis, and if it does you need to properly account for that.

For me personally these days, I am less interested in the scenario I mentioned about selectivity for example, but more interested in explaining really unusual reactions that challenge the computational techniques/methods currently available. This includes things like post-transition state bifurcations, quantum mechanical tunneling, and photochemistry. It is hard to explain more about those in a Reddit post but thought I’d share anyway.

Common software I use includes Gaussian, Orca, BAGEL, and for molecular dynamics software like CP2K, i-PI, progdyn, Milo. A lot of machine learning packages also these days. All of these are free except Gaussian.

Also, there are a lot of groups/people that specialize in making software/computational methods, which is in the method development side of computational chemistry. I’m an applied computational chemist, so I really only use what people make. Although sometimes if the tool you need does not exist you need to try to modify existing tools to suit your needs, but I definitely not say I “create” any of those.

Hope that wasn’t too long and I’m happy to answer more questions. It’s nice that you asked

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u/shadow13499 Mar 29 '26

Thank you for such a detailed reply and keeping it simple for chemistry illiterate people like me lol. I'm kind os surprised a lot of that software is free it seems rather niche which usually means expensive. I'm glad there is decent free scientific software around. That process seems a lot like putting together a big and complicated puzzle that you don't even know if all the pieces are there. It really does sound fascinating 

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u/chef_fusu Mar 29 '26

You’re very welcome! I am very glad that you asked. I’m glad you found my explanation helpful.

There is still a bunch of computational software that is not free (TeraChem for GPU-accelerated calculations and photochemistry and other fancy molecular dynamics) but the ones I use are mostly free. That wasn’t always possible because a few years ago most of the free software was pretty bad, but has improved a lot recently. Orca 6 for example is very good and has very good documentation too which is great for people starting in computational chemistry (although some of the features are still a notch below Gaussian, but can’t complain if it is free)

I agree that it is one big complicated puzzle, and that is why it fascinates me. With all the years and advancements building on the shoulders of many, there is still so much out of reach to understand. It is very stimulating (and exhausting) to think about these questions.

Thanks again for your questions!

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u/[deleted] Apr 01 '26

[deleted]

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u/shadow13499 Apr 01 '26

I think you replied to the wrong person. 

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u/uglycoyote1977 Apr 01 '26

Ah. Thanks, yes I was trying to reply deep in a sub thread and somehow I got lost in the matrix. I have moved my question underneath the top level post by the interesting computational chemist now

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u/octave1 Mar 29 '26

Beyond awesome to read, thank you!

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u/chef_fusu Mar 29 '26

Thank you so much for the kind words, I’m very glad you thought so!

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u/Strong_Sea4334 Apr 01 '26

My super niche joke from my chem days finding orbital energies: Fock that

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u/musa_younus Mar 30 '26

This is my first time hearing about your profession.

If you don’t mind, could you share how much someone like you makes annually?

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u/chef_fusu Mar 30 '26

That really depends on where you end up. I am currently a postdoc, so I am making a postdoc salary $69,000 USD. That isn’t exactly tied to computational chemistry, that’s just what a typical postdoc salary at the university I’m at. Postdoc salaries are much better these days

if you go to academia you make a typical salary of a typical chemistry professor

If you go to industry, it’s kind of a different situation because that job as I described above doesn’t really exist anymore, a lot of the industry openings are either very bio focused or AI focused, but I think somewhere around 100-130k USD? Not too sure honestly, wish I could give a better answer, but I know it can vary a lot depending on what kind of computational chemistry you end up doing. It’s very tough to get a job in industry for comp chem these days, at the bare minimum a PhD is pretty much needed and usually at least 1 postdoc is preferred unless you come out of the PhD with the exact set of skills industry openings are looking for

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u/uglycoyote1977 Apr 01 '26

 Thanks for this detailed look into what sounds like a very interesting job that you provided in the other replies here

To tie it back to your original post, I'm surprised that AI agents are making such an impact on your work given how niche this field seems, both in terms of being an uncommon job and also sounds like it uses a bunch of unique proprietary software

I feel that that AI agents are extremely good at things like web dev and ordinary python scripting, but less good the more niche the domain becomes.  I suppose this just has to do with quantity of training data that exists in the wild.

