Why 90% of Engineers May Be Replaced by AI by 2035 (And How to Survive It)

Last Updated on December 3, 2025 by Admin

By 2035, the most dangerous thing an engineer can be is not unemployed — it’s being average. Not because engineering is dying…

But because technology is learning to do what millions of engineers do every day — faster, cheaper, and without fatigue.

AI doesn’t replace people.

It replaces patterns.

And most engineering work today still is just a pattern.

The Hard Truth Engineers Avoid

Routine Work Is Easy to Automate

Most engineers spend their days performing repetitive analyses, tweaking CAD models or writing small scripts—work that is highly structured and ripe for automation. Large language models such as GPT-5.1 provide design assistants that answer engineering questions and iterate pile designs in real time. AI design tools like NVIDIA’s CircuitVAE produce digital circuits two to three times faster than conventional methods and often outperform commercial tools.

Siemens’ generative design platforms capture veteran engineers’ knowledge and automatically generate electrical system architectures, drastically accelerating design iterations and reducing errors. A McKinsey survey reports that generative AI can reduce product development cycle times by up to 70 percent, giving teams more time for testing and refinement.

Degrees Are Becoming Commodities

For decades, a degree in civil, mechanical or electrical engineering was a ticket to a stable career. That credential alone is no longer enough. The World Economic Forum’s Future of Jobs 2025 report estimates that 92 million roles will be displaced by automation by 2030, while 170 million new jobs will be created. Forty-one percent of employers already intend to reduce their workforce because of AI.

More importantly, the report predicts that 39 percent of today’s core skills will be obsolete by 2025. Companies expect employees to continuously upskill; 85 percent plan to invest in workforce training, and 63 percent cite a skills gap as their biggest barrier to transformation. Credentials are less valuable than demonstrated ability to learn, adapt and apply new technologies.

Technology Evolves Faster Than Human Skills

Engineers often view experience as a shield against obsolescence. Yet AI and automation are advancing exponentially. By 2030, 80 percent of the U.S. workforce could have at least 10 percent of their tasks impacted by large language models. Low-code and no-code platforms already let business users build apps with drag-and-drop interfaces; Gartner predicted that 65 percent of application development would happen on such platforms by 2024.

Meanwhile, cloud automation tools like Chef treat infrastructure as code, automating the manual processes once handled by DevOps engineers. As AI tools become more capable, the half-life of technical skills shortens dramatically. Engineers who stop learning quickly become obsolete.

Technology Isn’t Coming – It’s Already Here

AI Design Tools

Generative design has moved from experimental labs into mainstream engineering workflows. The technique uses AI to autonomously generate design options within constraints defined by the engineer. Tools like Autodesk Fusion or Siemens NX can explore hundreds of thousands of permutations simultaneously, producing lightweight structures or optimized layouts that humans might never conceive.

Engineers evaluate and refine these options rather than modeling everything from scratch. The TechTarget guide explains that this approach is used across architecture, aerospace, construction and manufacturing to generate more ideas faster and bring products to market more quickly.

For construction professionals looking to master these emerging technologies, our guide on AI skills every construction professional should learn provides comprehensive training pathways. McKinsey’s research shows that AI-powered product design tools can help companies create dozens of high-fidelity design variations in hours rather than weeks.

Automation Software and Cloud Platforms

Automation doesn’t just design parts; it builds, deploys and maintains systems. In software engineering, continuous integration/continuous deployment (CI/CD) platforms automate testing and deployment pipelines. Infrastructure-as-code tools convert configuration into scripts, eliminating manual provisioning and scaling tasks.

According to nOps’ 2025 guide to cloud automation, such tools improve quality control, accelerate innovation, reduce costs and free engineers from mundane maintenance. Cloud providers now offer managed services for databases, machine learning and observability, effectively outsourcing entire chunks of traditional engineering work.

Robotics in Construction and Manufacturing

On construction sites, robots are doing the heavy lifting. The U.S. National Institute for Occupational Safety and Health notes that robots already perform utility excavation, roadwork, bricklaying and drywall finishing.

An article from the Association for Advancing Automation highlights how robotics is revolutionizing construction: autonomous heavy equipment uses GPS and LiDAR to dig and move earth, while Fastbrick Robotics’ Hadrian X robot lays more than 1,000 bricks per hour, enabling builders to construct a house in a single day.

Drones survey sites, 3-D printers create building components layer by layer, and robotic inspectors detect cracks and perform minor repairs. These tools don’t just improve productivity; they also reduce waste and enhance safety. Those interested in understanding how these changes are reshaping construction career pathways can explore our comprehensive career guides.

