Drop redundant paragraph

khnumdev · khnumdev · commit b7c7cd0eaaf7 · 2026-02-01T12:54:13.000+01:00
diff --git a/_posts/2026-01-31-ai_wonderland.markdown b/_posts/2026-01-31-ai_wonderland.markdown
@@ -62,25 +62,27 @@ Once I felt confident with AI coding, I decided to build my own home network and
 
 At work, we started using AI. GPT models worked well for test generation and seed data, though achieving good code generation was harder. Claude models proved better for code generation. If you want to measure AI usage, check this post about [real-time employee AI usage in Worklytics](https://www.worklytics.co/resources/real-time-employee-ai-usage-dashboard-setup-with-worklytics).
 
-But let me be honest here. My coding skills have declined somewhat. Why? I still code extensively. However, before AI, I spent more time thinking and digging through Stack Overflow comments and posts until finding a suitable solution. Now, I often ask AI for a solution, and if the solution looks good, I can use it. If not, I refine it or try a different approach. I remember being stuck on problems for hours before finding a solution. Now I can try multiple approaches with AI until something works. I'm not thinking less, just differently. 
+But let me be honest here. **My coding skills have declined somewhat**. Why? I still code extensively. However, before AI, I spent more time thinking and digging through Stack Overflow comments and posts until finding a suitable solution; then I was spending hours to write the code and thinking about each line I was writing. Now, I often ask AI for a solution, and if the solution looks good, I can use it. If not, I refine it or try a different approach. I remember being stuck on problems for hours before finding a solution. Now I can try multiple approaches with AI until something works. I'm not thinking less, just differently. 
 
-There's also the question of code quality and programming languages. For personal projects or my GitHub repositories, I don't worry about the language used. I spent 15 years coding in C#; Java was my focus the last five years. My home server's frontend is built in JavaScript, the backend in Python. The [distributed app design tutorial](https://khnumdev.github.io/dist-app-tutorial/) is written in Node.js. I don't care at all. Is that bad? I'm not sure. I just want things to work.
+There's also the question of code quality and programming languages. For personal projects or my GitHub repositories, I don't worry about the language used. I spent 15 years coding in C#; Java was my focus the last five years. My home server's frontend is built in JavaScript, the backend in Python. The [distributed app design tutorial](https://khnumdev.github.io/dist-app-tutorial/) is written in Node.js. The language doesn't matter—what matters is choosing the right tool for the problem. As soon as the code works and does what I want, I'm fine with it.
 
-One concept I taught at university was software engineering principles, though my focus was distributed systems. I emphasized core software principles for two main reasons: first, "doing the right things" (which I have on my CV), and second, "code needs to be maintained, understood, and improved." That's true because code was written by humans for humans, and much of our work as software engineers is "cleaning house"—improving existing code so the next person faces fewer problems. But if AI writes the code, *who cares*? If the app works, that's *what matters*. I'm not saying code quality isn't important, but the mindset is changing. AI generates working code, and new models will generate even better code. So why spend time improving code that'll be replaced in a few years with the effort of one prompt? The mindset is shifting across most organizations. At least for my personal projects I've lowered the barrel about quality, as soon as code works and do whatever I want, I'm fine with it. 
+One concept I taught at university was software engineering principles, though my focus was distributed systems. I emphasized core software principles for two main reasons: first, "doing the right things" (which I have on my CV), and second, "code needs to be maintained, understood, and improved." That's true when humans write and maintain the code. Much of our work as software engineers is "cleaning house"—improving existing code so the next person faces fewer problems.
 
-On the professional side thins are different. Code quality matters as well as other factors. Using AI helped me to deliver features faster and I can do in minutes what took hours before. But when AI isn't working well is a painful, as 
-you can keep iterating with promtps and never get a good result. At the end you have spent more or less same time than coding by yourself. The other thing as I notice whith this is the loss of "perception of the tracking the progress". If I write code by myseflf I know what and where I'm doing, starting with style and the way of doing things. With AI I have all the files modified at once and I lost the feeling of doing the things bit a bit. Sometimes doing a small changes, add a prompt focused on some part or just rewrite from scratch. That depends on the complexity but the "mind effort" is different than coding by myself. 
+But here's what's changing: if AI generates code and AI can also maintain it, the definition of quality shifts. It's not that quality doesn't matter anymore—it's that we're optimizing for different things. For personal projects, I prioritize speed and functionality. The code works, solves the problem, and if it needs to change, I can ask AI to regenerate or improve it. For professional work, the calculus is different. Code still needs to be secure, performant, and correct. But the obsession with human-readable, perfectly formatted code matters less when AI handles maintenance.
 
