In the face of AI-driven constant change, what advice would you give to those entering the tech industry next year?

Despite 2025 having been another year marred by layoffs, tech industry hiring overall continued to grow.

Add to this, more than 5 million computer science or equivalent graduates globally and another roughly 400,000 bootcamp grads are entering the market this year.

We’re not saying it’s the most optimistic tech market to be entering — and entry-level hiring has dropped again — but that doesn’t mean that it’s hopeless. Someone’s going to have to clean up all the mess vibe coding leaves behind!

I spoke to a dozen engineering leaders, each with over a decade of experience in the field. For most of them, sure, they started their careers in more favorable job markets. But that doesn’t mean they don’t have actionable advice — and well wishes — to give you.

Read on for advice for those just starting out in the tech industry in 2026. Listen and learn — because those are probably the most important software engineering skills you can hone in this time of AI-driven rapid change. Good luck!

Stay Open To Continuous Learning

Everyone in the tech industry — and soon all roles in almost any industry — is in the midst of a huge change. As Mustafa Suleyman, CEO of Microsoft AI, posted this past June on X, formerly known as Twitter, the biggest career accelerator in the next decade is to “get really, really good at learning.” This was echoed by everyone I spoke with in their advice to you.

“Be proactive and curious about what you can improve around you. That could be about your own learning, your own team or organisation, or the industry. If you learn how to ask great questions, recognize what can be done within the context you’re working in and then actually do something about it, you’ll be developing skills that you can apply throughout your career.

“Your impact as an engineer goes beyond just the code — it’s how you pick up on what challenges and opportunities there are, how you communicate those to others, and how you analyze and plan what can be done.

“With AI doing more and becoming more commonplace and taking on more of the technical, I think human skills become even more important. We’ll always need people within tech, and the people who can make sense of the chaos and ambiguity, make decisions and help teams come to decisions and communicate those things clearly are only going to be more valued.”

— Melinda Seckington, leadership coach and trainer at Learn Build Share

“Don’t try to compete with AI; rather, learn to collaborate with it.

“The people who thrive will be those who use AI to accelerate learning, explore ideas faster and reduce the fear of experimentation. But the fundamentals still matter, like understanding systems, writing clean code, communicating clearly and thinking critically about trade-offs.

“AI can generate answers, but it can’t replace your judgment. Focus on becoming someone who knows which problems matter, how to ask the right questions and how to turn ambiguity into clarity. Tools will change every year; these instincts will last your whole career.”

— Subho Halder, CEO of Appknox

Reflect on How AI Changes You

If you graduated with a computer science degree or equivalent recently, you remember a time without AI. You also may have had to take whole courses or at least entire exams without AI. That isn’t practical or even recommended in a modern job interview that should reflect the modern experience of AI-enriched software development, but hold onto that experience — and sometimes choose to intentionally do work without AI.

“Pay attention to where you are defaulting to ‘interpassivity,’ where you are allowing the computer to do a simulation of work that is neither very productive nor teaching you anything new. There’s no shortcut that lasts for long, and without the strong fundamentals that come from hard work, you will be much more exposed to the risk of becoming obsolete.”

— Camille Fournier, author of “Platform Engineering” and “The Manager’s Path”

“Look at what you need, not what everyone else is doing — there are so many tools, you could drown.”

— Molly Clarke, platform engineering lead at easyJet

“Become an expert in using AI as a tool, but also know its weaknesses. It’s there to supplement, not supplant.”

— Chris Billingham, product director at Etiq AI

“Learn how to harness these new technologies and stay curious.”

— Dana Lawson, CTO of Netlify

Get Back to Basics

One of the flaws of many engineering education programs is that they focus on greenfield application building. At the rate at which AI-generated code is extending codebases, not only will even new apps not be truly greenfield, we run the risk that no one on a team has actually read — let alone written — the code. Now, more than ever, from progressive delivery to platform engineering to testing coverage, engineering best practices are essential.

“Don’t skimp on security. The more vibe coding everyone globally does, the more security vulnerabilities will be created. You’re better at code with AI, but so are bad actors.”

— Fergus Kidd, CTO of FieldPal.ai

“First: Don’t rely on AI. Second: Read tech books from the 2000s on how to build software. A lot of the development work I see is making basic errors in development that were well understood as best practice in the 2000s. We need to bring some of those ideas back.”

