Last Update: 3/13/2026
Your role’s AI Resilience Score is
Median Score
Changing Fast
Evolving
Stable
This reflects the reliability of your score based on the number of data sources available for this career and how closely those sources agree on the outlook. A higher confidence means more consistent evidence from labor experts and AI models.
What does this resilience result mean?
These roles are undergoing rapid transformation. Entry-level tasks may be automated, and career paths may look different in the near future.
AI Resilience Report for
They create and improve computer programs and apps by writing code, solving problems, and making sure everything works smoothly.
This role is changing fast
The career of software development is "Changing fast" because AI tools like GitHub Copilot and ChatGPT are automating routine tasks such as coding snippets, bug fixing, and drafting documentation. These advancements allow developers to work faster, but they still need to review and refine AI-generated work.
Read full analysisLearn more about how you can thrive in your career
Learn more about how you can thrive in your career
This role is changing fast
The career of software development is "Changing fast" because AI tools like GitHub Copilot and ChatGPT are automating routine tasks such as coding snippets, bug fixing, and drafting documentation. These advancements allow developers to work faster, but they still need to review and refine AI-generated work.
Read full analysisContributing Sources
We aggregate scores from multiple models and supplement with employment projections for a more accurate picture of this occupation’s resilience. Expand to view all sources.
AI Resilience
AI Resilience Model v1.0
AI Task Resilience
CareerVillage's proprietary model that estimates how resilient each occupation's tasks are to AI automation and augmentation
Microsoft's Working with AI
AI Applicability
Measures how applicable AI tools (like Bing Copilot) are to each occupation based on real usage patterns
Anthropic's Observed Exposure
AI Resilience
Based on observed patterns of how Claude is being used across occupational tasks in real conversations
Althoff & Reichardt
Economic Growth
Measured as "Wage bill" which is a long term projection for average wage × employment. It's the total labor income flowing to an occupation
High Demand
We use BLS employment projections to complement the AI-focused assessments from other sources.
Learn about this scoreGrowth Rate (2024-34):
Growth Percentile:
Annual Openings:
Annual Openings Pct:
Analysis of Current AI Resilience
Software Developers
Updated Quarterly • Last Update: 2/17/2026

What's changing and what's not
Today’s software developers often use AI tools to speed up routine work. For example, coding assistants like GitHub Copilot or ChatGPT can suggest snippets, find bugs, and even rewrite parts of code [1]. AI can also scan user feedback and documents to pull out requirements or generate user stories automatically [1].
Even writing tasks – such as drafting status reports or code documentation – can be partly automated: AI models help create inline comments and draft reports from project data [1] [2]. However, these tools aren’t perfect or on their own. In fact, Bureau of Labor Statistics analysts note that AI-generated code often still fails or needs human fixes [3].
And tasks that require judgment – like choosing hardware based on costs and security, or supervising a team – remain largely human jobs [3]. In short, AI today augments many developer tasks (especially repetitive coding and writing), but people are still in charge of reviewing work and handling the complex parts.

AI in the real world
AI tools for developers are widely available, so many companies are experimenting with them. One big reason firms adopt AI quickly is potential payoff: McKinsey estimates generative AI could boost developer productivity so much that it adds $2.6–$4.4 trillion to the global economy [2]. In other words, businesses see a huge benefit if coding goes faster.
At the same time, using AI can be expensive and tricky: there are costs for powerful hardware or cloud services and for integrating AI into existing systems. Experts also warn of risks like security holes or bias in AI-written code [1], so companies move carefully. Finally, the software job market still has strong demand: the BLS reports IT jobs growing even as AI tools emerge [3].
In practice, firms balance these factors – cost savings and developer shortages push adoption forward, while costs, trust, and ethics issues slow it down. Overall, AI is seen as a helpful assistant, but people’s creativity, problem-solving, and leadership remain key for software development [2] [1].

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Median Wage
$133,080
Jobs (2024)
1,693,800
Growth (2024-34)
+15.8%
Annual Openings
115,200
Education
Bachelor's degree
Experience
None
Source: Bureau of Labor Statistics, Employment Projections 2024-2034
AI-generated estimates of task resilience over the next 3 years
Specify power supply requirements and configuration.
Use microcontrollers to develop control signals, implement control algorithms, or measure process variables, such as temperatures, pressures, or positions.
Supervise the work of programmers, technologists and technicians and other engineering and scientific personnel.
Train users to use new or modified equipment.
Train users to use new or modified equipment.
Advise customer about or perform maintenance of software system.
Specify power supply requirements and configuration.
Tasks are ranked by their AI resilience, with the most resilient tasks shown first. Core tasks are essential functions of this occupation, while supplemental tasks provide additional context.

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