In 2025, Artificial Intelligence has become a major driver of job cuts in the US.
In the first seven months of this year, AI was directly blamed for over 10,000 layoffs. This is part of a bigger trend. The tech industry has cut over 89,000 jobs so far in 2025—a 36% increase from last year.
Why? Many companies are adopting an “AI-First” strategy. They are proactively cutting staff to become “leaner,” even after reporting strong profits.
The news about AI-driven layoffs can be unsettling. This guide is built to help you move from anxiety to action by outlining the skills and strategies that will make your career indispensable.
Table of Contents
Key Takeaways:
- AI drove over 10,000 layoffs in early 2025; the tech industry saw a 36% increase in job cuts, totaling over 89,000 positions.
- Roles based on routine mental work, like foundational coding (down 15% in job listings) and project coordination (80% of tasks at risk by 2030), are high-risk.
- Resilience comes from human-only “meta-skills” like emotional intelligence, strategic thinking, and ethical judgment, with Data Scientist jobs projected to grow by 36%.
- Professionals must build hybrid skillsets and partner with AI to stay ahead, as the World Economic Forum estimates 85 million global jobs are at risk by 2026.
What Is a Cognitive Layoff in 2025?
A cognitive layoff is the loss of a job due to AI. This happens when a company adopts AI tools that automate intellectual tasks. These are not factory-floor jobs. They are “knowledge work” roles that require human thought.
This includes tasks like writing basic code, analyzing data, and drafting reports.
This shift is hitting new graduates the hardest. Job listings for entry-level tech roles have fallen by 15% in the past year. AI is automating the basic tasks once given to junior developers. Now, companies expect new hires to already have the high-level skills of a mid-career engineer.
These layoffs are different from traditional cuts. A traditional layoff often happens when a company is losing money. A cognitive layoff happens when a company is growing. The business replaces a human task with AI to become more efficient. The role is not lost because of a bad economy. It is lost because a machine can do the same mental work faster.
The Evidence in 2025
Several companies made these cuts in 2025.
- Strategic Cuts: Tech firms cut thousands of jobs as part of new “AI integration” strategies. Microsoft, for example, cut over 15,000 jobs to realign its business around AI. Intel planned to reduce its workforce by 15-20% to shift focus to AI-driven projects.
- Creative Disruption: The education company Chegg laid off 45% of its workforce, or 388 employees. It cited the “new realities of AI” as the reason. Students began using AI chatbots for help, which disrupted Chegg’s business.
- Back-Office Roles: IBM announced plans to replace about 7,800 back-office positions with AI. These are roles in areas like human resources that handle routine administrative work.
Cognitive vs. Traditional Layoffs
| Criteria | Cognitive Layoff (AI-Driven) | Traditional Layoff (Economic) |
| Main Driver | Automation of intellectual tasks for efficiency. | Cost-cutting due to a weak market or low sales. |
| Roles Hit | Data analysis, report writing, admin support, routine coding. | Roles across all departments tied to company revenue. |
| Reason Given | “AI integration,” “strategic optimization.” | “Market headwinds,” “restructuring,” “cost savings.” |
The Psychological Impact
Losing a job to AI is a deep psychological disruption. Research on IT professionals who lost jobs to AI shows it causes severe “technostress.”
This is more than just financial worry. It includes a loss of professional identity and a feeling of betrayal by the organization. Workers feel chronic anxiety and a loss of control. Their sense of self, built on their skills, is often eroded.
Is My Mid-Level IT Job Safe from AI?
Which IT Jobs Are at Risk From AI?
An IT job’s risk depends on one main factor. How much of the job is just routine mental work? If a task is repeatable and follows clear rules, it is at risk.
Here is an analysis of common mid-level roles that are at risk right now:
High Risk: Entry-Level Coders
This group has the highest risk of being displaced. Generative AI is now very good at routine coding. It can handle basic debugging, run tests, and add simple features.
The job market is changing. Companies no longer hire people for the “fiddly coding details.” They need humans for high-level system design and architecture. The 2026 job market shows this trend.
