As organizations move deeper into 2026, several powerful trends are reshaping the HR function. From AI-powered analytics to skills-based hiring, these developments are changing the way companies build and manage their workforces.

AI Is Transforming Talent Management

Artificial intelligence is becoming one of the most influential technologies in human resources. HR teams are increasingly using AI-powered platforms to streamline recruitment, screen candidates, analyze employee performance, and forecast workforce needs.

Modern HR systems can process thousands of applications in seconds, identify skills gaps across departments, and recommend targeted training programs for employees. AI-driven workforce analytics also allow companies to predict employee turnover risks and detect engagement challenges earlier than traditional HR methods.

According to Gartner, organizations are rapidly adopting AI-enabled HR tools to improve decision-making and workforce planning. The research firm notes that “AI is helping HR leaders move from descriptive reporting to predictive and prescriptive insights about their workforce.”

Companies are using these insights to make more informed hiring decisions, allocate training budgets more effectively, and improve employee retention strategies. However, experts caution that the growing use of automation in HR must be accompanied by responsible governance.

Gartner also warns that HR leaders must ensure transparency and fairness when deploying AI tools in recruitment and talent management to prevent unintended bias in automated decision-making.

Skills-Based Hiring Is Replacing Traditional Credentials

Another major shift in HR strategy is the growing emphasis on skills-based hiring. Instead of focusing primarily on academic degrees or job titles, many companies are prioritizing demonstrable skills and practical experience.

According to the LinkedIn Global Talent Trends report, employers are increasingly adopting skills-based hiring to expand the talent pool and identify candidates who might otherwise be overlooked through traditional recruitment processes.

LinkedIn notes that “skills are becoming the new currency of work,” with companies prioritizing capabilities such as digital literacy, data analysis, and AI-related expertise.

This shift is particularly visible in the technology sector, where the pace of innovation often outpaces traditional education systems. As a result, organizations are investing more heavily in internal training programs, certification pathways, and continuous learning initiatives.

The trend reflects a broader realization that the future workforce will need constant reskilling to keep pace with technological change.

Employee Experience Becomes a Strategic Priority

Employee expectations have changed dramatically in recent years. Workers increasingly seek flexibility, purpose-driven work, and stronger support for mental health and wellbeing.

As a result, HR leaders are placing greater emphasis on employee experience — a concept that encompasses workplace culture, leadership quality, career development opportunities, and digital workplace tools.

According to Deloitte’s Global Human Capital Trends report, organizations are increasingly recognizing that employee experience has a direct impact on business performance. The report states that “organizations that prioritize the human experience are more likely to achieve stronger engagement, productivity, and retention outcomes.”

Companies are therefore investing in tools that measure employee sentiment through pulse surveys, engagement analytics, and real-time feedback platforms.

These technologies allow HR teams to identify emerging workplace issues early and implement targeted improvements before dissatisfaction spreads across teams.

Hybrid Work Is Becoming the Long-Term Model

The shift toward hybrid work has become a defining feature of the modern workplace. Many organizations now combine remote work flexibility with in-office collaboration to balance productivity, employee satisfaction, and organizational culture.

According to research by McKinsey & Company, hybrid work arrangements are expected to remain a permanent component of the global labor market. The firm notes that flexible work models can significantly influence employee retention and talent attraction strategies.

McKinsey reports that employees consistently rank workplace flexibility among the most important factors when evaluating job opportunities.

For HR leaders, hybrid work requires new management frameworks. Performance evaluation is increasingly shifting from measuring hours spent in the office to focusing on outcomes, project results, and team collaboration.

Workforce Analytics Is Becoming Central to HR Strategy

Data-driven decision-making is becoming a core capability for modern HR teams. Workforce analytics platforms combine performance data, engagement metrics, and operational insights to help organizations understand how teams function and where improvements are needed.

According to Deloitte, the increasing availability of workforce data is transforming HR into a strategic business function. The firm notes that advanced people analytics enables organizations to identify productivity patterns, forecast staffing needs, and evaluate the effectiveness of leadership programs.

By integrating HR data with financial and operational metrics, companies can align workforce strategies more closely with business objectives.

