Will AI Replace Software Engineers (Or Just Their Tasks)?

AI isn’t coming for software engineers someday. It already is (in the conventional sense). At companies like Amazon and Microsoft, generative AI now produces about 25% of their code, (1) and you can’t argue that’s not reshaping software teams in real time.

But let’s get precise here. AI isn’t replacing entire engineers; it’s replacing what they’ve traditionally done. Boilerplate, repetitive tasks, and basic feature scaffolding are handed over to the machines.

In this piece, we’ll break down what’s actually being replaced, where software engineers still matter most, how AI is augmenting rather than erasing the role, and what this shift means for career paths—including transitioning into product management and similar roles.

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The Rise of AI Coding Tools

AI has quickly made its way into software engineers’ daily workflows. Developers now commonly use AI-powered coding assistants like GitHub Copilot and ChatGPT to help write and review code. In fact, nearly two-thirds of software developers are already using AI tools as part of their work. 

These tools can autocomplete lines of code, suggest improvements, identify bugs, and even generate entire functions based on a prompt. For many programmers, AI helpers have become invaluable. As one software engineer put it (2), “I can’t imagine working without it now”.

The productivity boost from AI assistants is significant. GitHub Copilot's large-scale data found out through analysis that around 30% of Copilot users claimed an increase in productivity (3). Developers using GitHub Copilot also saved about 4.5 hours per week on average and produced higher-quality code. 

Similarly, an experiment by GitHub found that task completion rates jumped to 78% with Copilot, compared to 70% without it. These aren’t trivial gains. They hint at a fundamental shift in how software gets built. Routine tasks like writing boilerplate code or searching documentation without the RAG system can be offloaded to an AI, freeing up humans to focus on more complex aspects of product development.

Fear of AI Replacing Software Engineers

Despite the productivity benefits, the advent of AI in coding has also raised concerns about job security for programmers. High-profile tech leaders have made bold predictions that fuel these fears. 

For example, Meta’s CEO Mark Zuckerberg stated that by 2025 “Meta will probably have a mid-level engineer AI that can write code, and over time it will replace human engineers.” Likewise, OpenAI’s CEO Sam Altman has argued there is a “high probability that AI will replace coding jobs in a gradual but accelerating manner.” 

In a 2025 interview, Altman suggested that in the near term each software engineer might simply become far more productive with AI. He said eventually fewer engineers may be needed overall. Even NVIDIA’s CEO Jensen Huang mused that with advanced AI, “coding might be dead in the water,” advising young people to explore other fields.

These kinds of statements have led to dramatic headlines about an AI-driven “coding apocalypse.” The worry is that if AI can handle most programming tasks, companies might sharply reduce hiring of human developers, especially at the junior level. 

There are some early signs of this impact. In Aug 2025, Stanford researchers, led by economist Erik Brynjolfsson and the Digital Economy Lab, found that, over the past three years, employment for early-career workers in AI-exposed fields declined by 13% . 

Nicholas Daniel, the CPO at Etsy, also said on The Product Podcast:

Our PM-to-engineer ratio has shifted from 1:10 to 1:6. AI is accelerating discovery and delivery. Team design has to evolve with it.

According to Reuters, new graduate hiring for software roles in 2023 was 50% lower than in 2019, which observers call “one of the fastest job shifts in any profession, ever.” Coding bootcamps have reported much lower placement rates for their graduates as AI coding tools handle many tasks that junior developers used to do. 

It’s no wonder that many aspiring programmers are anxious about what these trends mean for their careers. So, we wonder…

Will AI Replace Software Engineers?

AI will not fully replace software engineers, but it is already reshaping their work by automating routine coding tasks and boosting productivity. The role of engineers will shift toward higher-level design, problem-solving, and guiding AI, rather than disappearing altogether.

Which engineers will not be replaced by AI?