Venture reports also show that new grad hiring has dropped about 25% year-over-year, and coding bootcamps face challenges as the jobs they prepared learners for are shrinking due to AI automation.New coders need much stronger portfolios to get hired. AI has shrunk the number of foundational coding jobs.
For Coders (High Risk)
Your job is at high risk. Stop focusing on writing every line of code. AI now handles routine coding and debugging. Your new job is to direct the AI and design the systems.
- Master AI Direction: Learn Prompt Engineering for Code Generation. This is the skill of writing precise instructions. It guides the AI to produce clean, secure, and efficient code. You are no longer just a builder; you are the architect telling the AI what to build.
- Move to Architecture: Use your new time to focus on a safer role: system architecture. This is the high-level design of large-scale systems, a task AI cannot do. Start by earning a top-tier cloud certification. The AWS Certified Solutions Architect or Google Professional Cloud Architect credentials prove you can design complex systems.
High Risk: Project Coordinators
This job is at high risk. The role is mostly administrative. It involves tasks like tracking schedules, managing resources, and sending status updates.
New AI project management tools now automate all of these functions.
Industry reports highlight that by 2030, AI may eliminate up to 80% of project management tasks, including many performed by coordinators. Most organizations are already adopting AI-driven tools that reduce the need for manual coordination and administrative support.
AI can schedule meetings, assign tasks, track deadlines, and summarize meetings. These tools are so efficient that they reduce the need for a person to do this work manually.
For Project Coordinators (High Risk)
Your job is also at high risk. AI is automating administrative tasks. Stop being an administrator. Become a Super-Coordinator who manages the AI and the people.
- Master AI Tools: Become an expert in AI-powered Project Management suites. Tools like Asana, Wrike, and ClickUp now use AI to automate schedules, flag risks, and write status reports. You use these tools to manage more projects at once. You only step in to handle the serious problems the AI finds.
- Move to Human Leadership: This frees you to focus only on the human side of the job. Get a Certified Scrum Master (CSM) certification. A Scrum Master is a human-centric leader and coach. The role is 100% about people. It involves managing team conflict, removing obstacles, and improving team dynamics. AI cannot do this.
Low Risk: Data Analysts
This role is not disappearing. It is changing. The US Bureau of Labor Statistics expects data scientist jobs to grow 36% between 2023 and 2033.
AI is a powerful tool for analysts. It can sort, find patterns, and report on large datasets. But the AI cannot understand why the data matters. The human job is to interpret the data. Analysts must explain the context and help the business make decisions. The new role focuses on critical thinking, communication, and ethical data use.
For Data Analysts (Low Risk)
Your job is low-risk, but it is changing. AI can find patterns in data. Your value is in explaining why those patterns matter and if they are fair.
- Master Data Storytelling: This is the skill of turning complex data into a simple, persuasive story for leaders. You are the human translator. You do not just show a chart; you explain what that chart means for the business and what action to take.
- Focus on Ethics: Specialize in Ethical AI and Bias Auditing. This is a new, critical human role. You are the check on the AI. You must audit AI models for fairness and bias before the company uses them. Use official guides like the NIST AI Risk Management Framework as your standard. This job requires human judgment.
How to Know If Your Job Is at Risk
Look at your daily work to assess if you are replaceable:
- Routine Thinking: Is your job built on repeatable steps? Tasks that follow clear, defined rules are the most vulnerable. This includes jobs like insurance underwriting or running repetitive security scans.
- Clear Inputs and Outputs: Can you easily define the start and end of a task? If a process is logical, AI can learn to manage it.
- “Human-Only” Skills: Does your job require negotiation, emotional intelligence, or managing unclear situations? Roles that depend on these “meta-skills” are safer. AI cannot yet handle strategic or ethical judgments.
Jobs AI Will Replace in the Next 5 Years (Projection through 2030)
Which Jobs Will AI Replace by 2030?
The 2026 Forecast
Job loss from AI is set to accelerate. In 2025, 21% of companies reported replacing roles with AI. For 2026, nearly 33% of business leaders plan to implement deeper automation. This shows companies are moving from small tests to using AI across their entire business.