This shift is also changing the skillset required of HR professionals. Data literacy, analytics capabilities, and technological expertise are becoming essential competencies for HR leaders.

Regulation and Responsible AI Governance

As AI systems become more deeply integrated into hiring and workforce management, governments are introducing new regulations to ensure ethical use of employee data and automated decision-making systems.

The Society for Human Resource Management (SHRM) has highlighted growing regulatory attention on algorithmic hiring tools and employee monitoring technologies. According to SHRM research, organizations must establish clear governance frameworks to ensure transparency, fairness, and data protection.

Failure to address these issues could expose companies to legal risks as well as reputational damage.

HR leaders therefore face a growing responsibility to balance technological innovation with ethical and regulatory compliance.

HR Is Becoming a Strategic Business Function

Perhaps the most important shift in recent years is the transformation of HR itself. Rather than functioning solely as an administrative department, HR is becoming a strategic partner in shaping organizational success.

According to Deloitte, the role of HR is evolving from operational support to “architect of the workforce experience,” with responsibility for aligning talent strategies with long-term business goals.

Chief Human Resources Officers are increasingly involved in digital transformation initiatives, leadership development strategies, and workforce planning efforts designed to prepare organizations for the AI-driven economy.

In an era defined by rapid technological change and evolving employee expectations, the organizations that succeed will be those that treat talent strategy as a core component of business strategy. HR leaders who embrace data, technology, and employee-centric thinking will play a critical role in building resilient and future-ready workforces.

The outgoing General Manager, Georgi Ganev, continues his career in a new international role within the company, taking on the position of General Manager Data & AI Central Eastern Europe Territories. Georgi Ganev is one of the most recognizable leaders of IBM Bulgaria and in the country’s IT sector in recent years. He has played a key role in the development of the local office, the business partner ecosystem, the expansion of the business portfolio, and the positioning of IBM as a strategic partner for both the business community and the public sector. His transition to an international role reflects the high appreciation the global organization has for his long-standing experience and achievements.

Images: knowbox

In recent remarks, Huang said the company’s growth in AI is no longer driven solely by chip design, but by the ability to deliver end-to-end AI platforms that combine hardware, software, networking, and large-scale systems. That strategy is shaping where NVIDIA is hiring—and which skills it values most.

Key AI roles NVIDIA is prioritising

Huang indicated that NVIDIA’s hiring focus spans several high-impact technical areas:

• AI and machine learning engineers working on model optimisation, inference efficiency, and deployment at scale
• Software engineers specialising in CUDA, AI frameworks, compilers, and developer platforms
• Data-centre and systems engineers integrating GPUs, networking, power, and cooling for large AI clusters
• Cloud and AI infrastructure specialists supporting hyperscalers, enterprises, and sovereign AI initiatives
• Research scientists advancing next-generation AI architectures, performance techniques, and training methods

The emphasis reflects NVIDIA’s belief that its competitive edge lies in deep integration across the AI stack, rather than in hardware alone.

Why talent matters more than ever

Huang has stressed that as customers explore alternative accelerators and custom chips, NVIDIA’s software ecosystem and engineering expertise remain difficult to replicate. He has described people as one of the company’s most durable advantages, particularly in areas such as high-performance computing, distributed systems, and energy-efficient AI workloads.

Despite broader volatility in the technology job market, NVIDIA continues to signal that AI-focused hiring remains a priority, even as some peers slow recruitment or restructure teams.

What this means for AI professionals

For engineers and researchers, NVIDIA’s hiring priorities point to where long-term demand is strongest. Skills in infrastructure, optimisation, and production-grade AI systems are increasingly valued over narrow or experimental roles.

As AI shifts from research to critical enterprise and national infrastructure, NVIDIA’s message is clear: the next phase of AI growth will be built by specialised teams, not just faster chips.

Material by Iva Abadjievа

IMAGE: NVIDIA

The difference lies less in job titles and more in skills, scope, and mindset.

The QA Roles Most at Risk

Manual Testers Focused on Repetitive UI Testing

Roles centered on executing predefined test cases by hand — especially regression and UI-only testing — are the most exposed.

