When people ask me what I expect from 2026, I usually pause for a second.

Not because I don’t have an answer, but because the honest one isn’t simple. We’re entering a period where technology, business, geopolitics, and human expectations are no longer moving in parallel lanes. They’re overlapping, sometimes uncomfortably, and that overlap is where most of the real challenges and opportunities will come from.

This is not a technical forecast. It’s closer to a conversation. The kind you’d have if you sat across from me and asked how things really look from inside an IT company that still ships software every day, but also has to think long-term about where all of this is heading.

The broader context we’re operating in

Geopolitics used to feel distant from day-to-day engineering work. Over the past few years, that illusion has disappeared.

The concept of friendshoring, building partnerships in regions you trust rather than simply where costs are lowest, is becoming a real and practical factor in how companies make decisions. Clients care about stability, predictability, cultural alignment, and long-term reliability.

For our region, this creates both opportunity and responsibility. Trust is not something you claim. It’s something you earn through consistency, transparency, and delivery. In 2026, where you build from will matter almost as much as what you build.

Speed is no longer the advantage. Preparation is.

One of the biggest misconceptions I see today is that speed alone wins.

Yes, products are being built faster than ever. Yes, tools allow teams to prototype, code, and iterate at an incredible pace. But in 2026, the real differentiator won’t be how fast you build. It will be how well you prepare before you build.

Teams that invest time upfront in understanding the problem, the user, the constraints, and the business context don’t just move faster later. They avoid entire categories of mistakes. When preparation is done right, modern development approaches and fast feedback loops become a force multiplier rather than a liability.

Poorly defined products move fast in the wrong direction. And the faster the tools, the more expensive those mistakes become.

What this means for engineers

From an engineering perspective, 2026 will feel less like a clean break and more like a quiet but profound shift in how the stack itself is understood.

For a long time, we described engineering roles primarily through programming languages and frameworks. That mental model is slowly breaking down. What I increasingly see, both in real projects and in client conversations, is a different way of thinking about the stack altogether: energy, infrastructure, data, models, and only then applications.

This shift explains a lot of what’s happening on the market. When clients ask for engineers who understand AWS and agentic systems, they’re not chasing a trend. They’re reacting to reality. Models are evolving extremely fast and becoming capable of handling more and more logic that used to live deep inside application code. As that happens, the real leverage moves down the stack, into infrastructure, orchestration, data flows, and the way intelligence is embedded and governed.

In that sense, infrastructure plus agentic systems are starting to resemble a new version of full stack development. Not full stack in the classic sense of frontend and backend, but full stack across systems, intelligence, and execution. Engineers who understand how these layers connect will increasingly define how products are built.

At the same time, the pace of tooling is impossible to ignore. Tools like Claude can already generate complete frontend implementations for new platforms, and increasingly solid backend structures as well. That changes the expectations placed on younger engineers in particular. The question is no longer how fast you can write code, but how quickly you can move from an idea to a working, meaningful system by using these tools intelligently.

That kind of speed does not come automatically. It’s not just a technical shift, it’s a cultural one. It requires unlearning habits that were built over years, changing how problems are approached, and being comfortable with constant experimentation and iteration. Not everyone will make that transition easily.

This tension becomes even more visible in mission critical areas like infrastructure and DevOps. These domains were traditionally conservative for good reasons. Reliability, security, and predictability matter. But even here, AI driven tools are increasingly taking on monitoring, optimization, incident detection, and operational decision support.

Trying to prove that this won’t work is usually a losing game. The more productive approach is to stay on top of what’s emerging and consciously adapt habits, workflows, and expectations. Engineers who do that will not just keep up, they’ll shape how these systems are used responsibly.

In 2026, the engineers who stand out won’t be defined by a single language or framework. They’ll be defined by how well they understand the stack as a whole, how fast they can learn, and how effectively they can translate new capabilities into real, reliable outcomes.

AI as part of the system, not the whole story

As AI moves from controlled showcases and pilots into real, everyday operations, expectations are changing fast.

