If you thought the 2025 tech story was “AI everywhere,” you only got the first chapter. Yes, AI is transforming how software gets built—but that’s only one part of the plot. Behind the scenes, engineering workflows are shifting, data pipelines are being rethought, development methodologies are wobbling, hardware ceilings are showing, and even energy and sustainability are back on the table.

To make sense of what comes next, we sat down with AMC Bridge experts working across training, engineering, and delivery. Their perspectives cut across tools, workflows, engineering culture, system design, and big-picture tech forces. Here’s where things are heading next year—and what’s worth watching as the curve continues to bend.

Oleksandr Myroshnychenko, Manager, Training

AI in Engineering: From Magic Wand to Normal Tool  

If I look at the past year, I’d say AI finally started being treated as a regular engineering tool rather than a magic wand from Harry Potter—a miracle solution that fixes everything. At the beginning of 2025 there was a sense that a few more months and we’d all be flying to space on schedule. Reality is slower. AI became better, but not magical—and that’s actually healthy.

In my view, AI is becoming the same kind of infrastructure as the Internet. There was life before the invention of the Internet and life after it. Did the Internet make life better? Yes. Can I live without it now? No. I think AI is moving in that same direction.

engineering software specifically, I see active development of AI agents. Not as replacements for developers, but as assistants that can automate specific tasks. And this is how the industry is adapting them—consciously, realistically. You cannot remove twenty years of experience from software development and hope that an agent will compensate. It can’t. At least not now.

API-First Software and Globalized Workflows

Another trend I noticed is the shift toward API-first engineering software. For many years the industry would build the core product first and only later add an API layer so external tools could interact with it. Now the API is laid down from the beginning.

This doesn’t mean APIs are new—they have been used for a long time, and many integrations already relied on them. What’s new is timing and intention.

When APIs are planned up front, different tools can integrate more naturally. Instead of patching connectivity after the fact, products become part of an ecosystem from day one. It still doesn’t eliminate manual workflows entirely—humans were (and sometimes still are) “the glue”—but the share of programmatic interoperability is growing.

This leads to something I would call globalization of engineering workflows—products stop being isolated islands and become part of ecosystems. I first noticed this in 2025 and expect it to continue through 2026. I’m not saying this is the biggest trend of the decade, but I appreciate it as someone who has been around engineering software for many years.

High-Quality Data Becomes the New Resource

Another important shift: the industry realized that models are already powerful, but they require good data. Training AI requires datasets, and in engineering domains these datasets must be high-quality. So now there is a trend to create those datasets. As usual: the better the input, the better the output.

Development Methodologies May Need to Evolve  

Historically, when new capabilities appear, development methodologies adapt. Over the years, different approaches replaced one another as the industry learned how to build software more effectively. With AI becoming part of development, I think the process will need to evolve again. Scrum assumes humans do all the work, which may not fit when some tasks are delegated to agents. I don’t know what the next methodology will look like, but processes usually change when tooling changes.

Green Energy vs. Quantum Computing: Very Different Timelines  

Last year we discussed two broader technological directions: green energy and quantum computing. Here is how I see them now:

Green energy will continue developing. I’m optimistic about thermonuclear synthesis. In 2025, we already saw record results for maintaining clean energy from fusion experiments. If humanity learns to control the power of the sun, electricity becomes cheap and accessible, and we can stop thinking about its price in every decision. This can change everything.

Quantum computing, on the other hand, feels stuck. It’s as if someone invented an extremely good hammer but there are no nails. Even competitions are now being announced to come up with meaningful workloads for quantum computers. The technology is impressive, but without application pathways it remains a very expensive hammer.

Market Effects: The Overheated AI Economy  

One aspect that is often overlooked is how the AI boom affects adjacent markets. Datacenters require enormous amounts of RAM and compute. As demand shifts toward AI infrastructure, consumer hardware gets more expensive. Memory is required everywhere—from vehicles to routers—and when one sector absorbs most of the supply, others feel the pressure.

AI cannot grow infinitely; markets eventually hit ceilings. Personally, I think we are entering a phase where the economic dynamics of AI adoption will become just as interesting as the technical ones.

Expectations for 2026: The Price of Tokens  

One expectation for 2026 that may sound funny is that I hope tokens will become cheaper. Tokens are the currency of interacting with AI. Every request spends tokens, and tokens cost money. The cheaper the token, the more accessible it becomes to ask questions and experiment.

