Being a CTO in the Age of AI

The role of CTO has always been complex—part technologist, part strategist, part team builder. But 2025 has added new dimensions that require a fundamental rethinking of what technology leadership means. AI hasn't just changed what we build; it's changed how we think about building, leading, and delivering value.

Having spent decades in technology leadership and currently serving as CTO at 4Square Capital, I've had a front-row seat to this transformation. The skills that made CTOs successful even three years ago are necessary but no longer sufficient. Let me share what I've learned about navigating this new landscape.

The New Essential Skills

The conversation around CTO skills has shifted dramatically. We're no longer just talking about architectural patterns and technology stacks. Today's CTOs need to master an entirely new skillset:

AI Forensics and Model Understanding

Understanding how different AI models fail has become as important as understanding how they succeed. At 4Square Capital, we've built askdiana.ai, a financial AI assistant, and one of the most critical skills has been recognizing hallucination patterns in generated outputs.

This isn't just about reading research papers. It's about developing an intuition for when an LLM is confident versus when it's confabulating. Our Genius² engine exists precisely because we understood that LLMs are fundamentally pattern generators, not truth engines. That understanding shaped our entire verification architecture.

"A CTO who doesn't understand how AI fails is like a surgeon who only knows how to operate when everything goes well."

Strategic Prompt Engineering

Prompt engineering has evolved from a technical curiosity to a strategic capability. But it's not about crafting clever prompts—it's about understanding how tokenization works, how attention mechanisms influence outputs, and how to structure interactions to get reliable, consistent results.

I use AI tools daily—Claude Code for development, various LLMs for research and analysis. But the difference between dabbling and mastery is understanding the underlying mechanisms. When you know how a model processes context, you can architect solutions that work with the model's strengths rather than fighting against its limitations.

Judgment, Synthesis, and Connection

Here's what surprised me most about the AI era: the most valuable CTO skills aren't technical at all. They're about judgment—knowing which problems AI should solve and which it shouldn't. They're about synthesis—combining AI capabilities with traditional approaches to create something neither could achieve alone. And they're about connection—seeing patterns across domains that AI, trained on isolated datasets, might miss.

When we designed askdiana.ai's architecture, the critical decisions weren't about which LLM to use. They were about where to let AI operate and where to enforce deterministic behavior. That's pure judgment, informed by decades of experience building systems that have to work.

How I Actually Use AI as a CTO

Let me be specific about how AI tools have changed my day-to-day work, because there's a lot of hype and not enough honest discussion about practical impact.

Development Acceleration with Claude Code

I use Claude Code extensively in my development work. Not because it writes perfect code (it doesn't), but because it dramatically accelerates the iteration cycle. Here's my typical workflow:

• Scaffolding – I'll ask Claude Code to generate initial implementations. This is where AI truly shines—creating boilerplate, setting up patterns, implementing standard algorithms.
• Refactoring – When I need to restructure code, AI tools can handle the mechanical aspects while I focus on the architectural decisions.
• Documentation – AI excels at generating initial documentation from code, which I then refine and enhance.
• Debugging assistance – Describing a bug to an LLM often helps me think through the problem differently, even if its suggestions aren't directly applicable.

But here's what I don't do: I don't blindly accept AI-generated code. Every line gets reviewed. The architectural decisions are still mine. The AI is a productivity multiplier, not a replacement for thinking.

Strategic Research and Analysis

LLMs have fundamentally changed how I research technologies, evaluate approaches, and stay current. I can process far more information than before, but—and this is critical—I still need to verify, cross-reference, and apply judgment.

For example, when evaluating different approaches to LLM deployment for askdiana.ai, I used AI to quickly survey the landscape. But the decision to implement private infrastructure came from understanding business requirements, regulatory constraints, and the actual physics of data security—areas where human judgment remains essential.

Team Augmentation, Not Replacement

One of the most powerful applications has been enabling smaller teams to punch above their weight. At 4Square Capital, we've built sophisticated AI capabilities on what VCs would call "concerning capital efficiency." How? By using AI to multiply the effectiveness of our team.

But this requires a specific culture: experimentation is encouraged, iteration is expected, and failures are learning opportunities. AI tools work best when teams aren't afraid to use them creatively.

What Hasn't Changed (and Why That Matters)

Here's the uncomfortable truth: most of what makes a great CTO hasn't changed at all. AI has added new tools and capabilities, but the fundamentals remain the same.

First-Principles Thinking

Understanding the actual problem—not just the trendy solution—remains paramount. When everyone else was focused on which LLM to use, we focused on the core challenges: accuracy, privacy, and reliability. That first-principles approach led us to architectural decisions that solved real problems rather than chasing technological fashion.

Clear Communication

The ability to translate technical complexity into business value hasn't diminished; it's become more critical. Executives don't need to understand transformer architectures; they need to understand what AI can and can't do for their business. That translation is still a human skill.

System Thinking

AI doesn't exist in isolation. It's part of a broader system—technical, organizational, and business. Understanding how components interact, where bottlenecks form, and how changes propagate through systems remains a fundamentally human capability.

When we architected askdiana.ai, the AI components were important, but equally important were the data pipelines, security architecture, deployment infrastructure, and user experience. System-level thinking integrated all these pieces into something coherent.

