The Basics
To understand how AI is affecting programming, a short introduction to programming is necessary. Programming is the process of creating software, which involves multiple tasks such as problem analysis, requirements gathering, system design, coding, testing, debugging, integration, deployment, and maintenance.
In software development, the Software Development Lifecycle (SDLC) is essential because it provides a structured framework to guide and manage these tasks. While there are several SDLC models—today, the Agile model is among the most widely known and applied—the fundamental steps remain closely aligned with the tasks mentioned above.
- Requirement Analysis
- Planning
- Design
- Implementation (Coding)
- Testing
- Deployment
- Maintenance & Updates
Skills needed in SDLC
I asked ChatGPT to create a table with the skills needed in the different steps of the SDLC, and which can be replaced by AI. This is the result:
| SDLC Phase | Professional Roles | Key Skills Required | AI Replacement Potential |
|---|---|---|---|
| Requirement Analysis | Business Analyst, Product Owner, Consultant | Communication, domain knowledge (finance, pharma, etc.), stakeholder management, requirements documentation | ❌ Low – AI can summarize but human interaction & negotiation are irreplaceable. |
| Planning | Project Manager, Scrum Master | Project management (Agile, Scrum, PRINCE2), risk management, resource allocation, leadership | ⚠️ Medium – AI tools can suggest timelines/resources, but leadership & decision-making remain human tasks. |
| Design | Solution Architect, UX/UI Designer | System architecture, database design, UI/UX principles, creativity, design thinking | ⚠️ Medium – AI can propose architectures & design mockups, but creativity & domain fit need humans. |
| Implementation (Coding) | Software Developer, Data Engineer, Mobile App Dev | Programming languages (Java, Python, C++), debugging, clean coding, DevOps basics | ✅ High – AI copilots can already write/debug code, but deep architecture and complex problem-solving still need humans. |
| Testing | QA Engineer, Test Automation Specialist | Manual/automated testing, test case design, bug tracking, performance/security testing | ✅ High – AI is very good at test automation & bug detection, but exploratory/manual testing still valuable. |
| Deployment | DevOps Engineer, Cloud Engineer | CI/CD pipelines, containerization (Docker, Kubernetes), cloud platforms (AWS, Azure, GCP) | ⚠️ Medium – AI can optimize deployments, auto-scale infrastructure, but strategic oversight remains human. |
| Maintenance & Updates | Support Engineer, Site Reliability Engineer (SRE) | Monitoring, troubleshooting, patch management, security updates | ⚠️ Medium/High – AI can auto-detect issues and even self-heal systems, but complex incident handling still human-driven. |
This may reflect the current status of AI’s influence on the SDLC. For me, the most important point is the business impact of AI. I have already heard from several programmers that the market no longer looks as promising as it once did. A few years ago, companies were actively hunting for programmers and offering excellent conditions. Today, however, the hype seems to have faded. A shift in the programmer job market has already occurred—not because programming is no longer necessary, but because fewer programmers can now accomplish much more with the help of AI.
As a result, the expected level of expertise for entry-level programmers will rise. This may be good for companies and the overall market, but it represents a real challenge for new graduates entering the field.
And what about the other steps of the SDLC? The same trend will apply. There is no reason why professionals should not become more effective with AI. For example, a requirements engineer can gather client inputs, let AI generate summaries, highlight key questions, and then review and refine the results. A skilled engineer can validate these answers, make corrections if necessary, and finalize the work. As long as AI is not perfect—which may take a long time—experts will remain essential. However, the amount of repetitive work is already being reduced dramatically.
Market Behavior
- In February 2025, the company Ocado has cut 500 technology and finance jobs because AI is reducing costs.
- In July 2025, Scale AI layed 200 employees, because they ramped up their GenAI capacity too quickly.
- In July 2025, Microsoft cut about 4% of jobs amid hefty AI bets.
- In September 2025, Fiverr layed off 250 workers in AI refocus effort.
- In September 2025, Software is laying off 20% of the employees in shift to bold AI bests.
And here a large list of tech layoffs in 2025 can be found.
Discussion
What we are witnessing now is a shift in the market. This is not an apocalyptic scenario, but rather a transition in the demand for specific skills. Some skills are becoming less valuable or even obsolete, while others are increasingly sought after. Roles that have become less productive due to AI are particularly vulnerable, and many companies are already planning to reduce them.
On the other hand, there is a growing demand for AI/ML engineers, DevOps specialists, data annotation and review professionals, as well as experts in automation and AI system design. This transformation presents opportunities, but also risks.
In theory, such a shift might not need to result in job losses if workers successfully adapt and reskill. However, since AI is promising significant gains in efficiency, companies focused on boosting revenue may be faster to cut jobs than workers can adjust to the changes.
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