Why:

• AI and record-and-replay tools can already cover repetitive flows
• CI/CD pipelines require speed humans can’t match
• UI tests are increasingly auto-generated and self-healing

Manual testing itself isn’t obsolete — but manual-only roles with no automation or domain depth are.

Testers Isolated from the Development Process

QA specialists working as a downstream “approval gate” are being phased out.

Common characteristics:

• No involvement in requirements or design
• Testing starts after development is “done”
• Limited understanding of system architecture

Modern teams expect QA to be embedded, not external.

Low-Code / Script-Only Automation Engineers

Automation engineers who rely heavily on fragile scripts without understanding:

• system internals
• APIs
• data flows
• failure modes

are increasingly replaceable by AI-assisted test generation and smarter frameworks.

The market now values test engineers, not “script maintainers.”

QA Roles That Are Growing Stronger

Test Automation Engineers with Engineering Depth

Automation specialists who understand:

• APIs and contracts
• backend logic
• cloud environments
• version control and CI/CD

are seeing increased demand, not less.

These roles often overlap with software engineering and are critical to release velocity.

QA Engineers Embedded in Agile & DevOps Teams

QA specialists who:

• participate in sprint planning
• influence acceptance criteria
• design test strategies early

are becoming strategic contributors.

They don’t just test features — they shape what gets built and how.

Reliability, SRE & Quality Engineering Roles

As uptime and performance become business-critical, QA is merging with reliability engineering.

Growing areas include:

• resilience testing
• chaos engineering
• performance validation
• production verification

These roles often collaborate closely with SRE teams and platform engineers.

Security- and Compliance-Oriented QA

With rising supply-chain attacks and regulatory pressure, QA specialists who focus on:

• secure testing
• dependency validation
• auditability
• data integrity

are increasingly hard to replace.

Security-aware QA is now a risk management function, not a cost center.

The New Reality: QA Titles Matter Less Than Capabilities

The market is not asking:

Are you a manual or automation tester?

It’s asking:

Can you help us ship reliable software faster, with fewer incidents?

Future-proof QA specialists typically:

• write code (even if not production code)
• understand systems, not just features
• use AI as a tool, not a crutch
• think in terms of risk, not checklists

What QA Professionals Should Do Now

To stay relevant in the next 3–5 years:

• Learn automation fundamentals (APIs, assertions, test design)
• Get comfortable with CI/CD pipelines
• Develop domain expertise (finance, healthcare, infra, AI)
• Understand observability and production metrics
• Use AI tools — but verify everything they produce

Bottom Line

QA jobs aren’t vanishing — narrow QA jobs are.

The future belongs to QA specialists who think like engineers, collaborate like product partners, and understand that quality is not a phase — it’s a system.

For those willing to evolve, QA may actually become more influential than ever.

Material by Iva Abadjievа

Image: Freepik

Artificial intelligence is often discussed in abstract terms—models, scale, efficiency. But inside Amazon, some of the company’s most senior leaders are experiencing AI in far more personal, human ways: helping a dog gear up for kayaking, turning family recipes into conversations, organizing chaotic family schedules, and even discovering better books.

Across teams—from retail and sustainability to transportation, advertising, and devices—Amazon executives describe AI not as a distant technology, but as a daily companion that adapts, remembers, and increasingly feels intuitive.

Shopping That Knows You (and Your Dog)

For Doug Herrington, CEO of Worldwide Amazon Stores, AI’s impact is clearest in how it personalizes everyday shopping.

I’ve been using Rufus, Amazon’s AI-powered shopping assistant, to shop recently. I talk to Rufus about Arno a lot,

Herrington says, referring to his dog.

Rufus remembers his breed, his size, and his favorite food and treats.

That memory paid off when Herrington mentioned a kayaking trip in Puget Sound.

When I told Rufus about our upcoming outing, it recommended a great life vest for him without hesitation.

Beyond recommendations, Rufus is also changing how customers interact with pricing.

You can go to any product detail page and press the ‘price history’ link… and ask Rufus to alert you if the price drops, and even automatically buy it for you once it does,

Herrington explains.