-As software engineers, we think our code is the *end goal*, but it's not. Sometimes we forget that code is a tool for solving problems. If AI can do that better, why fight it? But surprisingly, this is where software engineers will have more value: in system design, architecture, and decision-making. AI can generate code, but it can't decide what to build, how to build it, or why to build it. That's our job. Without understanding the basics, you're lost and can't evaluate whether AI results are good or bad. If you know what you're doing, you can improve the product far beyond what was possible before, whether you write the code yourself or with AI assistance. The good news is, learning new things is easier than ever.
+I'm not saying we should abandon standards. I'm saying that teams are now asking: "Does it work? Can we iterate on it quickly?" instead of "Is every line beautifully crafted for the next developer?" Not all code needs to be perfect. If you're building a startup, shipping fast matters more than pristine architecture. Why spend weeks refactoring for maintainability when AI can regenerate the codebase in minutes? The traditional justification for design patterns was that humans would maintain code for years. When AI handles that, patterns become less critical. 
 
-Another related point is proper code quality. Coding is hard; writing good code is harder. Code isn't art or inherently beautiful (though it can be ugly). Code is a tool for solving problems. It needs to be readable, understandable, and maintainable. AI isn't perfect yet and sometimes generates suboptimal, insecure, or incorrect code. We need to review AI-generated code, test it thoroughly, and ensure it meets quality standards. AI can't do this yet. However, not all code *needs to be perfect*. If you're building a startup and want to ship fast, iterate, and experiment, you can now accomplish in days what took months before. You can build an MVP in days instead of weeks. That's a game changer for startups. 
+On the professional side, things are different. Code quality still matters, and AI helps me deliver features faster. What took hours before now takes minutes. But when AI underperforms, it's painful. You keep refining prompts, iterating endlessly, and eventually realize you've spent as much time as if you'd coded it yourself.
+
+There's another aspect I've noticed: the loss of "sense of tracking progress." When I code manually, I know exactly what I'm doing—the style, the approach, the incremental steps. With AI, all files change at once. Sometimes it's a small refinement, sometimes a complete rewrite. The progression feels invisible, and the "mind effort" feels different than hands-on coding. 
+
+As software engineers, we think our code is the *end goal*, but it's not. Sometimes we forget that code is a tool for solving problems. If AI can do that better, why fight it? But surprisingly, this is where software engineers will have more value: in system design, architecture, and decision-making. AI can generate code, but it can't decide what to build, how to build it, or why to build it. That's our job. Without understanding the basics, you're lost and can't evaluate whether AI results are good or bad. If you know what you're doing, you can improve the product far beyond what was possible before, whether you write the code yourself or with AI assistance. The good news is, learning new things is easier than ever. As a reminder, for AI too as well.
 
 ## Is John Connor ready to play?
 
-But the question is: will AI take my *current* job? Probably. It's a matter of time—whether 2 years or 10 years, it will happen. My home server will involve several people in the past. Any landing page or corportate page can be *done* by AI; imagine how many people you don't need here (designers, frontends, backends). I'm not saying that all jobs will disappear but for certain tasks you can that yourself instead of hiring/contracting someone.
+But the question is: will AI take my *current* job? Probably. It's a matter of time—whether 2 years or 10 years, it will happen. My home server project would have involved several people in the past. Any landing page or corporate page can be done by AI; imagine how many people you don't need for that work (designers, frontend developers, backend developers). I'm not saying that all jobs will disappear, but for certain tasks, you can do that yourself instead of hiring or contracting someone.
 
-Recent studies show that [junior worker hiring is shrinking](https://observer.com/2025/09/ai-shrinking-job-market-junior-workers-harvard-study/). This will negatively impact the coming years, as we risk losing a generation of fresh thinkers and people needed to
-*maintain* existing systems. Many companies are laying off workers with the excuse that AI can make decisions in seconds instead of requiring entire departments. A clear example is lawyers: you can consult a lawyer or input your case into AI to get a report with possible outcomes, similar cases, etc. Same applies to accountants, financial advisors, and marketing experts. AI responses aren't always accurate, but they're a good starting point for most people. AI will improve further in coming years. As I read recently, *we are cooked*—meaning we're facing a serious challenge. 
+Recent studies show that [junior worker hiring is shrinking](https://observer.com/2025/09/ai-shrinking-job-market-junior-workers-harvard-study/). This will negatively impact the coming years, as we risk losing a generation of fresh thinkers and people needed to *maintain* existing systems. Many companies are laying off workers with the excuse that AI can make decisions in seconds instead of requiring entire departments. A clear example is lawyers: you can consult a lawyer or input your case into AI to get a report with possible outcomes, similar cases, and so on. The same applies to accountants, financial advisors, and marketing experts. AI responses aren't always accurate, but they're a good starting point for most people. AI will improve further in coming years. As I read recently, *we are cooked*. 
 
 My job isn't just coding anymore. Throughout my career, I've had to learn new languages, frameworks, platforms, architectures, [devops](https://es.slideshare.net/slideshow/devops-cult-what/128327583), and tools—now including AI. Not using AI as a software engineer today is like using horses for transportation instead of a Formula 1 car. I'm not saying we'll stop coding or developing software, but how we do it is changing—fast.
 
@@ -90,18 +92,17 @@ But with great power comes great responsibility, and AI brings serious ethical c
 
 Then there's the question of consent. AI models were trained on massive datasets scraped from the internet—books, articles, artwork, code—often without permission or compensation to creators. Artists discover their styles replicated, writers find their prose mimicked, and photographers see their images used to train systems that could replace them. It's a Wild West of intellectual property rights, and the legal frameworks haven't caught up.
 
