AI Archives - Ivan Turkovic

20+ Years as a CTO: Lessons I Learned the Hard Way

Being a CTO isn’t what it looks like from the outside. There are no capes, no magic formulas, and certainly no shortcuts. After more than two decades leading engineering teams, shipping products, and navigating the chaos of startups and scale-ups, I’ve realized that the real challenges and the real lessons aren’t technical. They’re human, strategic, and sometimes painfully simple.

Here are the lessons that stuck with me, the ones I wish someone had told me when I started.

Clarity beats speed every time

Early in my career, I thought speed meant writing more code, faster. I would push engineers to “ship now,” measure velocity in lines of code or story points, and celebrate sprint completions.

I was wrong.

The real speed comes from clarity. Knowing exactly what problem you’re solving, who it matters to, and why it matters that’s what lets a team move fast. I’ve seen brilliant engineers grind for weeks only to realize they built the wrong thing. Fewer pivots, fewer surprises, and focus make a team truly fast.

Engineers want to care, they just need context

One of the most frustrating things I’ve witnessed is engineers shrugging at product decisions. “They just don’t care,” I thought. Until I realized: they do care. They want to make an impact. But when they don’t have context, the customer pain, the market reality, the business constraints,they can’t make informed decisions.

Once I started sharing the “why,” not just the “what,” engagement skyrocketed. A well-informed team is a motivated team.

Vision is a tactical tool, not a slogan

I’ve been guilty of writing vision statements that sounded great on slides but did nothing in practice. The turning point came when I started treating vision as a tactical tool.

Vision guides decisions in real time: Should we invest in this feature? Should we rewrite this component? When the team knows the north star, debates become productive, not paralyzing.

Great engineers are problem solvers first

I’ve worked with engineers who could write elegant code in their sleep, but struggle when the problem itself is unclear. The best engineers are not just builders, they’re problem solvers.

My role as a CTO became ensuring the problem was well-understood, then stepping back. The magic happens when talent meets clarity.

Bad culture whispers, it doesn’t shout

I’ve learned to pay attention to the quiet. The subtle signs: meetings where no one speaks up, decisions made by guesswork, unspoken assumptions. These moments reveal more about culture than any HR survey ever could.

Great culture doesn’t need fanfare. Bad culture hides in silence and it spreads faster than you think.

Done is when the user wins

Early on, “done” meant shipped. A feature went live, the ticket closed, everyone celebrated. But shipping doesn’t equal solving.

Now, “done” only counts when the user’s problem is solved. I’ve had to unteach teams from thinking in terms of output and retrain them to think in terms of impact. It’s subtle, but transformative.

Teams don’t magically become product-driven

I used to blame teams for not thinking like product people. Then I realized the missing piece was me. Leadership must act like product thinking matters. Decisions, recognition, discussions, they all reinforce the mindset. Teams reflect the leadership’s priorities.

Product debt kills momentum faster than tech debt

I’ve chased the holy grail of perfect code only to watch teams get bogged down in building the wrong features. Clean architecture doesn’t save a product if no one wants it. Understanding the problem is far more important than obsessing over elegance.

Focus is a leadership decision

I once ran a team drowning in priorities. Tools, frameworks, and fancy prioritization systems didn’t help. The missing ingredient was leadership. Saying “no” to the wrong things, protecting focus, and consistently communicating what matters that’s what accelerates teams.

Requirements are not the problem

If engineers are stuck waiting for “better requirements,” don’t introduce another process. Lead differently. Engage with your team, clarify expectations, remove ambiguity, and give feedback in real time. Requirements are never the bottleneck leadership is.

The hard-earned truth

After twenty years, I’ve realized technology is the easy part. Leadership is where the real work and the real leverage lies.

Clarity, context, vision, problem-solving, culture, focus these aren’t buzzwords. They are the forces that determine whether a team thrives or stalls.

I’ve seen brilliant teams fail, and ordinary teams excel, all because of the way leadership showed up. And that’s the lesson I carry with me every day: if you want speed, impact, and results, start with the leadership that creates the conditions for them.

