This is not a fable. This story actually happened in front of my bewildered eyes. Armed with a custom GPT model, an analyst sought to streamline the daunting task of querying voluminous financial data. It was, at first glance, a stroke of genius—a breakthrough tool designed to make data analysis accessible to those without a background in coding. No more grappling with Python or SQL—just the power of natural language at your fingertips. The potential was enormous. But what started as a promising innovation took a bizarre and disappointing turn when the analyst’s own rigidity stifled what could have been an even greater leap forward. The project had undeniable merit. By uploading a complex dataset on historical spending and budget allocations into a finely-tuned GPT model, the analyst had transformed a traditionally opaque subject into something approachable and understandable. The model could answer questions in plain English, making it possible for anyone to interrogate budget data without needing a technical skillset. It was a tool that promised to democratize data analysis, offering a window into public spending that had previously been closed to most. But the excitement in the room dimmed when a critical question was raised: How do you know if the chatbot is accurate? With surprising naïveté, the analyst replied that it was impossible for the bot to be wrong. “It’s generating Python code,” he confidently stated, as though the act of generating code automatically ensured the validity of the results. It was a moment of dismay for those watching. The assertion was not only technically flawed but exposed a fundamental misunderstanding of how AI and coding work. Any experienced data professional understands that generating code is not the same as ensuring its correctness. Python code can easily harbor logical flaws, even if it’s syntactically perfect. The model, in interpreting and responding to the uploaded data, could misrepresent figures, make incorrect assumptions, or simply produce erroneous outputs due to the quality of the data it was trained on. Yet, the analyst stood firm, unable to consider that his model might produce mistakes—a kind of tunnel vision that can be dangerous in data-driven work. But that wasn’t where the real disappointment lay. After all, everyone can misjudge their own creations at times. The more troubling moment came when someone suggested an enhancement that could have elevated the project: Why not use the chatbot to generate thought-provoking questions about the budget? If the model had been trained on all this data, surely it could be used to stimulate new lines of inquiry and suggest areas that warranted deeper scrutiny. The analyst’s reaction was, frankly, perplexing. He flatly rejected the suggestion, insisting that any questions the chatbot might generate would be random and lack relevance. It was as though he had completely forgotten the context he’d given the model, which made it perfectly capable of producing insightful, data-driven questions. His view of the model was limited to being a data query tool—nothing more. Instead of seeing AI as a partner in analysis, he closed his mind, refusing to entertain the idea that it could be a collaborator in generating new ideas. It was a turning point—the analyst had created something that could revolutionize the way people interacted with data, only to refuse to let it evolve beyond his own narrow vision. He had built a car but refused to consider driving it anywhere new. The GPT model, which could have empowered non-technical users to explore the budget, analyze trends, and even brainstorm innovative questions, was reduced to a glorified search engine. In shutting down the very possibility of using AI as a tool for exploration, the analyst effectively undermined the transformative potential of his own project. What makes this turn of events truly disappointing is not just the missed opportunity but what it says about our broader mindset in the face of new technologies. There is a tendency to see AI as a mere extension of what we already do—a faster calculator, a more efficient script writer, a streamlined data query interface. In confining AI to such narrow applications, we do it a disservice, but more importantly, we limit ourselves. We fail to see it as a tool that can help us think, challenge our assumptions and even suggest avenues we might not have considered. This data analyst got far, indeed, with his custom GPT model. He demonstrated how new technology can lower barriers, allowing more people to engage with complex datasets. But then, just as he was poised to break through to something truly revolutionary, his brain shut down. He stopped innovating. He refused to imagine. And he dismissed AI’s greatest asset: its ability to generate new perspectives, new connections, and new questions that humans might never have formulated on their own. The lesson here is clear. In the era of AI, the greatest challenge is not building more capable models—it’s maintaining an open mind. As we push the boundaries of technology, we must ensure that we don’t box ourselves in with outdated thinking. Because when we stop dreaming and exploring, even the most powerful AI can’t help us break new ground. About Me: 25+ year IT veteran combining data, AI, risk management, strategy, and education. 