The start of a new year often sees individuals and industry leaders alike putting forward their predictions for what lies ahead. It’s a season when analysts, tech communities, and experts share their forecasts about software development, offering insights into the trends and changes the coming year may bring.
In preparing this small guide, we examined leading industry reports such as Forrester’s 2026 predictions, Stack Overflow’s 2025 developer survey, JetBrains’s State of Developer Ecosystem Report 2025, and LeadDev’s 5 uncomfortable predictions for engineering leaders in 2026.
Drawing from these authoritative sources and our own discussions, we have compiled a tailored list of predictions that reflect what we believe will define software development in 2026.
Here’s a look at what these thought leaders are forecasting and what it might mean for your day-to-day work.
AI Won’t Replace Developers…
But it will reshape how we build and create in the years ahead.
JetBrains’ Report shows 85% of developers already use at least one AI tool, with the top desired use cases being generating boilerplate code (62%), understanding and fixing bugs (58%), and generating tests (57%). Developers expect AI to handle low-level implementation while they focus on problem-solving and high-level design. And so, the most valuable developers will be those who can effectively prompt, review, and refine AI-generated output.
LeadDev is a bit more cautious stating that despite the hype, 2026 is shaping up to be a reality check year for AI in software development. According to them, research suggests AI coding assistants can be up to four times faster than humans alone but also ship code that is 10 times riskier.
While these perspectives seem opposed, they actually describe two sides of the same trend: AI accelerates development dramatically, but without mature review processes, that speed multiplies the risk of shipping flawed code. Teams with strong engineering discipline will benefit; those without it, are likely to experience LeadDev’s predicted “reality‑check.”
It’s becoming clear that the future of software development will not be defined by AI replacing developers, but rather by developers who know how to use AI judiciously. The most successful professionals will combine the efficiency and automation offered by AI with the discernment needed to review, refine, and sometimes reject its output. Ultimately, embracing AI as a tool while maintaining rigorous standards and critical oversight will be the hallmark of the most valuable developers in 2026.
What it means for you: If you haven’t yet, now’s the time to get serious about understanding how AI tools fit into your workflow. Not as a replacement for thinking, but as a multiplier for what you can accomplish. Start integrating at least one AI-driven tool into your daily workflow. Document the impact it has and decide whether it’s worth keeping or not. It’s not only about your speed, but also about your decision-making process, code quality, and security of the implementation.
The Junior Developer Paradox
Here’s where things get uncomfortable. Forrester forecasts a 20% drop in computer science enrolments and doubling of the times it takes to fill developer roles.
Although Forrester doesn’t state that companies explicitly intend to replace juniors with AI, the slowdown in entry‑level hiring combined with increased demand for senior AI‑literate engineers suggests many organisations are deprioritising junior pipelines, intentionally or not.
The straightforward yet harsh logic behind it is that some organizations believe they can replace entry-level developers with AI, reducing demand for new graduates. At the same time, companies are hunting for senior developers with AI experience and strong architecture skills, which are harder to find.
This creates a strange bottleneck. Organizations want experienced developers who can work effectively with AI, but they’re not investing in growing that talent from the ground up.
It’s all about long- and short-term hiring strategy and a proper growth mindset. A company could very well decide to halt junior hiring, hoping AI will cover basic coding. But months later, they would most likely struggle with knowledge gaps and a lack of internal growth. Meanwhile, a company that invests in mentoring juniors alongside AI, is on a good path to creating a pipeline of adaptable, AI-fluent engineers.
What this means for you:
- If you’re early-career: Don’t just learn to code. Learn to code with AI tools and focus on building the architectural understanding that AI can’t easily replace. Document your experience and logic behind working with AI in development; it’s becoming a differentiator.
- If you’re senior: Your mentorship matters more than ever. The pipeline of junior talent is getting squeezed, and organizations that don’t invest in growing developers internally will struggle long term.
- If you’re hiring: Forrester’s advice is worth heeding, don’t abandon entry-level hiring. Look within your organization for people eager to learn and consider that someone trained on your systems with AI assistance might outperform a senior hire who’s never used these tools.
Why Vibe Coding Isn’t Taking Over Yet
“Vibe coding”, which means describing features in plain English and letting AI fill in the blanks, captured significant attention in 2025. But according to Stack Overflow, the reality on the ground tells a slightly different story.
A striking 72% of developers say vibe coding is not part of their professional work, with an additional 5% emphatically rejecting it. Only around 15% have adopted it to any degree, with just 0.4% describing themselves as enthusiastic practitioners.