The engineers who won’t be replaced by AI are those who work beyond just writing code, focusing on system architecture, solving complex problems, and translating business needs into technical solutions. AI also can’t replace engineers who handle security, compliance, debugging edge cases, and leading cross-functional collaboration to turn ideas into real products.

While AI is indeed changing the job market, most experts don’t believe that software engineers are facing extinction. Historically, waves of automation have transformed developer roles rather than eliminated them. 

For decades, new tools have emerged to automate parts of coding (from compilers to code libraries), yet experts say the demand for skilled engineers has only increased. The introduction of AI follows this pattern. 

Industry analysts, experienced engineers, and academics tend to envision an evolution of the software engineering role, not a mass displacement.

When the words ‘will ai take over coding’ pop up in your mind, know these several reasons humans are still very much critical:

• AI has limitations: Current AI coding assistants are powerful but not infallible. Developers often find that an AI can get you “70% of the way” to a solution, but the last mile (debugging, handling edge cases, refining for production) requires human expertise. This is sometimes called the “70% problem” in AI-assisted coding. As one engineer quipped, “AI is like having a very eager junior developer on your team... they can write code quickly, but they need constant supervision and correction.” In other words, AI might generate code drafts, but skilled developers must review and fix the AI’s output.

• Beyond coding: Writing code is only part of a software engineer’s job. Engineers also engage in high-level tasks like architectural design, product analysis, user research, and problem-solving that go far beyond typing out functions. They make judgment calls about product features, trade-offs, and system constraints. These are the things that require understanding context and human needs. Most software engineers do far more than churn out code”; they design products, choose product stacks, troubleshoot complex issues, gather user feedback, and more. AI currently has no genuine understanding of real-world context or customer needs. It cannot truly replace the creative and design thinking of a human developer.

• Changing skill set, not no skill: Rather than making engineers obsolete, AI is expected to change which skills are most important. “The skills software developers need will change significantly, but AI will not eliminate the need for them. Not anytime soon anyway,” says Arnal Dayaratna, a technology analyst at IDC. Developers may spend less time writing boilerplate code and more time in guiding AI, verifying its output, and focusing on design and strategy. But those who can adapt will remain in demand.

Even Mark Zuckerberg, after making his provocative prediction, later clarified that AI coding tools could free up engineers to be more creative rather than simply replace them. 

This points to a future where human developers work alongside AI, with each doing what they do best. AI can handle repetitive or highly structured tasks, while humans concentrate on creativity, critical thinking, and the non-technical aspects of software.

AI Can Assist, Not Replace Software Engineers

A more likely scenario than a total AI takeover is that software engineers will work in tandem with AI. This collaboration is already the new normal. Many in the industry now describe AI coding tools as “pair programmers” or virtual assistants. 

GitHub’s CEO Thomas Dohmke even predicted that in the coming years, AI could write “80% or even 90% of code” for a typical project. Human developers will then guide and refine the AI’s work. Rather than writing every line themselves, engineers will increasingly shape the direction, verify the outputs, and handle the tricky integration pieces that AI tools can’t manage.

This kind of human-AI partnership can make developers massively more productive. Senior engineers we have spoken with especially report that AI helpers amplify what they can already do. 

For example, an experienced coder can use AI to quickly generate code snippets or tests for known solutions. This allows them to concentrate on overall architecture or solving novel problems. 

Which jobs can't AI replace?

AI can’t replace the parts of software engineering that require creative problem-solving, system design, and translating business needs into technical solutions. It also can’t take over tasks like debugging complex edge cases, ensuring security and compliance, or collaborating with cross-functional teams to shape products.

The AI is a force multiplier for skilled developers 

In effect, AI (like agentic AI or RAG) amplifies existing knowledge and skills rather than replacing them. Those developers who learn to leverage AI will outperform those who don’t, which only suggests an engineer using AI will replace engineers who don’t use AI.

Importantly, completely autonomous software development is far from reality given current limitations. 

Recent research identified major roadblocks that AI must overcome to code software without human help (5). These include understanding large codebases, handling vague problem definitions, and debugging effectively. 

