Ethical AI in Web Development: AI's Impact on Developers and the Industry

Artificial Intelligence (AI) has rapidly become intrinsically integrated into web development, offering automation, enhanced productivity, and new ways to solve problems. From AI‑powered code suggestions to automated debugging, AI tools are reshaping the way that we as developers work and the code that we produce. However, as AI becomes more prevalent, it also raises ethical questions, particularly around its impact on developers, security, and hiring trends.

Are AI‑driven tools genuinely helping developers, or are they undermining the profession? Is AI making web development more efficient, or is it encouraging shortcuts that could degrade long‑term expertise? As companies reconsider the need for junior developers and security concerns around AI‑generated code continue to emerge, it's worth asking: what does an AI‑assisted future mean for the web development industry?

Today, I'm focusing on how AI is changing web development, particularly its impact on developers, hiring practices, security risks, and the open‑source community. For a discussion on AI's ethical implications around sustainability, responsibility, and the future of development, take a look at my companion article "Ethical AI: Sustainability, Ethics, and the Future".

AI and the Decline of Junior Developers

AI‑powered coding tools like GitHub Copilot and ChatGPT are becoming ever more common in web development. For junior developers, these tools offer an easy way to generate code quickly, but at what cost? Whilst AI can speed up workflows, there is a growing concern that it's being used as a shortcut rather than as a learning aid. If junior developers rely too heavily on AI to write their code, they may not develop the essential problem‑solving skills that experienced developers have built over time.

AI as a Crutch: Are Juniors Learning?

One of the biggest risks with AI‑assisted coding that I've seen is that it can become a crutch. It is easy to ask AI to generate a function, copy it into a project, and move on without fully understanding how it works. The problem is when things go wrong; many junior developers struggle to debug the code because they never truly wrote it in the first place.

I've seen this first‑hand with my students. In one of my previous roles, I mentored newly hired junior developers who had joined the company straight out of development bootcamps with varying degrees of earlier formal development background (usually very little). Whilst pair‑programming or reviewing pull requests with them, I would often ask them to explain the code that they had submitted as a way of reinforcing their understanding.

On more than one occasion, I received a shrug and the explanation that AI had told them to do it. They didn't understand the code that they had written and were proposing to merge into a live production codebase. This raises an important issue: if juniors aren't learning to think through problems and write their own solutions, are they really developing their skills as engineers?

AI should be a tool to assist learning, not a replacement for it. There's a big difference between using AI to fill in gaps in knowledge compared to using it to replace learning altogether. If new developers never take the time to build their own understanding, they risk becoming reliant on AI rather than growing into skilled engineers and progressing past the 'junior' stage of their careers.

The Hiring Shift: Fewer Junior Developers

Beyond learning challenges, AI is also affecting hiring trends. As AI becomes more capable, some companies are starting to question whether they even need junior developers. If AI can generate basic boilerplate code and automate repetitive tasks, why would they hire someone inexperienced to do the same work?

This shift is already happening. My LinkedIn feed is littered with examples of companies announcing that they will no longer hire at the junior level. Some hiring managers have admitted they are recruiting fewer juniors because AI can handle entry‑level coding tasks. But if companies stop hiring at the junior level, how will the next generation of developers gain experience?

Without a steady pipeline of junior developers learning and growing into mid‑level and senior roles, there's a very real risk that the industry will face a long‑term problem where the lack of entry‑level opportunities leads to fewer experienced developers in the future. This in turn could make the industry all the more reliant on AI.

This raises a difficult question... are we training developers, or are we training AI to replace them?

AI as a Productivity Booster, Not a Replacement

Despite concerns about AI's impact on junior developers, it is important to recognise that AI is not all bad. When used correctly, AI can be an incredibly powerful productivity tool, helping developers work faster, automate repetitive tasks, and improve efficiency. The key is to use AI as an assistant, not a substitute for real coding skills.

AI as an Assistant for Developers

One of AI's biggest strengths is its ability to automate repetitive coding tasks. Writing boilerplate code, setting up basic project structures, and generating simple functions are all areas where AI can save time.

For example, tools like GitHub Copilot can suggest CSS rules, API requests, and reusable components, reducing the need for developers to write the same patterns repeatedly. Instead of spending time on routine setup work, developers can focus on solving more complex problems.

