Month: April 2025

Today, my LinkedIn feed was overflowing with hot takes about AI and the future of programming. A recurring theme? That AI will make software engineers obsolete. No-code platforms, AI-assisted builders, and vibe-based coding were all being hailed as the future.

Here’s my take:
AI is about to increase the demand for software engineers — not replace them.

And we’ve seen this kind of thing before.

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The Swedish Renovation Effect

Years ago in Sweden, a popular TV show demonstrated how easy it was to renovate your own house. Enthused by what they saw, thousands of Swedes began renovating their homes on their own.

The result?
Disaster. Half-done kitchens. Electrical fires. Poorly installed plumbing.

And what followed was a massive surge in demand for professional carpenters, electricians, and contractors.

AI-powered programming is heading down the same path. We’re about to see lots of excited builders — and just as many messes to clean up.

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What Is “Vibe” Coding?

“Vibe” coding is the idea that you can build software by simply describing what you want in natural language.

“Make me an app that helps me track my fitness and suggests recipes.”

And boom — AI tools like ChatGPT, GitHub Copilot, or Replit Ghostwriter produce working code.

But building something functional isn’t the same as building something reliable. AI can’t:

• Ensure secure architecture
• Integrate across complex systems
• Handle scaling issues
• Manage technical debt
• Think critically about edge cases and long-term impact

That’s where skilled software engineers step in.

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Real Examples of Where Engineers Will Still Be Needed

Let’s go through three common scenarios that show why engineers aren’t going anywhere.

1. The Startup Founder MVP

A non-technical founder uses AI to create a prototype. It works and gains users. But now they need:

• API design that scales
• Data security and privacy compliance
• Frontend polish and accessibility
• DevOps for deployment pipelines and monitoring

They’ll be hiring engineers soon.

2. The Corporate DIY Tool

A marketing team builds an internal dashboard with AI. It’s fast — until:

• Traffic grows and it starts crashing
• Security holes appear
• It can’t integrate with enterprise systems

Enter: The IT and engineering team to fix and rebuild it.

3. The Indie Hacker Problem

An indie developer builds a cool tool with AI and gets traction. Now users want new features. Stripe integration is flaky. Bugs start piling up.
Suddenly, vibe coding hits its limits — and real development work begins.

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Just Like Excel… But Bigger

We’ve seen this before with Excel. Millions use it for complex calculations, planning, and even pseudo-apps. But when the stakes rise — in finance, logistics, or reporting — companies bring in:

• Excel consultants
• VBA programmers
• Business analysts

The same will happen with AI-built software. It’ll democratize access, but it’ll also raise expectations.

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Engineers Will Be the New Advisors, Builders, and Maintainers

Software engineers in the AI age won’t just code. They’ll:

• Audit AI-generated systems
• Refactor MVPs into production-ready platforms
• Coach teams on maintainability and design thinking
• Design better APIs and AI integration points
• Create guardrails and tooling that make AI-generated code safer and smarter

In other words: we’ll be more important than ever.

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AI Is the New Hammer — But You Still Need Builders

AI coding tools are like giving hammers to the world.

Some people will build amazing things. Others will build something that looks good but falls apart in the rain.

And just like the Swedish renovation boom, someone will need to fix, maintain, and scale all that newly built infrastructure.

That someone is you — the software engineer.

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About the Author

Ivan Turkovic is a seasoned tech expert, fintech and blockchain platform architect, and former CTO of multiple startups. With 20+ years of experience building products and teams using Ruby on Rails, TypeScript, and cloud-native platforms, he helps businesses turn complex systems into reliable, scalable solutions.

Whether you’re an early-stage founder, a company struggling with technical debt, or a team trying to level up your AI integration strategy — Ivan can help.

🖥️ Visit ivanturkovic.com for more articles, insights, and contact info
📧 Reach out via LinkedIn or email ivan.turkovic@gmail.com

Artificial intelligence is transforming industries at an unprecedented pace, redefining the way we design, develop, and deploy products. However, with great power comes great responsibility. As AI automates more processes and decision-making, the need for thoughtful product design, robust security, and meticulous attention to edge cases has never been more critical.

While AI can handle vast amounts of data and make predictions, it lacks human intuition. The real challenge for companies leveraging AI is ensuring that the technology is implemented in a way that minimizes risks while maximizing efficiency and accuracy. This is where experienced tech architects and CTOs come in. Their deep understanding of system design, security, and data modeling is becoming a key differentiator in creating AI-powered products that are reliable and resilient.

