Ivan Turkovic

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.

---

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.

---

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.

---

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.

---

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.

---

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.

---

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.

---

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.

Copy and Paste from AI: $20

Actually Knowing What’s Going to Work: $150,000

Knowing Where to Place It: Priceless

The rise of AI-powered tools has made it easier than ever for people to generate code, write content, and create designs with minimal effort. Platforms like ChatGPT, Copilot, and other AI-driven assistants can spit out working solutions in seconds. But while AI can provide fast results, there’s an undeniable gap between “having code that runs” and “having code that works in the real world.”

Just because something compiles, doesn’t mean it scales. Just because it executes, doesn’t mean it’s secure. And just because AI gives you a function, doesn’t mean it’s the right one for your specific problem.

This is the difference between merely copying and pasting code and actually knowing what will work—and more importantly, where to place it.

In this post, we’ll explore why experience is irreplaceable, the hidden costs of not knowing what you’re doing, and examples of how things can go wrong when you rely solely on AI without the expertise to back it up.

---

The Trap of “It Works, So It’s Fine”

One of the biggest pitfalls for beginners relying on AI-generated code is assuming that if something works, it must be correct. AI can generate code that runs, but without experience, it’s easy to overlook issues like:

• Security vulnerabilities
• Performance bottlenecks
• Maintainability nightmares
• Scalability limitations
• Legal and compliance risks

Here are some real-world examples where blindly trusting AI (or generic code from the internet) can lead to disaster.

---

Example 1: SQL Injection – The Classic Security Oversight

Let’s say you ask an AI to generate a login system in Python using Flask and SQLite. You get something like this:

@app.route('/login', methods=['POST'])
def login():
    username = request.form['username']
    password = request.form['password']
    query = f"SELECT * FROM users WHERE username = '{username}' AND password = '{password}'"
    result = db.execute(query).fetchone()
    if result:
        return "Login successful"
    return "Invalid credentials"

It works! You enter a username and password, and it checks if they exist in the database. But here’s the problem—this code is vulnerable to SQL injection. Anyone with basic knowledge of security can enter a malicious string like:

' OR '1'='1' --

And suddenly, they’re logged in as an admin.

An experienced developer would know that SQL queries should always use parameterized statements:

query = "SELECT * FROM users WHERE username = ? AND password = ?"
result = db.execute(query, (username, password)).fetchone()

But if you don’t know what to look for, you wouldn’t even realize there’s a problem—until it’s too late.

---

Example 2: “Just Use This AI-Generated Smart Contract”

With blockchain and smart contracts becoming more popular, AI can generate Solidity contracts for you. But Solidity is tricky—one wrong move and you could introduce vulnerabilities that cost millions.

For example, imagine an AI generates this Solidity contract:

contract SimpleBank {
    mapping(address => uint256) public balances;
    
    function deposit() public payable {
        balances[msg.sender] += msg.value;
    }
    
    function withdraw(uint256 amount) public {
        require(balances[msg.sender] >= amount);
        payable(msg.sender).transfer(amount);
        balances[msg.sender] -= amount;
    }
}

It looks fine, right? But this contract is vulnerable to reentrancy attacks. A malicious actor can drain the contract using a recursive function call before the balance updates. An experienced blockchain developer would immediately see this issue and fix it by using the checks-effects-interactions pattern or a reentrancy guard.

---

Example 3: The Nightmare of Scaling Bad Code

An AI can generate a sorting algorithm for you. But let’s say you need to process millions of data points efficiently. An AI might give you something like:

def sort_list(lst):
    return sorted(lst)

For small datasets, this works fine. But what if you’re sorting billions of records? An experienced developer would ask:

• What’s the computational complexity of this approach?
• Is there a more efficient algorithm like Merge Sort, Quick Sort, or even a parallelized approach?
• How does this impact memory usage?
• Should I use a database index instead of sorting in memory?

AI won’t always make those considerations for you. Without experience, you might not even realize there’s a problem—until your system grinds to a halt under real-world loads.

---

Why Experience Is Priceless

The AI tools available today are powerful, but they are not a replacement for deep expertise. The ability to:

• Anticipate edge cases
• Understand security implications
• Optimize for performance and scalability
• Write maintainable code
• Know the right architecture for the problem

…is something that only comes with experience. AI can be an incredible assistant, but it won’t teach you intuition. It won’t tell you what’s actually going to work in production. It won’t warn you when something is subtly broken.

