Teach leaders of AI era (as per Jun 2025)

A few weeks ago, I started experimenting with Opus 4 — and honestly, the quality of the AI-generated code scared me. It wasn’t just fast and clean; it was better than a junior. That moment forced me to step back and ask: how do I stay relevant as a CTO in the AI era? So I went deep — read forecasts, analyzed trends, and mapped out the future of engineering leadership.

The future of labour economics and AI

I read many studies on how labour economics and software engineering might look over the next 10 to 25 years. There are lots of speculations and opinions, but on average, most researchers and think tanks agree that we are going to witness three major shifts in the economy.

Phase I – Deep task automation

Likely time‑frame: 2025-2032
Probability: ~80 %

Gen‑AI tools diffuse at scale; 20‑40 % of hours in advanced‑economy jobs are automated.

Phase II – Labour‑light growth

Likely time‑frame: 2032-2042
Probability: ~50 %

Whole occupations start to disappear faster than new ones form; full‑time employment rates fall, average weekly hours shrink.

Phase III – Post‑labour equilibrium

Likely time‑frame: 2045–2065
Probability: ~15-30 %

Economic output largely decouples from human labour; income is mainly redistributed through public or platform dividends.

There are different timelines and probabilities, but majority of reseaarchers agrees that almost 40% of global jobs will be exposed to large shocks. Software engineering is one of the first candidates for automation: engineers write code in strict formal languages which are easy to verify and, therefore, easy to automate and validate via AI tools.

It means tech leaders and seniour engineers have to start rethinking their roles in industry right now.

10-Year Horizon (Approx. 2035)

Over the next 10 years, the software industry is poised for significant but uneven transformation. By 2035, AI will very likely handle a majority of routine coding tasks, yet humans are expected to remain in the loop for critical decision-making, creative design, and oversight. If current trends continue, generative AI models will become dramatically more powerful and reliable in coding by the early-to-mid 2030s.

As per today (Jun 2025), Alphabet CEO Sundar Pichai disclosed that over 25% of new code at Google is now AI-generated. But despite all of this, most analysts do not foresee the complete obsolescence of human software engineers by 2035.

First of all AI goes for routine coding tasks. It means that the amount of entry jobs in software will drop to zero (or almost to zero). And it will be expected from tech leaders and senior engineers to do more work using AI. Majority of forecats agress that seniors and tech leadership won’t be replaced by 2035. Even more, some researches predict increased demand (≈+23%) for technology jobs by 2030 even as AI is adopted because businesses will undertake more digital projects.

But it doesn’t mean seniors and leads can kick back and relax.

Enterprises might shift some workforce from pure coding roles into roles like AI systems supervisors, data curators, or customer-focused engineers customizing AI-driven software. There is also an expectation of cost savings. In order for us to maintain our levels of income, we will need to evolve, acquire new skills and get better at what we are doing.

One likely outcome in large organizations is the rise of “Hybrid AI Developer Teams.” A typical project team in 2030 might consist of a few human engineers paired with AI coding assistants, rather than many human programmers. For example, instead of 10 developers, a team might have 3 humans who supervise and orchestrate AI agents generating code, tests, and documentation.

We also have to expecti higher level of competition. With cheap AI models coding won’t bbe a limiting factor anymore. Staying ahead of competirots which have access to everything you have, will emphasize the importance of wisdom, experience, domain knowledge and insights.

What Remains Uniquely Human by 2035

Of course, problem-solving, creativity and system thinking. Also translating a real-world need into a software design requires understanding context, anticipating edge cases, and prioritizing trade-offs. And there are high chanses that this skill won’t be automated by 2035.

Customer Collaboration and Domain Knowledge: Engaging with clients or end-users to gather requirements, iterate on feedback, and ensure a software solution truly solves the right problem will remain a human-centric activity. Because we still expect humans to make a final purchasing decision.

The ethical considerations of software (privacy, fairness, security) will be even more important than now. Humand engineers will need to ensure software produced by AI agents is following law and ethical guidelines.