The Governance Challenge

One area where CTOs are being tested like never before is AI governance. LLM-powered tools are proliferating across organizations—compliance checkers, code assistants, data analyzers, content generators. The question isn't if AI will show up in your stack; it's how to govern, scale, and extract value from it.

This requires new frameworks:

• Model selection criteria – Understanding which models for which tasks, balancing cost, capability, and risk
• Data governance – Ensuring AI tools don't inadvertently expose sensitive information
• Output validation – Building verification layers that catch AI errors before they cause problems
• Performance monitoring – Tracking not just technical metrics but business impact
• Ethical guidelines – Establishing clear boundaries for what AI should and shouldn't do

At 4Square Capital, we addressed this by implementing private infrastructure—your data literally never leaves your environment. This isn't just a security decision; it's a governance decision. Can't have data leakage if there's nowhere for data to leak to.

Building AI-First Culture

The technical aspects of AI integration are actually the easy part. The hard part is cultural transformation. How do you build teams that effectively leverage AI without becoming dependent on it?

The Paradox of AI Competence

Here's a paradox I've observed: the people who benefit most from AI tools are those who need them least. Experienced developers who already understand architecture, patterns, and best practices can use AI to accelerate their work dramatically. Junior developers who rely too heavily on AI tools can fail to develop the foundational understanding they need.

This means team development in the AI era requires more attention, not less. We need to ensure people are building competence, not just using tools.

Creating Safe Experimentation

The teams that extract the most value from AI are those that experiment freely. But experimentation requires psychological safety—the freedom to try things that might fail. This is a cultural challenge, not a technical one.

In our team, we encourage prompt engineering workshops, internal hackathons focused on AI applications, and explicit rewards for finding innovative uses of AI tools. But we also emphasize verification, testing, and validation. Experimentation without discipline is just chaos.

Looking Forward: What's Next for CTOs

The next phase of AI leadership will focus on integration and optimization rather than exploration. We're past the "what can AI do?" phase and entering the "how do we make AI reliable, governable, and valuable?" phase.

AI Literacy Across Organizations

CTOs need to become AI evangelists, not in the hype sense, but in the education sense. Everyone in the organization needs AI literacy—understanding what it can do, what it can't do, and how to use it effectively. This is a teaching challenge, not just a technology challenge.

Hybrid Architectures

The future isn't "AI or traditional approaches." It's hybrid systems that use each approach where it's strongest. Our Genius² engine embodies this: LLMs for understanding and context, deterministic systems for verification and mathematics, human judgment for final validation.

Designing these hybrid architectures requires understanding both paradigms deeply. That's increasingly the core CTO competency.

Ethical Leadership

As AI capabilities grow, CTOs face increasingly complex ethical questions. Not just "can we build this?" but "should we build this?" Not just "does it work?" but "who does it work for and who might it harm?"

These aren't abstract philosophical questions. They're practical decisions that affect real people. And they require the kind of judgment that comes from experience, values, and a willingness to say no when necessary.

The Actual Reality

Let me be blunt about something: despite all the hype, AI hasn't made the CTO role easier. It's made it more complex. We now need all the traditional skills plus a new layer of AI-specific competencies. The learning curve hasn't flattened; it's steepened.

But here's the opportunity: CTOs who embrace this complexity, who develop both the technical and judgment skills required, become force multipliers for their organizations. The gap between CTOs who understand AI deeply and those who don't is widening rapidly.

"The CTOs who will thrive aren't those who best use AI tools. They're those who best understand when not to use them."

Practical Takeaways

If you're a CTO navigating the AI era, here's what I've learned actually matters:

1. Develop AI forensics skills – Learn to recognize when and how AI fails in your domain. This is more valuable than understanding how it succeeds.
2. Use AI tools daily – You can't lead AI transformation without hands-on experience. Make AI part of your regular workflow.
3. Build verification layers – Never trust AI outputs directly. Always verify, especially in high-stakes domains.
4. Focus on hybrid architectures – The best systems combine AI with traditional approaches. Design for both.
5. Invest in team AI literacy – Your team's AI competence will determine your organization's AI success more than any technology choice.
6. Maintain first-principles thinking – Don't get swept up in hype. Solve real problems with appropriate tools.
7. Establish clear governance – AI without governance is a liability. Build frameworks before you build solutions.
8. Stay grounded in fundamentals – AI changes tools, not principles. Good architecture, clear communication, and system thinking remain essential.

The Bottom Line

Being a CTO in 2025 means mastering a paradox: embrace AI capabilities while maintaining healthy skepticism. Use AI tools extensively while understanding their limitations. Move fast while building in verification. Experiment freely while maintaining governance.

It's more challenging than ever before. But for those willing to develop both the new skills and maintain the timeless fundamentals, it's also more impactful than ever before.

At 4Square Capital, we've shown that understanding these principles—not just chasing the latest AI models—leads to solutions that actually work. askdiana.ai exists because we asked hard questions, maintained high standards, and refused to compromise on the fundamentals just because AI made certain shortcuts possible.

That approach, more than any specific technology choice, is what defines successful CTO leadership in the age of AI.

The question isn't whether AI will transform technology leadership. It already has. The question is whether you're building the skills—both new and foundational—to lead through that transformation effectively.

Because at the end of the day, technology leadership is still about leadership. AI just changed the terrain we're navigating.

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