AI is also proving to be useful for debugging. AI‑powered tools can identify syntax errors, flag inefficient code, and even suggest fixes. Rather than manually searching through documentation, developers can use AI to get immediate guidance on common mistakes. This is especially helpful when dealing with unfamiliar languages or frameworks.

However, AI is not a perfect debugging tool. It can still make incorrect suggestions, and developers need to understand the underlying issue rather than blindly trusting AI‑generated fixes. This goes hand‑in‑hand with the points I made earlier about junior developers. I often use ChatGPT to help me when it comes to transforming or manipulating data from one structure into another. Because I understand the code it suggests, I then know whether it is any good or needs further refinement. That experience only comes with time and exposure to problem‑solving.

When used properly, I don't believe that AI is replacing developers. It is making them more productive. The challenge is ensuring that developers use AI to assist their work, not to do their work for them.

Levelling the Playing Field

AI is not only making developers more efficient but also making learning and development more accessible. In the past, becoming a web developer required spending countless hours reading documentation, digging through Stack Overflow, and experimenting with different approaches. Now, you can use AI to provide instant explanations and suggestions, bounce ideas off, and formulate plans, all helping developers of all levels improve their skills.

AI as a Learning Tool

For those who are new to web development, AI can act as a mentor, offering explanations for complex concepts, breaking down coding patterns, and even suggesting best practices. Instead of spending hours searching for solutions, developers can ask AI direct questions and get immediate answers, similar to how they might interact with a senior developer.

I feel that this accessibility is particularly valuable for those who don't have access to formal education or mentorship. AI‑powered tools lower the barrier to entry, allowing more people to learn to code and contribute to the industry.

AI and Accessibility in Web Development

AI is also playing a role in making the web more accessible. AI‑powered tools can automatically detect poor contrast ratios, flag missing alt text, and suggest improvements for screen reader compatibility. Instead of relying solely on manual audits, developers can now use AI‑driven accessibility checkers to ensure their websites meet best practices.

For businesses and developers focused on inclusive design, AI can help highlight areas for improvement, making it easier to build websites that work for all users. This is a clear example of how AI can be used as a force for good, not just for productivity but also for ethical and inclusive development.

A Balancing Act

Whilst AI is clearly helpful, it is important to strike a balance. I've already discussed above that developers should use AI to enhance their skills, not replace their learning process. The most effective approach is to treat AI as a learning assistant, a productivity tool, and a way to improve accessibility rather than something to blindly trust and depend upon.

The Limitations of AI in Web Development

AI tools have made significant strides in assisting developers, but they are still far from perfect. That's not to say that this isn't going to continue improving over time, but whilst they can generate code, automate tasks, and improve efficiency, they also come with notable limitations. AI is often only partially correct, and in some cases, it can generate code that is completely incorrect or even insecure. So: how much should we trust AI‑generated code?

AI is Only Ever 75% Accurate

I'll happily admit, I've made that 75% value up. It comes from a place of personal experience, but I've no empirical data to back up just how often AI is wrong. AI is impressive for sure, but it is not infallible. Many developers have observed that AI‑generated code is useful but rarely perfect. In many cases, AI produces solutions that are roughly accurate, leaving developers to refine and debug the remaining issues themselves. This means that anyone using AI needs to understand what they are doing rather than copying and pasting suggestions without review.

One of the issues I see frequently is that AI can generate outdated or inefficient code simply because it has learned from outdated data. Since models like ChatGPT and Copilot rely on training data from the past, they may suggest deprecated functions, inefficient algorithms, or security risks. Developers must be vigilant in making sure that AI‑generated solutions align with current best practices and with the coding standards already in place for their project.

When AI Makes Things up

One of AI's biggest ‑ and well‑recognised ‑ flaws is its tendency to hallucinate. This is where it can generate code, functions, or even entire libraries that simply do not exist. I've personally seen many cases where AI suggests functions that seem reasonable at first glance but fail when tested because they were never part of any real programming language or framework.

This can be particularly dangerous for junior developers who may not realise that the AI has produced incorrect information or made‑up code. If AI‑generated code is blindly trusted, it can very easily introduce hard‑to‑detect bugs or security vulnerabilities.

Recognising AI's Mistakes: A Generational Advantage

In my experience, more experienced developers tend to spot AI‑generated errors quickly because they already know what good code should look like. They can tell when a function seems off, where syntax is slightly wrong, or when an approach is not efficient.

