You Think AI Speeds You Up by 24%; It Actually Slows You Down by 19%

One of the most talked-about topics in the tech world recently is how much artificial intelligence speeds up our software development processes. In many articles, blog posts, and conferences, I read claims that AI increases developers' productivity by 20%, 30%, or even 50%. Some even mention 24%. I, too, was initially swept up in this wave.

However, nearly 20 years of experience in the field have taught me this: numbers often don't reflect reality, especially when a "measurement" is claimed. Since I started actively using AI tools in my own projects and for clients I consult for, I've noticed the exact opposite of these "acceleration" claims. The truth is, I feel like AI is slowing me down by 19%, at least in certain areas.

AI's Promises and My Expectations

When AI tools first emerged, I had high hopes, especially for code completion, debugging, and boilerplate code generation. Working on a production ERP, constantly writing repetitive operator screens or data integration APIs could sometimes be tedious. I thought AI would lighten this load.

My dream was to write a complex PostgreSQL JOIN query or a specific FastAPI endpoint in seconds, deploy it with just a few tweaks. This could be a huge lifesaver, especially when deadlines were approaching. I even aimed to save time by having AI write some data processing logic for the backend of my Android spam application.

ℹ️ The Quick Start Fallacy

The "quick start" illusion offered by AI is often appealing at the beginning of complex projects. However, in systems requiring deep technical understanding and tightly coupled with business processes, this acceleration often remains superficial, leading to underlying maintenance, security, or performance issues.

The 24% productivity increase claims circulating in the market even bothered an experienced person like me. I questioned, "Am I not using it efficiently enough?" But as I delved deeper, I realized how shallow the perspective behind these numbers was. Writing code quickly doesn't always mean delivering value quickly.

The Cost of Prompt Engineering and Context Loss

The key to getting the right output from AI is what they call "prompt engineering." While it seems easy at first, explaining a complex system to AI sometimes takes longer than writing the code from scratch. For example, in a client project, I asked it to write an Nginx reverse proxy configuration working on a specific VLAN segmentation.

AI gave me a standard configuration. But what I wanted was a custom configuration that routed to different upstream servers based on the source IP, validated JWT tokens, applied specific rate limiting rules, and also did DSCP marking. Explaining these details to AI one by one, clarifying why each parameter was important, and correcting AI's misunderstandings took hours.

# Simple example initially provided by AI
server {
    listen 80;
    server_name example.com;

    location / {
        proxy_pass http://backend_servers;
    }
}

To adapt this simple output to the complex scenario I wanted, I had to go back and forth with AI dozens of times. AI often provides generic solutions and struggles to understand edge cases, the specific architecture of the system, or security requirements. This leads to significant time loss, especially for sensitive configurations like systemd unit files or when setting up logical replication in PostgreSQL.

The Maintenance Burden and Hidden Debts of "Fast" Code

The fact that AI-generated code often "works" at first glance is a big trap. For a new module in a manufacturing company's ERP, I quickly asked AI for a CRUD API set. It generated a draft using FastAPI and SQLAlchemy. Initial tests seemed fine.

However, when I delved into the code, I encountered serious performance and reliability problems such as N+1 query issues, insufficient ORM eager-loading strategies, and the lack of a transaction outbox pattern. AI, often focused on producing the simplest solution, can overlook architectural patterns critical for enterprise systems like event-sourcing or idempotency. This later came back to me as hours of refactoring and optimization costs.

# Example of a (simple) FastAPI endpoint generated by AI
@app.get("/items/{item_id}")
async def read_item(item_id: int, db: Session = Depends(get_db)):
    item = db.query(models.Item).filter(models.Item.id == item_id).first()
    if item is None:
        raise HTTPException(status_code=404, detail="Item not found")
    return item
# This code can lead to an N+1 problem if it doesn't load related tables of the item (e.g., category).

Another example was with the financial calculators for my side product. I quickly asked AI for a date range calculation function. The function worked but couldn't correctly handle leap year situations or different time zones. In a production environment, such errors can lead not only to time loss but also to loss of customer trust. I always have to put AI-generated code through an audit process, which means additional effort.