Low-Code and No-Code Platforms

Low-code development environments let business professionals build sophisticated applications with minimal coding. Built-in’s overview of the movement notes that low-code platforms can deliver software ten times faster than traditional programming.

Gartner predicted that 65 percent of app development would occur on these platforms by 2024. Salesforce’s platform is used by 99 of the Fortune 100 companies, demonstrating enterprise adoption. While these tools empower engineers to focus on architecture and integration, they also mean that many straightforward coding tasks will be done by non-engineers.

Robotics and AI in Manufacturing

In manufacturing, AI-enabled robots perform welding, assembly, quality inspection and even predictive maintenance. Generative design software integrated with additive manufacturing (3-D printing) creates optimized parts with less material, reducing weight and cost.

Robots can run continuously, delivering consistent quality and drastically reducing human error. AI-driven predictive maintenance analyzes sensor data to schedule repairs before machines fail, reducing downtime. Engineers now oversee fleets of robots and algorithms instead of operating equipment directly.

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The Myth of Job Security in Engineering

Myth 1: “Engineering Is Always in Demand”

Engineering will remain essential, but the nature of engineering work is changing. The U.S. Bureau of Labor Statistics points out that for architecture and engineering occupations, productivity gains are expected rather than substantial employment reductions. However, those gains will come from automating routine tasks and amplifying the output of a smaller number of engineers.

The Future of Jobs report predicts 92 million roles will be displaced by automation; many of those roles are entry-level or mid-career engineering positions that involve repetitive analysis or documentation. Demand for engineers remains, but roles are shifting toward systems thinking, integration and oversight. Understanding these shifts is crucial for those pursuing construction management careers.

Myth 2: “Experience Will Protect Me”

Experience can be a double-edged sword. Engineers who have spent decades doing the same tasks are most at risk. The Technology Magazine summary of the WEF report notes that 39 percent of core skills will become obsolete. In other words, nearly half of what experienced engineers know today may be irrelevant in a few years. Meanwhile, 85 percent of employers plan to prioritize upskilling. Staying relevant requires continuous learning and a willingness to embrace new tools.

Myth 3: “AI Can’t Replace Creativity”

It’s tempting to believe that human creativity is beyond automation. Yet generative design tools can explore design spaces that humans can’t imagine. AI-generated images, code and architectures often surprise even experts. The creative advantage of human engineers lies not in generating initial ideas but in defining the problem and selecting the best solution.

Design engineers now use AI-assisted systems to generate hundreds of validated options and must decide which aligns with project goals.

Test and validation engineers supervise predictive simulation models rather than running test scripts; manufacturing engineers train algorithms that anticipate yield deviations. Creativity still matters, but it operates at a higher level. Those looking to develop these higher-order skills can explore our AI-powered career simulator for personalized guidance.

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The New Class System Among Engineers

Technological change is splitting engineers into three distinct categories:

1. System Thinkers

These engineers understand entire systems—business models, supply chains, user needs—and integrate technological solutions accordingly.

They combine technical expertise with strategic thinking. The Rutgers article highlights that AI proficiency is becoming a critical requirement across civil, industrial, mechanical and aerospace engineering.

Organizations are creating dedicated positions for AI engineers; those who combine domain expertise with AI skills enjoy expansive opportunities.

System thinkers leverage generative design, data analytics and automation to deliver outcomes rather than outputs. For civil engineers specifically, our guide on how civil engineers can thrive in the age of AI provides a comprehensive roadmap.

2. Tool Operators

This group relies on existing tools and follows established workflows. They might be proficient in CAD, simulation packages or programming languages, but they do not adapt when tools evolve. When AI systems take over the rote aspects of their work, they risk becoming redundant.

Low-code platforms mean that business analysts can perform tasks that once required software engineers. Cloud automation eliminates much of the configuration work done by DevOps teams. Tool operators may find their roles commoditized and squeezed by cost pressures.

3. Irrelevant Skill Holders

These engineers fail to adapt entirely. They cling to obsolete technologies or processes and ignore emerging trends. As systems move to the cloud and AI becomes pervasive, such individuals become irrelevant.

The WEF notes that almost four in ten workers will need reskilling within five years, yet only half of companies have transition plans. Engineers who do not take responsibility for their own upskilling may find themselves in this third category.

Income Inequality Within the Profession

Technological change exacerbates income inequality. System thinkers who master AI and business strategy command higher salaries and often share in the value they create. Tool operators may see wages stagnate as their tasks become commoditized. Irrelevant skill holders risk unemployment.