Why AI won’t solve these problems

With all the excitement around AI today, it’s tempting to think that tools can fix everything. Need better requirements? There’s AI. Struggling with design decisions? AI can suggest options. Want faster development? AI can generate code.

Here’s the hard truth I’ve learned: none of these tools solve the real problems. AI can assist, accelerate, and automate but it cannot provide clarity, set vision, or foster a healthy culture. It doesn’t understand your users, your market, or your team’s dynamics. It can’t decide what’s important, or make trade-offs when priorities conflict. Those are human responsibilities, and they fall squarely on leadership.

I’ve seen teams put too much faith in AI as a silver bullet, only to discover that the fundamental challenges alignment, focus, problem definition, and decision-making still exist. AI is powerful, but it’s a force multiplier, not a replacement. Without strong leadership, even the most advanced AI cannot prevent teams from building the wrong thing beautifully, or from stagnating in a quiet, passive culture.

Ultimately, AI is a tool. Leadership is the strategy. And experience with decades of trial, error, and hard-won insight is what turns potential into real results.

Cocoa, Chocolate, and Why AI Still Can’t Discover

Imagine standing in front of a freshly picked cocoa pod. You break it open, and inside you find a pale, sticky pulp with bitter seeds. Nothing looks edible, nothing smells particularly appetizing. By every reasonable measure, this is a dead end.

Yet humanity somehow didn’t stop there. Someone, centuries ago, kept experimenting, steps that made no sense at the time:

Picking out the seeds and letting them ferment until they grew mold.
Washing and drying them for days, though still inedible.
Roasting them into something crunchy, still bitter and strange.
Grinding them into powder, which tasted worse.
Finally, blending that bitterness with sugar and milk, turning waste into one of the most beloved foods in human history: chocolate.

No algorithm would have told you to keep going after the first dozen failures. There was no logical stopping point, only curiosity, persistence, and maybe a bit of luck. The discovery of cocoa as food wasn’t the result of optimization, it was serendipity.

Why This Matters for AI

AI today is powerful at recombining, predicting, and optimizing. It can remix what already exists, generate new connections from vast data, and accelerate discoveries we’re already aiming toward. But there’s a limit: AI doesn’t (yet) explore dead ends with stubborn curiosity. It doesn’t waste time on paths that appear pointless. It doesn’t ferment bitter seeds and wait for mold to form, just to see if maybe, somehow, there’s something new hidden inside.

Human discovery has always been messy, nonlinear, and often illogical. The journey from cocoa pod to chocolate shows that sometimes the only way to find the extraordinary is to persist through the ridiculous.

The Future of Discovery

If we want AI to go beyond optimization and into true discovery, it will need to embrace the irrational side of exploration, the willingness to try, fail, and continue without clear reasons. Until then, AI remains a tool for extending human knowledge, not replacing the strange, stubborn spark that drives us to turn bitter seeds into sweetness.

Because the truth is: chocolate exists not because it was obvious, but because someone refused to stop at “nothing edible.”

This path makes no sense. At every step the signal says stop. No data suggests you should continue. No optimization algorithm rewards the action. Yet someone did. And that’s how one of the world’s favorite foods was discovered.

This is the gap between human discovery and AI today.

AI can optimize, remix, predict. It can explore a search space, but only one that’s already defined. It can’t decide to push through meaningless, irrational steps where there’s no reason to keep going. It won’t follow a path that looks like failure after failure. It won’t persist in directions that appear to lead nowhere.

But that’s exactly how discovery often works.

Cocoa to chocolate wasn’t about efficiency. It was curiosity, stubbornness, and luck. The same applies to penicillin, vulcanized rubber, even electricity. Breakthroughs happen because someone ignored the “rational” stopping point.

AI is far from that. Right now, it’s bounded by what already exists. It doesn’t yet invent entirely new domains the way humans stumble into them.

The lesson? Discovery is still deeply human. And the future of AI will depend not just on making it smarter, but on making it willing to walk blind paths where no reward signal exists until something unexpected emerges.

Because sometimes, you need to go through moldy seeds and bitterness to find chocolate.