The Increasing Complexity of AI-Driven Product Design

Unlike traditional software, AI-driven products require a fundamentally different approach to design and development. Instead of writing explicit rules for every scenario, AI models learn from data, which introduces new challenges in predicting behavior, handling unexpected cases, and preventing security vulnerabilities.

One of the biggest challenges is handling edge cases. AI models are trained on data, but real-world applications often introduce unexpected situations that weren’t part of the training set. A lack of foresight in handling these cases can lead to significant issues. Consider these examples:

• Self-driving cars: AI systems are trained on millions of traffic scenarios, but rare or unusual events (like an overturned truck or a person walking with an unusual posture) can confuse the system. Tesla’s Autopilot has been criticized for failing in such edge cases, sometimes leading to accidents.
• AI chatbots: Microsoft’s Tay AI was released in 2016 and quickly turned into a PR disaster when users manipulated it into making racist and offensive statements. The lack of robust content moderation mechanisms exposed the bot’s vulnerability to adversarial manipulation.
• Healthcare AI: A medical AI model trained primarily on data from Western countries may perform poorly when deployed in regions with different demographic data, leading to incorrect diagnoses or biased treatment recommendations.

To prevent such failures, experienced architects must proactively model possible failure scenarios and ensure that fallback mechanisms are in place. Anticipating these issues requires deep knowledge of system architecture and an understanding of human behavior—qualities that experienced CTOs and technical leaders bring to the table.

Data Modeling: The Foundation of AI Success

AI systems are only as good as the data they are trained on. Poorly modeled data can introduce biases, inaccuracies, and unpredictable behaviors. This is another reason why experienced tech architects are invaluable.

• Bias in AI systems: Amazon once had to scrap an AI-powered recruiting tool that discriminated against women because it was trained on past hiring data, which was predominantly male. An experienced AI architect would have identified this risk and designed the system to counteract historical biases.
• Data drift: AI models degrade over time as real-world data changes. If data pipelines aren’t continuously monitored and updated, performance will decline. Google’s AI for identifying diabetic retinopathy struggled when deployed in real-world clinics because the image quality was lower than in its training dataset.
• Scalability challenges: AI models that work well in development often fail at scale due to inefficient data pipelines. A well-designed architecture ensures that data ingestion, preprocessing, and storage can handle increasing loads without performance bottlenecks.

Tech architects who understand data engineering, pipelines, and real-time processing can build more resilient AI systems that stand the test of time.

Security and Data Privacy: A Major Concern in AI Systems

One of the biggest risks with AI-driven systems is security and data privacy leaks. Companies without experienced leadership often underestimate the attack surface that AI systems create. Some high-profile failures include:

• Samsung’s AI mishap (2023): Employees used ChatGPT for internal coding assistance, accidentally leaking sensitive source code. The lack of internal security policies and oversight allowed this breach to happen.
• Deepfake abuse: AI-generated deepfakes have been used for identity fraud, political misinformation, and even scams impersonating executives. Companies need AI-specific security measures to detect and prevent such misuse.
• GDPR violations: AI models that store or process personal data without clear consent can lead to massive fines. Meta (Facebook) has faced repeated regulatory scrutiny for mishandling user data.

Experienced CTOs and security-focused architects play a vital role in identifying potential AI security risks before they become major breaches. This includes designing secure data pipelines, implementing differential privacy techniques, and ensuring AI models do not memorize sensitive information.

Why Experienced CTOs and Tech Architects Will Thrive

AI is reducing the need for repetitive coding, but it is increasing the demand for high-level system thinking, security awareness, and strategic planning. Companies that blindly rely on AI without understanding its risks are setting themselves up for failure.

The future belongs to tech leaders who can:

• Design AI-powered systems that handle edge cases gracefully.
• Build scalable and unbiased data models.
• Prioritize security and data privacy in every AI-driven product.

While junior developers and AI automation can accelerate coding and prototyping, only experienced architects can prevent catastrophic failures before they happen. As AI continues to reshape industries, those with deep technical expertise will be in higher demand than ever.

If you’re a CTO or tech architect, now is the time to double down on your expertise. AI is not replacing your role—it’s making it more valuable than ever.

Looking for Expert Help? Let’s Work Together!