This bifurcation reflects a broader trend where technology amplifies productivity for a few while rendering others redundant. Understanding which side of the divide one is on is critical for career planning.

Why 90% Will Disappear (Not Engineers, But Roles)

The headline statistic—that 90 percent of engineers will be replaced by technology—is not a prophecy of mass unemployment; it is a recognition that tasks, titles and value propositions will change dramatically. Here’s why:

Entry-Level Roles Are Highly Automatable

Junior engineers often perform calculations, data entry, CAD drafting and documentation. These tasks are already being taken over by AI models and automation. For example, generative design software automatically generates multiple models based on constraints, exploring hundreds of thousands of design iterations. Large language models can write code, draft technical reports and even interpret regulatory documents.

Data entry clerks and basic analysts are among the occupations predicted to lose the most jobs. As a result, entry-level engineers will need to start their careers at a higher level of abstraction, focusing on system integration, AI oversight and business context. For those wondering about whether college marks still matter, the focus has shifted dramatically toward demonstrable skills.

Mid-Career Roles Will Be Redefined

Mid-career engineers who become subject matter experts in narrow technical domains may find their expertise embodied in AI systems. Siemens’ generative design platform encodes rules and lessons learned by veteran engineers. As more knowledge becomes embedded in software, the premium on memorized rules diminishes.

Instead, mid-career engineers must become educators, curators and trainers of AI systems—ensuring that algorithms encode best practices, avoid bias and align with organizational goals.

They will also need to develop skills in data analytics and automation, as highlighted by Rutgers’ emphasis on AI proficiency, data analytics and automation expertise. Those looking to transition can explore construction consulting career paths that leverage their existing expertise.

Traditional Specializations Are Being Commoditized

Specializations such as structural analysis, circuit design or mechanical drafting are being packaged as cloud services. NVIDIA’s CircuitVAE outperforms traditional design tools; companies can obtain optimized circuits from a cloud API rather than hiring specialists.

Low-code platforms provide drag-and-drop interfaces for building software. In construction, autonomous robots handle tasks from excavation to bricklaying. The value in these specializations moves from performing the work to orchestrating the ecosystem of tools.

Demand Shifts to System Integration, Ethics and Business Strategy

As automation takes over the “what” and “how,” the human role shifts toward the “why.” Engineers must ensure that systems align with ethical guidelines, regulatory requirements and societal values. They will need to integrate AI outputs into broader business strategies, weighing trade-offs between cost, sustainability and human impact.

The WEF report emphasizes that the jobs created by automation will be in roles that require problem solving, creativity and human interaction. Those roles require a different mindset and skill set than traditional engineering. For those interested in exploring these emerging roles, our article on discovering your true calling in AI-driven construction provides valuable insights.

What the Top 10% Will Do Differently

The engineers who thrive amid disruption will not simply be the smartest coders or the most experienced designers. They will embody a new set of behaviors and capabilities.

They Will Be Business-Minded

Future-proof engineers understand how their work impacts revenue, cost and customer experience. They speak the language of finance and strategy. Rather than optimizing a widget’s weight by 2 percent, they ask whether that improvement increases market share or reduces total cost of ownership. Understanding business models allows engineers to prioritize projects that drive value and to advocate for investments in upskilling and automation.

The Technology Magazine piece notes that 86 percent of employers expect AI and information processing technologies to transform their business. Engineers who can translate technical opportunities into business outcomes will be indispensable.

They Will Create Tools, Not Just Use Them

The top 10 percent will write the scripts, APIs and frameworks that automate tasks for everyone else. They will build generative design plugins, bespoke low-code modules and custom robotics interfaces. By creating tools, they embed their expertise into scalable systems.

This is the difference between using ChatGPT to answer a question and building an AI-driven design assistant tailored to your company’s standards. Manufacturing engineers already train algorithms to anticipate yield deviations rather than manually adjusting machines.

They Will Be Technologically Fluent

Technological fluency goes beyond knowing one programming language. It includes understanding AI fundamentals, data analytics, cloud architectures and cybersecurity.

Rutgers’ report highlights that AI proficiency is becoming critical across all engineering domains. Engineers must be comfortable choosing when to automate, evaluating AI outputs and debugging ML models.

They will also need to understand the limitations of AI—such as bias and quality issues in generative design—and know how to mitigate them. Our comprehensive guide on BIM 2.0 and AI transformation explores these critical competencies in depth.