When to Hire Real Engineers Instead of Freelancers for Your MVP

Building a startup is a race against time. Every day you wait to ship your idea is a day your competitors could gain an edge. That’s why many founders start with freelancers or “vibe coding” to launch their MVP (Minimum Viable Product) quickly. But this fast-track approach comes with hidden risks. There comes a point when hiring real engineers is no longer optional, it’s critical for your startup’s survival.

In this post, we’ll explore when it’s the right time to transition from freelancers to full-time engineers, and why vibe coding with low-cost freelancers can be dangerous for your MVP.

Why Start With Freelancers?

Freelancers are often the first choice for early-stage founders. Here’s why:

Speed: Freelancers can help you quickly prototype your idea.
Lower Cost: You pay for work done, without the overhead of full-time salaries or benefits.
Flexibility: You can scale the workforce up or down depending on the project stage.

Freelancers are perfect for validating your idea, testing market demand, or building proof-of-concept features. However, relying on freelancers too long can create technical debt and slow your growth when your product starts attracting real users.

The Hidden Dangers of Vibe Coding With Low-Cost Freelancers

Many founders are tempted by freelancers offering extremely low rates. While the idea of saving money is appealing, vibe coding with bargain-rate developers comes with serious risks:

Poor Code Quality: Low-cost freelancers may cut corners, leaving messy, unmaintainable code.
Lack of Documentation: Your codebase may be difficult for future engineers to understand or build upon.
Delayed Timelines: Cheap freelancers often juggle multiple clients, causing unpredictable delays.
False Confidence: Founders may assume their MVP is “production-ready” when it’s not.
Hidden Costs: Fixing technical debt later often costs more than hiring quality engineers from the start.

Using low-cost freelancers is fine for prototyping ideas quickly, but it becomes risky when your MVP starts attracting real users or paying customers.

Signs You Need Real Engineers

Here are the main indicators that your MVP has outgrown freelancers:

1. Product Complexity Increases

Your MVP is no longer a simple prototype.
Features require backend scalability, integrations, or complex logic.
Codebase is hard for freelancers to maintain consistently.

2. Customers Expect Stability

Paying users begin using your product regularly.
Bugs, downtime, or inconsistent updates start hurting your credibility.
You need reliable, professional code that can scale.

3. You Plan for Rapid Growth

You anticipate increasing traffic, user engagement, or data volume.
Your MVP needs a scalable architecture to handle more users efficiently.

4. Security and Compliance Matter

Sensitive user data, payment systems, or regulatory requirements are involved.
Freelancers may lack the expertise to ensure security best practices.

How to Transition Smoothly to Full-Time Engineers

Once you’ve decided to hire real engineers, plan the transition carefully to avoid disruption:

Audit Existing Code: Identify areas of technical debt and create a roadmap for refactoring.
Hire Strategically: Look for engineers with startup experience who can handle rapid iteration and product scaling.
Document Everything: Ensure all features, APIs, and infrastructure are well-documented for the new team.
Maintain Continuity: Keep a few top freelancers for short-term tasks during the handover period.
Invest in Tools: Use code repositories, CI/CD pipelines, and testing frameworks to support professional development practices.

Cost Considerations

Hiring full-time engineers is an investment. While freelancers may seem cheaper upfront, consider the long-term costs:

Technical Debt: Fixing poor-quality code can cost far more than hiring engineers initially.
Lost Customers: Product instability can lead to churn and missed revenue.
Opportunity Cost: Delays in scaling and adding features can let competitors win market share.

Think of full-time engineers as insurance for your product’s future success.

Conclusion

Freelancers are invaluable for testing your idea and building a lean MVP quickly. But relying on low-cost vibe coding can be dangerous: messy code, delayed timelines, and hidden costs can stall your startup before it even takes off. Once your product gains traction, complexity, and paying users, hiring real engineers ensures stability, scalability, and long-term growth.

Key Takeaway: Use freelancers for prototyping, but transition to full-time engineers before your MVP becomes a product your customers depend on. Planning the move carefully saves time, money, and frustration.