If you need expert guidance to bring your ideas to life, I’m here to help. Whether it’s building innovative solutions, refining your tech strategy, or tackling complex challenges, let’s connect and create something great. Reach out today on ivan.turkovic@gmail.com, and let’s make things happen!

AI-powered development tools like Lovable, Bolt, and others have captured the imagination of developers and non-developers alike. The promise? Build complete applications with just a few prompts. The reality? A much harsher learning curve, hidden complexities, and an eventual realization that these tools, while powerful, are not yet capable of fully replacing traditional software engineering.

The Hype: Why AI-Powered Development Feels Revolutionary

There’s a reason why so many are flocking to AI-powered coding platforms. They offer something unprecedented—turning natural language descriptions into working code, reducing development time, and making software engineering more accessible to those without deep programming knowledge.

For a while, it seems magical. With just a few prompts, a prototype can be generated, UI components materialize, and APIs are wired up. For solo entrepreneurs, product managers, and designers who have always relied on engineers to bring their ideas to life, AI-powered development tools feel like an emancipation. They provide the illusion of democratization, allowing anyone to create software—until they hit the brick wall of reality.

The Reality: Why These Tools Are Not Enough (Yet)

Building a functional app is not just about writing code. It involves architecture, performance optimization, security, state management, backend integrations, database design, debugging, and deployment. These aspects of software development are where AI-generated code often struggles or outright fails.

Many, myself included, have tried to build and deploy simple applications using these AI tools, only to run into major roadblocks:

• Database Connection Issues: AI-generated code frequently struggles with database connections, especially when dealing with cloud environments, ORMs, or different types of data persistence strategies.
• Authentication & Security Concerns: Many platforms generate basic authentication flows, but real-world implementations require fine-tuning for access control, session management, and compliance with security standards.
• API Integrations & Rate Limits: AI may generate API calls, but it doesn’t always handle edge cases, pagination, throttling, or error responses properly.
• Frontend Hydration & State Management: AI-generated frontend code often runs into hydration errors, especially in React or other component-based frameworks.
• CORS Policy Errors & DevOps Challenges: Cross-Origin Resource Sharing (CORS) issues plague AI-generated projects, requiring manual intervention. Similarly, deployment is far from a one-click experience, as infrastructure knowledge is often required.

These problems aren’t just annoyances; they are project killers for those without the technical expertise to debug them.

Why Churn is Inevitable

Many people jumping into AI-powered development tools do so because of FOMO (Fear of Missing Out). They see impressive demos and believe they can bypass years of software engineering experience. However, after a few frustrating attempts, reality sets in. Without a foundational understanding of software engineering principles, many will abandon these tools entirely.

Mismatched Expectations

The expectation is that AI will do everything for them. The reality is that AI can accelerate certain aspects of development but cannot (yet) replace the problem-solving skills of an experienced developer. This gap between expectation and reality inevitably leads to frustration and churn.

Lack of Debugging & Support

Unlike traditional development, where countless Stack Overflow threads, GitHub issues, and community discussions exist, AI-generated code can be unpredictable. Debugging issues with AI-generated code often requires real software engineering skills, something many early adopters of these tools do not have.

Dependency on Experts

In my own experience, I only got past these obstacles because I had access to people who actually understand software engineering. Many others won’t have that same support network, making it even more frustrating when things don’t work.

The Future of AI-Powered Development

Despite these challenges, I’m still building with AI and learning a ton. AI-assisted development is undoubtedly the future—but it’s not the present solution many believe it to be. Here’s what needs to happen before these tools can truly democratize software development:

• Better Abstraction of Complexity: AI tools need to handle real-world complexities like authentication, database management, and security without requiring deep expertise from users.
• Improved Debugging & Documentation: There must be AI-assisted debugging and more robust documentation around generated code.
• Integration with Traditional Development Workflows: Instead of aiming to replace engineers, AI tools should become better copilots that assist rather than automate everything.

AI-powered development will continue to evolve, but the current wave of enthusiasm will likely be followed by a period of disillusionment. Many will churn out of frustration, while others—especially those willing to learn and adapt—will reap the benefits of being early adopters.

For now, AI-generated code is a powerful tool, but not a replacement for the art and science of software engineering. The hype is real, but so are the limitations. Those who acknowledge and navigate these challenges will be the ones who truly benefit from this technological shift.