It’s clear that the toothpaste is out of the tube. AI content creation is here, and its use is only going to grow. Everyone from big brands to solo bloggers is using ChatGPT, Google’s Gemini, and other generative AI tools to churn out articles, social media posts, and webpage content. However, while these tools are great at writing content that is clear and easy to read, they are not without limitations. In this article, we’ll explore some of the common pitfalls of AI content creation that should be avoided to maintain quality and diversity in your work should you choose to incorporate AI into your workflow.
1. Content Quality and Diversity
Homogeneity: It used to be pretty easy to spot a bad website—think typos everywhere and poor translations that make no sense. Now, with everyone starting to use AI for their content, it’s a lot harder to tell who’s legit. Everyone kind of sounds the same, like they all hired the same writer. It’s tough to find truly great writing because even though the worst content has gotten better, the best hasn’t really moved up. Everyone’s stuck in this middle ground where it might not be bad, but it’s not exactly good either.
Overly Verbose and Grandiose: Anyone who has used AI to write for them has probably experienced the more-is-more approach of these systems. AI content commonly produces long-winded pieces of text that lack conciseness and focus more on using overly grandiose language that feels very sales-y even when you’re not trying to sell a product. Though you can get better, more concise responses with better prompts, the overly long text that seems like someone typing next to a worn-out thesaurus can be tedious to read, and less effective at converting users.
Shallow Content: While AI can generate content on virtually any topic, it sometimes fails to go too deep into subjects, offering a surface-level perspective that might not satisfy readers seeking in-depth analysis or insights. You can prompt your way to stronger, more thorough answers, but this time spent conversing with the AI could be used to read through sources with different perspectives.
2. Intellectual Property and Privacy
Intellectual Property Issues: There are significant concerns around the creation of text and image content that may infringe on copyrights or trademarks. This issue is particularly relevant as AI tools can produce outputs similar to existing works without clear distinctions, and they rarely give you text with cited sources. Many sites are blocking AI crawlers to prevent them from incorporating their content in their training systems.
Privacy: When using AI systems such as ChatGPT, it’s important to be aware that these systems learn from interactions and store data from these exchanges. Handling sensitive or personal information with AI presents significant risks, including the potential for breaches of confidentiality. This is a critical factor for any user that processes personal information, as there is always a risk that, despite the intentions of using the data responsibly, systems can be hacked. If compromised, this could lead to unintended disclosure of sensitive information and potential harm. Therefore, you always need to consider the security of the data and the implications of its potential exposure when using AI technologies.
3. Understanding and Contextual Limitations
Limited Understanding of Context: One of the challenges with generative AI models is their limited ability to navigate the nuances and subtleties of specific topics and contexts. These models, like the predictive text function on your phone, work through pattern recognition and statistical probabilities rather than conscious thought. Even though they are trained on massive amounts of data and can be quite impressive, generative AI can still struggle with humor, sarcasm, and complex questions.
Incomplete Responses: Because of AI’s limitations in contextual understanding, it can lead to responses that seem incomplete or off-target. For example, when prompted for detail on specialized subjects, the AI might provide overly wordy or vague answers, or even repeat information in an attempt to appear more knowledgeable. AI-generated answers may not fully address users’ queries or concerns, leading to a sense of dissatisfaction or the need for further clarification.
4. Bias and Ethical Considerations
Bias: AI systems mirror the biases found in the datasets used to train them. When training data includes prejudiced representations or even lacks data on a certain topic, the resulting content can seemingly endorse biases or produce incorrect information due to a lack of proper information. This can result in content that discriminates against or misrepresents certain groups, leading to fairness and equity issues in various AI applications, from recruitment to law enforcement and beyond.
Ethical Considerations: Much of the discussion so far has been around problems caused by AI’s poor performance. However, issues also occur when AI performs too well. When generative AI creates realistic and persuasive content, such as deepfakes or misleading information, it introduces serious ethical challenges. This raises concerns about the integrity of information and the potential for malicious uses in scenarios like the dissemination of fake news and impersonation scams. These ethical considerations highlight the urgent need for guidelines and oversight to ensure AI technology is used responsibly and safely.
5. Reliability and Up-to-dateness
Outdated Information: AI-generated content relies on the training data it was built from, leading to potential inaccuracies or outdated information for topics that are constantly changing due to new information. Some systems can access the internet now, but this can introduce new biases. If users cannot verify the authenticity of the information within the system itself, they have to turn to conventional research methods.
Overreliance: Overreliance on AI for tasks such as content creation can lead to a decline in human skills and less oversight. Also, these systems depend on online servers that occasionally experience downtime. When these systems are unavailable, it can severely disrupt those who depend on AI for day-to-day tasks, potentially causing significant issues if they lack the skills to make up for it.
6. Operational Limitations
Usage Limits: Many AI platforms enforce usage limits that cap the amount of content that can be generated within a certain timeframe or under specific conditions. These restrictions are often in place to manage server load and ensure service stability but can significantly limit scalability and make it difficult for users to work through complex tasks with AI.
Scaling Issues: As the use of AI for generating content becomes more widespread, scaling up while maintaining the quality and distinctiveness of the output presents challenges. Some are already trying to create mass amounts of content at scale and Google has responded by de-indexing hundreds of these sites through manual action. More AI content can lead to a dilution of unique value that human-written content used to offer. This saturation can make it harder for any written content to stand out, whether you are using AI to create spam or not.
In light of these limitations, it’s crucial for writers of web content to adopt a balanced approach, using AI for its strengths while being mindful of its weaknesses. While Google may have changed its tune on scaling with AI content, it has been consistent on suggesting quality and relevance as factors that guide your content creation. Writers should focus on producing content that genuinely benefits their audience. If they choose to use AI as a tool to enhance, but not replace, the human element of writing, then they should be fine.