Have you experienced the vibe code tax firsthand? Share your story in the comments and tell us how you decided when to hire full-time engineers.

On AI-Generated Code, Maintainability, and the Possibility of Disposable Software

Over the past two years, I’ve been using various AI-assisted tools for programming like Codeium, GitHub Copilot, ChatGPT, and others. These tools have become part of my daily workflow. Mostly, I use them for code completion and to help me finish thoughts, suggest alternatives, or fill in repetitive boilerplate. I’m not doing anything too wild with autonomous agents or fully automated codebases. Just practical, incremental help.

That said, even in this limited use, I’m starting to feel the friction.

Not because the tools are bad but actually, they’re improving fast. Individual lines and even complete methods are cleaner than they used to be. The suggestions are smarter. The models are more context-aware. But one thing still nags at me: even with better completions, a lot of the output still isn’t good code in the long-term sense.

Maintainability Still Matters

The issue, to me, isn’t whether AI can help me write code faster. It can. The issue is whether that code is going to survive over time. Is it going to be easy to understand, extend, or refactor? Does it follow a style or pattern that another human could step into and build on?

This matters especially when you’re not the only one touching the code or when you come back to it after a few months and wonder, “Why did I do it this way?”

And here’s the contradiction I keep running into: AI helps you write code faster, but it often creates more problems to maintain. That’s especially true when I’ve tested more advanced setups where you let an agent plan and generate entire components, classes, or services. It sounds great in theory, but in practice it causes a lot of changes, inconsistencies, and small bugs that end up being more trouble to fix than if I had just written it myself from the start.

So for now, I stay close to completions. Code at the scale of a line or a method. It’s easier to understand, easier to control. I can be the architect, and the AI can be the assistant.

The Self-Fulfilling Trap

There’s a strange loop forming in AI development. Since the generated code is harder to reason about or maintain, people often treat it as throwaway. And because it’s throwaway, nobody bothers to make it better. So it stays bad.

Self-fulfilling prophecy.

The more AI you use to generate logic, the more you’re tempted to not go back and polish or structure it. You get into a loop of “just generate something that works,” and soon you’re sitting on a pile of glue code and hacks that’s impossible to build on.

But maybe that’s okay? Maybe we need to accept that some code isn’t meant to last.

Disposable Software Might Be the Point

This is where I’m starting to shift my thinking a little. I’ve always approached code as something you build on something that lives and evolves. But not all code needs that.

A lot of software today already is disposable, even if we don’t admit it. One-off internal dashboards, ETL jobs, scripts for events, MVPs for marketing campaigns, integrations that won’t live beyond a quarter. We often pretend we’re building maintainable systems, but we’re not. We just don’t call them disposable.

With AI in the mix, maybe it’s time to embrace disposability for what it is. Write the code, run the code, get the result, throw it away. Next time, generate it again maybe with better context or updated specs.

This mindset removes a lot of the pressure around maintainability. And it fits the strengths of today’s AI tools.

When Not to Use Agentic Systems (Yet)

I’ve played with more autonomous agent systems what people call “agentic AI” or multi-agent code platforms. Honestly? I’m not sold on them yet. They generate too much. They make decisions I wouldn’t make. They refactor things that didn’t need to be touched.

And then I spend more time reading diff views and undoing changes than I saved by delegating in the first place.

Maybe in the future I’ll be comfortable letting an AI agent draft a service or plan out an architectural pattern. But today, I’m not there. I use these tools more like smart autocomplete than autonomous developers. It’s still very much my code and they’re just helping speed up the flow.

Final Thoughts

There’s a real risk of overhyping what AI can do for codebases today. But there’s also an opportunity to rethink how we treat different classes of software. We don’t need to hold everything to the same standards of longevity. Not every project needs to be built for 10 years of feature creep.

Some software can (and should) be treated like scaffolding and built quickly, used once, and removed without guilt.

And that’s where AI shines right now. Helping us build the things we don’t need to keep.

I’ll keep experimenting. I’ll keep writing most of my own code, and using AI where it makes sense. But I’m also watching carefully because the balance between what’s worth maintaining and what’s better thrown away is shifting.