Artificial Intelligence (AI) has revolutionized software development, making it faster and more efficient. However, many developers still struggle with AI coding tools, often making mistakes that lead to frustration, inefficiencies, and broken codebases. In this guide, we’ll explore the best practices for AI-assisted coding, focusing on the most common mistakes and how to improve your workflow.

1. No Planning

Jumping straight into coding without a plan is a recipe for disaster. A well-thought-out plan can save hours of debugging and restructuring later.

My Planning Hack

I turn on ChatGPT voice and have a one-on-one conversation about what I want to build. After 15 minutes, I ask:

“Write me a well-structured draft on all the things we’ve finalized in this conversation.”

This way, I use AI as my:

• Brainstorming buddy
• Critique
• Web researcher
• Draft writer

The result? A one-page document outlining the core features of my MVP.

Don’t build blindly. Plan before you hunt.

2. No Knowledge Base for AI Models

AI coding models work best when they have a reference point. Reducing hallucinations and irrelevant code generation requires a structured knowledge base.

How to Build a Knowledge Base for AI Coding Models

After drafting my idea, I create structured documentation:

• Product Requirements Document (PRD) – What the app does
• App Flow Document – How users interact with the app
• Tech Stack Document – The technologies being used
• Frontend Guidelines – UI/UX rules
• Backend Structure – API architecture and database schema

This documentation acts as a reference, ensuring AI models generate relevant and accurate code. Learn more about AI Documentation

3. Not Picking the Right Tools

Every AI coding tool has its own superpower. Choosing the wrong tool can lead to inefficiencies.

AI Coding Tool Comparison:

Tool	Strengths	Weaknesses
ChatGPT-4	General-purpose coding, debugging, writing docs	May hallucinate in complex logic
Claude 3.5	Excellent for writing and structuring code	Not great for debugging
Gemini Flash 2.0	Scanning and refactoring large codebases	Lacks execution speed
Cursor/Windsurf	AI-powered IDE for smart code execution	Needs AI-specific setup

Check out the best AI coding tools

4. Not Picking the Right Tech Stack

AI models are trained on specific programming languages. Using the right tech stack leads to better quality code with fewer errors.

AI-Friendly Tech Stacks

Frontend

• Next.js
• Vite
• Flask (for Python-based projects)

Database

• Supabase (PostgreSQL)
• Firebase

Authentication

• Clerk.dev
• Supabase
• Firebase

AI Models

• OpenAI
• Claude
• Gemini

Best AI-Compatible Programming Languages

Language	AI Model Effectiveness
Python	Excellent
JavaScript/TypeScript	Great
Rust	Decent
Ruby	Limited (but improving)

5. Not Building Step by Step

Letting AI plan the entire development process often results in messy, unstructured code.

The Fix: My 60-Step Implementation Plan

Instead of letting AI decide everything, I am creating a step-by-step execution plan with 60+ checkpoints to ensure nothing gets skipped. AI should only be used to execute the plan, not to decide what to do next.

6. No Debug Prompting

Debugging AI-generated code can be painful. Use these techniques to improve debugging:

• Attach the error and say: “Use chain of thought reasoning to find the core issue first and then plan step by step to fix the issue.”
• Ask AI to “follow best coding practices. Search the web and find the fix for this issue.”
• Only attach relevant files to improve AI’s focus.

7. Not Using Multiple AI Models

No single AI model can do everything. Use different models for different scenarios:

In Cursor/Windsurf:

• Claude Sonnet 3.5 → Best for writing and executing code
• GPT-4 o1/o3-mini-high → Best for debugging complex errors
• Gemini Flash 2.0 → Best for scanning and updating entire codebases

8. Not Using Starter Kits

Why start from scratch every time? Use boilerplates to speed up development and reduce errors.

I am preparing 4+ pre-built boilerplates specifically designed for AI coding to reduce token usage and avoid unnecessary errors.

9. Quitting Too Early

AI coding is exciting at first, but frustration kicks in when errors start piling up. Expect hundreds of build issues and AI mess-ups.

The Solution:

• Strong Foundation → Build rules and documentation to keep AI coding structured
• Version Control → Use Git to manage source code changes effectively Learn more about Git version control

Final Thoughts

AI-assisted coding is powerful, but only if used correctly. By avoiding these common mistakes and applying structured workflows, you can build faster, smarter, and more efficient applications.

