Software development used to be simpler, with fewer choices about which platforms and languages to learn. You were either a Java, .NET, or LAMP developer. You focused on AWS, Azure, or Google Cloud. Full-stack developers learned the intricacies of selected JavaScript frameworks, relational databases, and CI/CD tools.

In the best of times, developers advanced their technology skills with their employer’s funding and time to experiment. They attended conferences, took courses, and learned the low-code development platforms their employers invested in.

Only now are many developers realizing they must radically upskill due to new AI capabilities. Others may be out of work and should use the time to develop new skills to improve their employability. AI is taking a bite out of employment, and more than 100,000 tech employees have been laid off in 2025, according to layoffs.fyi.

As more coding is done with code generators, vibe coding tools, and low-code automation platforms, the choice of what to learn isn’t straightforward.

Gauging the market for new skills

Should developers follow their passions and interests? Or is it more pragmatic to review what skills and certifications are in demand and focus your learning in these areas?

Developers should look beyond today’s job specifications and anticipate where demand is likely to grow over the next few years. As more enterprise platforms deliver agentic AI capabilities, there will be a shift in what businesses will develop themselves and which platforms they invest in. 

“When evaluating where to build skills, developers should look beyond tools and focus on ecosystems that foster learning, collaboration, and adaptability,” says Gloria Ramchandani, SVP of product at Copado. “Choose platforms that encourage experimentation across low-code, automation, and cloud development, because the future favors hybrid roles that blend creativity, technical depth, and soft skills. Invest in communities where you can learn from peers, share best practices, and grow with technology.”

Ways to track the skills market include the Stack Overflow Developer Survey and the State of Developer Ecosystem Report. Review DORA’s State of AI-Assisted Software Development and Stanford University’s AI Index Report for detailed benchmarks on AI models and platforms.

Junior developers should go deep fast

Developers should also optimize learning based on their experience levels. One strategy for junior developers is to broaden their skill set and go deep into implementation details. Junior developers should prioritize learning tools that go deep into a skill or tool without requiring a significant time investment.

“New or junior developers, as strange as this may sound, should pick up tools that are well covered with YouTube tutorial videos,” says Matthew Makai, VP of developer relations at Digital Ocean. “No-code and low-code tools, IDEs, and developer services are difficult to learn from text-based tutorials alone. Videos can show you specific ways that more experienced developers use those tools that you can readily copy to build your own projects.”

A second consideration for junior developers is to learn new implementation domains. For example, application developers may want to learn more about API development, data pipelines, platform integrations, and AI agent development to demonstrate a broad range of software development skills.

“When choosing which new platforms to learn, focus on durable thinking over transient tools,” says Facundo Giuliani, developer relations engineer at Storyblok. “The most valuable skills lie in understanding system design, data flow, and how to combine automation, AI, and APIs to create outcomes. Low-code and no-code tools are great entry points, but the long-term edge comes from mastering composability, reasoning, and interoperability—the skills that apply across any emerging tech stack.”

Dave Killeen of Pendo recommends this test for developers evaluating platforms: Can you go from idea to working prototype in under a day with this platform? “The platforms worth mastering are the ones that collapse the feedback loop. Whether it’s low-code, AI-assisted development, or cloud platforms, choose what helps you iterate and validate assumptions in hours, not sprints.”

Preparing for senior-level dev responsibilities

Developers looking to advance to senior-level roles, or those already at that level, need to demonstrate a strong understanding of software engineering principles.

“Developers should focus on platforms that build transferable skills and teach core concepts like API integration, workflow automation, and modular design rather than locking them into one ecosystem,” says Phillip Goericke, CTO of NMI. Seek technologies that reinforce abstraction layers and extensibility, showing how concrete implementations emerge from abstract ideas. Prioritize environments that enable rapid prototyping and scalable, secure design, strengthen understanding of common programming structures, and develop critical thinking to narrow focus when solving real problems.”

Josh Mason, CTO of Recordpoint, adds, “Optimize for skills that travel: machine learning concepts, data modeling, API design, testing, and security.”

Developers must also explore how to use generative AI tools to write agile requirements, develop software, automate testing, maintain documentation, and drive other efficiencies across the software development life cycle (SDLC). Developer experiences are evolving from IDEs focused on writing code to natural language dialogs, code validation tools, and AI agents performing development tasks. 

“AI SDLC tools are just another new way for developers to use powerful assistants in their development pipelines, much like the modern IDE,” says Simon Margolis, associate CTO of AI and machine learning at SADA. “The developer’s core goal doesn’t change: applying logic to command tools, whether using English and Gemini CLI or writing Python directly.”

Diving into SaaS and low-code development

Some developers have staked their careers on building skills and delivering results from enterprise SaaS platforms. On the other hand, some developers have focused on pro-code development and open source frameworks, leaving low-code and proprietary platforms aside.

Developers should cast a wide net when learning to increase job opportunities and be open-minded about platforms, especially if they want to work in large enterprises. Some enterprise SaaS platforms with extended development capabilities include SAP, Salesforce, and Workday. Here’s a primer on the breadth of their development capabilities.

• Business Technology Platform (BTP) from SAP includes SAP Build for developing AI agents and solutions, SAP Integration Suite for connecting to other enterprise systems, and SAP Business Data Cloud for data governance and connecting to third-party data sources.
• Salesforce’s developer platforms include AgentForce 360 for developing AI agents, Mulesoft for implementing integrations, and Data 360 for connecting to third-party data sources.
• Build, the developer platform at Workday, incorporates multiple technologies including Extend for developing solutions, Orchestrate for workflow automation, Data Cloud for zero-copy access to selected data lakes, and Flowise for developing agents.

Developers should also consider learning enterprise-ready low-code platforms that embrace AI, such as Appian, OutSystems, Pega, and Quickbase.

“The biggest hurdle is not understanding the platform, and actually, the tool set in most platforms is pretty simple,” says Matt Grippo, SVP of core software at Workday. “The part that takes a little bit more knowledge is understanding the environment, the security model, and the business processes you have to work with.”

With LLMs and agentic AI capabilities being introduced by SaaS and low-code development platforms, developers may be considering whether learning them is a smart career path.

“I don’t believe that AI will replace developers,” says Dr. Philipp Herzig, CTO at SAP SE. “AI can generate code, but the abstraction is only as good as understanding what’s going on under the hood. There are tons of new challenges in this new AI stack, so while it’s easy to get started, much hard work is still required to make it enterprise-ready, make it at scale for the organization, and that’s where the complexity lies.”

Focus on the role, not just the tools

Developers looking to upskill should also focus on going upstack. Coding is a means of solving business challenges, testing new ideas, developing new customer experiences, automating workflows, delivering insights from data, and enabling innovation. In addition to coding skills, companies will expect developers to have strong business acumen and to participate more in agile planning for what gets developed.

“Technology will keep evolving, but your real edge is curiosity, critical thinking, and how you build with purpose,” says Christian Birkhold, VP of product management at KNIME. “Master AI to move faster, but never stop understanding what it builds. Sweat the details, stay close to the code, and master the fundamentals—that’s how you stay in control when everything else accelerates.”

Developers must embrace lifelong learning, as the problems to be solved, the tools, and the business opportunities are changing rapidly because of AI. Developers must learn new software development tools, strengthen their understanding of AI capabilities, and adopt a solutions-oriented mindset to expand their devops career paths.