These are complex challenges that will likely take many years (and breakthroughs in AI) to solve. In the meantime, companies will still need human engineers, though their responsibilities may shift toward overseeing AI product strategy and tackling the high-level work.

The corporate world seems to be embracing this augment-and-evolve mindset. Gartner analysts predict that by 2027, generative AI will create new roles in software engineering rather than destroy the profession (6). 

In their view, about 80% of software engineering teams will have to upskill to work effectively with AI. Instead of firing developers, product-led organizations will expect them to become proficient in using AI tools, much as they adapted to new programming frameworks or DevOps automation in the past. 

The outcome Gartner foresees is a transformed software engineering discipline with AI-augmented developers and entirely new job categories. This optimistic outlook suggests that evolution is more likely than extinction for coding careers.

How AI Is Changing the Software Developer’s Role

AI isn’t replacing developers, but it is shifting their work. The future engineer’s day-to-day will look different, with less manual coding and more oversight, integration, and strategy.

• Problem definition and design: More focus on understanding what needs to be built, working with PMs, and shaping architecture.

• AI code supervision: Reviewing, testing, and refining AI-generated code for accuracy, security, and performance.

• Integrating multiple tools: Combining AI-produced components with APIs, agentic AI, libraries, and existing systems.

• New specialties: Emerging roles like AI software engineer, AI product manager, prompt engineer, or AI auditor.

• Continuous learning: Keeping up with rapidly evolving AI frameworks and practices.

• Security and compliance checks: Engineers will need to guard against vulnerabilities or biases introduced by AI-generated code.

• Cross-functional collaboration: Developers will spend more time aligning with design, product, and business teams to translate AI-assisted output into real-world value

Overall, software engineering is becoming more multidisciplinary, blending coding with product analysis, product thinking, and AI orchestration.

Preparing for an AI-Augmented Future

AI isn’t eliminating developers, but it is reshaping what makes them valuable. To stay relevant, software engineers need to adapt their skills, mindset, and career trajectory.

1. Embrace AI as a tool

The developers who thrive won’t be the ones avoiding AI, but the ones mastering it. 

Coding assistants like GitHub Copilot, GPT-4, and Amazon CodeWhisperer can already reduce development time by 20–50% depending on the task. 

Learn to guide them with precise prompts, integrate their output into real projects, and know when their code is “good enough” versus when it needs human correction. Engineers, not machines, who use AI will replace those who don’t.

2. Strengthen your fundamentals

AI can produce functional code, but it often misses edge cases, introduces inefficiencies, or fails security checks. Without solid foundations in computer science, debugging, and architectural thinking, you won’t be able to spot those weaknesses. 

Engineers with strong fundamentals not only supervise AI output better but also use AI as a multiplier instead of a crutch.

3. Develop soft skills

As AI takes over repetitive tasks, engineers will spend more time communicating with cross-functional teams and aligning tech with business priorities. Skills like clear communication, negotiation, and product sense will distinguish those who lead projects from those who just implement them. 

These are the areas where AI adds little value, so they become your strongest differentiators.

4. Think beyond coding

Future-proof engineers are those who connect technical execution with product outcomes, not outputs. That means understanding how features affect product experience, how design choices impact scalability, and how to balance business trade-offs. 

This product-thinking mindset is also what makes career pivots into product management a common path for developers looking to expand beyond coding.

5. Commit to lifelong learning

Generative AI is evolving at a breakneck pace. What feels cutting-edge today could be obsolete in two years. Engineers must adopt a mindset of continuous education. They need to keep up with new frameworks, experiment with emerging AI tools, and learn how AI systems themselves work. 

This adaptability has always separated the best engineers from the average, but the gap will widen dramatically in the AI era.

6. Explore AI specialties

AI itself is a booming career domain. Engineers can branch into machine learning engineering, AI product management, prompt engineering, or even roles like AI system auditor. These jobs didn’t even exist a few years ago. 

