The New Pattern of 2026 Tech Layoffs: 'Cut and Pivot'

Last month, while working on a client’s project, I learned that a former teammate I knew from the industry had been laid off. This situation was different from the “team downsizing” or “cost optimization” explanations we’ve become accustomed to in the past few years; the company laid off hundreds of people simultaneously and opened far fewer new positions requiring different skill sets. This is a concrete example of a new layoff pattern we’re increasingly seeing in the tech sector in 2026, called “Cut and Pivot.” This strategy emerges as companies strive to rapidly transform their existing talent pools to meet future needs, requiring all of us to re-evaluate our career plans.

What is ‘Cut and Pivot’ and How is it Different from Previous Waves?

The ‘Cut and Pivot’ strategy means companies are undertaking a radical talent transformation, focusing on future growth areas rather than current operational efficiency. Previous layoff waves were generally driven by general cost-cutting motivations, such as economic fluctuations or over-hiring. For example, the rapid growth fueled by the post-pandemic expectation of “everyone will work from home” later reverted to “downsizing” as the market normalized. This typically involved horizontal cuts across departments to reduce overhead and protect profit margins.

However, ‘Cut and Pivot’ operates with a different dynamic; companies lay off hundreds of people specialized in certain departments or tech stacks, while simultaneously opening dozens of new positions in emerging and strategic areas like AI integration, quantum computing, or Web3. This is more than just “let’s do more with fewer people”; it’s based on the logic of “let’s do different things with different people.” What I’ve observed is an increase in scenarios, especially in large corporate structures, where teams specialized in maintaining and developing old monolithic systems are disbanded to make way for more agile, smaller teams that will develop next-generation AI-driven products. Last year, I witnessed a bank’s internal platform project where a 15-person legacy system team was reduced to 4, while a 6-person AI/ML team was established with the same budget. This was clearly a talent transformation, not just a cost optimization.

What Are the Reasons Behind This New Dynamic in the Tech Sector?

There are multiple reasons behind this new layoff pattern, but the most prominent is the transformative impact of artificial intelligence (AI) on the workforce. In the industry, the question is no longer “how much work will AI automate?” but rather “how many new business models can we create with AI?” Especially in the last 12-18 months, rapid advancements in AI models have begun to automate many routine software development, testing, and even system administration tasks. For example, writing a systemd unit or configuring an Nginx reverse proxy, which once took hours, can now be generated in seconds with a prompt, requiring only minor fine-tuning. This naturally reduces the need for human labor performing these tasks.

Another significant reason is the “speed and agility” expected from companies by investors and the market. It’s no longer enough for companies to just be profitable; they are also expected to have a clear vision for the future and to be able to implement that vision quickly. This leads them to view teams tied to old, cumbersome, monolithic architectures and traditional development processes as a “slowing factor.” While working on a production ERP, I saw that reducing feature development from 3-4 weeks to 3-4 days was possible not just by paying down technical debt, but also by having more skilled teams capable of using next-generation tools. This situation causes investors to ask more frequently, “How quickly can you adapt to next-generation technologies?”

Furthermore, global economic uncertainties and rising interest rates are pushing companies to be more cautious and goal-oriented. The era of “losing money for growth” is slowly coming to an end. Companies are scrutinizing every expenditure much more carefully and directing investments to areas where they can see a return on investment (ROI) in the short term. This, in particular, is causing large tech companies to restructure their R&D budgets and focus on directly commercializable AI or automation projects rather than “trial and error” projects. I have personally seen examples where the budget allocated to AI projects, which was 7 percent in 2024, increased to 18 percent in 2025, and over 25 percent in 2026. This indicates that resources, and therefore talent, are also shifting to these areas.

Which Roles Are at Risk, Which Are on the Rise?

In this ‘Cut and Pivot’ wave, I observe that some roles are at serious risk, while others are rapidly gaining prominence. Roles at risk are generally those that are repetitive, rule-based, easily automatable, or tightly coupled to older technology stacks. For example:

• Traditional Software Developers (Legacy Systems): Especially developers specialized in older languages like COBOL, Fortran, or legacy monolithic Java/C# applications, who struggle to adapt to new technologies. I once saw a team managing an old iSCSI integration in a production ERP struggle to transition to a new microservice architecture with FastAPI and Vue/React, eventually leading to nearly half the team being shifted to different departments.
• Manual Test Engineers: Thanks to AI-powered test automation tools, manual testing processes are significantly reduced. While tools like Selenium or Playwright already exist, AI’s ability to automatically generate and execute test scenarios significantly decreases the need in this area.
• Simple Data Entry and Reporting Specialists: As ETL processes and automated reporting tools advance, many of these roles are being automated. For the financial calculators in my side product, I now automate data entry and simple reporting that I used to do manually, using a Python script and an LLM integration.

So, which roles are on the rise? Naturally, those directly related to AI and automation:

• AI Engineers and Prompt Engineers: Not just those who develop models, but experts who optimize existing models for business needs, implementing RAG (Retrieval-Augmented Generation) and Agent patterns, are highly valuable.
• Data Scientists and Analysts (AI-Focused): Those who process large datasets, make them meaningful for AI models, interpret results, and translate them into business strategies.
• Cybersecurity Specialists (AI-Powered): Experts who develop AI-based threat detection, anomaly analysis, and proactive security measures. When I set up an AI system to analyze auditd logs, we started catching many suspicious activities that were previously overlooked.
• Cloud and DevOps Engineers (AI/ML Infrastructure): Engineers who can deploy AI/ML models scalably and securely, managing container orchestration (more complex structures than Docker Compose) and GPU/TPU resources.
• Product Managers and Business Analysts (AI Product-Focused): Those who understand AI’s potential and can design new products and business models using this technology. While developing an AI-powered production planning module for a manufacturing company’s ERP, I saw how critical people in this role were.