I’ve got price alerts for Arno’s fetch toys and chew rings—so if they go on deal, Rufus loads us up. I’m happy—and Arno is too.

AI as a Connector, Not a Replacement

For Kara Hurst, Chief Sustainability Officer, AI has become a bridge—between family members and between complex ideas.

My parents live across the country, but music helps us stay connected,

Hurst says.

My son and I used AI to surprise my dad with custom songs based on his interests.

The result exceeded expectations.

The app produced country and rock tracks—good ones!—and my dad was absolutely blown away. It’s a great family memory.

AI also plays a practical role in her nonprofit work.

Outside of work, I serve on the board of Water.org, and AI has become invaluable for meeting prep,

she says.

I recently used it to summarize research and pull salient points from a long document, reminding me of the key questions I wanted to ask.

Better Books, Fewer Misses

For Beryl Tomay, Vice President of Transportation, AI has quietly reshaped how she reads.

Reading is a big part of my day,

Tomay says.

To help me choose what to read next, I added all my past books, ratings, and notes into an AI tool.

The system didn’t just learn what she liked—it learned what she didn’t.

The AI identified patterns and extrapolated things I tend to not enjoy, so the recommendations have been very aligned with what I like across a diverse set of genres.

The impact is measurable.

My yearly average book rating has even gone up as a result,

she notes.

Some of my favorite books from 2025 were found this way—and 2026 is already off to a strong start with two 5-star reads.

An AI “Chief of Staff” for Family Life

For Kelly MacLean, Vice President at Amazon Ads, AI helps manage something far more complex than campaigns: a household with three kids, two careers, and a dog.

The family calendar can feel like its own full-time job,

MacLean says.

I started experimenting with AI as a lightweight ‘AI family operating system’—something that thinks through logistics like a human chief of staff.

By connecting calendars, school schedules, sports, and travel, the system creates clarity.

Every Sunday it summarizes the week, flags conflicts before I ever see them, and offers daily adjustments that help us avoid scrambling.

It even handles the small things.

Snack duty, jersey colors, when to leave based on traffic, weather—it offloads the mental juggle,

she says.

Honestly, it almost feels a little magical.

Coding, Cooking, and Reading—Together

For Panos Panay, Senior Vice President of Devices and Services, the most meaningful AI moments happen side by side with family.

One of my favorite AI hacks right now is sitting down with my son and writing code together,

Panay says.

Starting from zero and creating something reminds you that AI isn’t just about consuming—it’s about building and learning together.

In the kitchen, AI becomes a collaborator.

I took a photo of my mother-in-law’s handwritten kibbeh recipe and uploaded it to Alexa,

he recalls.

Step by step, Alexa became my sous-chef.

The experience was more than practical.

It turned into a conversation about substitutions, techniques, and timing. It’s deeply emotional—it brings family, memory, and tradition to life.

Even reading has changed.

Kindle’s ‘Story So Far’ completely changed how I read,

Panay says.

It pulls you right back into the story and the characters you care about.

A More Human Future for AI

Across these stories, a common theme emerges: AI works best when it fades into the background and amplifies what matters most—time, connection, creativity, and presence.

As Panay puts it:

My advice? AI should be useful and keep you present. Try one simple thing—talk to Alexa about a family recipe, ask Kindle about a book you haven’t picked up in a while, or create something entirely new that inspires you.

Material by Iva Abadjievа

Source: Amazon

Pinterest noted that the decision reflects a strategic shift rather than a short-term financial crisis, as the company continues to invest in machine learning to enhance user experience and advertiser value. Alongside job cuts, Pinterest is also reducing office space and operational costs to support its long-term transformation. The move highlights how even consumer-facing platforms are reshaping their organizations around AI capabilities, often with significant implications for human resources.

Material by Veronika Atanasova

Source: Pinterest Newsroom / Regulatory Filing

Image by Dima Solomin on Unsplash

Amazon: Efficiency and AI Drive Large-Scale Cuts

Amazon is reportedly preparing to eliminate up to 14,000 roles globally, affecting divisions such as Amazon Web Services, retail operations, Prime Video, and human resources. The reductions are widely viewed as part of a broader push to simplify management layers and improve efficiency as automation and AI systems increasingly handle logistics, forecasting, and internal operations. While Amazon continues to invest heavily in AI infrastructure, the company is signaling that future growth will not require the same level of headcount expansion as in prior years.