It’s no longer enough for AI to be impressive. In 2026, clients increasingly expect AI to be accountable. That means traceability, explainability, and proof of quality. Decisions need to be inspected. Outputs need to be verified. And systems need to make it clear what happened, why it happened, and who or what was involved.

This shift is driven partly by regulation, but even more by reality. Once AI systems start influencing core business processes, compliance, security, and auditability stop being optional. They become part of the basic contract between technology providers and the organizations that rely on them.

What we’re seeing aligns closely with a broader pattern identified in recent industry research. Despite massive experimentation, most organizations have not seen real, organization‑wide transformation from AI. Individual productivity went up, but teams did not become meaningfully more coordinated or effective.

The core issue isn’t model capability. It’s context and integration. AI tools that live outside of real workflows, data, and systems can generate content, but they can’t reliably execute work or coordinate across teams. That creates speed without control, and output without ownership.

As AI enters regulated and mission‑critical environments, this gap becomes impossible to ignore. Organizations don’t just want answers, summaries, or suggestions. They want systems that can operate inside their existing tools, respect permissions, leave audit trails, and behave predictably under pressure.

In 2026, the real value of AI will come from moving beyond isolated assistants toward systems that act as connective tissue across tools, teams, and processes. AI that understands where work happens, how it flows, and what constraints apply.

That’s also where security expectations rise sharply. When AI is embedded into daily operations, it must be designed with governance in mind from day one. Not bolted on later. Visibility, control, and responsibility become just as important as capability.

This is why AI should be treated as part of a larger system, not as a standalone solution. When integrated thoughtfully, it can elevate how organizations work together. When treated as a shortcut, it creates risk.

Traditional systems still matter

There’s a tendency in our industry to treat anything that isn’t new as obsolete. That’s a mistake.

In highly regulated environments, fintech is a good example, reliability, auditability, and compliance still matter more than novelty. Core systems and enterprise platforms continue to play a critical role, not because they’re exciting, but because they’re trusted.

The real challenge in 2026 won’t be choosing between old and new. It will be integrating them intelligently. Bridging modern development practices with stable, battle-tested systems is hard work, but it’s also where a lot of real value is created.

Hardware, infrastructure, and energy enter the spotlight

Software has moved incredibly fast over the past decade. Hardware, much less so. That gap can’t last forever.

As more products connect the digital and physical worlds, retail, logistics, hospitality, energy, pressure is building to make hardware smarter, more adaptable, and more tightly integrated with software systems.

At the same time, every system consumes energy. Every line of code runs somewhere. In 2026, efficiency, infrastructure choices, and long-term sustainability will become harder to ignore, both economically and socially.

The human side remains the hardest part

With all the technology in the world, the biggest challenge is still human.

People want purpose, clarity, and room to grow. They don’t want to be replaced by tools. They want tools that make their work more meaningful.

The companies that succeed in 2026 will be the ones that understand this balance. Where technology supports people, not the other way around. Where learning is constant, but pressure is sustainable.

A final thought

There’s a quote I’ve repeated more than once over the years: Chaos is a ladder.

It’s usually used dramatically, but the idea behind it is simple. Periods of instability, whether geopolitical, technological, or regulatory, feel overwhelming when you’re inside them. But they also create openings that don’t exist in calmer times. If you can see through the noise and understand what’s forming on the other side, you can move upward while others are still trying to regain balance.

That’s how I see 2026. Not as a year to fear, and not as a year for blind optimism, but as a year where clarity becomes a competitive advantage. Keeping your eyes on the prize means understanding which parts of the chaos matter, which don’t, and where long-term value is actually being created.

There’s another idea I often come back to as well. Not every breakthrough comes from making the existing thing slightly better. At some point, progress requires a shift in perspective, not just refinement. The same is true for our industry. Incremental improvement alone won’t be enough. Real progress will come from rethinking how systems are designed, how responsibility is shared, and how technology fits into the real world.

If we get this right, the outcome isn’t just better software or faster delivery. It’s something more meaningful. The chance to evolve into a new kind of force on the global market, grounded in trust, adaptability, and maturity rather than hype.

For those of us building technology in this part of the world, this moment carries real potential. Not just to grow, but to evolve. And that, I believe, is a challenge worth embracing.

‍