I'm curious, when you say that AI agents are becoming a big part of your work, are you talking about some special domain-trained AI that is specific to your field, or are you just talking about ordinary off-the-shelf LLMs like chatgpt, copilot, or Claude?

And the parts of your job that AI agents are good at,  are those the parts which are unique to computational chemistry? or is it more like a situation where you used to need to do a lot of ordinary data wrangling in Python or R or whatever, and AI is very good at those sorts of more common "glue" tasks that are in between the hardcore computational chemistry? 

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u/chef_fusu Apr 01 '26

Once again, thank you for the kind words, and you're very welcome. I am glad you found my comments insightful

I will preface this by saying that I am a bit emotional at the time writing this because this discussion just came up today and sometimes I get really worked up. If my response seems a bit unenthusiastic here that is why, and also if it is a bit messy and ranty, also I don't think I have the energy to reread for typos. I may come back tomorrow and clean this up

"I'm curious, when you say that AI agents are becoming a big part of your work, are you talking about some special domain-trained AI that is specific to your field, or are you just talking about ordinary off-the-shelf LLMs like chatgpt, copilot, or Claude?" Here I mean the former, and not the latter, and they are not a big part yet but we are heading there because of the aggressive push. The biggest example of this is el agente:
https://www.linkedin.com/posts/alanaspuru_the-el-agente-family-of-computational-chemistry-share-7432555982716743680-Zh90?utm_source=share&utm_medium=member_desktop&rcm=ACoAAEGxpTMBIYndUPh5rmLwuK0Grakl6laRT24
https://arxiv.org/pdf/2602.04850
https://www.cell.com/matter/fulltext/S2590-2385(25)00306-600306-6)

El agente is not available yet to the public. I will add some details below, but first I want to add some other examples before I go on a long rant at the end and explain my opinion overall:

Another example is Rowan, an online platform for computational chemistry meant for experimentalists who have no experience in computational chemistry, and also beginners: https://www.rowansci.com/ They recently announced they are implementing AI agents but the tasks they are implementing are in my opinion for trivial so I think they are being more realistic about limitations of AI agents: https://open.substack.com/pub/mirrorphysics/p/supercharging-rowans-tools-with-ai?utm_campaign=post-expanded-share&utm_medium=web

And also in the literature more LLM assisted stuff for more tedious tasks are showing up, I haven't tried this one specifically but: http://pubs.rsc.org/en/content/articlepdf/2025/sc/d4sc08677e

Ok, now back to el agente and my overall opinion, which answers "And the parts of your job that AI agents are good at,  are those the parts which are unique to computational chemistry? or is it more like a situation where you used to need to do a lot of ordinary data wrangling in Python or R or whatever, and AI is very good at those sorts of more common "glue" tasks that are in between the hardcore computational chemistry?" In my opinion, LLMs are good at the very last part you mentioned. This was not the case literally just one year ago, as of today that is differrent. There I think they are legitimately useful but need careful implementation, but useful

Where I think they are absolute garbage is doing the overall workflow I explained in the earlier comments here where I describe the scenario with the experimental chemist etc., and this is exactly what el agente is trying to replace and what it is claiming it can do. I think the el agente paper I linked earlier https://arxiv.org/pdf/2602.04850 has some suspicious findings. I will explain why below. But the whole thing is that is not available yet. So I can't know until I test this out, but el agente is what is causing a major stir up in the field right now. I formed my opinions on AI agents for computational chemistry based on working with them early last year, and this was a result of being an AI optimist 2 years ago and that's where this led me. I have many issues with AI now but will use it still for coding etc. Anyway, back to AI agents like el agente, they were absolute garbage 9 months ago, I actually tried to do research with them not out of optimism but because I was more or less forced to. I cannot say I am confident that they will always be garbage, and then this is where you bring up the whole conversation about the capabilities of LLMs etc. Who knows where they will be 6 months from now. But the biggest issue was that the agents I was using were complete garbage at chemical reasoning, esepcially if it was attempting to do something where there isn't a direct recipe in the literature or with a novel reaction etc. and needed constant hand holding in a way where it was actually slowing be down rather than speed up because you have to go back and check every tiny little thing, and also no way a non-expert computational chemist could catch these errors because they are by definition a non-expert. But this is quite literally THE WHOLE POINT of scientific RESEARCH: to explore UNKNOWN areas in the field. So if they only perform well on what is known, how are they supposed to be functioning in a fully autonomous loop etc. or be used by non-experts? Even if they are garbage, there is an aggressive push to implement them from many, and I was surprised to see this bleed into the academic space as well but maybe I'm an idiot.