—Paul Johnston, fractional CxO

Keep ‘Soft Skills’ Core

Cross-organizational communication is more important than ever in the Age of AI. After all, 95% of AI pilots fail due to organizational silos, according to a study released in July by MIT NANDA. Being able to talk to both tech and business colleagues is essential — and always try to get closer to your users. In an already highly competitive job market, you also have to stand out against the AI as well. The most human skills are more valuable than ever.

“Learn to build relationships as well as systems. LLMs [large language models] can scaffold your code, draft your docs and help you reason through complexity, but it can’t build trust on your behalf. Trust building as well as good judgment remain an edge that’s not going away.”

— Helen Greul, vice president of engineering at Multiverse.io

“Develop industrious habits, learn to step back, try not to worry, but don’t be complacent. The tech industry has changed, it’s not as easy as it was, but it has also stayed the same. The bullies and sexists are still here. Find mentors, stay safe.”

— Jamie Dobson, co-founder of Container Solutions and author of “Visionaries, Rebels and Machines”

“My advice to anyone entering the tech industry next year is that you are entering the industry at the most interesting time in history — it would have been unthinkable that someone entering the tech industry could overtake someone with decades of experience in a matter of months, but this is now possible.

“Embracing AI is not optional; it is essential and this will come naturally to those with limited experience. Therein lies an opportunity to climb the career ladder faster than ever before, or as an entrepreneur, build a business with meaningful scale in record time.”

— Paul Payne, CTO of scale-up SaaScada

“It’s a tough market. I don’t have any specific advice. Prepare for your lucky opportunities and make the most of them when you stumble upon one.”

— Hazel Weakly, architect V for global engineering and reliability at ING

You Are Still Welcome

The tech industry has always been one of constant flux. The change is just happening faster than ever, at least for right now. I wanted to end this with a reminder that you are still welcome here. Good luck!

“Welcome, we’re glad you’re here! Get ready for things to change dramatically over the next five years and know that continuous learning will be your strongest attribute throughout your career.”

— Nathen Harvey, DORA lead at Google Cloud

The post Advice for People (Still) Entering the Tech Industry in 2026 appeared first on The New Stack.

If there’s something we realized in 2025, it’s that AI is an amplifier of human-led software development teams — not a replicant or replacement of human talent.

Over the past year, developer experience (DevEx) has been defined by platform engineering and AI — which is merging into one and the same, as the first emerges as the gold standard for properly, safely and efficiently deploying the second.

But developer productivity, we’ve learned, has been defined by how leadership strategically approaches the adoption of these two trends. Only the organizations that understand AI enablement, via a platform strategy, will stand out in the new year. Here’s how.

The Role of Operational Platform Engineering in AI Adoption

Platform engineering is the adoption of an internal platform to create golden paths that make regular activities — such as deploying to the cloud and requesting services from another team — easier.

Following the Cloud Native Computing Foundation’s Platform Engineering Maturity Model, I asked the experts I interviewed for this post where they were on their platform engineering journey. Overwhelmingly, their responses aligned with the second tier of maturity, called “operational,” which means their organizations have standardized tooling that is centrally tracked by a dedicated platform or DevEx team.

Another quarter was at the final stage of optimization via qualitative and quantitative metrics, integrated and managed services, an enabled ecosystem and a broadly adopted Platform as a Product mindset. The smallest company I interviewed — Etiq AI, with fewer than a dozen developers — is still using provisional, custom and ad hoc request processes, which is appropriate while it’s still in its startup phase.

Granted, I generally spoke with platform leads whom I have interviewed over the last couple of years. But my findings could be part of the overall trend that platform engineering is ready to exit the trough of disillusionment. It’s also a natural response to AI adoption demanding at least an internal developer portal, if not a full-fledged platform, laying down guardrails and sometimes gates for this new level of speed.

Dave Bresci, senior manager of site reliability engineering at PagerDuty, told me that Backstage and now Spotify Portal have acted as a “lynchpin” of platform engineering through “its enablement of golden paths and centralization of technical documentation.”

Within Portal, he said, he has found that “AI coding assistants have been transformative in facilitating experimentation and prototyping for our developers, as well as coding throughput.” Then, on top of that, PagerDuty engineers have built some custom internal tooling with AI to make large-scale changes across the codebase, which he said have been “extremely impactful.”

Of course, any strategy is only as good as how it’s measured.

