Tech Layoffs Analysis: Which Skills Are Still in High Demand

In the past year, more than 152,000 tech employees have lost their jobs. According to Layoffs.fyi, almost 10,000 tech workers were laid off by mid-February 2025 alone:

Key Factors Behind Tech Layoffs

What has led to the situation?

• Slowing economies and rising inflation are putting financial pressure on businesses globally.
• Many companies hired too many people during the pandemic and now can’t afford to keep them.
• AI is reshaping the industry, automating jobs with repetitive tasks.

Adapting to the Changing Tech Landscape

Despite the layoffs, opportunities are emerging for those with the right skills. The key to staying competitive is continuous learning and adaptation. Some skills are becoming less relevant, while others are in high demand.

This article will highlight the most valuable skills today and practical steps to grow in your career.

Tech Skills in High Demand

The order of skills listed here is not a ranking of importance but a logical grouping of related fields. Of course, you don’t need to know all of these skills! Skim through and pick the ones that interest you the most.

1. Artificial Intelligence (AI)

What it is:

AI refers to systems that can perform tasks requiring human-like intelligence, such as decision-making, pattern recognition, and automation. AI engineers design, build, and deploy AI systems by developing models, training algorithms on data, and integrating AI solutions into applications for tasks like automation, analysis, and decision support.

Why it matters:

More and more companies are starting to use AI to speed up work, improve products, and save money. AI has a wide use in self-driving cars, recommendation systems, robotics, and predictive analytics.

Key skills to get started:

• Mathematics and statistics: Linear algebra, and probability.
• Programming: Python is the most widely used, with libraries like TensorFlow and PyTorch.
• Algorithms and data structures: Understanding search algorithms, graph theory, and data structures is essential.
• Data handling: Working with large datasets, data preprocessing, and feature engineering.
• AI Ethics and bias awareness: Understanding ethical AI design and avoiding biased models.

2. Machine Learning (ML)

What it is:

ML is a subset of AI that enables computers to learn patterns from data without being explicitly programmed. It includes techniques like supervised learning (classification, regression), unsupervised learning (clustering, anomaly detection), and reinforcement learning.

Why it matters:

You can find ML in personalized recommendations and predictive maintenance across healthcare, finance, and manufacturing. It helps professionals create smarter, more efficient, and automated solutions.

Key skills to get started:

• Probability and statistics: Essential for understanding models like decision trees, Bayesian networks, and support vector machines (SVMs).
• Machine learning algorithms: Knowledge of regression, neural networks, and ensemble methods.
• Model evaluation and tuning: Hyperparameter tuning and cross-validation.
• Big Data tools: Experience with frameworks like Apache Spark for large-scale ML applications.

3. Natural Language Processing (NLP)

What it is:

NLP is a branch of AI that enables computers to understand and process human language. It uses rules, statistics, or machine learning to process text.

Why it matters:

NLP helps us interact with technology through voice assistants (e.g., Siri) and chatbots (e.g., Pi). It also aids industries like healthcare and customer service improve communication and detect fraud.

Key skills to get started:

• Linguistics and text processing: Tokenization, stemming, lemmatization, and named entity recognition(NER).
• Deep learning for NLP: Working with transformers (e.g., BERT, GPT) and RNNs/LSTMs.
• Vectorization techniques: Word embeddings like Word2Vec, GloVe, and TF-IDF.
• Speech and audio processing: Tools for speech recognition like Whisper.
• NLP libraries: Hands-on experience with spaCy, NLTK, and Hugging Face Transformers.

4. Generative AI

What it is:

Generative AI is a subset of AI that uses generative models to create new content, such as text, images, music, and even code. It is behind tools like ChatGPT, Copilot, Gemini, Stable Diffusion, Midjourney, and DALL-E.

Why it matters:

Generative AI helps companies save time, money, and stay competitive. It drives innovation in content creation, automates tasks, and supports better decision-making.

Key skills to get started:

• Deep learning architectures: Understanding transformers, GANs, VAEs, and diffusion models.
• Neural network training: Fine-tuning large models and optimizing performance.
• Prompt engineering: Crafting effective prompts for LLMs to generate useful output.

5. Data Science, Analysis, and Engineering

What it is:

All these fields help extract value from large datasets. However, they have different purposes:

• Data Analysis examines historical data to find trends, patterns, and insights. Analysts use tools like SQL, Excel, and visualization software to interpret data for decision-making.
• Data Science applies statistical methods, machine learning, and predictive modeling to analyze and forecast trends. It combines programming, math, and domain knowledge to build data-driven solutions.
• Data Engineering collects, cleans, and structures raw data for analysis. Engineers build scalable data pipelines, manage databases, and optimize data storage for performance.