-Deepfakes represent another danger. We've moved beyond silly Will Smith videos to convincing fake political speeches, non-consensual intimate imagery, and sophisticated scams. The erosion of trust in media is accelerating—we're reaching a point where seeing is no longer believing. Democracy itself faces threats when you can't distinguish real from fabricated. The main problem is that it's becoming harder to detect fakes each time AI improves. As AI can simulate voices, scams are more present than ever. Each security breach in a company means that a lot of bots can be trained with real human voices, used to impersonate employees or even relatives of the victim. Imagine receiving a call from your boss asking for sensitive information, or from a family member in distress requesting money. The emotional manipulation is powerful, and AI makes these scams more convincing than ever.
+Deepfakes represent another danger. We've moved beyond silly Will Smith videos to convincing fake political speeches, non-consensual intimate imagery, and sophisticated scams. The erosion of trust in media is accelerating—we're reaching a point where seeing is no longer believing. Democracy itself faces threats when you can't distinguish real from fabricated. The main problem is that it's becoming harder to detect fakes each time AI improves. As AI can simulate voices, scams are more present than ever. Each security breach in a company means that a lot of bots can be trained with real human voices, used to impersonate employees or even relatives of the victim. Imagine receiving a call from your boss asking for sensitive information, or from a family member in distress requesting money. The emotional manipulation is powerful, and AI makes these scams more convincing than ever. But what happens when AI is training using these content? It's a vicious cycle.
 
 And let's not forget the environmental cost. Training large AI models consumes enormous amounts of energy—[some estimates suggest training a single model generates as much carbon as five cars over their lifetimes](https://www.technologyreview.com/2025/05/20/1116327/ai-energy-usage-climate-footprint-big-tech/). As AI usage scales, so does its carbon footprint. Data centers running inference queries 24/7 require massive electricity and cooling. We're solving problems faster, but at what environmental cost?
 
 ## "It's always tea-time"
 
 AI feels like a true third revolution—something that will fundamentally change how we live, work, and interact. It's like having all content available with just a prompt—a superpower if used correctly. It feels like ages since I didn't use AI daily, but it was just months ago. Things are moving rapidly: new models, startups, and companies appear constantly. Every week there's something new about AI, and it's hard to stay updated.
 
-I also suspect we're in a bubble. Eventually, funding will dry up and some AI companies will disappear, just like the dotcom bubble. [History will repeat](https://jasonzweig.com/lessons-and-ideas-from-benjamin-graham-2/), and most AI companies aren't profitable or lack sustainable models—much like [Lucent Technologies](https://en.wikipedia.org/wiki/Lucent_Technologies). But that doesn't matter. AI will prevail, and there will be two types of users: those who use AI and those who don't. Like then, many people *didn't understand what the internet is*; like in the 1970s-80s, many people *didn't want to use computers because they were too complicated*. Now we can't imagine an architect without AutoCAD, a doctor without access to online medical databases, or a finance department without Excel. I want to emphasize my post [Some thoughts about technology](/2023/12/30/tech_thoughts/) again: barely 15 years ago, video calls from mobile phones weren't possible. 
+I also suspect we're in a bubble. Eventually, funding will dry up and some AI companies will disappear, just like the dotcom bubble. [History will repeat](https://jasonzweig.com/lessons-and-ideas-from-benjamin-graham-2/), and most AI companies aren't profitable or lack sustainable models—much like [Lucent Technologies](https://en.wikipedia.org/wiki/Lucent_Technologies) will cause issues in the system. But that doesn't matter. AI will prevail, and there will be two types of users: those who use AI and those who don't. Like then, many people *didn't understand what the internet is*; like in the 1970s-80s, many people *didn't want to use computers because they were too complicated*. Now we can't imagine an architect without AutoCAD, a doctor without access to online medical databases, or a finance department without Excel. I want to emphasize my post [Some thoughts about technology](/2023/12/30/tech_thoughts/) again: barely 15 years ago, video calls from mobile phones weren't possible. 
 
-Every day I read cases where people ask AI about medical issues and it usually gives good responses, or [how AI helps in protein research](https://www.science.org/content/article/ai-revolution-comes-protein-sequencing). The kinds of new things that can be done are almost
-impossible to imagine now. Just think about the possibilities in the next 5-10 years. Personally, I expected quantum computing to be the next big thing for problems that seemed unsolvable, but AI is here now and impacting our lives.
+Every day I read cases where people ask AI about medical issues and it usually gives good responses, or [how AI helps in protein research](https://www.science.org/content/article/ai-revolution-comes-protein-sequencing). The kinds of new things that are now possible are almost impossible to imagine. Just think about the possibilities in the next 5-10 years. Personally, I expected quantum computing to be the next big thing for problems that seemed unsolvable, but AI is here now and impacting our lives.
 
 For that reason, I believe AI will become normal, just like home internet is now. I won't speak in future tense: AI is changing labor and how we consume information. What about the effects? Unknown yet. But I can be comfortable knowing my name isn't John Connor.
 