However, for junior developers who rely too heavily on AI, this ability is not yet developed. If they never take the time to understand the fundamentals, they may struggle to differentiate between a useful AI suggestion and a misleading one. I can see a scenario where this creates a gap in the industry, where experienced developers know how to challenge AI‑generated code, but newcomers accept it at face value.

AI can be a powerful tool, but it is not a replacement for critical thinking. Developers must learn to question and verify AI‑generated code rather than just assuming that it is always correct.

AI and Security Concerns

AI is making development faster, but it is also introducing new security risks. Developers using AI‑generated code must be mindful of how it handles sensitive information, security vulnerabilities, and proprietary data. Without proper safeguards, AI can expose confidential details, introduce weaknesses, and even leak credentials.

AI Leaking API Keys and Sensitive Data

One of the more troubling issues with AI‑powered coding tools is their tendency to auto‑complete sensitive information. There have been multiple reports of AI tools like GitHub Copilot accidentally generating valid API keys, credentials, and other private data. This happens because AI is trained on vast datasets, and if similar keys have appeared in public repositories, it may reproduce them in code suggestions.

For example, Copilot has been known to suggest Google Maps API keys, AWS credentials, and database passwords within code snippets. If a developer accidentally commits this AI‑generated key to a public repository, they could expose their project to potential security breaches too.

To mitigate this, developers should:

• Never use AI‑generated credentials

  in production code.

• Regularly scan for exposed keys

  using tools like GitGuardian or TruffleHog.

• Use environment variables

  to store sensitive information instead of hardcoding credentials.

Uploading Proprietary Code to AI Models

Another growing concern is that developers often copy and paste proprietary code into AI tools for debugging or optimisation. This can be risky, as it may unintentionally expose private application logic, business‑sensitive algorithms, or even confidential client data.

Most AI models do not provide clear transparency about how user data is stored, processed, or used. Whilst some companies claim that input data is not retained, others have been criticised for using interactions to further train their models. This means there is always a risk that proprietary code could be absorbed into an AI's training dataset.

DeepSeek and National Security Concerns

There has been growing debate about where AI tools are based and who controls them. One particular concern is DeepSeek, a China‑based AI model. Given the legal landscape around data privacy and government surveillance in China, some developers worry that proprietary code or personal data uploaded to DeepSeek could be accessed or retained in ways that are not fully disclosed.

This raises an important ethical question: should developers be cautious about where they submit proprietary data? Companies that deal with highly sensitive applications, financial data, or government systems need to think carefully before using AI tools that are operated by entities outside their jurisdiction.

Striking a Balance Between AI and Security

AI can assist developers in writing better security practices, such as suggesting input sanitisation or highlighting vulnerabilities. However, the risks must be managed carefully. Developers should:

• Use AI responsibly

  , avoiding the upload of confidential or proprietary code.

• Double‑check AI‑generated security recommendations

  , rather than assuming they are safe.

• Stay informed about where AI models store and process data

  , choosing tools with clear privacy policies.

AI can be a useful assistant in software development, but security must remain a priority. A careless approach to AI‑generated code could expose businesses to data breaches, compliance issues, and reputational damage.

AI and Bias in Web Development

AI models are only as good as the data they are trained on. Whilst AI can generate code, assist with design decisions, and even recommend accessibility improvements, it can also perpetuate biases hidden within its training data. If AI‑generated code reflects past biases, how do we ensure the web remains fair and inclusive?

The Hidden Bias in AI‑Generated Code

AI does not think for itself. It learns patterns from existing data, which means that any biases present in that data can be reinforced and repeated. In web development, this can manifest in several ways:

• Exclusionary design patterns

  : AI may generate layouts or colour schemes that do not prioritise accessibility.

• Discriminatory hiring tools

  : AI models used in recruitment may favour certain demographics over others if trained on biased hiring data.

• Algorithmic bias in user experiences

  : AI‑generated personalisation features may inadvertently exclude or favour certain users.

Studies have shown that some AI‑generated image recognition models have misclassified people based on race or gender, simply because they were trained on datasets that were not diverse enough. Similar risks apply to AI‑generated web layouts, accessibility checks, and even automated moderation tools.

Ensuring AI Aligns with Best Practices

Developers need to be critical of AI‑generated suggestions and ensure they align with modern best practices. AI may recommend outdated coding patterns, omit accessibility features, or fail to consider inclusivity in design.