Why it matters:

Data-driven decisions are important in various industries, like:

• Retail & E-commerce: Data science improves product recommendations (e.g., Amazon).
• Finance: Data analysis helps in fraud detection and risk assessment.
• Healthcare: Predictive analytics helps in early disease detection and resource optimization.
• Marketing: Customer segmentation and sentiment analysis drive campaigns.
• Technology and AI: AI applications depend on structured data.

Key skills to get started:

• Foundational data skills: Learn SQL for querying and managing databases. Use Excel for basic data manipulation and visualization.
• Programming languages and tools: Python is popular in data fields. R is another option, especially for statistical analysis. Learn libraries like Pandas for data manipulation and NumPy for numerical operations.
• Statistical and analytical concepts: Get familiar with probability, hypothesis testing, and regression analysis.
• Work with real-world data: Practice on open datasets (e.g., Kaggle, and Google Dataset Search). Build personal projects (e.g., analyzing sales trends).
• Visualization and business intelligence tools: Learn Tableau, Power BI, or Python visualization libraries (Matplotlib/Seaborn).

6. Cybersecurity

What it is:

Cybersecurity protects systems, networks, and data from cyber threats like hacking, malware, phishing, and ransomware. These attacks aim to steal sensitive information, disrupt operations, or demand ransom payments. Security includes measures like encryption and firewalls.

Why it matters:

Cyberattacks are happening more often and becoming more complex. A breach can cost a lot, damage a company’s reputation, and cause legal problems. Skilled cybersecurity experts help prevent these risks.

Key skills to get started:

• Networking and security fundamentals: Learn TCP/IP, encryption, firewalls, and authentication.
• Hands-on practice: Use security tools like Wireshark, Metasploit, and Snort to analyze threats.
• Penetration testing: Use platforms like TryHackMe to practice ethical hacking.
• Gain certifications: For example, CompTIA Security+, CEH, or CISSP.

7. Cloud Computing

What it is:

Cloud computing lets you store and access data and apps online, so you don’t need to invest in expensive hardware. It allows you to pay only for what you use. Popular cloud services include SaaS, PaaS, and IaaS. Here’s a breakdown explaining the differences.

Why it matters:

Most companies are moving to the cloud for its flexibility, scalability, and cost-saving features. Cloud services enable easy access to data from anywhere. For instance, Netflix uses AWS to stream content globally.

Key skills to get started:

• Understand cloud environments: AWS, Azure, and Google Cloud.
• Core concepts: Usage of virtual machines (EC2, Azure VMs), storage options (S3, Azure Blob), networking (Amazon VPC, Azure Virtual Network), and security (IAM, Cloud Identity).
• In-demand certifications: AWS Certified Solutions Architect, Microsoft Azure Fundamentals, or Google Cloud Associate.

8. DevOps and Automation

What it is:

DevOps integrates software development and IT operations to improve collaboration, automate workflows, and speed up software delivery. It includes CI/CD practices. Automation replaces manual steps in testing, deployment, and system management.

Why it matters:

Companies need to deliver software faster while maintaining good quality. For example, Google applies Site Reliability Engineering (SRE) to keep services stable while deploying changes at scale.

Key skills to get started:

• Core DevOps tools: Docker, Kubernetes, Terraform, Ansible.
• Hands-on with CI/CD tools: GitHub Actions, GitLab CI, or Semaphore CI.
• Cloud environments: Understand how cloud platforms like AWS, Azure, and Google Cloud work, similar to cloud computing.
• Useful learning resources: This DevOps Roadmap offers project ideas and pairs well with the learning path in Semaphore CI’s CI/CD Learning tool.

9. Blockchain Development

What it is:

Blockchain development involves creating and maintaining blockchain networks and decentralized applications (dApps). It includes two main areas: core blockchain development for designing blockchain protocols and smart contract development for building decentralized applications.

Why it matters:

Blockchain has applications across multiple industries:

• Finance: Banks and fintech companies use blockchain for digital assets (e.g., JPMorgan’s Kinexys).
• Healthcare: Blockchain secures medical records and improves data privacy.
• Supply Chain: Companies like Walmart track food safety using blockchain.

Key skills to get started:

• Understand blockchain basics: Distributed ledgers, consensus mechanisms, cryptographic security.
• Explore platforms: Ethereum (smart contracts), Solana (fast transactions), Hyperledger.
• Learn blockchain programming: Solidity, Rust, Go.
• Use blockchain development tools: Hardhat and Truffle for testing and deployment, Ethers.js and Web3.js for blockchain interaction.
• Build projects: Create smart contracts, ERC-20 tokens, or decentralized apps.