Vimeo: Layoffs Follow Post-Acquisition Restructuring

Vimeo has announced another round of layoffs following its $1.38 billion acquisition by Bending Spoons, marking the company’s second workforce reduction since the deal closed. Although Vimeo has not disclosed exact numbers, the layoffs reflect a classic post-acquisition consolidation strategy, aimed at streamlining operations and refocusing on core product priorities. The move mirrors a broader trend in which newly acquired tech firms undergo rapid restructuring to reach profitability faster.

Autodesk: Strategic Reorganization Despite Strong Financials

Autodesk confirmed plans to cut approximately 1,000 employees, about 7% of its global workforce, as part of a restructuring of its go-to-market and sales operations. Importantly, the company emphasized that the layoffs are not driven by declining demand or direct AI replacement, but by a strategic realignment toward cloud platforms and long-term efficiency. Autodesk’s decision highlights a growing reality in tech: layoffs can occur even amid solid financial performance.

xAI: No Layoffs, but an Extreme Talent Model

Unlike its peers, xAI has not announced broad layoffs. Instead, the company—founded by Elon Musk—represents the other side of the AI labor equation. Reports from former employees and recent media coverage describe an ultra-lean, high-pressure work environment, characterized by aggressive timelines, intense GPU utilization, and unconventional incentive structures.

Elon Musk Offered a Bonus Cybertruck for Completing a High-Pressure GPU Training Run

Rather than reducing headcount, xAI appears to be concentrating investment into a small number of highly paid, high-impact roles, while relying on extreme productivity expectations to move quickly. This approach reflects a growing divide in the tech labor market: while many large firms reduce staff, elite AI teams compete fiercely for a narrow pool of top-tier talent.

A Broader Shift in Tech Workforce Strategy

Across the industry, layoffs are becoming more targeted and strategic than the broad cuts seen in earlier cycles. Workforce data suggests that while the pace of layoffs has slowed compared to 2022–2023, companies are still trimming middle management, sales, HR, and non-core initiatives—often while continuing to hire selectively for AI, infrastructure, and security roles.

At the same time, some organizations have adopted a “no-hire, no-fire” posture, leaning on AI-driven productivity gains to meet growth targets without expanding payrolls. The result is a paradoxical labor market: fewer overall jobs, but intensified competition for specialized skills.

What This Means for HR and Tech Workers

For HR leaders, the lesson is clear: workforce planning is no longer just about growth forecasts, but about automation roadmaps and skill relevance. For employees, job security increasingly depends on alignment with AI-driven priorities rather than company size alone.

As companies like Amazon, Vimeo, Autodesk, and xAI demonstrate in different ways, layoffs in tech are no longer simply a response to downturns—they are a mechanism for reshaping organizations for an AI-first future.

Material by Iva Abadjievа

Image: Freepik

Ghori described the challenge as emerging during a critical phase of large-model training, where delays could significantly slow progress and impact competitiveness. Musk’s offer was framed as a motivational push rather than a formal incentive program, reflecting his belief that extreme goals can drive exceptional outcomes. While it remains unclear whether anyone ultimately earned the Cybertruck, the anecdote illustrates how urgency and high stakes shape day-to-day work at xAI.

The episode adds to a growing body of firsthand accounts portraying xAI as an environment defined by intense timelines, heavy GPU utilization, and direct executive involvement. Training large-scale AI models requires tight coordination across infrastructure, software optimization, and rapid iteration—conditions that often translate into long hours and high pressure for engineering teams.

Though light-hearted in tone, the Cybertruck challenge highlights a deeper reality of the current AI race: execution speed is as critical as model quality. Musk’s willingness to deploy bold, personal incentives underscores how leadership style can directly influence productivity, morale, and retention in cutting-edge AI ventures—sometimes energizing teams, and at other times testing their limits.