More than half of those interviewed responded that they run a mix of developer surveys and other qualitative data, with the core four DORA metrics that track throughput and stability. About a third said they leverage the SPACE framework and/or the DX Core 4.

How AI Is Already Increasing Developer Productivity

While many of the teams consulted for this post are still in the process of deciding which AI tooling, if any, works across their whole engineering organization, some touted big developer productivity wins already.

“These tools don’t just make us faster; they make the work feel lighter,” said Subho Halder, CEO at Appknox, whose security vulnerability detection scale-up has seen the biggest productivity gains so far from what he calls practical AI tools.

“Claude and ChatGPT help our engineers think faster, CodeRabbit speeds up [pull request] reviews and Windsurf/Cursor boosts everyday development,” he continued. “We even allocate a separate budget for AI because the return is clear. And as a security company, every tool is vetted for the highest levels of privacy and compliance.”

For his team at Spotify, Niklas Gustavsson, the company’s chief architect and vice president of engineering, said that the tools that really move the needle for them fall into three buckets:

• AI coding tools, with Claude Code, Cursor and GitHub Copilot used by 90% of developers daily, according to Google’s latest DORA survey.
• Background coding agents integrated into the existing fleet management system.
• Knowledge assistants built into Spotify internal platforms, which are typically dogfooded and then either open sourced or commercialized.

“We plug these AI coding agents into its existing fleet management system as the ‘brain’ that generates code changes from natural-language instructions, while fleet management remains the ‘muscle’ that safely runs those changes across thousands of repos via automated PRs,” Gustavsson said.

At the time of our interview, AI agents had generated more than 1,500 merged pull requests, delivering 60% to 90% time savings on some of Spotify’s larger migrations.

“Under the hood, we have an internal framework that orchestrates prompts, linting, [Model Content Protocol] tools, [large language models] as a judge [of] validation and observability,” he said. “So we can swap the underlying agents and models in and out without disrupting teams.”

Spotify is further ahead of most organizations in its agentic AI journey. And its technical and nontechnical teams are also the first and some of the heaviest users of the AiKA AI knowledge assistant, to “find answers to all kinds of Spotify-specific questions, from learning how a particular system or tool works, to getting reference code, to company policies,” Gustavsson said.

He added, “AiKA has helped reduce internal support requests and the number of random questions in Slack and other channels. We’ve seen a 47% reduction in the time it takes to resolve internal support requests at Spotify.”

Paul Payne, CTO of scale-up SaaScada, said that Claude Code is completely redefining the software engineering processes of his open API platform for financial services.

“Twelve months ago, we wrote code by hand and we were very proud of that code,” he said. “Today, we rarely write any code — we build specialized agents and skills using Claude Code so that we can operate at a higher level and unlock the massive productivity benefits AI coding agents bring.

“They really are a force multiplier the likes of which we have never seen, and it is allowing us to build entire features and even products in days and weeks that would have taken many months of manual coding.”

This is aligned with the latest “DORA Report,” released in September, which focused on the impact of AI as an amplifier of software development. Unlike the previous year’s report, which found mixed results for AI in software development, the 2025 survey found that AI assistants had become ubiquitous, triggering a huge impact on individual developer productivity.

An internal developer platform was pinpointed as the best foundation upon which to build AI adoption and to add the guardrails and gates necessary for your own sector’s or your users’ risk profile.

The Future Is Agentic: Fleets of Autonomous Agents Ahead

While the last couple of years were more about preparing data and systems for generative AI (GenAI) at scale, engineering leaders are really confident in the agentic near future.

Those who are hopeful for what AI brings are definitely looking forward to a greater embracing of agentic AI.

“By 2026, we’ll start seeing AI agents move beyond single-company experiments and into limited cross-organization collaboration, but it will still be cautious and deliberate,” said Max Marcon, MongoDB’s director of product management. “Right now, most companies are focused on the basics,” which includes important tasks like making sure agents behave safely, access only the right data and operate under clear governance.

“Full interoperability between agents is still early, much like where Open Banking was in its first decade,” he noted.

While 2025 was about early-stage agents, many interviewees predicted that 2026 will be about agentic memory, where these AI agents build on their own past experiences to influence their own decision-making.

“As technology evolves and matures, we will see an increase in examples of true agency where AI agents become more autonomous,” João Freitas, general manager and vice president of engineering for AI and automation at PagerDuty, agreed.