And we should all be ready for what that means.

The AI isn’t going to be on call at 2 AM when things go down.

Large Language Models (LLMs) like ChatGPT, Copilot, and others are becoming a regular part of software development. Many developers use them to write boilerplate code, help with unfamiliar syntax, or even generate whole modules. On the surface, it feels like a productivity boost. The work goes faster, the PRs are opened sooner, and there’s even time left for lunch.

But there’s something underneath this speed, something we’re not talking about enough. The real issue with LLM-generated code is not that it helps us ship more code, faster. The real issue is liability.

Code That Nobody Owns

There’s a strange effect happening in teams using AI to generate code: nobody feels responsible for it.

It’s like a piece of code just appeared in your codebase. Sure, someone clicked “accept,” but no one really thought through the consequences. This is not new, we saw the same thing with frameworks and compilers that generated code automatically. If no human wrote it, then no human cares deeply about maintaining or debugging it later.

LLMs are like that, but on a massive scale.

The “Average” Problem

LLMs are trained on a massive corpus of public code. What they produce is a kind of rolling average of everything they’ve seen. That means the code they generate isn’t written with care or with deep understanding of your system. It’s not great code. It’s average code.

And as more and more people use LLMs to write code, and that code becomes part of new training data, the model quality might even degrade over time, it becomes an average of an average.

This is not just about style or design patterns. It affects how you:

Deliver software
Observe and monitor systems
Debug real-world issues
Write secure applications
Handle private user data responsibly

LLMs don’t truly understand these things. They don’t know what matters in your architecture, how your team works, or what your specific constraints are. They just parrot what’s most statistically likely to come next in the code.

A Fast Start, Then a Wall

So yes, LLMs speed up the easiest part of software engineering: writing code.

But the hard parts remain:

Understanding the domain
Designing for change
Testing edge cases
Debugging production issues
Keeping systems secure and maintainable over time

These are the parts that hurt when the codebase grows and evolves. These are the parts where “fast” turns into fragile.

Example: Generated Code Without Accountability

Imagine you ask an LLM to generate a payment service. It might give you something that looks right, maybe even works with your Stripe keys or some basic error handling.

But:

What happens with race conditions?
What if fraud detection fails silently?
What if a user gets double-charged?
Who is logging what?
Is the payment idempotent?
Is sensitive data like credit cards being exposed in logs?

If no one really “owned” that code because it was mostly written by an AI and these questions might only surface after things go wrong. And in production, that can be very costly.

So What’s the Better Approach?

LLMs can be great tools, especially for experienced engineers who treat them like assistants, not authors.

To use LLMs responsibly in your team:

Review AI-generated code with care.
Assign clear ownership, even for generated components.
Add context-specific tests and documentation.
Educate your team on the why, not just the how.
Make accountability a core part of your development process.

Because in the end, you are shipping the product. The AI isn’t going to be on call at 2 AM when things go down.

Final Thoughts

LLMs give us speed. But they don’t give us understanding, judgment, or ownership. If you treat them as shortcuts to ship more code, you may end up paying the price later. But if you treat them as a tool and keep responsibility where it belongs they can still be part of a healthy, sustainable development process.

Thanks for reading. If you’ve seen this problem in your team or company, I’d love to hear how you’re dealing with it.

AI Isn’t Leveling the Playing Field, it’s Amplifying the Gap

We were told that AI would make development more accessible. That it would “level the playing field,” empower juniors, and help more people build great software.

That’s not what I’m seeing.

In reality, AI is widening the gap between junior and senior developers and fast.

Seniors Are 10x-ing With AI

For experienced engineers, AI tools like ChatGPT and GitHub Copilot are a multiplier.

Why?

Because they know:

What to ask
How to evaluate the answers
What matters in their system
How to refactor and harden code
When to ignore the suggestion completely

Seniors are using AI the same way a great chef uses a knife: faster, safer, more precise.

