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If you’re thinking about graduate school for software development you’ve probably had the same nagging thought as a lot of working developers and career changers: Am I investing in a field that AI is about to shrink? It’s a fair concern, especially when headlines make it sound like code writes itself and whole applications appear from a prompt.

Software development careers have always been shaped by moments like this, when new capabilities force the field to reconsider what expertise actually looks like. AI introduces that kind of moment by shifting where skill and responsibility show up in the work, which makes questions about education, career paths, and long-term value worth examining.

“The most significant change in AI is the ease of access to Gen AI tools to regular users,” says Interim Dean Li-Chiou Chen of 91��Ƶ’s Seidenberg School of Computer Science and Information Systems. “That is, everyone with internet access can use AI tools to some extent. Therefore, AI is impacting every aspect of our lives and is helping us make decisions in many areas, such as healthcare, communications, education, legal, marketing, and more.”

That accessibility is reshaping what it means to work in software. To understand where the field is headed, it helps to look at how AI is already changing day-to-day development work.

How Has AI Helped Transform the Role of Software Developers?

AI has changed how work is distributed across projects. Tasks that used to absorb large amounts of time now move faster, which shifts how engineers approach design, implementation, and review.

In day-to-day workflows, AI-assisted systems often support routine development work by:

• Generating first drafts of code that can be reviewed and refined
• Reducing time spent on boilerplate setup and common patterns
• Suggesting fixes or improvements during implementation
• Producing images, icons, and visual assets from text descriptions

Testing and quality assurance have shifted in similar ways. AI systems can analyze codebases for patterns associated with defects or failures, which changes how teams approach coverage and validation.

Common uses include:

• Automatically generating test cases from existing code
• Surfacing edge cases that are easy to miss in manual testing
• Flagging higher-risk areas earlier in the development cycle

Security and reliability benefit from related analysis. AI-driven scanning can flag anomalies and potential vulnerabilities, helping teams focus review time where it matters most. These systems don’t replace secure coding practices, but they can help teams catch issues sooner, especially in large codebases.

Dr. Christelle Scharff, professor of computer science and co-director of the 91��Ƶ AI Lab, sees this shift as part of a broader pattern, explaining, “Everybody will have to be able to use AI and this will be a differentiator. Large Language Models and image generators will permit professionals to be more productive and be used as supportive tools in all possible sectors.”

Across these use cases, AI primarily handles pattern recognition and repetition. Developers remain responsible for correctness, tradeoffs, and ensuring systems behave as expected in production.

How AI Helps Increase Efficiency Across the Software Development Process

AI’s impact shows up most clearly through efficiency gains. By reducing friction in common tasks, AI-assisted systems can help teams maintain momentum across planning, building, and review. In one GitHub survey, .

Early-stage development is where many teams feel the difference first. AI tools are often used to get features or components off the ground faster, including:

• Producing initial code drafts that can be adapted to project needs
• Filling in standard patterns without manual setup
• Moving from idea to working example more efficiently

AI support can extend into design and interface work at a basic level. When tools generate initial layouts or page structures, designers and engineers can focus more attention on interaction, usability, and refinement instead of building everything from scratch.

As projects grow, efficiency gains continue through analysis and maintenance. AI-driven scanning can help identify duplicated logic, performance issues, or refactoring opportunities across large codebases. Instead of searching manually, teams receive targeted suggestions to support maintainability work.

Natural language interfaces can speed up implementation, too. Developers describe desired behavior in plain language and receive relevant code suggestions, reducing context switching. Related techniques assist with debugging by flagging likely sources of errors and prioritizing issues based on past patterns.

In collaborative environments, these efficiencies can support steadier delivery. AI tools may help draft pull requests, summarize changes, and support reviews, all of which are useful when code volume increases and review bandwidth gets tight.

5 AI Tools That Help Software Developers

AI tools are now part of many standard development environments. Each tends to support a specific part of the workflow, and their value depends on fit and oversight.

1. GitHub Copilot

Developed by GitHub in collaboration with OpenAI, provides inline code suggestions based on context within an editor.

• How it helps:
  • Generates drafts of functions and common patterns
  • Reduces time spent on boilerplate and repetitive syntax
  • Supports multiple languages and frameworks
• Pros:
  • Deep integration with popular IDEs
  • Useful for accelerating routine coding tasks
• Cons:
  • Output requires careful review
  • Less effective in highly domain-specific codebases

2. Claude AI

, developed by Anthropic, is a conversational AI assistant that supports software development through code generation, explanation, debugging, and document analysis.