Plan before you code. Structure before you execute. Debug with intelligence. Adapt and improve.

The rapid advancement of AI has sparked an ongoing debate in software development: Should companies replace junior developers with AI-powered tools and a few senior engineers, or should they invest in training juniors to become future seniors while leveraging AI to enhance their productivity?

While both perspectives have merit, the ideal solution is often a balance. A well-structured team that combines the fresh enthusiasm of junior developers with the experience of senior engineers, amplified by AI, can outperform any one-dimensional approach.

The Case for AI Replacing Junior Developers

AI-powered coding assistants like GitHub Copilot, Tabnine, and ChatGPT have dramatically improved code generation, debugging, and even architectural decision-making. These tools allow senior developers to move faster and reduce the need for a large number of junior developers.

Real-Life Example: Stripe

Stripe, a leading fintech company, experimented with reducing its reliance on junior developers by leveraging AI-powered development tools. They streamlined their engineering teams, allowing senior engineers to complete tasks in half the time with AI assisting in boilerplate code, debugging, and automated testing. This helped Stripe reduce costs while maintaining high-quality output.

Pros:

• Lower payroll costs
• Faster development cycles
• Fewer mistakes and more consistent code quality

Cons:

• No pipeline for future senior developers
• Loss of diverse perspectives and innovation
• Higher risk if the senior developer leaves

The Case for Training Junior Developers

On the other hand, investing in junior developers creates long-term sustainability and cultivates a strong company culture. AI should be seen as a tool to accelerate their learning rather than a replacement.

Real-Life Example: Shopify

Shopify has a strong internal development culture that emphasizes mentorship and junior developer growth. They implemented a program where junior developers used AI tools to assist with coding, debugging, and understanding best practices. Within two years, many juniors had transitioned to mid-level roles, reducing hiring costs and increasing team loyalty.

Pros:

• Builds a pipeline of future senior engineers
• Encourages innovation and fresh perspectives
• Creates a collaborative and diverse work environment

Cons:

• Higher initial training costs
• Slower development speed in the short term
• Risk of juniors making mistakes early on

The Ideal Team: A Mix of Juniors, Seniors, and AI

The best-performing teams strike a balance between juniors, seniors, and AI tools. Here’s why:

• Juniors bring fresh ideas and enthusiasm but lack deep experience.
• Seniors provide technical leadership and decision-making skills.
• AI accelerates productivity and helps fill knowledge gaps.

Example: The Hybrid Approach at Supercell

Supercell, a gaming startup, faced a dilemma: Hire more juniors, bring in an expensive senior team, or leverage AI? They opted for a hybrid approach:

• A few senior developers focused on core architecture and mentoring.
• Juniors were onboarded with structured training programs and AI-powered coding tools.
• AI helped automate repetitive tasks, reducing junior mistakes.

After a year, productivity was up 40%, and juniors were significantly more skilled.

How to Start Implementing This Approach

1. Invest in AI Tools – Provide teams with AI-powered coding assistants, but don’t rely on them blindly.
2. Create a Structured Training Program – Develop mentorship programs where seniors guide juniors.
3. Encourage AI-Assisted Learning – Let juniors use AI for debugging and code explanations to accelerate learning.
4. Pair Programming and Code Reviews – Ensure juniors work alongside seniors to absorb knowledge.
5. Set Clear Growth Paths – Provide juniors with clear milestones for career advancement.

Conclusion

Rather than replacing junior developers with AI, the smarter approach is to use AI to enhance their growth. Companies that cultivate junior talent will be better positioned for long-term success, while AI can help both juniors and seniors become more productive.

By striking the right balance between automation and human development, teams can maximize efficiency, innovation, and sustainability in the ever-evolving tech landscape.

In the modern tech landscape, product owners often assume that software will “just work” safely. But security isn’t automatic, and a lack of foundational understanding can lead to serious vulnerabilities. While AI has become a valuable tool in development, it cannot replace the expertise of seasoned developers who can anticipate, diagnose, and resolve complex security flaws. In this article, we’ll explore why relying solely on assumptions—or even AI—can put users at risk, and how experienced developers have rescued failing projects from disaster.

The Illusion of Safety in Software Development

Product owners, especially those without technical backgrounds, often focus on features, user experience, and business objectives. Security tends to be an afterthought—if it’s considered at all. They might assume:

• “Our framework handles security automatically.”
• “AI-generated code must be secure.”
• “Hackers won’t target us because we’re a small company.”