They Will Be Learning-Obsessed

Continuous learning is no longer optional. With 39 percent of skills expected to become obsolete, the only way to stay ahead is to treat learning as part of the job. Future-proof engineers allocate time each week to explore new tools, read research papers, participate in hackathons and take online courses.

They build personal “learning systems” to track emerging trends and experiment with them. They also share knowledge widely within their organizations, fostering a culture of experimentation and adaptation.

For structured learning paths, consider exploring our essential construction skills guide for 2025.

They Will Be Financially Literate

As roles evolve, compensation structures may shift away from hourly billing toward value-based pricing or equity. Engineers who understand finance can negotiate better, identify profitable innovations and even participate in entrepreneurial ventures.

They know how to build business cases, evaluate return on investment and align technical decisions with budget constraints. Financial literacy also helps engineers to navigate the impact of automation on wages and to invest in their own upskilling.

They Will Be Automation-Aware

Automation awareness means understanding which tasks should be automated, which require human judgment and how to supervise automated systems. It also means recognizing when automation introduces new risks. For example, generative design tools can create thousands of options, but they may also produce unrealistic or unsafe designs; human oversight is required.

Similarly, robots improve productivity but require safety protocols and skilled operators. Engineers who anticipate these issues and design safeguards will be valued.

The New Engineering Survival Formula: T.A.R.E.

To help engineers remember what matters most, consider the T.A.R.E. framework:

• Technology Fluency – Master AI, automation and data analytics. Understand how to integrate them ethically and effectively. Stay current with rapidly evolving tools.
• Adaptability – Embrace change rather than resist it. Develop the habit of continuous learning, experimenting with new methods and pivoting when necessary.
• Revenue Thinking – Tie technical work to business outcomes. Understand the economic impact of engineering decisions and contribute to strategy.
• Experimentation – Build prototypes, run simulations, conduct A/B tests. Use data to validate ideas and iterate quickly. Treat failures as learning opportunities.

Engineers who embody T.A.R.E. will ride the waves of technological change instead of being swept away by them. For personalized guidance on implementing this framework, our AI Construction Career Mentor provides 24/7 support tailored to your specific situation.

A Brutally Honest Self-Assessment

Use the following checklist to evaluate your readiness for the AI-driven future:

Are you building future skills?

• Have you completed a course on machine learning, cloud architecture or data analytics in the past year?
• Can you build a simple AI model or automate part of your workflow?

Are you automation-aware?

• Do you routinely look for tasks you can automate?
• Have you implemented at least one script or tool to eliminate manual work?

Do you understand business?

• Can you explain how your engineering work affects revenue, cost and customer satisfaction?
• Do you participate in discussions about budgets and strategy?

Can you work with AI instead of avoiding it?

• Have you integrated generative design, LLMs or predictive analytics into your projects?
• Do you know the limitations and ethical considerations of these tools?

If you answered “no” to any of these questions, you have identified a learning opportunity. Use it as motivation to invest in yourself.

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Average engineering is ending.

Elite engineering is just beginning.

This decade will decide whether you work with technology — or get outrun by it.

Final Wake-Up Call

Engineering isn’t dying. Average engineering is. The roles that revolve around memorizing formulas, drafting similar designs or following checklists will vanish. In their place, new roles will emerge—roles that require engineers to be strategists, ethicists, storytellers and leaders. AI and automation are neither villains nor saviors; they are tools that magnify human intent.

The World Economic Forum predicts that while 92 million roles will be displaced, 170 million new jobs will be created. Those jobs will go to engineers who can harness technology to solve complex problems, who can think across disciplines, who can communicate with both machines and executives.

The urgency is real. Skills are becoming obsolete faster than ever, and companies are already shedding jobs because of AI. Yet the opportunity is equally real. Engineers who commit to lifelong learning, cultivate business acumen and build tools rather than merely use them will not just survive; they will lead. They will design the systems that define our world, from sustainable cities to autonomous factories and personalized healthcare. They will prove that the human element—curiosity, judgment, empathy—remains irreplaceable.

So when you walk into the office tomorrow, don’t fear that your profession has become software. Recognize that your profession has expanded. Embrace the technologies that are changing engineering, and decide which side of the 90/10 divide you want to be on. The future will not wait.

Related Posts:

• AI in Construction Hiring: Smart Tool or Silent Job Killer?
• Human engineering skill – will it be replaced by computers?
• AI in Construction: How AI is shaping the future of the construction industry
• Top 10 Construction Jobs That Will Be Automated by 2030

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