If tokens become cheap enough, the number of questions becomes unlimited and adoption becomes much faster. This is a real economic factor that can influence the pace of AI implementation across companies.

Bohdan Nashylnyk, Tech Expert

From “Google Replacement” to Full-Fledged Development Tools

If I look at 2025 from a developer’s perspective, I’d say there was a kind of mini-revolution. A year ago, AI was basically just a replacement for Google. You could give it a small function or a class and get an answer, but that was it. In just one year, these models turned into full-fledged tools that can generate entire features, work with dozens of files, and operate on huge code bases.

In other words: AI already solved the problem of writing code. And it’s not just about capabilities—adoption changed dramatically too. A year ago, most people just used ChatGPT® and didn’t pay attention to anything else. Now, the majority of developers have started mastering new AI tooling. At this point, just using ChatGPT doesn’t mean you’ve mastered AI—it actually means you’re behind. The new baseline is at least using GitHub Copilot® (GitHub’s pair-programmer plugin), and ideally tools like Cursor® (AI-powered IDE) or Claude Code® (cloud-assisted code workflow), and whatever else will follow.

Too Much Code, Not Enough Review

One thing I expect in 2026 is a new problem: too much code. If AI speeds up coding two or three times, then the amount of code we need to review also grows two or three times. Developers love writing code; reviewing code is what you do because you must. If this balance breaks, people will burn out and review will become a bottleneck. So either tools will expand beyond just generation, or the development process itself will change. I don’t know what the new format will look like, but I’m convinced the next wave won’t be “AI writing code faster”—it will be the development process changing altogether.

When More Code Means More Vulnerabilities

Security will become a bigger concern as well. The more code gets written, the harder it is to verify. If models are trained on specific datasets that contain certain vulnerabilities, those vulnerabilities can appear everywhere. Attackers will know what to look for in advance. So I expect stronger security frameworks and standards.

From Automation to Agents

Another trend that hasn’t fully materialized yet is the shift from traditional automation (software helping humans) to agents (software doing the work). This year everyone tried to “add AI somewhere,” usually as a feature pasted on top. I expect that to change. AI won’t be an add-on anymore—it will become a software layer, just like a database is today. Part of the application will stay deterministic, part will be AI-driven.

Connected to this idea is Generative UI (GenUI), where the interface is generated for the user and the task. An engineer could get a richer view, a director a simpler one. Google is already experimenting with this through Gemini ®. It’s early, but interesting.

The Cost Divide: Access vs. No Access

Another trend that doesn’t get enough attention is the growing gap between developers who have access to these tools and those who don’t. Tools like Cursor or Claude Code are expensive. Someone must pay for that—either customers or companies. If you can’t afford them, you will not be as fast as those who can. The speed and productivity gap could create a real divide: not between senior and junior, but between enabled and not enabled.

The Plateau, the Hardware Ceiling, and What Was Overpromised

There were loud promises last year: AGI, replacing developers, 90% of code written by AI. These predictions did not materialize. Yes, capabilities improved. Yes, the tooling is getting better. But large language models as general knowledge engines are already hitting a plateau. At the same time, we’re running into hardware limitations—the world simply cannot produce enough memory and GPUs to satisfy demand. The next wave of improvement will probably come from specialized models and better wrappers—things like improved prompting, better context handling, and better integration—rather than raw model scaling.

Autonomy Is Not Here Yet

We’re still in a “human-in-the-loop” stage. Fully autonomous agents still do funny things. Recently I saw a Gemini-based bot on GitHub that spent hours adding and removing labels in a loop because of conflicting rules—until someone turned it off. Microsoft has similar experiments where Copilot creates pull requests and answers comments. It’s impressive and hilarious at the same time, but still not autonomous.

Green Development: Still Relevant, Still Complicated

Green sustainability around compute is becoming more relevant—especially in regions that care about environmental impact. Developers are already joking about how much energy is spent on generating code with these tools, and I think the environmental cost of data centers will become a serious topic. We talk a lot about accelerating development, but rarely about what it costs in energy.

What Surprised Me Most and What I Expect in 2026

I was genuinely surprised by how radically development changed in just one year. For ten years I wrote code roughly the same way. This year, the workflow became completely different. And I was surprised by how ready developers were to adopt new tools. Even those who didn’t love them still stepped forward and adapted.

In that sense, software development really is ahead of the curve. I think in other industries adoption will be slower and far more resistant.