Global forecasts show the same trend. The World Economic Forum estimates AI could replace 85 million jobs globally by 2026. This means professionals must learn new skills now. The IT, banking, and financial services sectors face the highest immediate risk.
Tech Jobs at Highest Risk (2026–2030)
The next five years will see a decline in jobs based on routine tasks.
- Foundational Coding: Entry-level coders and data processors are at high risk. These roles will shrink as AI handles the routine work. The human job will shift from writing code to telling the AI what to build.
- Project Coordination: The administrative project coordinator role is also at high risk. AI tools now automate scheduling, task tracking, and status reports. This reduces the need for people to manage these tasks.
- IT Operations: Clerical and IT operations jobs are vulnerable. This includes data entry, system monitoring, and rules-based jobs like insurance underwriting. IBM’s plan to replace 30% of its back-office roles with AI confirms this trend.
The Safest Jobs in the AI Economy
The most resilient jobs require skills that AI cannot replicate. These include complex strategy, managing human interactions, and making difficult ethical judgments.
- Senior Leadership: Managers and CEOs are safe. Their work involves leadership, setting company values, strategic negotiation, and managing culture. AI cannot do this.
- AI Ethicists: This is a new, critical role. An AI ethicist manages the technology’s impact on society. They handle problems like AI bias, privacy, and transparency. This job requires a mix of technical skills, philosophy, and law. They test new systems to ensure they are fair and follow regulations.
How to AI-Proof Your Tech Career
Keep Learning to Stay Ahead
Job security in tech requires adaptability. This means you must stay curious, experiment, and learn new skills quickly. Technology changes fast.
Professionals must update their knowledge with courses, certifications, and real projects. This allows you to learn new skills and drop old ones. Staying current keeps your job relevant.
Use AI as a Tool, Not a Threat
A secure career involves working with AI, not against it. Generative AI tools help you finish tasks faster. Companies that integrate AI well are more productive and competitive.
Success is often measured. For example, a company may set a goal for a 5% improvement in efficiency. When AI helps employees meet this goal, the company invests more in the tools and the training. Employees must learn to use these platforms. This helps them change their roles to work with the AI. The competition is no longer just human vs. human. It is the human who uses AI vs. the human who does not.
Build Hybrid Skills
The most secure tech careers combine two different skill sets. You need technical knowledge and strategic business skills.
Professionals must understand how AI algorithms work. They must also master non-technical skills like ethics, business, and human behavior.
The “AI Ethicist” is a perfect example of a hybrid role. This job requires a strong base in computer science. It also requires deep knowledge of law, philosophy, and social science. This mix of skills is necessary to guide AI development, manage bias, and follow complex legal rules.
Your 5-Day Plan To Prepare Yourself In The Face Of Ai
Here is a practical, five-day plan to build your career resilience in the face of AI.
Day 1: Audit Your Skills
Your first step is to get an honest look at your job. Use the criteria from our previous discussion to score your daily tasks.
- Routine Thinking: Does this task follow the same rules every time? (e.g., running a monthly report, basic debugging).
- Clear Inputs and Outputs: Is this task a predictable process? (e.g., taking meeting notes and turning them into a schedule).
Score these tasks on a 0-10 vulnerability scale. A score of 10 is “highly routine” and at high risk. A score of 0 is “highly creative” and at low risk. This audit shows you exactly where your job is vulnerable.
Day 2: Identify Your ‘Human Edge’
Now, look at the tasks that scored low on the vulnerability audit. These are your “meta-skills.” They are tasks AI cannot easily replicate.
List your top three human-edge skills. These are non-technical abilities. Examples include:
- Negotiating with a difficult client.
- Mentoring a junior employee.
- Explaining a complex technical problem to a non-technical executive (systems thinking).
- Calming a teammate during a crisis (empathy).
These are your most valuable skills. You must now focus your career on them.
Day 3: Find a ‘Hybrid’ Certification
Your goal is to merge your technical skills with strategic knowledge. Research one certification that combines technology with business or ethics.
This is not about learning more code. It is about learning the why behind the code.
- For business focus: Look for certifications like “Microsoft Certified: Azure Fundamentals” or “AWS Certified Cloud Practitioner.” These courses are designed to teach you how the technology drives business value.
