8d • AI News
AI Generated Content – Should You Use It?
What is Chat GPT and should you use it?
ChatGPT is a variant of the popular language model GPT-3, designed specifically for use in chatbots and other conversational systems. It has the ability to generate human-like responses to user input, making it an attractive option for developers looking to build realistic chatbots. However, like any technology, ChatGPT has its own set of pros and cons that developers should consider before using it.
So as we can see, ChatGPT was originally built to be a support Chatbot. It was for use in AI support systems.
I asked ChatGPT5 what it saw as the pros and cons of using AI in 2025 and how had things changed since it was first released to the general public.
Here's what it came back with;
Great question — the pros and cons of AI, especially in 2025, are more nuanced than ever. And yes — things have definitely changed since AI first went public in the earlier years. Here's how I see it (warts and all).
What "AI" means now (2025) vs early days
Before diving into pros/cons, it helps to clarify that when people say “AI” today, they often mean machine learning, deep learning, and generative AI (e.g. text, image, audio generation). Early public AI systems were far more limited (rule-based systems, narrow expert systems, simpler automation). So the context has shifted.
Pros of using AI (in 2025)
- Scale & speed - AI can analyse vast amounts of data in seconds (something no human team could do). It can spot patterns, trends, and anomalies faster than we ever could.
- Automation of repetitive / tedious work - Tasks like data entry, summarising documents, first drafts, initial customer support — these get offloaded to AI, freeing humans to focus on more creative or strategic work.
- Augmentation, not just replacement
- The “AI as assistant” model — where humans + AI together produce better outcomes — is more common now. For example, AI helps authors with brainstorming, marketers with campaign ideas, engineers with code scaffolding.
- Democratization of tools - In the early days, only big tech orgs or rich labs could afford large-scale AI. Now, thanks to open models, APIs, compute access, even small businesses or individuals can use AI (or plug into existing ones). That levels the playing field.
- Innovation in new domains - AI is pushing breakthroughs in healthcare (diagnostics, drug discovery), climate modelling, personalized education, creative fields (music, visuals), and more. It’s a force multiplier for possibility.
- Cost reduction (in many cases) - Over time, AI-powered systems reduce labour/time costs. Once integrated, marginal cost for many tasks is low.
- Continuous improvement / learning - Modern AI systems often adapt (or can be updated) as new data comes in, improving over time if built well.
Cons & risks of AI (2025)
- Bias, fairness, and discrimination - AI models are trained on human data — including all the biases in history. If not carefully audited, AI can perpetuate or amplify unfair treatment (in hiring, insurance, policing, lending, etc.). Many systems still struggle with demographic fairness.
- Opacity / “black box” problems - For many models, especially large ones, it’s hard to know why a particular decision or text was produced. That lack of interpretability makes debugging, accountability, and trust harder.
- Misinformation / hallucinations - Generative models sometimes “make up facts,” confidently stating falsehoods as truths. In 2025, this remains a big challenge — relying blindly on AI outputs is dangerous.
- Intellectual property, plagiarism, originality concerns - Who owns the output? Did the AI use copyrighted data in training and replicate too closely? Questions around attribution, originality, and rights are thorny.
- Job disruption and transition costs - Some roles are at risk of being automated (especially the repetitive or lower-skilled ones). Even where new jobs emerge, people may struggle with reskilling, displacement, or inequality in who benefits.
- Dependence & de-skilling - Over-reliance on AI for thinking or creativity might atrophy human skills over time — people might lose the practice of critical thinking, writing, or problem-solving in domains now handled by AI.
- Security, misuse, malicious use - AI can be used to generate convincing phishing emails, deepfakes, automated hacking tools, even bioweapon designs in worst-case scenarios. The risk landscape expands.
- Privacy, data surveillance, and consent - Many AI systems rely on huge datasets with personal or sensitive data. How that data is collected, used, consented upon, or anonymized is a major concern. Misuse or leaks are serious risks.
- Concentration of power & inequality - Most cutting-edge AI infrastructure, research, compute, and datasets are controlled by a few big players or countries. That centralization can lead to power imbalances, monopolies, or geopolitical tension.
- Safety and alignment risk (in future / advanced systems)As AI systems become more capable, ensuring their goals align with ours (i.e. “you optimise X, but don’t kill the cat”) becomes nontrivial. The field of AI alignment / AI safety is trying to address this.
How things have changed from “first public AI” to 2025
Capabilities - Vast leap in flexibility and “general-like” behaviour in narrow domains
Accessibility / cost - More voices, more use cases, more innovation coming from smaller actors
Trust / expectation - The “wow” factor is less, but ethical, social issues are more front-of-mind
Mistakes / “hallucination” - More generative, more creative — but also more risk of confident wrong outputs. The “trust but verify” approach is more vital
Regulation & policy - Governments and international bodies are more engaged
Scale of impact - Integrated into many sectors (healthcare, finance, marketing, media, security). Ripple effects are bigger; interdependencies more complex.
In short: AI has shifted from “interesting toy / research curiosity” to a core infrastructure layer in many fields. And with that shift, the stakes, risks, and responsibilities have grown proportionally.
My “take” (as your skeptical-but-hopeful friend)
I see AI in 2025 as a powerful amplifier — for good and bad. If you build systems or businesses with AI, you must build in ethics, transparency, human oversight, and error handling. The gains are real (speed, insight, creativity), but so are the costs (trust, damage from errors, inequality).
If I were you, I’d lean into AI but cautiously. Start with “assist me, don’t replace me” setups. Use human-in-the-loop patterns. Always validate outputs with critical thinking. And watch for regulation, public perception, and fairness issues—they’re as real as the tech itself.
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AI Generated Content – Should You Use It?
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