Agents, he said, will “adapt better to their context and to the environment, can make better decisions given a wide range of tools and resources, and independently take actions towards a goal.”

The rise of standards like the Model Context Protocol (MCP) and Agent2Agent (A2A), Freitas said, will help establish best practices around agent deployment and monitoring: “This will unlock new use cases and business opportunities, and increase the return on investment from AI.”

Tech leaders tend to be an optimistic bunch, with the startup founders the most doggedly so.

“AI will positively disrupt the entire software development life cycle,” said Payne, of SaaScada. “Existing software engineering teams can achieve significant productivity gains if they embrace this paradigm shift.”

He added that, “10x engineers could become 100x engineers, but each team will need to carefully navigate the intense rate of innovation and change among model and tool providers to fully unlock these gains.”

In response to the speed of change, he predicted that we won’t just see AI infused into traditional project management tools, but 2026 will likely welcome a new era of fully integrated, end-to-end software delivery life cycle tools, “providing all-in-one environments for AI-powered product planning, requirements analysis, technical design, prototyping, coding, deployment and testing.”

He also voiced the common concern that security risks will only rise in this AI-powered near future, but he thinks that AI will be part of the protection, too.

“The best engineering teams will take calculated risks on where and how to constrain AI behavior so their software performs better with each subsequent model release,” Payne said, “allowing them to ride the wave of innovation.”

Why the Right Developer Experience Is Crucial for Success

The changes roiling the tech world aren’t just technical ones. Communication with your developers — and in fact, your entire organization —  will be the biggest differentiator in 2026.

“It’s easy to get swept up in the excitement of AI, but the biggest differentiator in 2026 will still be leadership that models calm, clarity and structure,” said Helen Greul, vice president of engineering at Multiverse.io. “Technology is accelerating — people need steadiness. The teams that thrive will be the ones whose leaders remember that.”

This was emphasized this December with the results of a new white paper by Multitudes, which determined that leadership’s actions are one of the most important factors in ensuring AI adoption success. Buying a tool does not equal adoption or drive AI usage, the report found. Only when leaders make adoption a vocalized priority or “clear expectation” — mandates aren’t as welcome by developers — did they witness accelerated AI tooling adoption.

“Ask the users what they want,” said Molly Clarke, platform engineering lead at easyJet, who only predicted an increase in prioritization of developer experience in 2026.

In the Age of AI, users include both internal developers and any external customers — don’t go shoving features, especially AI-enriched ones, on your users until you know they want them. Security and other gates should be mandatory, but everything else serving your internal developers — including any platform engineering strategy — must be optional.

The goal of any developer productivity strategy should be to make it easiest to do the right thing, so developers don’t want to leave that golden pathway.

For the last three years, DORA research has proven that user centricity is a huge amplifier of organizational performance. The 2025 “DORA Report” focused on the state of AI-assisted software development, but still found a “user-centric focus” as core to positive outcomes.

The more the survey respondents agreed with the following, the more successful they were with AI adoption:

• Creating value for users is their focus.
• Users’ experience is their top priority.
• Focusing on the user is the key to the success of the business.

In terms of serving internal users, the report further found that internal developer platforms became “nearly universal” over the past year, and that it’s imperative to make your platform the foundation for your AI strategy.

When trying to improve developer productivity in 2026, especially via AI adoption and/or a platform engineering approach, make sure that you listen to your internal developers. They likely know better than anyone else what increases their productivity. This also creates a safe environment for them to innovate and experiment to build great new things.

“We’re entering a phase where small teams can do previously unthinkable things. The playing field is flattening,” Greul said. “I’m excited for the rise of creativity, all sorts of weird ideas and side projects that become billion-dollar companies.”

The post In 2026, AI Is Merging With Platform Engineering. Are You Ready? appeared first on The New Stack.

Now that Microsoft CEO Satya Nadella has appointed a new CEO to run Microsoft's biggest businesses, he has a little more time on his hands for other adventures. Beyond focusing on Microsoft's technical work, Nadella is now turning to the ancient art of blogging to discuss Microsoft's year ahead and why he thinks everyone needs to move "beyond the arguments of [AI] slop vs sophistication."

Nadella's first blog entry in "sn scratchpad" is all about Microsoft and other AI companies still needing to get a bunch of stuff right with AI. Chief among them is creating a new concept for AI that evolves the "bicycles for the mind" concept that Steve J …

Read the full story at The Verge.

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