Some programming tasks involve messy integration of legacy systems, cutting- edge hardware, or poorly specified requirements. Humans will still handle the “glue” work in many cases by 2035.

The roles of engineering managers, team leads, and CTOs involve much more than coding – they require leadership, mentorship, strategic thinking, and cross-functional coordination. These soft skills will grow in importance. A CTO in 2035 might oversee both human developers and AI systems.

TL;DR; human software engineers will provide the vision, domain context, and critical thinking, while AI will handle much of the brute-force coding and analysis.

25-Year Horizon (Approx. 2050)

The the uncertainties at this horizon grow much larger. I wouldn’t give much trust to any predictions for this period. Twenty-five years is enough time for revolutionary change. Or not changes can happen at all becase our current approach to AI architecture will run out of potential.

But there are two major scenarios we have to consider.

• Scenario 1: Near-Total Automation of Software Engineering (AGI Realized). AGI can entirely handle the conception, design, coding, and maintenance of software. And we have no tools or models to predict what will happen with entire humanity if that happens. It may be a beginning of new new era for humanity, but it also could be a catastrophe for entire civilization.
• Scenario 2: Predominantly Human-AI Hybrid Teams (Advanced Augmentation). That would be the best outcome from human-AI collaboration. Software will be cheap to produce, but we also may see increasing demand. With AI handling absolute majority of coding tasks, engineers will become “product directors”, “AI supervisors”, “technology strategists”. Some tasks likely still resist full automation even with advanced AI. These may include directly understanding human values and needs. In this hybrid future, the teach leader’s role is even more pivotal: they must orchestrate a complex symphony of AI tools and human talent. The effective teach leader is one who knows exactly how to allocate tasks between AI and humans to maximize innovation and quality.

Action plan

We still have time to prepare for what is coming. It’s already clear the role of tech leaderip will be redefined i nearest feature. And it means we have to re-think our skills and responsibilities. I prepared a list of things to master in order to be relevant in AI era.

Embrace AI as a Force Multiplier, Not a Threat (Mindset Shift)

The worst thing a tech leader can do is ignore or reject the AI tooling revolution. Instead, actively pilot generative AI in your software processes. Encourage your developers to use coding assistants, and share best practices internally on how to leverage them.

Invest in Upskilling and “Hybrid Skills” Development

According to labor experts, the future belongs to those who can blend technical skills with uniquely human strengths.

• Domain & Product Expertise: Deep understanding of the business domain you’re in (finance, health, etc.) will allow your team to ask AI the right questions and verify its outputs meaningfully
• Systems Architecture & Integration: Train engineers in high-level system design, distributed systems, and integration of multiple components. These are tasks where human architects will likely still guide AI (ensuring systems fit together coherently in the enterprise context).
• Creative Thinking & Design: Encourage participation in design thinking workshops, user experience (UX) research, and problem-solving hackathons. Creativity and user-centric design are harder for AI to replicate.
• AI/Machine Learning Literacy: While not everyone needs to be an ML researcher, having a solid understanding of how AI models work, their limitations (bias, errors, security issues), and how to fine-tune or prompt them is crucial.

Reconfigure Team Roles and Hiring Criteria

Start redefining job roles in your organization to align with the coming changes. You might create positions like AI Tooling Lead, responsible for evaluating and integrating the latest AI dev tools into your pipeline. When hiring, place greater weight on adaptability, problem-solving ability, and communication skills, relative to just proficiency in a programming language.

Leverage AI for Competitive Advantage in Business Strategy:

Beyond engineering, look at how AI can transform your business model. For example, if AI drastically cuts development time, you can iterate on customer feedback faster – make that a selling point (market your company as one that responds lightning-fast to user needs due to AI agility).

Monitor and Engage with AI Policy & Ethics Initiatives

The regulatory environment for AI and automation is likely to evolve significantly in coming years. As a tech leader, you should stay ahead of legal/policy changes that could impact how you use AI or how workforce automation is handled.

Focus on Human-Centered Value Creation

As the playing field levels on the technical side (everyone has AI coding power), doubling down on the human elements of your product and company can differentiate you.