To mitigate these risks, developers should:

• Manually review AI‑generated recommendations

  to ensure they meet accessibility and ethical standards.

• Use diverse training datasets

  where possible, ensuring AI tools are not reinforcing existing biases.

• Stay informed about ethical AI development

  , following discussions on bias detection and fairness in machine learning.

The Role of Developers in Ethical AI

Ultimately, AI does not make decisions, developers do. If AI suggests something that is exclusionary or biased, it is up to the developer to spot it, correct it, and ensure fairness. Treating AI as an assistant rather than an authority is the only way to make sure that the web remains accessible, fair, and inclusive for all users.

AI in Open Source Development

The open‑source community has always thrived on collaboration, human creativity, and shared knowledge. AI‑generated code is now entering this space, raising questions about authorship, contribution quality, and the impact on developer communities.

Diluting Open‑Source Contributions

Open‑source projects rely on developers sharing well‑thought‑out, tested, and documented code. AI‑generated contributions, however, can be lower in quality and may lack proper context or understanding. Since AI generates code based on patterns rather than genuine problem‑solving, some worry that the rise of AI‑assisted contributions could lead to:

• More low‑quality pull requests

  , requiring maintainers to spend extra time reviewing and fixing AI‑generated code.

• A decline in meaningful contributions

  , as developers rely on AI instead of writing well‑structured solutions themselves.

• A loss of innovation

  , as AI can only generate code based on existing knowledge rather than coming up with truly novel ideas.

Who Owns AI‑Generated Code?

There are also legal and ethical questions about ownership. If a developer submits AI‑generated code to an open‑source project, who owns the rights to that code? Some licensing models are unclear on whether AI‑generated contributions should be treated differently from code that has been human‑written.

This becomes more complex when considering that AI tools like Copilot are trained on vast amounts of publicly available open‑source code. If AI generates code that is similar to an existing open‑source project, does this violate licensing agreements?

Ensuring Responsible AI Use in Open‑Source

To prevent AI from negatively impacting open‑source development, contributors should:

• Only submit AI‑generated code they fully understand and can justify

  .

• Manually test and document all AI‑assisted contributions

  to ensure quality.

• Be mindful of licensing issues

  when using AI‑generated snippets in projects.

AI can be a useful tool, but open‑source development thrives on human oversight, collaboration, and innovation. Without careful management, AI‑generated code could create more problems than it solves.

Wrapping up

AI is changing the landscape of web development, offering new levels of efficiency and automation. It can generate boilerplate code, assist with debugging, and even improve accessibility. However, these benefits come with serious ethical considerations.

Over‑reliance on AI has the potential to create a whole generation of developers who lack problem‑solving skills, as juniors skip foundational learning in favour of AI‑generated solutions. Hiring trends are already shifting, too, with some companies reducing junior roles because AI can handle the basic tasks that a junior might otherwise be hired to do. This raises concerns about the long‑term sustainability of the industry.

AI also introduces security risks, from leaking API keys to exposing proprietary code in AI models. Developers must remain cautious about how they use AI, ensuring they do not compromise privacy or introduce vulnerabilities into their projects.

At the same time, AI has the potential to level the playing field, making web development more accessible, assisting with learning, and improving web inclusivity. The key is to treat AI as an assistant rather than a replacement, using it to enhance rather than undermine development practices.

Key Takeaways

• AI is reshaping web development

  , but it should be used as a tool to assist rather than replace developers.

• Junior developers risk losing learning opportunities

  , as AI allows them to bypass key problem‑solving skills.

• Hiring trends are shifting

  , with some companies reducing entry‑level roles due to AI automation.

• Security concerns must be addressed

  , including the risk of AI leaking sensitive data and proprietary code.

• Bias in AI‑generated code

  must be carefully managed to ensure fairness and inclusivity.

• Open‑source development faces challenges

  , as AI‑generated contributions raise concerns about quality and ownership.

AI is a powerful tool, but it is not a silver bullet. Developers must remain critical of its limitations and take responsibility for how they integrate AI into their workflows.

If you found this discussion insightful or even just a little thought‑provoking, you may also be interested in the ethical considerations around AI's environmental impact, its role in responsible development, and the future of AI in the industry, which will be this article "Part 2". Have a read here: "Ethical AI: Sustainability, Ethics, and the Future".