Testing Processes and Trust Verification

The trust placed in AI-generated code is a big problem for me. Especially when working on a bank's internal platform or a production ERP, even the smallest error can lead to millions of liras in damage. Therefore, I have to meticulously test every piece of code written by AI, as if a junior developer wrote it, from the very beginning.

This isn't just about writing unit tests; it also requires integration tests, performance tests, and even security vulnerability scans. Last month, I developed a Redis cache mechanism for my own site with AI's help. AI had suggested allkeys-lru for the Redis OOM eviction policy. It sounded logical, but it overlooked that it could exhibit unexpected behavior when working with cgroup memory.high limits. After encountering an OOM-killed situation in production, I questioned AI's suggestion and realized that different policies like volatile-lru or noeviction might be more compatible with cgroup limits.

⚠️ Trusting AI is Risky

AI-generated code or configurations are often built on assumptions and do not fully understand your specific system architecture or business logic. Therefore, blindly using AI output in critical systems carries serious security, performance, and business continuity risks. Always verify with your own expertise.

This verification process more than consumes the time AI claims to save me. Debugging AI-written code can be harder than debugging my own code because I don't have access to AI's "thought process" or "logic."

Cognitive Load and Decision Fatigue

AI often offers multiple solutions for a problem. For example, when I asked it to write a systemd unit file for a Linux service, it offered different Restart policies, ExecStartPre or ExecStopPost options. Evaluating the pros and cons of each option creates an additional cognitive load for me.

This is especially evident in complex network segmentation or Zero-Trust Network Architecture (ZTNA) designs. AI offers me different firewall policy suggestions, VLAN configurations, or routing authentication (OSPF/IS-IS) methods. Making the right decision among these options, predicting the impact of each on the system, more than eats up the time AI claims to save.

Last month, during a CVE tracking task, I received suggestions from AI for kernel module blacklist. AI suggested blacklisting some modules like algif_aead. While this suggestion seemed logical for a specific security vulnerability, analyzing other dependencies and potential side effects on the system still fell to me. AI is insufficient in performing such in-depth analyses, which increases the responsibility and stress of making the final decision.

Using AI Efficiently: My Approach

Despite these negative experiences, I haven't completely written off AI. I've just learned to position it as an "assistant," not a "replacement." I have some principles I've set to use AI more efficiently in my workflow:

1. Boilerplate and Initial Drafts: I use AI to generate tedious and repetitive boilerplate code or initial drafts for a concept. For example, it works well as a starting point for a Docker Compose file or a simple shell script.
2. Information and Syntax Check: I use it to quickly recall parameters of a Linux command I've forgotten or the syntax of a specific language. This is quite useful for topics like Redis commands or journald filters.
3. Debugging Assistance (Guidance Only): I get help from AI to understand a complex error message or list possible causes. However, I always filter its solution suggestions through my own expertise.
4. Investment in Prompt Engineering: I continue to improve my prompt engineering skills to get better outputs from AI. I try to write prompts that are more specific, more contextual, and clearly express my expectations.
5. Use of RAG (Retrieval-Augmented Generation): I provide AI with more specific context by using RAG systems trained on my own documents and codebase. This helps AI generate solutions more suitable for the project's specific architecture.

💡 Use AI Like a Mentor

Think of AI not as an "oracle" that gives you direct solutions, but as a "mentor" that offers different perspectives or reminds you of certain topics. The final decision and responsibility should always be yours.

For example, when writing a fail2ban filter, I ask AI for a general regex pattern, and then manually refine this pattern based on my own journald logs. Or, I get information about partition strategies in PostgreSQL and evaluate the options AI provides to choose the most suitable one for my data structure.

Conclusion: AI is a Tool, Not Expertise

One of the biggest misconceptions I've seen in my career is the idea that tools will automatically provide solutions. AI is undoubtedly a powerful tool, but it doesn't replace a developer. There's a big difference between writing code quickly and building quality, maintainable, secure, and performant systems.

In my experience, the 24% acceleration promised by AI often turns into a 19% slowdown. The main reason for this is AI's inadequacy in understanding context, performing in-depth analysis, making architectural decisions, and managing trade-offs in critical systems. AI only offers us more options and more data, but making the right decision among these options still depends on our expertise and experience. My clear position is this: AI can increase efficiency when used correctly, but only when it passes through an experienced hand. Otherwise, while you think it's saving you time, it could actually cost you much more.