• How it helps:
  • Generates, reviews, and explains code across multiple languages
  • Assists with architecture decisions, documentation, and technical writing
  • Handles long-context inputs, which is useful for analyzing large files or complex requirements
• Pros:
  • Strong reasoning and explanation capabilities
  • Useful for both code tasks and broader technical problem-solving
• Cons:
  • Operates as a conversational tool rather than an IDE plugin
  • Responses benefit from clear, specific prompts for technical tasks

3. Amazon CodeWhisperer

provides real-time code suggestions inside IDEs, with an emphasis on cloud-based development.

• How it helps:
  • Assists with writing cloud services and integrations
  • Flags potential security issues during development
• Pros:
  • Strong support for AWS workflows
  • Built-in security scanning
• Cons:
  • Suited primarily for teams using AWS
  • Less flexible for non-cloud projects

4. Perplexity

is an AI-powered research and answer engine that combines large language model capabilities with real-time web search, which makes it useful for developers who need up-to-date technical information.

• How it helps:
  • Quickly surfaces documentation, tutorials, and technical references with source citations
  • Answers implementation questions with current information rather than relying on static training data
  • Supports research on unfamiliar libraries, APIs, or frameworks
• Pros:
  • Combines conversational AI with live web search for current results
  • Cites sources directly, making it easier to verify information
• Cons:
  • Not designed for direct code generation inside an IDE
  • Output depends on the quality and availability of indexed sources

5. Qodo

(formerly CodiumAI) is an AI-powered code review tool that analyzes changes in real time to identify bugs, logic issues, and quality concerns before code is merged.

• How it helps:
  • Flags potential errors and inconsistencies during development
  • Reviews pull requests to surface higher-risk changes
  • Supports automated quality checks across codebases
• Pros:
  • Improves code quality early in the workflow
  • Useful for scaling review across larger projects
• Cons:
  • Requires configuration to match team standards
  • Still depends on human review for final decisions

What Are the Risks of Using AI in Software Development?

AI tools can improve speed and reduce friction, but they also introduce risks that teams need to manage deliberately, especially when tools are used without clear limits or oversight.

One set of risks involves data quality and reliability. AI systems depend on large training datasets, and output quality often mirrors dataset quality.

Common challenges include:

• Time and cost associated with sourcing, cleaning, and maintaining datasets
• Inaccurate suggestions when training data doesn’t reflect real-world conditions
• Gaps around edge cases that fall outside common patterns

There are also limits to how well AI handles unfamiliar or complex situations. These tools perform best with well-defined tasks but can struggle in systems with tightly coupled components or highly specific business rules.

Transparency is another concern. AI-generated code doesn’t always make its reasoning clear, which can make inefficient logic or subtle errors harder to detect during review, especially when output looks superficially correct.

Operational dependency can introduce risk as well. Teams that build workflows around constant AI availability may see slowdowns if access changes, policies shift, or tools become unavailable.

Privacy and security remain ongoing considerations. Many AI systems process large volumes of data, raising questions around consent, sensitive information, and compliance. Teams typically address this through access controls, anonymization, encryption, and clear internal usage policies.

Dr. Soheyla Amirian, assistant professor of computer science at Seidenberg, frames the tension this way: “The opportunity lies in AI’s potential to revolutionize industries, improve efficiency, and solve global challenges. But it becomes a threat if not used ethically and responsibly. By fostering ethical frameworks and interdisciplinary collaboration, we can ensure AI serves as a tool for good, promoting equity and trust.”

Will We Still Need Software Developers in the Future?

As AI tools become more capable, questions about the future of software development are unavoidable. While AI can assist with implementation, several core responsibilities continue to depend on human expertise.

Areas where software developers remain essential include:

• System design and architecture. Developers make decisions about structure, scalability, integration, and long-term maintainability, i.e., work that requires understanding constraints and downstream impact.
• Context and continuity. Long-lived codebases reflect years of decisions and tradeoffs. Developers provide the historical and situational awareness needed to make safe changes.
• Review and refinement. AI-generated output often needs adjustment to meet performance expectations, security requirements, and team standards.
• Collaboration and problem-solving. Software development requires clarifying requirements, resolving ambiguity, and coordinating across roles. That work shapes what gets built and how it functions in practice.

Brian McKernan, PhD, assistant professor of communication and media studies at 91��Ƶ’s Dyson College of Arts and Sciences, puts it simply, “There are great uses for AI, particularly in cases with huge amounts of data. But we will always need humans involved in verifying.”

Dr. James Brusseau, a Dyson faculty member and close collaborator with Seidenberg, echoes that perspective, stating, “AI, more than anything else, is just a tool. That is, I do not think it is capable of producing thought as you and I do.”

As AI takes on more routine programming tasks, the developer role shifts toward guiding decisions, evaluating outcomes, and staying accountable for what ships.

