18 Aug Why Technical Talent Is Harder to Assess in the Age of AI
Artificial intelligence has transformed how software is designed, built, and reviewed in the Age of AI. Developers can now generate code, explain unfamiliar systems, write tests, improve documentation, and troubleshoot errors with support from AI tools, including GPT-based applications.
This progress creates a new challenge for employers. A candidate may complete a coding task quickly, but speed alone no longer proves technical ability. The real question is whether the candidate understands the problem, evaluates AI-generated output and can make sound engineering decisions when the answer is incomplete or incorrect.
For technology leaders, hiring teams and recruitment partners, technical assessment must now measure more than code production. It must reveal how a person thinks, communicates and applies judgement. It should also recognise how people use new technology across various fields while considering its wider impact on society.
AI has changed what coding performance means
The Age of AI is transforming how software is designed, developed, tested, and maintained. AI tools can help developers generate code, explain complex systems, write tests, improve documentation, and identify potential errors more efficiently. (github.blog)
These results do not mean that AI, sometimes described as a.i., can replace engineering expertise. They show that the assessment target has shifted. Employers need to understand whether a candidate can:
- Define the problem accurately
- Choose an appropriate technical approach
- Identify risks and trade-offs
- Review AI-generated code
- Test and secure the solution
- Explain decisions clearly
- Adapt when requirements change
A candidate who writes less code but makes better decisions may be more valuable than someone who produces a large amount of code without understanding its impact.
The difference between code generation and engineering judgement
The Age of AI is reshaping how businesses operate, innovate, and compete. From automating routine tasks to improving decision-making and creating new products, artificial intelligence is becoming an essential part of modern business strategy.
This is where assessment becomes more difficult. A technically polished answer may hide weak reasoning. Candidates can also use AI to produce explanations that sound convincing without reflecting genuine understanding.
Recruiters and hiring managers should therefore focus less on whether a candidate knows every syntax rule. Instead, they should explore how the candidate approaches uncertainty and validates their work.
Useful questions include:
- What assumptions did you make before starting?
- Why did you select this architecture?
- What could fail in production?
- How would you test this solution?
- Which parts would you change after reviewing the code?
- What would you do if the system had to support ten times more users?
- How would you explain this decision to a non-technical stakeholder?
The quality of the answer often lies in the reasoning behind it, not in the number of lines written.
Online tests can create a false sense of confidence
The Age of AI is changing how businesses attract, develop, and retain top technology talent. Companies that embrace AI-driven recruitment can identify qualified candidates faster, improve hiring decisions, and build stronger teams for the future.
For example, a timed exercise may not show whether a candidate can:
- Work with an existing codebase
- Read unclear documentation
- Collaborate with designers or product managers
- Prioritise competing requirements
- Investigate a difficult bug
- Improve an imperfect solution
- Communicate progress during a project
AI can make the limitations of these tests more visible. If candidates are allowed to use AI, the assessment may measure prompt quality or tool familiarity. If AI is prohibited, the exercise may not reflect the environment in which the candidate will actually work.
A stronger process makes the rules clear. Employers can choose to assess candidates without AI, with AI or in both conditions. The important point is to match the format to the role.
For a junior developer, an assessment without AI may reveal core learning ability. For a senior cloud engineer, a realistic exercise using approved AI tools may provide better evidence of how the person will work in the real role.
The most important skills are becoming harder to observe
The World Economic Forum’s Future of Jobs Report 2025 identifies AI and big data, networks and cybersecurity, and technological literacy among the fastest-growing skills. At the same time, employers continue to value analytical thinking, creative thinking, resilience, flexibility, leadership and collaboration. (weforum.org)
The Age of AI is transforming technology recruitment, helping companies identify skilled candidates faster, streamline hiring processes, and build high-performing teams prepared for the future.A strong technical candidate may not have the most impressive list of tools. They may instead demonstrate:
- Curiosity about how systems work
- A habit of asking precise questions
- Comfort with changing requirements
- The ability to explain complex subjects simply
- Willingness to challenge an unsafe or inefficient approach
- A clear understanding of business priorities
- Consistent learning habits
These qualities are especially important in software development, cloud infrastructure, AI and machine learning, fintech and other sectors where errors can create serious operational or financial consequences. They also matter when organisations assess the economic benefits of automation while trying to avoid the worst inequities associated with unequal access to education, technology and opportunity.