These assumptions are dangerous. Security must be deliberately designed into a system, and failing to do so can lead to catastrophic breaches.

Real-World Examples of Projects Gone Wrong (And How Developers Saved Them)

1. The E-Commerce Disaster: Exposed Customer Data

A startup built an e-commerce platform using a popular low-code solution. The product owner assumed that security was baked into the platform. What they didn’t realize was that their database had been left publicly accessible, exposing thousands of customers’ personal and payment data.

How an Experienced Developer Saved It:

A senior developer was brought in after an independent security researcher exposed the vulnerability. The developer:

• Implemented proper authentication and access controls.
• Added encryption for stored customer data.
• Set up automated security testing to prevent future leaks.

Without this intervention, the company would have faced lawsuits and lost customer trust.

2. The AI Code Generator Mistake: Unsecured API Keys

A SaaS company decided to speed up development using AI-generated code. Their AI assistant provided clean-looking API integrations, but it didn’t consider security best practices. The AI-generated code stored API keys directly in the frontend, exposing them to anyone who inspected the browser’s developer tools.

How an Experienced Developer Saved It:

A security-conscious engineer audited the code and:

• Moved API keys to a secure environment variable setup.
• Implemented proper authentication (OAuth2) instead of static keys.
• Set up monitoring alerts for unusual API usage.

The fix prevented potential attackers from hijacking API calls and exploiting user data.

3. The Blockchain Nightmare: A Flawed Smart Contract

A startup launched a DeFi project, assuming that a popular AI tool could generate Solidity smart contracts with minimal oversight. The result? A flawed contract that allowed attackers to drain funds due to a reentrancy bug.

How an Experienced Developer Saved It:

After losing some initial funds, they hired a blockchain security expert who:

• Refactored the smart contract using best practices.
• Implemented reentrancy guards to prevent exploit attempts.
• Conducted thorough security audits before redeploying.

Without an expert, the project could have been completely compromised.

AI is a Tool, Not a Replacement for Expertise

AI-generated code can be helpful, but it lacks real-world context. AI doesn’t understand business logic, legal requirements, or evolving security threats. An experienced developer:

• Thinks critically about how code will be used (and misused).
• Understands regulatory compliance (GDPR, PCI-DSS, HIPAA, etc.).
• Can adapt and apply security measures based on the specific application.

The Takeaway: Invest in Expertise Before It’s Too Late

Security isn’t something that should be bolted on at the end of a project—it should be a core consideration from the start. Product owners must:

• Consult experienced developers early in the development cycle.
• Prioritize security just as much as features and user experience.
• Recognize that AI is a helpful assistant, but not a substitute for human expertise.

Ignoring security can lead to costly breaches, reputational damage, and legal consequences. But with the right developers involved, businesses can build not just functional, but also secure, reliable, and scalable software.

---

Have You Seen Security Mistakes Firsthand?

What’s the worst security oversight you’ve encountered in a project? Share your experiences in the comments!

Artificial intelligence has experienced an explosion of growth in recent years, with companies like OpenAI, Anthropic, and Google leading the charge in providing powerful foundational models. But despite their immense computational capabilities, these AI providers are increasingly finding themselves in a familiar position—one that resembles the fate of telecom giants in the past.

In the telecom industry, infrastructure providers built the backbone of communication networks, yet they eventually found themselves competing not on innovation but on price, reliability, and scale. Meanwhile, a new wave of companies emerged, offering user-friendly interfaces and value-added services on top of these networks, becoming the real consumer-facing brands. Today, a similar pattern is emerging in AI, where a new breed of AI wrappers is rapidly capturing market attention and customer loyalty.

AI Wrappers: The New Kings of the AI Economy

AI wrappers are companies or platforms that use existing AI models but differentiate themselves by offering improved usability, domain-specific expertise, or additional automation capabilities. They act as intermediaries between the raw power of foundational models and the end user, providing a tailored, more accessible experience. These wrappers often build agentic systems that extend AI capabilities beyond simple text generation, integrating AI into real-world workflows more seamlessly.