- For ethics focus: Look for programs like the IAPP’s “Certified AI Governance Professional (AIGP).” This training teaches you how to manage AI risk, ensure fairness, and understand new AI laws.
Day 4: Become an AI Ally
You cannot outwork AI in routine tasks, so do not try. Instead, learn to use it as a tool.
Commit to using one generative AI tool for 30 minutes every day.
- If you are a coder, use a tool like GitHub Copilot to write your boilerplate code.
- If you are a project coordinator, use an AI project manager to automate your status reports.
Track the time you save. Use that saved time to focus on your “human edge” skills from Day 2.
Day 5: Network and Translate
Contact two people you have worked with in the past. Do not ask for a job. Your goal is to practice translating your value.
When you talk about a past project, do not list your tasks. Explain why your human judgment was critical.
- Instead of saying: “I ran the weekly status meeting.”
- Say: “I was responsible for translating the team’s technical problems into clear business risks for our vice president. This helped us get the new resources we needed.”
This reframes your identity. You are not the person who runs the meeting. You are the person who provides critical judgment. This is the skill that keeps you valuable.
Skills to Survive AI Automation (The Human Edge)
What AI Cannot Do
AI is a powerful tool, but it cannot replace human leadership.
- Emotional Intelligence (EQ): An AI cannot “read the room.” It lacks the empathy to manage team motivation. It cannot resolve a complex, personal conflict between two team members. Leadership and teamwork require an emotional awareness that AI does not have.
- Stakeholder Management: Projects require human negotiation. A coordinator must manage the expectations of clients, executives, and team members. This involves handling office politics and arguing for the project’s needs. An AI cannot negotiate with a skeptical executive.
Creative Problem-Solving: AI is good at following rules. It fails when a project hits a new, unexpected problem. A human must use intuition and creativity to find a new solution. AI can optimize a known process, but it cannot invent a new one.
Advanced Data and Evaluation Literacy
AI can process data. Humans must question it. The most important human skill is now advanced data literacy. This is the ability to critically interpret what AI produces.
This skill includes:
- Identifying Bias: Recognizing if an AI’s answer is skewed by flawed data.
- Determining Value: Deciding which metrics matter for a business goal.
- Ensuring Validity: Checking that the AI’s output is accurate and true.
- Ethical Scrutiny: Understanding data privacy rules and ensuring data is used responsibly.
Critical Thinking That AI Cannot Replicate
AI operates on logic and patterns. It cannot handle problems that are new, unclear, or strategic. The human job is to decide “what is worth doing.”
This requires mastering “meta-skills,” which are abilities that help you learn and adapt.
- Seeing: This is systems thinking. It is the ability to see how all the complex parts of a business fit together.
- Dreaming: This is applied imagination. It is the ability to invent a new solution when the old rules no longer work.
- Making: This is creative design. It is the ability to build and test new ideas.
These skills are essential for creating new products and solving problems in ambiguous situations.
Communication and Leadership
The most secure professional skill is mastering human interaction. AI cannot show real empathy, build trust, or manage social dynamics.
Leadership in an AI-powered workplace is about managing people, not just tasks.
- Managing Technostress: AI creates new anxieties, such as the fear of job loss. A leader must manage this psychological impact and help the team adapt.
- Translating Complexity: A leader must be able to explain complex, technical data to non-technical partners and clients.
- Setting Purpose: An AI can complete a task, but a human leader must inspire a team and give their work meaning.
Emotional intelligence—the ability to understand and influence emotions—remains a uniquely human skill.
Conclusion
The AI era is redefining the IT job market. As routine cognitive tasks become automated, the demand for professionals who can handle strategic complexity, ethical governance, and human leadership is rising.
To stay resilient, you must build a hybrid skillset, use AI as a tool to enhance your efficiency, and focus on the “human edge”—the empathy and systems thinking that AI cannot replicate. The future belongs to the augmented strategic thinker who can merge technical skill with human judgment.
Take stock of your current abilities. Identify one “human edge” skill you can start developing today to secure your role in the post-2026 workforce.