Material by Yana Petrova

Source: Relentless, episode “WTF is happening at xAI” | Sulaiman Ghori

IMAGE: Relentness 

What skills do you think will be most important for cloud infrastructure specialists in the next 3–5 years?

With the constant development of technologies in the field of cloud solutions and the growing interest of companies in them, specialists in this field are increasingly in demand. The migration from private infrastructures to cloud solutions requires experts who can create secure, efficient, and financially sound plans, as well as architectures that do not disrupt the availability of business products.

The market is moving towards more complex roles that combine design, cost optimization, automation, working with multi-cloud and hybrid environments, and security. This is a natural evolution of the specialist profile and reflects the long-term goals of companies in the field of cloud solutions.

Candidates are increasingly valued for soft skills that enable them to translate technical skills in cloud solutions into business value. Presentation skills and the ability to adapt content for non-technical audiences will also find increasing application in the intertwined infrastructure and business solutions in the cloud.

What approaches do you use at Paysafe to develop and retain talent in a team that works with cloud technologies?

At Paysafe, we focus on technical work in which employees see meaning, opportunities for growth and development, a positive culture, and the idea that cloud infrastructure solutions have value equivalent to that of our business products. In modern business applications to end customers, infrastructure plays a key role in ensuring fast, secure, and reliable service for our users.

Beyond the ongoing maintenance of our systems, employees work on business tasks with infrastructure dependencies that provoke technical thinking and experimentation, leading to the construction of architecture and a model for its reliable long-term maintenance, often distributed across different teams and continents. Paysafe operates in a modern DevOps-oriented environment that encourages collaboration, autonomy in decision-making, and the use of modern infrastructure tools such as code, monitoring, and telemetry.

Employees have access to a variety of resources with up-to-date technical literature and certification opportunities provided by the company.

What role do you think automation and artificial intelligence will play in the development of talent in this field?

Automation and artificial intelligence are the main accelerators of technical development for candidates in the field of cloud technologies. They help create intelligent and predictable technical solutions, with an additional key feature: the ability to automatically recognize and correct errors.

By using automation for routine operations, specialists gain more time to invest in infrastructure development and decision-making for its improvement based on feedback from monitoring tools that track systems at multiple levels and build a comprehensive picture of behaviour.

What new roles or profiles do you expect to see emerge in the field of cloud infrastructure, and how should candidates prepare for them?

With the development of cloud solutions, artificial intelligence, and automation capabilities, traditionally known roles will evolve into new hybrid ones in line with market demand.

Some of these roles are:

Cloud-native Platform Engineer – focused on creating platforms for developing cloud-based business systems

Cloud FinOps Specialist – as infrastructure grows, so does the need for cost control and optimization

SecOps Engineer – working with infrastructure as code for security control, as well as building systems for early problem detection

To prepare, candidates need to keep up with trends in the field, as well as the increasing number of products appearing in the portfolios of companies offering cloud solutions. Researching and experimenting with new technologies gives candidates visibility into new opportunities that they can use as part of their work, apply them, and improve existing infrastructure solutions.

The material and image are provided by Paysafe

Why did you choose Java and its ecosystem?

I wasn’t particularly enthusiastic about Java at university, but my professional path led me to it in a very logical way. I started with data science and BI, then at Paysafe I mainly worked with Oracle and PL/SQL.

Gradually, while working on real projects, I began to get into Java through smaller tasks. I liked the structure, the capabilities of the language, and the strong ecosystem, especially in the context of large payment systems.

Over time, Java proved to be a natural extension of what I was doing. It provided stability, a rich ecosystem, strong tooling, and excellent integration with the architecture and SDKs we use at Paysafe. I moved from small tasks to more complex initiatives, and at one point I simply found myself in a role where Java was my main tool, and it was completely conscious and logical.

How has your work as a Java programmer changed in recent years?

My work as a Java engineer has changed significantly in recent years.

The language and ecosystem are evolving rapidly, which has reduced boilerplate code and increased productivity, making work more enjoyable.

Also, almost everything is now cloud-based. Technologies such as AWS bring enormous advantages, but this also means that programmers now need to be much more familiar with cloud architecture rather than focusing purely on their code.