Juniors Are Being Left Behind

Many junior developers, especially those early in their careers, don’t yet have the experience to judge what’s good, bad, or dangerous. And here’s the issue:

AI makes it look like they’re productive until it’s time to debug, optimize, or maintain the code.

They’re often:

Copy-pasting solutions without understanding the trade-offs
Relying on AI to write tests they wouldn’t know how to write themselves
Shipping code that works on the surface, but is fragile underneath

What they’re building is a slow-burning fire of tech debt, and they don’t even see the smoke.

Prompting Isn’t Engineering

There’s a new kind of developer emerging: one who can write a great prompt but can’t explain a stack trace.

That might sound harsh, but I’ve seen it first-hand. Without a foundation in problem-solving, architecture, debugging, and security prompting becomes a crutch, not a tool.

Good engineering still requires:

Judgment
Pattern recognition
Systems thinking
Curiosity
Accountability

AI doesn’t teach these. Mentorship does.

Where Is the Mentorship?

In many teams, mentorship is already stretched thin. Now we’re adding AI to the mix, and some companies expect juniors to “just figure it out with ChatGPT.”

That’s not how this works.

The result? Juniors are missing the critical lessons that turn coding into engineering:

Why things are built the way they are
What trade-offs exist and why they matter
How to debug a system under load
When to break patterns
How to think clearly under pressure

No AI can give you that. You only get it from real experience and real guidance.

What We Can Do

If you’re a senior engineer, now is the time to lean into mentorship not pull away.

Yes, AI helps you move faster. But if your team is growing and you’re not helping juniors grow too, you’re building speed on a weak foundation.

If you’re a junior, use AI but don’t trust it blindly. Try to understand everything it gives you. Ask why. Break it. Fix it. Learn.

Because here’s the truth:

AI won’t make you a better engineer. But it will make great engineers even better.

Don’t get left behind.

Final Thoughts

AI isn’t the enemy. But it’s not a shortcut to seniority either. We need to be honest about what it’s good for and where it’s failing us.

Let’s stop pretending it’s a magic equalizer. It’s not.

It’s a magnifier.
If you’re already strong, it makes you stronger.
If you’re still learning, it can hide your weaknesses until they blow up.

AI Can Write Code. But Can It Build the Right Product?

AI is changing how we write code. But there’s a critical question not enough teams are asking:

Is it helping us build the right thing?

From GitHub Copilot to ChatGPT and beyond, the latest AI tools are letting engineers generate code faster than ever before. But faster coding doesn’t automatically mean better outcomes. It just means we get to the wrong destination quicker unless we rethink how we build.

If you’re a founder, CTO, engineer, or investor, this shift matters. Because in this new landscape, the real competitive edge isn’t AI-enhanced productivity. It’s clarity of vision, deep user understanding, and ruthless focus on solving the right problems.

AI Code Generation: Speed Boost or Strategy Trap?

The appeal of AI tools is obvious. With a few prompts or autocomplete suggestions, entire functions materialize. Repetitive boilerplate disappears. Developers feel empowered, moving quickly and staying in flow.

But what are they building?

The reality is: most teams don’t fail because they can’t ship fast enough. They fail because they’re solving problems no one cares about.

“AI just helps you build the wrong thing faster if you’re not thinking critically about what matters.”

In a sense, AI has become the new baseline. It’s like cloud infrastructure everyone has access to it. It’s not the edge. It’s the new floor. And once everyone has the same tools, what matters most isn’t how fast you build but what you choose to build.

Why Product Thinking Beats Faster Coding

Imagine two teams building similar features. One is using AI to crank out code at lightning speed. The other spends time talking to users, validating assumptions, and only writing code when it’s clear what matters most.

Which one wins in the long run?

The second team. Every time.

Great companies aren’t made by productivity alone. They’re built by making smarter bets.

There are already countless stories of AI-powered engineering orgs that pushed out entire platforms only to discover they’d misread the market or misunderstood the user. The cost of wrong decisions compounds faster when you’re building faster.

Insight: Your AI stack isn’t your differentiator. Your user understanding is.

Engineering Without Context Is a Risk and AI Just Amplifies It

One of the most overlooked risks of AI in development is that it can further isolate engineers from the real-world problems their users are facing.