How to design a better AI-era technical assessment
A modern assessment should reflect the work the candidate is expected to perform. It should also make the evaluation criteria visible to everyone involved in the process.
In the Age of AI, technology recruitment is becoming more efficient, data-driven, and globally connected. Companies can use AI-powered tools to identify qualified candidates, streamline hiring, and build high-performing teams for the future.
1. Start with a realistic business problem
Replace abstract questions with a scenario connected to the role. Ask candidates to design a service, improve an existing workflow or investigate a production issue.
The problem does not need to be large. A focused, realistic task can reveal more than a long collection of unrelated questions.
2. Decide how AI will be used
There are three practical options:
| Assessment approach | What it measures | When it may be useful |
|---|---|---|
| No AI allowed | Independent technical knowledge and problem-solving | Early-career hiring or foundational skills |
| AI allowed with disclosure | AI collaboration, review skills and engineering judgement | Most modern development roles |
| AI required as part of the task | Tool fluency, output validation and workflow design | AI-enabled engineering teams |
4. Include a review stage
Give candidates an AI-generated solution that contains errors, security weaknesses or unnecessary complexity. Ask them to review it and suggest improvements.
This tests a skill that is increasingly important in professional engineering: knowing when not to trust the output.
5. Add communication to the evaluation
Technical work rarely happens in isolation. Candidates must communicate with colleagues, customers and leadership teams.
Include a short explanation, design discussion or peer-review exercise. This can help reveal whether the candidate can make technical information understandable and connect it to business outcomes.
What hiring teams should look for in candidate evidence
Assessment results should be considered alongside other evidence. A portfolio, GitHub profile or previous project can show what a candidate has built, but it may not show how much AI assistance was involved or how the candidate contributed to the final result.
Structured interviews can add useful context. Ask candidates to describe a difficult decision, a failed implementation or a time when they had to learn a new technology quickly. Follow up with specific questions about their actions and results.
Reference checks can also explore practical behaviours, such as ownership, communication, technical leadership and reliability.
The goal is not to penalise candidates for using AI. AI is becoming part of normal software development, and developers who use it effectively may deliver more value. The goal is to understand how much responsibility the candidate takes for the work produced.
Recruitment partners can improve assessment quality
As technical roles become more specialised, many organisations find it difficult to assess candidates internally. Hiring teams may understand the business need but lack the time or expertise to evaluate every programming language, cloud platform or engineering discipline.
A specialist technology recruitment partner can help create a more consistent process. This may include role calibration, competency frameworks, technical screening, structured interviews and market insight.
YourCode Recruitment Group supports organisations hiring software engineers, cloud specialists and senior technology professionals across European markets. By combining technology-led recruitment with industry expertise, YourCode helps employers assess candidates against the skills, behaviours and working models that matter to the business.
This is particularly useful for companies scaling international teams, hiring contractors or building hybrid delivery models. A transparent assessment process can improve candidate experience while reducing the risk of hiring based on polished but unreliable signals.
The future of technical hiring is evidence-based
AI has not made technical skill irrelevant. It has made surface-level indicators less reliable.
A strong hiring process must now assess how candidates use tools, question outputs, solve unfamiliar problems and communicate decisions. It should measure the complete engineering contribution, from understanding the brief to maintaining the solution after launch.
The best technical assessments will be realistic, transparent and closely connected to the role. They will treat AI as part of the working environment while keeping human judgement at the centre of the decision. This includes understanding the boundaries of automated systems and their effects on energy use, water consumption and climate change as data centres expand.
For employers, the key question is no longer simply whether a candidate can write code. It is whether they can use every available tool responsibly to create secure, scalable and valuable software. That principle should guide the wider UN global dialogue about artificial intelligence, technology and the future of work.