Skills Needed as a Software Developer in the Age of AI

With AI becoming a standard part of software development workflows, expectations for developers continue to expand. Strong programming foundations are still important, but today’s most effective engineers also combine AI literacy, data skills, cloud expertise, and the ability to collaborate and make strategic decisions.

Generative AI can accelerate coding and testing tasks, but it does not replace the need for developers who can design systems, evaluate risks, and guide projects from concept to deployment. That shift explains why technical depth, interpersonal skills, and decision-making are all becoming more important in AI-driven environments.

Gartner has projected that through 2027, generative AI will create new roles in software engineering and operations, .

Dr. Christelle Scharff reinforces this point, adding, “I believe that the big deal is that the tools related to AI are now in the hands of everybody, while earlier advances were confined and used by people in tech.”

Technical Skills

Modern software developers working with AI need a broad technical foundation that spans programming, data, infrastructure, and responsible system design.

• Programming and development:
  • Proficiency in languages such as Python, R, Java, C++, or JavaScript
  • Familiarity with AI frameworks and libraries like TensorFlow, PyTorch, Scikit-learn, and Keras
  • Strong understanding of object-oriented programming and data structures
• AI and machine learning:
  • Understanding of supervised, unsupervised, and reinforcement learning approaches
  • Experience with natural language processing and computer vision applications
• Data skills:
  • Ability to clean, analyze, and preprocess large datasets
  • Experience working with structured and unstructured data
  • Familiarity with databases and big data tools such as SQL, NoSQL, Hadoop, or Spark
• Cloud and DevOps:
  • Experience with cloud platforms such as AWS, Google Cloud, or Azure
  • Knowledge of DevOps practices and CI/CD pipelines for deploying and maintaining applications
• AI ethics and security:
  • Understanding of AI bias, privacy concerns, and regulatory requirements
  • Skills in designing secure and compliant AI-enabled systems

Interpersonal Skills

As AI automates portions of coding and testing, developers spend more time collaborating, translating requirements, and shaping how systems are built.

• Communication and collaboration:
  • Explaining technical concepts to non-technical stakeholders
  • Working effectively in cross-functional teams with product managers, designers, and data scientists
• Teamwork and knowledge sharing:
  • Mentoring junior developers and reviewing code
  • Contributing to shared standards and best practices
• Adaptability:
  • Staying current with emerging tools and frameworks
  • Adjusting quickly as project goals and technologies evolve

Decision-Making Skills

AI increases both the speed of development and the complexity of technical choices. Developers are increasingly responsible for guiding projects strategically and managing risk.

• Strategic thinking:
  • Aligning AI initiatives with business objectives
  • Identifying high-impact opportunities for AI integration
• Project management:
  • Planning timelines, resources, and deliverables
  • Using tools such as Jira, Trello, or Asana to track progress
• Risk management:
  • Anticipating technical, ethical, and operational risks
  • Ensuring compliance with security standards and regulatory requirements

These skill areas reflect how software development roles are changing alongside AI, where strong technical foundations are paired with collaboration, judgment, and system-level thinking.

6 In-Demand Software Developer Jobs That Use AI

Many roles now require AI experience. Some of the fastest-growing and highest-paying positions combine strong engineering foundations with experience building, integrating, or operating AI-enabled systems.

According to the , overall employment in computer and information technology occupations is projected to grow much faster than the average for all occupations from 2024 to 2034, with about 317,700 openings projected each year. Roles such as software developer and data scientist are among the occupations driving that momentum.

1. Machine Learning Engineer

Machine learning engineers build software systems that train, deploy, and monitor models in production. They work closely with application developers to integrate machine learning into real products and services.

• Typical education: Bachelor’s in computer science, software engineering, or related field; master’s often preferred
• Key skills: Python or Java, machine learning frameworks, data pipelines, model evaluation, deployment
• Salary (NYC): $136,000–$219,000 per year (median $172,000)
• Job outlook:

2. AI Engineer

AI engineers design and implement AI-powered features inside applications, such as recommendation systems, automation tools, and intelligent interfaces. Their work connects models to APIs, cloud infrastructure, and user-facing systems within development workflows.

• Typical education: Bachelor’s in computer science or engineering; graduate study increasingly common
• Key skills: Software development, APIs, cloud platforms, AI model integration
• Salary (NYC): $135,000–$227,000 per year (median $174,000)
• Job outlook:

3. Data Scientist

Data scientists build analytical pipelines and machine learning models that support software products and data-driven platforms. They collaborate closely with software developers to move models from experimentation into production.

• Typical education: Bachelor’s in a quantitative field; master’s often preferred
• Key skills: Statistics, machine learning, SQL, Python or R, data visualization
• Salary (NYC): $127,000–$217,000 per year (median $165,000)
• Job outlook:

4. AI Research Scientist

AI research scientists develop new algorithms and learning approaches that later become part of commercial software systems, platforms, and developer tools.