One of the most notable examples is Cursor, an AI-powered coding assistant that integrates deeply with software development environments. While OpenAI provides the underlying models, Cursor enhances them with context awareness, code-specific optimizations, and user-friendly interactions that cater specifically to developers. Similarly, Jasper has become a dominant force in AI-powered content creation, building atop existing LLMs but fine-tuning the experience for marketing and branding purposes.

Other examples include:

• Replit Ghostwriter: An AI-powered coding assistant built into the Replit platform, offering features customized for its user base.
• Notion AI: A productivity tool that integrates AI to help users generate content, summarize notes, and organize information in an intuitive way.
• Synthesia: A video generation platform using AI-powered avatars and voice synthesis, making it easier for businesses to create professional content without requiring actors or video production expertise.

These AI wrappers add convenience, improve user workflows, and tailor AI capabilities for specific needs—something foundational models alone struggle to do.

Why AI Providers Are Like Telecoms

Telecom providers built the infrastructure that powered the internet, but their value quickly became commoditized. Consumers didn’t buy “bandwidth”—they bought streaming services, social media, and communication apps that provided the real utility. The underlying networks remained essential but largely invisible.

AI is heading down the same path. Foundational model providers like OpenAI, Anthropic, and Google are in an arms race to create more powerful models, but they face two fundamental challenges:

1. Differentiation is difficult: At the model level, once competitors catch up in quality, the main differentiator becomes price and API access rather than unique features.
2. Customer experience is owned by wrappers: Just as customers interact with Netflix rather than their internet provider, most AI users will engage with AI wrappers rather than the underlying models.

Unless foundational AI providers can create a breakthrough that redefines the way AI is used, they will likely remain the equivalent of cloud computing providers—essential but invisible to end users.

Can AI Providers Compete?

AI model creators face a strategic dilemma. Do they continue improving their base models and risk becoming commodities, or do they move up the stack, integrating more directly with users?

There are a few potential paths they could take:

• Vertical integration: OpenAI, for instance, is already moving in this direction with ChatGPT, aiming to make its own application as sticky as possible. Google has integrated Gemini into its search products, while Anthropic is working on making Claude a more accessible assistant.
• Exclusive partnerships: By offering custom AI models for select partners, providers can maintain an edge. This strategy mirrors what Nvidia has done with its GPU dominance—selling to cloud providers while also enabling high-performance, exclusive partnerships.
• Fine-tuned solutions: Rather than offering general-purpose models, AI providers could develop industry-specific solutions for enterprise applications, ensuring they remain a step ahead of generic AI wrappers.

However, even with these efforts, the fundamental shift in value remains: users prefer tailored experiences over raw power, and AI wrappers are best positioned to deliver those experiences.

The Future of AI: Who Wins?

The current trend suggests AI providers will continue building the foundation, but the real profits and brand recognition will go to those who package and distribute AI in a way that users love. AI wrappers are already dominating certain verticals, and as agentic AI systems become more advanced, we will see an even greater separation between infrastructure and experience.

If AI providers fail to adapt, they risk becoming the “AT&T” of the AI age—powerful but replaceable. Meanwhile, the new kings of AI will be the platforms that understand user needs, integrate AI seamlessly, and create interfaces that feel indispensable.

The AI landscape is rapidly shifting, and while foundational models remain crucial, they are no longer the defining factor in AI’s success. The future belongs to those who can bridge the gap between raw AI power and user-friendly experiences. As we move forward, expect more AI wrappers to emerge, reshaping industries and pushing AI providers further into the background.

In the end, AI might power the revolution—but the wrappers will own the kingdom.

Artificial intelligence is revolutionizing software development, making it easier for less experienced developers to write code, generate solutions, and build applications faster than ever before. But as AI lowers the barrier to entry, it creates an unexpected challenge—an increasing burden on tech leads who must navigate a landscape filled with AI-assisted code that often lacks structure, scalability, and maintainability.

The Rise of AI-Assisted Development

Tools like GitHub Copilot, ChatGPT, and other AI-powered coding assistants have significantly boosted developer productivity. Junior and mid-level developers can now produce complex code snippets, automate repetitive tasks, and solve problems they previously struggled with. While this sounds like a win for the industry, the reality is more nuanced.

AI-generated code is often syntactically correct but semantically flawed. It may work in isolation but lack architectural integrity when integrated into a broader system. AI lacks the human intuition necessary to understand business logic, future scalability, and team-specific best practices. This means that while development velocity increases, so does the risk of accumulating technical debt.