The survey data points to why. The biggest frustration with AI tools, cited by 66% of developers, is dealing with “AI solutions that are almost right, but not quite.” This cascades into the second-biggest pain point: 45% say debugging AI-generated code is more time-consuming than debugging code they wrote themselves.
Trust remains a significant barrier. More developers actively distrust AI tool accuracy (46%) than trust it (33%), with only 3% reporting they “highly trust” AI output. Experienced developers with 10+ years in the field show even more skepticism, with the highest “highly distrust” rates.
Unless future AI models significantly improve accuracy and reduce debugging overhead, this gap between expectation and reality is likely to keep vibe coding in niche use cases rather than mainstream workflows.
What this means for you: Expect vibe coding to remain a niche practice rather than a mainstream development approach. The “almost right” problem creates a ceiling on adoption when debugging AI code takes longer than writing it yourself, the productivity promise breaks down. We’ll likely see vibe coding find its footing in specific contexts: rapid prototyping, proof-of-concept work, and scenarios where “good enough” code is acceptable. Developers who embrace AI as an enabler not coding replacement, while honing their skills in distributed architectures and software designing patterns will remain indispensable.
Everything Becomes “As-Code”
By 2026, Forrester expects 80% of enterprise teams to use genAI for “processes-as-code”; not just infrastructure, but observability, governance, and security policies.
If you’ve worked with infrastructure-as-code (Terraform, Pulumi, CloudFormation), you know the value proposition: declarative configurations that are version-controlled, reviewable, repeatable, and auditable. Your infrastructure becomes code that lives in Git alongside your application.
GenAI has the potential to shift the equation. Instead of digging through documentation to remember how to structure a Grafana dashboard in JSON, you explain the metrics you want to visualize in plain English.
The AI isn’t replacing the need to understand what you’re trying to accomplish. You still need to know which metrics you need to show system health. But it removes the friction of translating that understanding into yet another domain-specific language.
This is to say, AI lowers the barrier to adopting declarative, version‑controlled practices by abstracting away complex syntaxes, but actual adoption will still depend on organisational maturity, governance readiness, and risk tolerance. And these are factors that vary widely between teams.
What this means for you: The declarative, version-controlled, automated approach to managing systems is becoming the default across the entire Software Developer cycle. If you’ve avoided infrastructure-as-code or similar practices because the syntax felt like too much overhead, AI tooling may change that and your approach is definitely worth revisiting. This is an opportunity to push for better practices, not because you now have time to learn five new DSLs, but because you don’t have to.
The Bottom Line
Taken together, these predictions point toward a future where AI is deeply embedded in the development lifecycle, but its impact will depend entirely on how teams balance speed with safety, and how organisations invest in developing AI‑literate talent at all levels.
AI is reshaping, not replacing, the developer’s role. The most valued skills will be architectural judgement, the ability to guide and verify AI, and a commitment to ethical, continuous learning.
Potential AI Pitfalls to Avoid
AI is a powerful force-multiplier, but it does come with certain risks. It’s not a revelation anymore that over-reliance on artificial intelligence can result in buggy, insecure, or non-compliant solutions if its output is accepted without scrutiny. It is essential to review and test all AI-generated content, especially in critical systems.
Additionally, there are notable ethical concerns, as AI models may unintentionally introduce bias, leak sensitive data, or produce insecure code. Staying informed about the ethical guidelines relevant to your industry is crucial in mitigating these risks.
Another important consideration is skill atrophy; if you depend exclusively on AI for routine tasks, your core technical skills may stagnate. To avoid this, it is important to dedicate time for manual coding, deep learning, and architectural exercises.
To address these challenges, establish regular code review practices, even for code generated by AI. Engage in discussions about ethical AI and participate in relevant training opportunities. Furthermore, deliberately allocate time for “AI-off” days or sprints, focusing on manual problem-solving and sharpening your skills without the assistance of AI.
The takeaway
The developers who struggle will be those who either ignore AI entirely or rely on it without understanding what it produces. The ones who thrive will treat it as a powerful tool that still requires a skilled operator.
- Early-Career Developers: Embrace AI as a collaborator. Build both technical and architectural skills and showcase your AI fluency.
- Senior Developers: Lead by example, mentor, stay curious, and refine your judgement for when AI should (and shouldn’t) take the lead.
- Hiring Managers: Balance your hiring pipeline. Invest in developing talent internally, prioritise AI literacy, and keep your teams engaged with the latest tools and ethical standards.
Based on:
Forrester’s “Predictions 2026: Software Development” report (October 2025)
Stack Overflow 2025 Developer Survey
JetBrains’s State of Developer Ecosystem Report 2025
LeadDev’s 5 uncomfortable predictions for engineering leaders in 2026
