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.