Gartner predicts that by 2027, 80% of developers will need to reskill to collaborate effectively with AI, and entirely new roles will emerge around AI governance, compliance, and integration.

7. Modern DevOps with AI

AI is changing release engineering. Expect CI agents that auto-triage flaky tests, auto-label failures, and propose pipeline fixes. Pair that with trunk-based development, feature flags, canary and blue-green deploys, and you get faster, safer releases. 

Track the impact with DORA metrics: deployment frequency, lead time for changes, change failure rate, and mean time to restore. If you publish a deployment frequency piece, link it here to show teams how AI can lift that metric.

8. Testing and QA with AI

Move beyond simple unit tests. Use AI to generate hypothesis-driven test plans, property-based tests, boundary cases, and fuzz inputs. Add mutation testing to check test quality. For services, lean on consumer-driven contract tests. 

For data products, validate schemas and drift. For UI, use AI-assisted visual diffs and accessibility checks. Keep humans in the loop for critical paths and security.

9. Automation through agents

Agents can take on recurring engineering chores: opening dependency PRs, fixing trivial lints, hardening IaC templates, refreshing SDK clients from OpenAPI specs, backporting small patches, and compiling release notes from merged PRs. 

Start with low-risk, reversible actions. Gate everything behind code owners, enforce signed commits, and require passing checks before merge.

10. Code generation in the real world

Treat generative code as scaffolding, not a final product. Good targets include CRUD layers, API clients, migrations, telemetry hooks, and boilerplate tests. 

Add guardrails: repository-level prompts, style guides, license filters, secret scanners, and supply-chain checks (SAST, dependency policies). 

For runtime safety, prefer typed templates, constrained decoding, or pattern libraries, so generated code aligns with your architecture and quality bar.

Exploring New Career Options in Tech

It’s also worth noting that software engineering skills are highly transferable. If you find that pure coding roles are changing in ways that don’t excite you, there are plenty of alternative paths where your background is an asset. 

The tech industry is broad, and companies need talent in many roles beyond writing code. In fact, only a portion of people in tech are programmers. Others work in design, analysis, management, and more to make products successful. 

Here are a few alternative career options that software engineers often consider.

Product management

Moving into a product manager role is a popular choice for engineers who enjoy the strategic and customer-facing side of tech. What’s more, experts like Andrew Ng have suggested that while AI means we can do more with fewer engineers, product management is still quite time-consuming. The ratio of PMs to engineers on his teams is now 2:1, as opposed to the traditional 1:4-7 ratio.

Product managers define the vision for a product, decide what features to build, and coordinate between engineering, product design, and business teams. If you have a knack for understanding user needs and big-picture thinking, this could be a natural transition. 

Padma Chandramouli

Padma Chandramouli

My best advice for software engineers considering product management is to start acting like a product manager today, right in your current engineering role. Get obsessed with the customer and the business outcome of your work.

When I was at Qualcomm, I focused on how my image recognition model could improve the drone's battery life. Find that tangible outcome in your projects.

Don't just build the feature; ask your PM why it's important and what user problem it solves. Volunteer to sit in on customer interviews. Ask to see the product metrics and data.

The more you can show that you care about the "why" and can connect your technical work to a business result, the more you'll naturally start thinking like a PM. That curiosity is the most crucial skill you can build for the transition.

As Dhaval Shah elaborates for Product School:

When I hire a Product Manager, I look for individuals that can: Think big about the possibilities that your product/feature can achieve and not be limited to what exists today.

Software engineers often make excellent technical product managers because they can communicate well with technical teams and understand what’s feasible to build. Product management is in high demand right now. If this path intrigues you, check out our guide on how to become a Product Manager for a deeper dive into making the switch.

AI product managers

As AI adoption accelerates, a new role has emerged: the AI Product Manager. Unlike traditional PMs, AI PMs sit at the intersection of product vision, data science, and responsible AI deployment. They ensure that models are trained on the right data, outputs are aligned with ethical standards, and user trust is maintained.

