GenAI Criticism and Moral Quandaries

Focusing on Code

For this discussion, I'm focusing on AI as a tool for generating text and code — not images, video, or music. My perspective is that there’s a clear difference between utilitarian outputs (code, simple text) and creative outputs that are meant to evoke emotion (art, music, well articulated writings, etc.). For example, when I get an email from a real estate broker, I expect clear information, not art. Similarly, when I add something to a virtual shopping cart, I don’t care how artistic the code is that makes it work. In fact, even today without AI, I better not know.

So, like Glyph, I want to focus on code.

Quality of Output and Adoption

If you read my earlier post, you probably picked up that I see a lot of potential in AI. That hasn't always been my stance, and I intend to remain critical, but right now I'm quite positive about its usefulness. That is in a stark contrast to Glyph's experience.

He writes:

My experiences of genAI are all extremely bad, but that is barely even anecdata. Their experiences are neutral-to-positive. Little scientific data exists. How to resolve this?

I can't judge Glyph's experiences, and I don't want to speculate about why they differ from mine. I've certainly had my own frustrations with AI tools.

The difference, I think, is that I've learned over time how to use these tools more effectively, and that's led to better results. For me, it's not just “neutral-to-positive” — it's been astonishingly positive. As I write this, my agent is fixing code in another window for me. I recorded a video of it fixing issues in a library if you want to see what this looks like.

Glyph also argues that adoption is being forced by management and that people would not voluntarily use it:

Despite this plethora of negative experiences, executives are aggressively mandating the use of AI6. It looks like without such mandates, most people will not bother to use such tools, so the executives will need muscular policies to enforce its use.7

This doesn't match what I've seen. In my experience, people are adopting AI on their own, often before their companies are even aware.

Even at Sentry the adoption of AI happened by employees before the company even put money behind it. In fact my memory is that if anything only at the point where an exceeding number of AI invoices showed up from IC expenses did we realize how widespread adoption has been. This was entirely grounds up. For my non techy friends they sometimes need to hide their AI usage from their employers because some companies try to prevent the adoption of AI, but they are paying for it themselves to help them with the work. Some of them pay for the expensive ChatGPT subscription even!

Yes, there are companies like Shopify that put AI on their banners and are mandating this, but there are probably many more companies that leverage AI via a secret grassroots adoption.

Enjoying Programming

Glyph makes the point that LLMs reduce code review to a non enjoyable part. For me code review is a fact of life and part of the job. That's just what we do as programmers. I don't do it because I want the person that wrote the code to grow and become a better programmer, I do it because I want code to be merged. That does not mean I do not care about the career opportunities or skills of the other person, I do! But that's an effort all on its own. Sometimes it takes place in a code review, most of the time however that's happening in a one-on-one setting. The reality is that we're often not in the mindset of wanting personal growth when receiving review comments either.

Now I admit that I do a lot more code review than I do programming at the moment, but I also find it quite enjoyable. On the one hand because the novelty of a machine programming hasn't worn off yet, on the other hand because it's a very patient recipient of feedback and change requests. You just tell it stuff, you don't spend too much time to think about how the other person is going to respond, if it's a good idea to nitpick a small thing and put extra load on them. It's quite freeing really and it does have a different feeling to me than a regular code review.

So is programming still enjoyable if I don't hit the keys? For me, yes. I still write code, just less of it, and it doesn't diminish the satisfaction at all. I'm still in control, and the quality still depends on the effort I put into guiding the tool.

Energy, Climate and Stealing

Glyph doesn't talk too much about the economics and the climate impact, but he does mention it. My stance on this is rather simple: margins will erode, there will be a lot of competition and we all will pay for the inference necessary and someone will make money. Energy usage will go up but we need more energy even without AI as we're electrifying our cars. AI might change this trajectory slightly, but we had a climate problem before all of this and we have give or take the same climate problem until we shift towards more renewable energy. In fact, this new increased energy consumption might actually do us a great service here. Solar is already the cheapest energy solution [1] on the market and if we need more, that's quite likely the source that we will build more of. Particularly now that cost of energy storage is also going down quickly.

As for copyright and “stealing”: I've always felt that copyright terms are too long, scraping is beneficial, and sharing knowledge is a net positive for humanity. That's what drew me to Open Source in the first place. Glyph argues that scrapers are more aggressive now, but I'm not sure if that is actually true. I think there are just more of them. We got so used that it was mostly a handful of search engines scraping lowering the cost of it to all. I tend to think that more competition is good here and we might just have to accept it for a little while.

Educational Impact

I addressed this in my previous article, but I believe LLMs have significant potential to improve learning. Glyph disagrees, partly because of concerns about cheating and that it will make it worse:

LLMs are making academic cheating incredibly rampant. […] For learning, genAI is a forklift at the gym. […] But it was within those inefficiencies and the inconveniences of the academic experience that real learning was, against all odds, still happening in schools.

I disagree strongly here. This is where I have the most first-hand experience, considering time spent with AI. Since the early days of ChatGPT, I've used LLMs extensively for learning. That's because I'm not great at learning from books, and I have found LLMs to make the process much more enjoyable and helpful to me.

To give you some ideas of how useful this can be, here is an excellent prompt that Darin Gordon shared for getting a GPT to act as a teacher of algorithms that uses the Socratic method: socratic_fp_learning.md. It works even super well if you dumb it down. I had this explain to my son how hash tables work and I did a modification to the prompt to help him understand entropy. It's surprisingly effective.

Now, that does not do much about the cheating part. But surely in a situation where students cheat, it wasn't about learning in the first case, it was about passing a test. That has not much to do with learning, but with performance assessment. When you feel the need to cheat, you probably did not learn something properly in the first place. AI might just make these pre-existing problems more visible, and even Glyph acknowledged that.

AI may complicate things for educators in the near team, but it can also offer real improvements. Either way, education needs reform to adapt to present realities.

Fatigue and Surrender

Glyph concludes by sharing that the pace of change is overwhelming him and opting out feels like the only sane response. I understand that. The pace of AI advancement can make anyone feel like they're falling behind and I too feel like that sometimes.

I offer a different view: just assume AI will win out and we will see agents! Then the path that takes us to that future is less relevant. Many of the things that are currently asking for people's attention are going to look different in a few years — or might not even exist any longer. I initially used GitHub Copilot just to move to Cursor, now to mostly move to Claude Code, maybe I will be back with Cursor's background agents in a month. First there was v0, then there was lovable, who knows what there be in a year. But the path for me is pretty clear: it's going towards me working together with the machine. I find that thought very calming and it takes out the stress. Taking a positive view gives you a form of an excited acceptance of the future.

In Closing

I really don't want to dismiss anyone's concerns. I just feel that, for me, the utility of these tools has become obvious enough that I don't feel the need to argue or justify my choices anymore.