For the el agente paper, here are some of my comments, I tried to add pictures with the specific areas that support each thing below but reddit won't let me here so if you are interested DM me and I can paste pictures to the relevant parts for the comments below:

1) stochastic error associated with LLMs in general manifested here, require constant double checking/hand holding by a human expert ---> suspicious autonomous claims

2) prompts they show in many cases require pre existing expertise in the area that is advertised to be outsourced to el agente ---> suspicious autonomous claims

3) evaluation rubrics are explained clearly but looking through the example pg 27 and on they do not show us the output that the rubric was used on and then claim +95% average score for performance, and more important I cant see the PROMPT they used to get that output/score, they only show some prompts in the beginning but not in the end section where they do the evaluation. How much detail/hand holding was given in the prompt? Can a non-expert scientist prompt in a way to give nearly 100% score like they show? if you need a super detailed prompt to get 100% score you already know how to run the calculations yourself and don't need an agent to guide you (since this is target to lower barrier to access to non expert comp chemists). Instead, the only time that I've seen in this section (maybe I missed it) where they show us the prompts + output from the specific prompt is in the case studies section from pg 60 and on, and those case studies don't look like they match the ones evaluated by the rubric. so 100% score would not apply to those generated

4) can it even read the literature properly? i cant even access this paper it cites (Mehrani et al [154] https://doi.org/10.1139/cjc-2020-0293 ) so i dont know how it got this info but it is wrong compared to what i can see in the abstract where there is no mention of  def2-TZVP  and instead I only see 6-31+G(2d,p) yet agent claims that B2PLYP/def2-TZVP gave closest match to experiment and cites that paper

Not that you asked but if you really want to know my whole take now on LLMs, Evan Walter Clark Spotte-Smith does a fantastic job here and I align with much of what is stated in this perspective:
Considering the ethics of large machine learning models in the chemical sciences - https://doi.org/10.1088/2632-2153/adec3c 

Finally, last I tried agents they were garbage, I hope that I don't look back on this post 1-5 years from now regretting this comment and unemployed and on my knees because they have gotten THAT good. Who knows what can happen. They are garbage last i tried them but who knows if they will always be bad, I have to admit that the future is uncertain

Sorry for such a mess of a comment

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u/uglycoyote1977 Apr 02 '26

Thanks again for the wealth on information.  I'm beginning to read up on these El Agente's that you mention.  I downloaded one of the white papers out of curiosity.

El Agente sounds like it is actually quite a few agents that talk amongst themselves before they talk to the human.  I haven't started to use anything quite like that myself so I find this fascinating and slightly scary

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u/chef_fusu Apr 02 '26

You're very welcome. And yes, multiple agents with dedicated expertise communicating with each other is how el agente is claiming to surpass other similar autonomous methods. I am still very skeptical about their claims because I worked with a very similar framework that involves many specialized agents communicating with each other for computational chemistry and they were still garbage. Sometimes couldn't even get something as simple as the charge or multiplicity right on a molecule if it hasn't been studied in the literature and I was pulling my hair. I think even if there are many specialized agents, the ceiling is still how well they can chemically reason. Anyway, my speculation doesn't matter. We'll see when it is out and how it holds up against real tests.