Will Software Engineers Be Replaced by AI?

We Calculated the AI Replaceability Index which can indicate how endangered your job truly is. The results are more nuanced than you would think and the answer is not a simple yes or no.

With the skyrocketing adoption of AI tools, millions of software engineers worldwide worry about the future of their jobs. Will your engineering job be safe, or will it be replaced by AI? Turns out it depends on what you actually do all day.

AI Replaceability Index (ARI)

Luckily, there is a way for you to calculate just how worried you actually need to be. The AI replaceability index, or ARI, is a scoring system Elite Brains built to measure how likely a specific job is to be replaced by AI in the future.

The ARI takes into account different metrics from 2 dimensions: how capable is AI to do this job and what structural barriers are standing in the way of AI replacing this job. The scoring system ranges from 0-10 and the higher the score the higher the risk. Anyone who wants to keep their peace of mind can calculate their own score using the Elite Brains calculator. Most software engineers will likely score around 4, but some can go quite a bit lower or higher, depending on their daily responsibilities.

It only takes seconds to create a sample calculation

This is what a sample calculation for a software engineer could look like. How did we end up with a 4.35 score? Check the full logic behind the AI Replaceability Index -  then calculate the AI replaceability score for your own position.

What software engineers do day to day 

Each work day of a software engineer can look completely different as their tasks span from routine and pattern-based responsibilities to judgment-based tasks that cannot be reduced to a formula. On one end stand more routine tasks with clearly defined inputs and outputs, such as writing boilerplate code, building standard features, fixing straightforward bugs, writing tests, and documenting their work. On the other end stands context-dependent work built on relationships and accountability. For tasks like architectural decision making, navigating ambiguous or shifting requirements, debugging gnarly issues spanning across multiple systems, supervision and mentoring, or reviewing a colleague's code, those factors are more important than just technical correctness. This split matters because it maps closely onto where AI tools help most and where they still fall short.

What can AI handle or assist with

• Writing boilerplate code, CRUD operations, and standard implementations - AI can generate repetitive setup code (API endpoints, database models, config files) that follows well-known patterns, freeing engineers from typing out the same scaffolding repeatedly.

• Code completion and autocomplete suggestions - AI can predict the next few lines or completing a function as the engineer types, based on context from the surrounding code and common idioms.
• Explaining unfamiliar code or documenting functions - AI can read existing code and produce plain-language explanations.
• Refactoring code for readability or style consistency - AI can restructure code to follow naming conventions, reduce duplication, or match a style guide, without changing what the code actually does.
• Finding and fixing simple, well-defined bugs - AI can identify issues like typos or incorrect function calls when given an error message or failing test, and propose a fix.
• Translating code between programming languages - AI can convert a function or module from one language to another while preserving logic.
• Documenting and communicating code changes - AI can summarize code changes into clear, structured writing that communicates intent to reviewers or future maintainers.
• On-demand technical Q&A - AI can act as an on-demand reference for syntax, library usage, or common patterns.
• Generating sample data, mocks, and fixtures for testing - AI can create realistic-looking mock data to populate test environments without manual data entry.

Tasks AI cannot replace or struggles with

• System architecture decisions - AI cannot decide which technology to use, how should services talk to each other or how to scale. Those systematically structural tasks require weighing long-term business needs, team skills, and constraints that AI isn't familiar with.

• Debugging complex, gnarly, or novel issues - AI doesn’t have the deep level of familiarity with a system needed to solve unexpected, complex, or novel issues.
• Making tradeoff decisions - AI doesn't understand the nuances of individual situations and lacks reason and judgement. It cannot make snap decisions like whether to ship something quickly with shortcuts now versus building it properly later, or whether to prioritize speed over security.
• Code review with deep contextual judgment - Unlike a human reviewer, AI lacks the experience to catch a code that might be technically correct but will cause problems later because of how the business actually operates.
• Cross-team coordination and negotiation - AI cannot negotiate, compromise, or manage relationships to help two teams agree on how their systems will work together etc.
• Mentoring and team leadership - AI cannot provide the emotional intelligence and relationship-building skills needed to help a junior engineer grow, give career feedback, or navigate team conflicts.
• Accountability for outcomes - While AI can suggest a solution, someone has to take responsibility if that solution fails and that someone needs to be a person.

This split clearly demonstrates that software engineering is not one job, and whether you are a junior developer, a backend engineer, a DevOps specialist, or another type of engineer who worries for their future, now is the time to adapt. Use the Elite Brains ARI calculator to measure your replaceability risk and start adapting to the new reality. Because the real divide is between those who will adapt and those who won't.

How to protect yourself from being replaced

Adapting starts with making your abilities visible, not just listing them in your resume. As AI absorbs more and more of the routine work, hiring managers are paying closer attention to actual technical skills rather than resume buzzwords or years of experience alone. Nowadays, recruiters want tangible proof a candidate is actually able to do the job, not just say they can. More skills lead to more advanced specialization, which then opens up more opportunities. One of the ways you can prove your expertise and distinguish yourself from others is by verifying your skills. This gives you a concrete way to prove what you can do and add it directly to your profile, so employers see verified ability instead of just another line on a resume.