In-Demand Tech Jobs

According to LinkedIn’s “Jobs on the Rise” report, the most in-demand tech jobs for 2025 include roles like AI specialists, data scientists, software engineers, and cybersecurity experts.

Also, roles such as cloud architects, DevOps engineers, blockchain developers, and AI ethics specialists are gaining popularity.

In-Demand Programming Languages

As tech evolves, some programming languages remain in high demand. According to the Stack Overflow Developer Survey 2024, these languages are essential:

• JavaScript (62%) — Key for web development.
• Python (51%) — Widely used in data science and AI.
• SQL (51%) — Crucial for managing databases.
• Java — Common in enterprise applications.
• C# — Important for game development and business apps.
• Go and Rust — Gaining traction in cloud computing and system programming.
• Kotlin and Swift — Top choices for mobile apps (Android and iOS).

Learning some of these languages can lead to new career paths.

The Importance of Soft Skills in the Age of AI

As AI advances, it may seem that human skills are becoming less relevant. However, soft skills will be more important than ever. While AI can process tasks quickly, our human qualities will drive innovation and set us apart from machines.

Here are the top 5 soft skills to focus on:

1. Critical Thinking

AI generates a lot of information, but we need to evaluate, analyze, and make sound decisions. Critical thinking helps us separate facts from opinions and avoid bias.

Ways to enhance critical thinking:

• Ask “why” more often in your daily work.
• Look at problems from different angles. What might you have missed?
• Consider opposite viewpoints. Is there a reasonable argument on the other side?
• Break big problems into smaller pieces. Which part can you do first? What smaller questions need answers before you move on?

2. Emotional Intelligence (EQ)

EQ is the ability to understand and manage your emotions. People with strong EQ are often better leaders and teammates because they communicate more effectively.

Ways to grow EQ:

• Listen first, then speak: Focus on hearing your colleagues’ concerns before offering your thoughts. It helps build trust and makes others feel valued.
• Observe stress cues: If a teammate seems overwhelmed, ask how you can help.
• Offer help: Instead of assuming what others need, ask, “How can I support you?”
• Reflect on mistakes: After a miscommunication, ask yourself, “How can I approach the conversation differently next time to ensure better understanding?”

3. Creativity

Creativity is a uniquely human strength that AI can’t replicate. Thinking outside the box enables us to solve problems in innovative ways and generate fresh ideas.

Ways to boost creativity:

• Stay curious: Explore new topics, ask questions, and read often. The more you read, the more ideas you’ll come up with.
• Take breaks: Step away from a tough problem to return with fresh ideas.
• Brainstorm: Discuss ideas with others to gain new insights.
• Don’t fear failure: Not every idea will succeed, but learning from mistakes teaches the most valuable lessons.

4. Adaptability and Continuous Learning

The tools you use today might be outdated tomorrow. Change is constant, and the ability to learn quickly keeps you valuable.

Ways to promote adaptability and learning:

• Explore new fields: Challenge your comfort zone by exploring new roles or projects. Take online courses to develop new skills.
• Stay positive: Have a positive attitude towards change rather than seeing it as a threat.
• Practice: Improve current work processes using your new skills.

5. Situational Awareness

Situational awareness helps you understand what’s happening around you and how it affects your goals. It enables you to spot potential problems and seize opportunities.

Ways to foster situational awareness:

• Pay attention in meetings and actively listen to updates.
• Talk to people outside your team to understand different perspectives.
• Keep an eye on company news and major decisions.
• Notice when things feel “off” — like sudden project changes or team stress.
• Take a few minutes every day to check what’s new in your industry.
• Offer to help someone when you genuinely believe you can!

Strategies for Upskilling

Take these actionable steps to gain knowledge:

• Diversify your technical skills: Don’t put all your eggs in one basket. For example, if you’re a backend developer, learn some DevOps practices. Look for skills that enhance what you already know.
• Pursue certifications: Enroll in reputable courses (Microsoft, PluralSight, Coursera, Udemy). Pick ones that teach practical skills.
• Create a strong tech portfolio: Share your best projects on LinkedIn or GitHub to showcase your knowledge.
• Engage in tech communities: Stay updated through webinars and network in tech forums like Stack Overflow, Dev.to, Reddit, and Discord. Solve real problems to help others.

Be consistent — set aside time regularly to keep learning and improving.

Conclusion

We’ve seen that tech layoffs are tough, but they don’t mean the end of the road. By focusing on both technical and soft skills, you can stay competitive in a constantly changing landscape.

Remember, the more you invest in your growth today, the more secure your career will be tomorrow. The future belongs to those who adapt — keep learning, keep growing!

Originally published at https://semaphoreci.com on February 25, 2025.