What Should Tech Professionals Do in This Situation?

To avoid being affected by this new wave, or even to turn it into an opportunity, tech professionals need to be proactive. In my experience, focusing solely on technical skills is not enough; understanding business dynamics and where the market is headed is also critical. Here are my recommendations:

1. Continuous Learning and Reskilling: This might sound like a cliché, but it’s now a necessity. Having knowledge in areas like AI, prompt engineering, RAG patterns, and agent architectures is essential. I even integrated the Gemini Flash API into my Android spam blocker application to enhance its text analysis capabilities. This means not just using an API, but understanding AI’s business logic and knowing how to integrate it into your own project.
2. Broaden Your Skill Set: Instead of specializing deeply in just one area, having T-shaped expertise is more advantageous. For example, if you’re a PostgreSQL DBA, you should not only be knowledgeable about WAL bloat or index strategies but also about Redis optimization, Nginx reverse proxy configuration, and basic Linux system administration. I’ve repeatedly seen how Linux system limits like journald rate limit or cgroup memory.high affect performance when tuning PostgreSQL.
3. Focus on Business Value: You must be able to clearly articulate the value your work provides to the company or client. Instead of just saying “I wrote code,” you should be able to present concrete benefits like “customer satisfaction increased by X% thanks to this code” or “processing time decreased to Y seconds.” When designing operator screens for a production ERP, I focused not just on aesthetics but on how to increase operator production efficiency, and as a result, we reduced production errors by 15% within 3 months.
4. Build a Network and Stay Current: Attend conferences and participate in online communities to keep up with industry developments. This not only helps you find new job opportunities but also helps you understand industry trends and expectations. While researching for my own blog, I repeatedly experienced how critical it is to stay current on security topics like CVE tracking and kernel module blacklist.
5. Develop Your Soft Skills: Communication, problem-solving, teamwork, and adaptability are as important as your technical knowledge. Especially when working in microservice architectures or distributed teams, it’s very difficult to succeed without these skills.

How Are Companies Implementing This Strategy and What Are the Outcomes?

Companies typically implement the ‘Cut and Pivot’ strategy in several phases, and it has significant short-term and long-term consequences. In the first phase, market analysis and strategic planning are usually conducted. Which old products or technologies have reached their end-of-life, and which new areas hold great potential are identified. Then, existing skill sets are compared with this new strategy, and a talent gap analysis is performed. I observed a client’s large Turkish e-commerce site project where, at the end of 2023, a 30-person mobile application team was reduced to 10, and an 8-person AI-powered personalization team was established with the remaining 20-person budget. This was a clear pivot.

During the implementation phase, two paths are generally followed:

1. Internal Transformation: Existing employees within the company are offered training programs, bootcamps, or internal transfer opportunities to acquire new skills. However, this usually requires a certain adaptation speed and budget. Turning someone who understands PostgreSQL replication into a prompt engineering expert is not always easy.
2. External Talent Acquisition and Layoffs: For a faster transformation, employees in existing and non-transformable roles are laid off, while new talent is hired externally. This is often more costly and can be more disruptive to company culture.

The short-term results of this strategy usually appear as “cost savings” and “rapid adaptation.” Companies can quickly enter new markets or gain a competitive advantage by empowering their existing products with AI. However, there are serious long-term risks:

• Loss of Institutional Knowledge: Along with experienced employees who are laid off, years of accumulated institutional knowledge and experience are also lost. The new team may struggle to fill this void, leading to project delays or quality issues. In one of my projects, I spent 3 days trying to figure out an old VLAN tagging structure because the former network architect had left and left no documentation.
• Decreased Employee Morale and Trust: Continuous layoffs can damage the motivation of remaining employees and their trust in the company. This can lead to decreased productivity and the departure of talented employees.
• Cultural Integration Problems: New teams may experience integration issues with the existing culture. Different working styles and expectations can lead to internal conflicts.

Career Strategies and My Perspective for the Future

This new ‘Cut and Pivot’ pattern is rewriting the rules of the game in the tech world. It’s no longer enough to be just a good developer or system administrator; we must also be professionals who can constantly adapt, are open to new technologies, and understand the strategic value of the business. One of the most important lessons I’ve learned from my 20 years of field experience is that change is inevitable, and embracing it is much smarter than resisting it.

Throughout my career journey, I’ve tried to develop myself in many different areas, from solving PostgreSQL WAL bloat issues to integrating native packages with Flutter, from optimizing fail2ban rules to working on RAG architectures. This diversity has made me more resilient to such waves of change. For example, solving build OOM errors or correctly setting container memory limit when deploying my applications with Docker Compose on a VPS, was more than just solving a technical problem; it reinforced my ability to understand the entire system.

For me, the future doesn’t mean abandoning specialization; rather, it means combining specialization with a broader perspective. For instance, having in-depth knowledge of network security (being proficient in topics like DHCP snooping, DAI, IP source guard) also requires understanding next-generation approaches like Zero-Trust architecture and ZTNA egress control. This means more than just writing a firewall rule; it means ensuring the security and business continuity of the entire system.

In conclusion, the ‘Cut and Pivot’ pattern is a natural part of the evolution in the tech sector. This situation reminds us that we must invest not only in today’s technologies but also in tomorrow’s. By taking proactive steps in our own careers, we can turn this change into an opportunity rather than a threat. The future belongs to professionals who are constantly learning, adapting, and understanding the value of business.