The pace of work is also more dynamic. Releases are more frequent and require constant learning and adaptation.

The biggest change is the advent of AI, which I use as a tool for discussing ideas and speeding up routine tasks, but not for critical implementations. It is a valuable assistant, but it requires a careful and responsible approach.

Which new technologies or versions of Java do you think will have the greatest impact in 2025?

Over the past 10 years or so, the Java ecosystem has undergone continuous improvements. This applies to the language itself, as well as to the JVM and accompanying Java tools.

In my opinion, the technology that has had and will continue to have the greatest impact is undoubtedly Project Loom.

For me, this is a revolution in Java—virtual threads enable huge improvements in system performance and scalability without the need for particularly complex changes to the existing code or additional infrastructure resources.

Added to this are Structured Concurrency and Scoped Values, which further improve Java’s concurrency model and lead to more efficient management of parallel tasks.

At the same time, developments in the JVM and improvements in garbage collection make the ecosystem faster, lighter, and better optimized for cloud environments, a factor that is key for modern platforms and microservice architectures.

To what extent can Java adapt to cloud environments, microservices, and containerization (Docker, Kubernetes)?

I think Java is quite successful in adapting to industry requirements, mainly due to the development of the JVM itself, as well as frameworks such as Spring.

For example, effective management of hardware resources such as memory and CPU, due to the fact that the JVM is “resource-aware” to the container in which the application is deployed.

At the same time, modern Garbage Collection algorithms reduce overhead and make applications more stable and economical in cloud infrastructure.

The Spring Boot + Spring Cloud stack itself is the de facto standard in application development, specifically microservices in cloud environments.

It provides tools for service discovery, load balancing, centralized configuration, and much more.

This enables the Java ecosystem to create standalone, lightweight, fast-starting, fast, and easily scalable applications.

In my opinion, Java is not just adapting, but rather setting the standard in the industry.

What is the role of the open-source community and how does it support the development of Java?

The open-source community is perhaps one of the biggest forces behind the popularity and development of the ecosystem.

The OpenJDK project allows many companies and individual engineers to contribute improvements to the language, JVM, and tooling, which accelerates innovation and eliminates the risk of dependence on a single vendor. Without this model, the evolution of Java would be significantly slower and probably more expensive.

Open JDK allows everyone to have their own distribution, which they control without relying on a single vendor, and to use it according to their needs.

Many of today’s technologies in the ecosystem are due to it: Spring, Maven, Gradle, and many others arose precisely because the community had to solve a practical problem. Imagine dependency management without Maven/Gradle.

Every programmer can also challenge themselves and try to improve their skills by reviewing or even trying to contribute to an open source project.

I personally haven’t gotten there yet, but maybe in the future.

In short, I think that much of Java’s progress is due precisely to the active, strong, and innovative open-source culture behind it.

How do you keep your knowledge up to date in such a dynamic environment?

I try to keep my knowledge up to date by regularly reading articles, documentation, and technical blogs when time allows. Although I don’t always manage to pay attention to all the books and materials, I try to keep up with key developments in the ecosystem.

The team environment also plays a big role: conversations with colleagues, code reviews, and working on common tasks often reveal new approaches and technologies.

In my opinion, learning from other engineers is one of the most valuable things, regardless of position. The possibilities of the language are endless, and there is always something new to learn.

Here, I need to give a shoutout to one of my colleagues, Stefan Ivanov, who also works at Paysafe. He was the one who opened my eyes to virtual threads, which I had overlooked. Exactly one month later, I was already using them.

Do you think Java will remain one of the leading languages, and why?

I am convinced that Java will remain among the leading languages for many years to come. A huge part of global software—corporate systems, critical infrastructure, and government applications—is built on Java, and such a foundation cannot be replaced easily or quickly.

In practice, there is no other language that has proven itself and can consistently do a better job.

When we add the continuous innovations in the language and the JVM, as well as the strong open-source community, Java not only maintains its position but continues to evolve and become more accessible and attractive to new engineers.

The material and image are provided by Paysafe