Here’s how it plays out:

AI makes coding easier and faster.
Engineers get deeper into flow states.
Less time is spent in discovery, user feedback, or product discussions.
Features are built from assumptions, not insights.

In short, AI widens the gap between builders and users, unless leaders intentionally close it.

Your engineers don’t need to be product managers. But they do need context:

Who are we solving for?
What pain are we addressing?
What does success look like for the user?

Without that, they’re just solving technical puzzles. And AI makes that even easier to do without questioning the why.

AI Strategy Is Not Product Vision: Lessons for CTOs and Founders

In 2025, one of the most common strategic mistakes I see from tech leadership is confusing “having an AI strategy” with “having a product vision.”

CTOs and founders proudly proclaim their AI initiatives, touting LLM integrations and Copilot productivity gains. But often, the product itself lacks a core value proposition. There’s no unique insight. No user obsession. Just surface-level tech hype.

Warning sign: If your AI roadmap is longer than your product roadmap, you’re probably heading in the wrong direction.

AI should support your vision, not become it. It’s a tool to accelerate clarity not a substitute for it.

How Smart Teams Use AI in Product Development

So how should high-performing teams approach AI?

They don’t ignore it. But they use it intentionally, not reactively. Here’s how they do it:

Start with product clarity: AI gets plugged in after the problem is well understood.
Use AI to prototype, not finalize: It’s a great tool for drafts, ideas, and quick iterations, but not for skipping over design thinking.
Build user feedback loops into engineering: Teams that talk to users frequently make better use of AI because they know what’s actually needed.
Avoid cargo culting AI features: Just because you can plug in an LLM doesn’t mean you should.

A simple framework for alignment:

Write a 1-page problem brief before building.
Identify the user pain, not the feature.
Validate with a real customer, not a teammate.
Use AI to build faster once the path is clear.

What Investors Should Really Look For

If you’re an investor evaluating AI-native startups, this part is for you.

Don’t just look at AI usage. That’s table stakes. Instead, dig deeper:

Are they solving a real user problem with a compelling insight?
Is the team obsessed with learning, not just launching?
Are engineers looped into product and customer conversations?
Is AI being used for leverage, or as a distraction?

The best startups in the AI era won’t be those with the flashiest demos. They’ll be the ones with:

Small teams.
Sharp thinking.
Deep user insights.
Relentless focus.

In an AI World, Thinking Is Your Edge

AI is raising the average. It’s making every developer a little more productive. It’s removing drudge work and unlocking new workflows.

But it’s not a silver bullet.

The teams that will truly win in this era are the ones that combine AI with critical thinking, product obsession, and strategic clarity.

They’ll ask better questions. They’ll build less but better. And they’ll move faster because they know exactly where they’re going.

So, how is your team using AI?
To generate more code or to make smarter decisions about what to build in the first place?

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Let’s Build Smarter, Not Just Faster

AI isn’t going away. But how we use it and what we choose to build with it will define which teams win and which ones waste time.

If you’re a founder, CTO, or product leader trying to navigate this new AI-powered world, and you’re serious about building the right product, I can help.

I bring over 20 years of experience across fintech, crypto, payments, and startup ecosystems as a developer, CTO, and product strategist. I’ve helped teams go from messy ideas to crystal-clear roadmaps and scalable platforms.

🔧 What I help with:

Defining product strategy in AI-heavy contexts
Aligning engineering teams with real user needs
Avoiding technical overbuild and feature bloat
Building lean, fast-moving MVPs with Ruby on Rails and modern stacks
Coaching tech teams to think product-first, code-second

If you want clarity, speed, and smarter decisions baked into your product and engineering culture, let’s talk.

📩 Get in touch with me or visit ivanturkovic.com to learn more.

Let’s build something that matters.

AI “Vibe” Coding Will Increase Demand for Software Engineers; Here’s Why

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

The Age of AI: Why Experienced Tech Architects and CTOs Are More Crucial Than Ever

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!

The Inevitable Churn of AI-Powered Development Platforms

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.