For engineers who already understand technical systems, this path can be particularly rewarding. You’ll collaborate with data scientists, ML engineers, and compliance teams, translating technical possibilities into user-centric solutions. 

As more employees will reskill to collaborate with AI, AI-focused PMs become central to that transition. For developers curious about product leadership but still passionate about cutting-edge tech, AI product management offers one of the most future-proof pivots in the industry.

Technical program management or project management

These roles involve overseeing the planning and delivery of projects, ensuring that complex software initiatives stay on track. 

Engineers who move into program management use their understanding of development to realistically timeline projects and mitigate technical risks. It’s a way to be involved in tech without writing code every day, focusing instead on coordination and high-level problem solving.

Developer relations or evangelism

If you love interacting with other developers and sharing knowledge, DevRel could be a fit. This involves teaching and supporting a community of developers (for example, if you work for a company that provides an API or platform). 

With your coding background, you can create demos, write tutorials, and speak at conferences. You can be the bridge between the engineering team and the developer-users. It’s a more people-focused role while still staying close to technology.

Data science or data engineering

Some software engineers pivot into the data side of tech. Data scientists apply statistical analysis and machine learning to glean insights, while data engineers build data pipelines and infrastructure.

 The coding skills you developed (in Python, SQL, etc.) are directly useful here, and you’d get to solve different kinds of problems. Working with data is certainly a growth area.

UX design or product design

This might require learning new skills, but engineers with a creative streak sometimes move into user experience design. Your understanding of what’s technically possible can actually be a strength when designing products. 

To understand the difference between these roles better, it’s useful to check the illustration:

You might need to pick up design tools and methods, but many design teams value a mix of skills. Even if you don’t become a designer, having a sense for UX can complement other roles too.

Entrepreneurship or consulting

With a software engineering background, you might choose to start your own tech business or work as a freelance consultant. 

In these paths, you’d wear many hats. Not just coding, but also handling business decisions or advising clients on technical strategy. Your coding expertise gives you credibility to lead tech initiatives. 

AI tools can even help a solo entrepreneur (for instance, by handling some coding tasks while you focus on the vision). If you crave independence or variety, this could be a rewarding direction.

Will AI Take Over Software Engineering?

AI is rewriting the rules of software engineering, but it won’t erase the profession. 

The mundane parts of coding are being automated, while the uniquely human skills of problem-solving, architecture, creativity, and judgment are becoming more valuable than ever. 

Think of the future less as humans being replaced and more as humans augmented. Imagine engineers working alongside AI much like Tony Stark with JARVIS, commanding power that multiplies their reach.

For those willing to adapt, the horizon is wide open. Mastering AI tools, reskilling where needed, and leaning into the skills AI can’t touch will make today’s engineers tomorrow’s leaders. 

And if one day you choose to pivot, your foundation in software still opens doors to roles like product management, data science, or even AI product leadership. The world will always need sharp, adaptable problem-solvers. Whether you’re writing code or directing an AI that writes it, your ability to think critically and build solutions will keep you indispensable in the long run.

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(1): https://economictimes.indiatimes.com/news/international/global-trends/amazon-microsoft-use-ai-to-generate-25-of-their-code-will-it-take-away-jobs-of-software-engineers-in-2025/articleshow/122030620.cms

(2): https://www.thestar.com.my/tech/tech-news/2025/02/21/artificial-intelligence-is-prompting-an-evolution-not-extinction-for-coders

(3):  https://arxiv.org/abs/2306.15033

(4): https://www.thestar.com.my/tech/tech-news/2025/02/21/artificial-intelligence-is-prompting-an-evolution-not-extinction-for-coders

(5):  https://arxiv.org/pdf/2503.22625

(6):  https://www.gartner.com/en/newsroom/press-releases/2024-10-03-gartner-says-generative-ai-will-require-80-percent-of-engineering-workforce-to-upskill-through-2027