Looking at 2026, I feel like a year from now the day-to-day work of developers will change even more. Before, you knew exactly how your day would be divided: writing code, meetings, review. In a year, I’m not sure what that breakdown will look like at all—and that uncertainty is exciting.

Stanislav Bielkov, Delivery Manager, Tech Expert

Autocomplete Is the Past. Agentic Workflows Are the Future.

Remember when AI coding assistance meant watching suggestions appear as you typed? Those days are over.

The tools that defined 2023–2024—GitHub Copilot's autocomplete, ChatGPT's code snippets—have been replaced by something fundamentally different. In 2026, we've crossed the threshold from AI that assists to AI that acts. The distinction matters: a copilot suggests the next line of code; an agent takes an entire ticket, breaks it into subtasks, writes the code, runs tests, and submits a pull request.

What agentic coding actually looks like:

You describe what you want—“Build user authentication with OAuth 2.0” (of course, real requests are much more complex)—and review the result. The AI handles implementation. Tools like Claude Code, Cursor's Composer mode, and Devin (from Cognition) don't just wait for your next keystroke. They reason about your entire codebase, modify multiple files simultaneously, execute terminal commands, run validation tests, and iterate based on feedback—often while you work on something else entirely.

What Every Developer Should Know Now

Fundamentals matter more than ever, not less.

There's a painful irony in the AI era: shallow skills have become dramatically easier to detect. AI can produce tutorial-quality code effortlessly, which means developers who never learned the fundamentals get exposed quickly. It is important to reason about a bug, interpret an error, understand how an OS works, explain why a query is slow, or design a system that doesn't collapse under scale.

AI writes code, but developers solve problems—those are not the same thing.

Many developers wrongly assume that operating systems, networking, or computer architecture are “old-school” in an age of AI assistance. The opposite is true. AI is a powerful pair programmer, but without strong fundamentals, you're just a passenger in a self-driving car you can't steer.

Learn to work with AI, not around it.

At bare minimum, you should be fluent with tools like ChatGPT, GitHub Copilot, Cursor, and Claude Code. But proficiency means more than typing prompts—it means:

• Architectural prompting: Organizing your codebase and conversations so AI produces what you actually need. Using agents to generate product requirements documents (PRDs) before implementation. Knowing when to add context and when too many irrelevant details will degrade output quality.
• Verification as a core skill: The “new skill” of 2026 is verification, not just coding. For every line of AI-generated code you accept, you should be able to explain why it works. If you can't, go back and study the concept.
• Auditing AI output: A developer in 2026 must spot subtle logic errors or security flaws in AI-generated code that might look correct but fail under edge cases. Knowing that AI tools trained on historical repositories may lack real-time CVE awareness and will happily suggest vulnerable libraries.

Know the New Stack

• Model Context Protocol (MCP) has quickly become a standard for how agents interact with external tools. Understanding how to build and manage MCP servers is becoming as fundamental as knowing REST APIs.
• Retrieval-Augmented Generation (RAG) is no longer a niche NLP technique—it's the backbone of many AI-driven products. Understanding how to implement and optimize RAG systems is essential.
• Multi-agent orchestration: Many tools let you coordinate multiple AI agents through structured workflows. The vast majority of developers will build tools agents use rather than the agents themselves.

System design becomes your primary value:

As AI begins writing a significant portion of functional code, the human developer's role shifts toward system design. For instance, if you are a web developer, you need to understand how thousands of microservices communicate, how to manage distributed state, and how to design for 99.999% availability. The ability to think in systems—knowing when to use micro-frontends vs. monoliths, understanding eventual consistency in global databases—becomes your differentiator.

Soft skills aren't optional

Technical skills alone won't get you far. Strong communication, documentation, and cross-team collaboration help you translate technical work into business value. The developer of 2026 isn't just a coder—they're a translator between AI capabilities and business outcomes.

The mindset shift:

The developers who thrive in 2026 won't be those who use AI. They'll be those who use AI well. The key differentiator is knowing how to guide AI, use its suggestions critically, and maintain control over the final product.

Also, don't let AI become a crutch that hides gaps in your knowledge. The struggle of debugging and writing code line-by-line isn't a bug in your learning process; it's a feature.

The Bottom Line

The technology has changed, but the fundamentals of being a great engineer haven't. Master the new tools, but anchor yourself in principles that transcend any particular technology. The developers who succeed combine deep technical foundations with fluency in AI-augmented workflows—and the wisdom to know which parts of their work to delegate and which to own.

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