Beyond Boilerplate: The New Graduate Journey

My name is Jason Hughes - I'm a Talent Acquisition Manager at Jane, where I focus on technical hiring for positions across Development, Product Management and Product Design. Over the past year, I've watched the expectations for engineering positions evolve - both in what we're looking for at Jane and across the industry. This article shares what I've learned about how AI is reshaping what it means to start a career in software engineering.

From writing boilerplate to driving outcomes - The New Graduate Expectation

A lot has been written about the current AI boom we are going through, and much has been said about the impact this has had and will have on the labour market, in particular for those entering the workforce. There are a variety of data points which have indicated a contracting and evolution of the graduate hiring market, such as a recent survey which suggested 66% of global companies intend to reduce entry-level hiring due to AI.

In the past, in many businesses a software engineering graduate could expect their first year to be defined by syntax. They were the ones in the proverbial engine room, writing the boilerplate code that freed up more experienced engineers to focus on larger tasks. It was a typical early stage of someones development - a way to build muscle memory before moving on to bigger problems and challenges.

In 2026, that engine room looks a lot different. Technology has turned what used to be a week of manual coding into a much shorter period of supervised generation. Many "junior" tasks of yesterday are being handled by the technology of today.

All of that means that software engineering graduates - and  the programs and people that train them - have to adjust. Entry-level positions are not going away, however their responsibilities and the expectations associated with them are changing dramatically.

The Shift from Execution to Ownership

Across most work disciplines, the value of a junior role is shifting from task execution to outcome ownership. We are moving away from a world where we reward people in these roles for completing tasks well, and toward one where we more highly value the broader impact they're able to make. While completing tasks well and making an impact often go hand-in-hand, the emphasis is changing. Individuals have always needed to develop these outcome-focused skills to progress their careers, but with the advent of AI they've become essential at a much earlier stage.

This shift is difficult - our ability to think critically, and drive outcomes is often shaped considerably by our experience. It’s a chicken and egg situation for graduates: you need experience to build a higher level of competency, but increasingly you need this level of competency in order to get the experience.

Rising Above the Technology

To thrive today, a graduate's education cannot end at the keyboard - it needs to extend to critical thinking and reach beyond the technical domain. To "rise above" the technology, graduates need to focus on several core areas:

• Problem Decomposition: AI is brilliant at answering questions but often struggles to ask the right ones. An immensely valuable skill is the ability to take a messy, human problem and break it down into logical steps that a system can solve.
• The "Why", Not Just the "How": In many cases - especially when building software intended to work at scale - it’s not enough to know that a piece of code works. You have to understand why it works, and be able to reason about why it is superior to alternative approaches. This still requires a deep grasp of software fundamentals - data structures, algorithms, and system design etc - just as it always has.
• Impact over Output: At Jane, we don't just ship code; we’re thinking about how we best solve problems for our customers. A graduate who can understand and explain how their work affects the broader business and customers is often far more effective than one who simply closes tickets. Both may be equally capable technically, but the former is demonstrating critical thinking and curiosity - they will question why we are doing what we are doing, and what the benefit is for the business and/or the customer.
• Bigger Picture Thinking: Humans don't have a context window. We can still interpret the bigger picture, dynamics, nuances, and relationships necessary to make an impact better than any tool can. These skills are your competitive advantage.

Demonstrating Critical Thinking: The Experience Paradox

Education is important, but more than ever, experience matters. As I alluded to earlier however - how do you develop experience if no one is giving you the opportunity to get it?

A great way to build experience and demonstrate critical thinking to employers is to apply your skills to a real world problem - start building! There are multiple benefits to this:

• You flex your problem solving muscles
• You demonstrate the ability to be customer obsessed - a great quality in any engineer
• You get engineering reps in, and learn when and how to best use AI and other technologies to expedite your engineering workflows
• You will likely learn new concepts and technologies along the way, those which you had not encountered in your academic work
• Depending on what you build, maybe there is a career opportunity there - a chance for you to start a business, or to generate ideas that will lead you towards one. Maybe you don’t look for a job with a business - maybe you start your own?

There is something very powerful about not just sending a resume, but sending an end-to-end example of you at work. However, there is a significant limitation of this advice: it takes time, which not everybody can invest for a variety of reasons.

What You Can Do

If you're a graduate or soon-to-be graduate reading this, here's what actionable looks like in 2026:

Build something real, but be strategic about it. As highlighted above, if you can, build!

If building isn't feasible right now, contribute. Open source projects need code review, documentation, issue triage, and testing - all things you can do in small time increments. This still gives you experience with production codebases and demonstrates your ability to collaborate!

Focus on the skills AI can't replicate yet. Practice decomposing ambiguous problems. Work on your communication skills - the ability to explain technical concepts to non-technical stakeholders is just as valuable than ever. Learn to ask better questions. These are qualities which can make a difference.

Seek out mentorship wherever you can find it. Much of the tacit knowledge of how experienced engineers think is still transferred human-to-human, and getting access to that knowledge early is a meaningful advantage. Network and meet people working in industry who are happy to share their insights and experiences.

Highlight your ability to work with AI: Be ready to demonstrate how you work with AI in practice. In interviews and projects show that you know when to use AI, how to validate its output, and when to rely on your own judgment instead. This will be an expectation of every Software Engineer at all levels going forward.

At Jane, we're adapting too. Across our engineering group we have adjusted our hiring practices to focus on these AI-native competencies - emphasising problem decomposition, critical thinking, and the ability to work effectively alongside AI tools. Our interview process now includes evaluating how candidates validate AI-generated code, how they approach ambiguous problems, and how they connect their technical decisions to customer and business outcomes.

The Future Belongs to Those Who Adapt Thoughtfully

The graduates who will thrive in this new landscape aren't necessarily those who can prompt AI most effectively - though that matters. They're the ones who can think critically about complex problems, communicate clearly with diverse stakeholders, connect their work to the needs of the business and customer, demonstrate high-agency, and understand when to leverage AI versus when to leverage their own judgment.

There is nothing novel here - these have always been valuable skills for an engineer - however AI has moved the timeline forward. What used to be year-three competencies are now year-one expectations. That's challenging for someone entering the workforce, but it's also exciting. If you can develop these capabilities, you're not competing with AI - you're leveraging it to create impact that would have been significantly harder for graduates ten years ago.

At Jane, we're integrating these competencies into how we hire and evaluate engineering talent at all levels of seniority. If you're building and/or possess these skills, we'd love to see what you're working on - check out our open roles and come ready to show us how you approach problems in this AI-native world. If you have questions or thoughts I’d love to hear from you - you can connect with me here on Linkedin.