• Typical education: Master’s degree or PhD in AI, computer science, or related field
• Key skills: Advanced mathematics, ML theory, experimentation, research
• Salary (NYC): $165,000–$256,000 per year (median $204,000)
• Job outlook:

5. Robotics Engineer

Robotics engineers write and maintain the software that controls automated systems, sensors, and intelligent machines. They combine software development with AI-driven perception and control to operate in physical environments.

• Typical education: Bachelor’s in engineering or computer science; advanced degrees common
• Key skills: Embedded systems, controls, AI integration, hardware/software coordination
• Salary (NYC): $110,000–$184,000 per year (median $141,000)
• Job outlook:

6. AI Product Manager

AI product managers guide the development of AI-powered software products, translating business goals into technical requirements and working with engineering teams through build, testing, and deployment.

• Typical education: Bachelor’s degree; many roles favor a master’s in technology or business
• Key skills: Product strategy, technical communication, AI fundamentals, stakeholder coordination
• Salary (NYC): $173,000–$262,000 per year (median $211,000)
• Job outlook:

Bonus: Software Developers (General)

Software developers design, build, test, and maintain applications and systems across industries, increasingly incorporating AI tools, automation, and machine learning into modern software architectures.

• Typical education: Bachelor’s in computer science or related field
• Key skills: Programming, system design, debugging, collaboration
• Salary (NYC): $108,000–$178,000 per year (median $138,000)
• Job outlook:

Note: Salary figures are based on Glassdoor data collected in January 2026. Actual compensation may vary over time and can fluctuate based on factors such as location, employer, experience level, industry, and the number of reported salary submissions.

Overall Job Outlook for AI-Driven Software Careers

Employment across computer and information technology occupations is projected to grow much faster than average over the next decade. Expanding use of artificial intelligence, cloud computing, automation, cybersecurity, and data-driven systems continues to drive demand for professionals who can design, build, and manage modern software environments.

For a closer look at career options in AI-related fields, 91��Ƶ’s program pages for the MS in Artificial Intelligence, MS in Applied Artificial Intelligence, and BS in Artificial Intelligence include detailed career outcome information.

FAQ

What is AI in software development?
AI in software development refers to using machine learning systems to support tasks such as writing code, testing software, debugging, and reviewing changes. These tools analyze patterns in existing code and data, while developers remain responsible for design decisions, validation, and deployment.

How can AI be used in software development?
Common uses of AI in software development include generating code drafts, creating or expanding test coverage, spotting potential bugs, generating images and visual assets, flagging security risks, summarizing changes for reviews, and supporting debugging workflows.

What is an AI software developer?
An AI software developer is a developer who builds, integrates, or works alongside AI-driven systems. This can include developing machine learning models, embedding AI services into applications, or using AI tools to support development workflows.

Is AI replacing software developers?
AI is not replacing software developers. It is changing how development work is distributed by handling routine tasks and generating drafts, while developers continue to guide design, review output, and manage complexity in production systems.

Can AI do coding?
AI can generate, refactor, and suggest code based on learned patterns. It does not independently understand business requirements, system constraints, or long-term impact. Developers must review and adapt AI-generated code to ensure it works correctly within a broader system.

What jobs will AI replace?
AI is more likely to automate specific tasks rather than fully replace most technology roles. Repetitive, narrow-scope work such as basic code translation, routine testing, or simple script generation is increasingly handled by AI tools. However, roles that involve system design, problem-solving, collaboration, and oversight continue to grow as organizations adopt AI across software development.

Which jobs will not be replaced by AI?
Roles that require system-level thinking, human judgment, and accountability are far less likely to be fully replaced by AI. This includes software architects, senior developers, security and infrastructure leaders, platform engineers, and technical product roles that guide design decisions, manage risk, and take responsibility for real-world system performance.

Looking Ahead

AI has become part of how software is built, but it hasn’t removed the need for people who can design systems, make decisions, and take responsibility for outcomes. For students weighing graduate education, preparation focuses on building those capabilities alongside technical depth.

91��Ƶ’s Seidenberg School has been teaching and researching AI for over 30 years. Faculty like Dr. Christelle Scharff, co-director of the 91��Ƶ AI Lab, and Dr. Soheyla Amirian lead research across healthcare AI, computer vision, and ethical AI frameworks. Students work alongside these researchers through lab projects, hackathons, and industry partnerships. To learn more, read about Seidenberg’s legacy of AI excellence or explore how faculty are powering student discovery.

To explore how 91��Ƶ approaches that preparation, you can review the Master of Science in Software Development and Engineering program options:

• MS in Software Development and Engineering, on-campus format

If you’d like to talk with an admissions counselor about fit, timelines, and prerequisites, you can also request information anytime.