Studied Information Systems in Germany. Dropped out about 6 months ago. Here is what AI changed since 2022:
1. Actual coding skills decline
When I started my studies in 2022 I had general knowledge of the web: HTML, CSS, a bit of JS and some Python because I'd built a web scraper once. During the first semester ChatGPT launched. I was skeptical and two feet deep in my weekly Java coding assignments, spending about 20 hours each week on easy, medium, and hard Java programming tasks alongside introductory CS lectures. I'm extremely happy that I started adopting AI a little later, probably around Q2 2023. By then I had already built my first solo side projects and thought: that's it, I now need to build a business using AI! I still watched a lot of YouTube videos about Spring Boot back then and really taught myself the basics, aided by ChatGPT here and there.
Fast forward a few years: my university dropped the coding homework tasks because everyone did them with AI and introduced 3 in-person exams during the semester inside lecture halls where you had to solve coding exercises without AI.
I personally noticed my coding skills declined significantly because of heavy AI-assisted coding adoption. I used to be very skeptical of AI directly editing my code and used the ChatGPT on one screen, code editor on the other approach for a long time which was the sweet spot between increased productivity and still understanding everything I wrote. Nowadays I just prompt and accept.
2. Long-term retention of knowledge drops
Through tools like NotebookLM and Google Gemini (which was the go-to education AI at my university and from what I heard at others too) you don't even need to read actual sources, scripts, or papers anymore. You just upload everything and have it summarized. Upload your lecture scripts, homework assignments, old exams, and ask the AI to explain everything and create exercises for you. This works. But you only optimize for the exam. Like fine-tuning a model for a specific task, you fine-tune your knowledge input to pass the exam rather than building a holistic, deeply rooted understanding. When I used to write my own summaries, read through lecture scripts, and actually attend tutorials, the knowledge I gained stayed with me much longer.
3. You solve tasks instead of learning
Convenience is the enemy of every student. AI has become so convenient that you really have to kick yourself to do anything without it. Reading something, writing something... at every task you tend to think: "I know I could do it, I'm capable enough, but it's just not worth my time when I have other assignments." It has genuinely become too convenient. It's like your brain builds up friction against using itself and would rather outsource to AI because it's less demanding and exhausting.
I recently heard that Google researched something similar and is now trying to build friction into their products that sometimes guides users toward an answer instead of just giving it to them.
What I observed during my studies mirrors what I now see in my professional life: using AI for coding has become so convenient that you just prompt nearly everything and gradually lose the ability to code (and maybe even to think independently).
That convenience problem is actually what I'm trying to solve in a different context with my current project.
Everyone who doesn't have money for paid ads struggles with marketing in the beginning. When I used to build and sell websites a few years ago, I spent about 70% of my time on acquisition and cold calling.
Building something useful is easier than ever nowadays. Claude, Lovable, and similar tools enable people without specific technical skills to build software. Marketing has always been the bottleneck.
Even posting on subreddits isn't really possible anymore since most of them ban you for promotional content.
If you're self-employed or bootstrapping a business, I think the most realistic path to getting noticed is to put yourself out there by posting on social media and genuinely contributing until you and your product get noticed.
I tried to build a B2C AI resume generator back in 2022 while I was studying, and the most traction I got was from printing flyers and guerrilla marketing. In B2C, word of mouth is the most powerful free traffic source and the same holds true in B2B. A referral is always "free" and already comes with a warm lead.
That said, I generally agree with the point that being generous and providing free value first builds connection and trust. The question is how to engineer word of mouth early on when you have almost no users... that's the actual hard problem (and getting your first users/customers ofc but normally you know at least one potential user/customer through friends and family).
Probably should feel as bad about it as you do for owning and using your phone. The world is unfair and will stay that way, but now with AI it'll probably just get worse. And if you only look out for yourself - fine, that might work, but all of it has consequences. If you use AI for coding you will most certainly have noticed already that your actual skills might have declined - at least that's the observation I made personally and from talking to fellow engineers.
Well, but the data centers needed for AI are on a much different scale than what "big tech profitmaxxing" used to need. I also agree with the author and you. Morally, I also cannot support the toll it takes on the environment, workers, and society in general. However, what's the option? Either be part of it or get laid off. Build an AI startup or be employeed in one and get that money or well I really cannot imagine a third path that's both financially viable and keeps you relevant in the next decade.
We humans tend to be so wasteful, especially with intangible things like tokens. I don't support this "tokenmaxxing" trend at all. Any business should strive to optimize for efficiency. It's ridiculous to think that spending more tokens correlates to higher-quality output or more money made. The law of diminishing returns applies here too. I'd just put all my employees on the $20 per month Claude plan and teach them to:
1. Still use their brains for either very complex or very mundane tasks
2. Be efficient with their limits
3. Build a mental connection like "tokens = money" otherwise they stay intangible
No wonder games always use their own currencies that also makes money intangible and increases spending.
As long as you know about the Google Search Console you can just manually submit your page to be indexed - works all the time. I also regularly see bots crawling my page so I am not sure if this article is just trying to build artifical demand.
1. Actual coding skills decline When I started my studies in 2022 I had general knowledge of the web: HTML, CSS, a bit of JS and some Python because I'd built a web scraper once. During the first semester ChatGPT launched. I was skeptical and two feet deep in my weekly Java coding assignments, spending about 20 hours each week on easy, medium, and hard Java programming tasks alongside introductory CS lectures. I'm extremely happy that I started adopting AI a little later, probably around Q2 2023. By then I had already built my first solo side projects and thought: that's it, I now need to build a business using AI! I still watched a lot of YouTube videos about Spring Boot back then and really taught myself the basics, aided by ChatGPT here and there. Fast forward a few years: my university dropped the coding homework tasks because everyone did them with AI and introduced 3 in-person exams during the semester inside lecture halls where you had to solve coding exercises without AI. I personally noticed my coding skills declined significantly because of heavy AI-assisted coding adoption. I used to be very skeptical of AI directly editing my code and used the ChatGPT on one screen, code editor on the other approach for a long time which was the sweet spot between increased productivity and still understanding everything I wrote. Nowadays I just prompt and accept.
2. Long-term retention of knowledge drops Through tools like NotebookLM and Google Gemini (which was the go-to education AI at my university and from what I heard at others too) you don't even need to read actual sources, scripts, or papers anymore. You just upload everything and have it summarized. Upload your lecture scripts, homework assignments, old exams, and ask the AI to explain everything and create exercises for you. This works. But you only optimize for the exam. Like fine-tuning a model for a specific task, you fine-tune your knowledge input to pass the exam rather than building a holistic, deeply rooted understanding. When I used to write my own summaries, read through lecture scripts, and actually attend tutorials, the knowledge I gained stayed with me much longer.
3. You solve tasks instead of learning Convenience is the enemy of every student. AI has become so convenient that you really have to kick yourself to do anything without it. Reading something, writing something... at every task you tend to think: "I know I could do it, I'm capable enough, but it's just not worth my time when I have other assignments." It has genuinely become too convenient. It's like your brain builds up friction against using itself and would rather outsource to AI because it's less demanding and exhausting. I recently heard that Google researched something similar and is now trying to build friction into their products that sometimes guides users toward an answer instead of just giving it to them.
What I observed during my studies mirrors what I now see in my professional life: using AI for coding has become so convenient that you just prompt nearly everything and gradually lose the ability to code (and maybe even to think independently).
That convenience problem is actually what I'm trying to solve in a different context with my current project.