The Burden on Tech Leads

Tech leads, already responsible for guiding teams, making architectural decisions, and ensuring high code quality, now face an additional challenge: reviewing and correcting AI-generated code. Here’s why AI can be a curse for tech leads:

1. Increased Review Workload

AI accelerates code production, but not always in the right direction. More code means more pull requests, more code reviews, and more debugging. Tech leads spend significant time analyzing whether AI-assisted code is functionally correct and adheres to best practices.

For example, a junior developer might use AI to generate an API endpoint for handling user authentication. The AI might produce working code but fail to implement essential security measures such as rate limiting, input validation, or proper session management. The tech lead must then review, refactor, and educate the team on why these aspects are critical.

2. Poor Architectural Decisions

Junior and even mid-level developers often rely on AI to solve immediate problems without considering the long-term architectural impact. AI doesn’t inherently enforce good design patterns, leading to monolithic structures, poorly optimized queries, or brittle integrations that tech leads must later refactor.

Imagine a situation where AI generates multiple SQL queries inside a loop instead of optimizing them into a single batch query. While the code functions correctly, it causes unnecessary database load, which can lead to performance bottlenecks in production. A tech lead must identify and correct such inefficiencies before they escalate.

3. False Confidence in AI Solutions

Developers using AI often assume the generated code is correct because it “works.” However, AI does not understand the nuances of business requirements, security concerns, or industry-specific regulations. This creates hidden bugs and vulnerabilities that tech leads must catch before they become production issues.

For instance, an AI-generated function might handle user passwords but store them in plaintext instead of hashing them properly. If a less-experienced developer deploys this code, it could lead to a serious security breach. A tech lead must not only fix the issue but also implement processes to prevent similar mistakes in the future.

4. AI-Driven Technical Debt

Without proper oversight, AI can rapidly generate unmaintainable code that accumulates as technical debt. Tech leads must dedicate additional time to reworking solutions, refactoring poor implementations, and ensuring code consistency across the codebase.

Consider a scenario where an AI tool generates dozens of functions across different files with inconsistent naming conventions and redundant logic. While the code technically works, it becomes difficult to navigate, update, or debug. A tech lead must step in to unify the code, remove duplication, and enforce better organizational practices.

The Future: More Tech Leads, Not Fewer

Paradoxically, as AI tools become more advanced, the need for strong technical leadership increases. More tech leads will be required to:

• Establish clear coding guidelines and best practices for AI-assisted development.
• Educate teams on AI’s limitations and how to critically assess its output.
• Enforce architectural principles to prevent the system from collapsing under its own complexity.
• Prioritize code quality and long-term maintainability over short-term gains.
• Introduce automated testing and linting rules to catch common AI-generated mistakes before they reach production.

How Tech Leads Can Adapt

To thrive in this AI-assisted environment, tech leads need to adjust their approach to leadership and mentorship. Some strategies include:

1. Establish AI Review Processes

Tech leads should implement specific review processes for AI-generated code, ensuring that every piece of code passes through a structured evaluation. Code review checklists should include:

• Security best practices (e.g., input validation, authentication measures).
• Performance optimizations (e.g., avoiding redundant queries or inefficient loops).
• Maintainability and readability (e.g., clear variable names, well-structured functions).

2. Promote AI Literacy Among Developers

Instead of discouraging AI use, tech leads should train their teams to use AI responsibly. This means educating developers on when to trust AI-generated code, how to refine prompts to get better results, and how to critically evaluate AI suggestions.

3. Use AI to Help, Not Replace, Thoughtful Engineering

Tech leads can leverage AI themselves—not just to generate code, but to automate tedious tasks like refactoring, documentation generation, and code linting. By integrating AI into CI/CD pipelines, tech leads can ensure AI-generated code is continuously checked for quality issues before deployment.

AI is undoubtedly a powerful tool for accelerating development, but it is not a substitute for experience, intuition, and thoughtful engineering. While junior developers benefit from AI assistance, tech leads bear the responsibility of ensuring that AI-generated code aligns with best practices and long-term business objectives. As AI continues to shape the industry, the role of tech leads will become even more critical—not just in fixing what has been done, but in guiding teams to use AI responsibly and effectively.

In the end, AI doesn’t replace the need for great engineering—it amplifies the need for strong technical leadership. The companies that recognize this will be the ones that successfully integrate AI into their development processes without sacrificing code quality, security, or scalability.