4x hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here: https://docligot.com This is not a fable. This story actually happened in front of my bewildered eyes. Armed with a custom GPT model, an analyst sought to streamline the daunting task of querying voluminous financial data. It was, at first glance, a stroke of genius—a breakthrough tool designed to make data analysis accessible to those without a background in coding. No more grappling with Python or SQL—just the power of natural language at your fingertips. The potential was enormous. But what started as a promising innovation took a bizarre and disappointing turn when the analyst’s own rigidity stifled what could have been an even greater leap forward. The project had undeniable merit. By uploading a complex dataset on historical spending and budget allocations into a finely-tuned GPT model, the analyst had transformed a traditionally opaque subject into something approachable and understandable. The model could answer questions in plain English, making it possible for anyone to interrogate budget data without needing a technical skillset. It was a tool that promised to democratize data analysis, offering a window into public spending that had previously been closed to most. But the excitement in the room dimmed when a critical question was raised: How do you know if the chatbot is accurate? With surprising naïveté, the analyst replied that it was impossible for the bot to be wrong. “It’s generating Python code,” he confidently stated, as though the act of generating code automatically ensured the validity of the results. It was a moment of dismay for those watching. The assertion was not only technically flawed but exposed a fundamental misunderstanding of how AI and coding work. Any experienced data professional understands that generating code is not the same as ensuring its correctness. Python code can easily harbor logical flaws, even if it’s syntactically perfect. The model, in interpreting and responding to the uploaded data, could misrepresent figures, make incorrect assumptions, or simply produce erroneous outputs due to the quality of the data it was trained on. Yet, the analyst stood firm, unable to consider that his model might produce mistakes—a kind of tunnel vision that can be dangerous in data-driven work. But that wasn’t where the real disappointment lay. After all, everyone can misjudge their own creations at times. The more troubling moment came when someone suggested an enhancement that could have elevated the project: Why not use the chatbot to generate thought-provoking questions about the budget? If the model had been trained on all this data, surely it could be used to stimulate new lines of inquiry and suggest areas that warranted deeper scrutiny. The analyst’s reaction was, frankly, perplexing. He flatly rejected the suggestion, insisting that any questions the chatbot might generate would be random and lack relevance. It was as though he had completely forgotten the context he’d given the model, which made it perfectly capable of producing insightful, data-driven questions. His view of the model was limited to being a data query tool—nothing more. Instead of seeing AI as a partner in analysis, he closed his mind, refusing to entertain the idea that it could be a collaborator in generating new ideas. It was a turning point—the analyst had created something that could revolutionize the way people interacted with data, only to refuse to let it evolve beyond his own narrow vision. He had built a car but refused to consider driving it anywhere new. The GPT model, which could have empowered non-technical users to explore the budget, analyze trends, and even brainstorm innovative questions, was reduced to a glorified search engine. In shutting down the very possibility of using AI as a tool for exploration, the analyst effectively undermined the transformative potential of his own project. What makes this turn of events truly disappointing is not just the missed opportunity but what it says about our broader mindset in the face of new technologies. There is a tendency to see AI as a mere extension of what we already do—a faster calculator, a more efficient script writer, a streamlined data query interface. In confining AI to such narrow applications, we do it a disservice, but more importantly, we limit ourselves. We fail to see it as a tool that can help us think, challenge our assumptions and even suggest avenues we might not have considered. This data analyst got far, indeed, with his custom GPT model. He demonstrated how new technology can lower barriers, allowing more people to engage with complex datasets. But then, just as he was poised to break through to something truly revolutionary, his brain shut down. He stopped innovating. He refused to imagine. And he dismissed AI’s greatest asset: its ability to generate new perspectives, new connections, and new questions that humans might never have formulated on their own. The lesson here is clear. In the era of AI, the greatest challenge is not building more capable models—it’s maintaining an open mind. As we push the boundaries of technology, we must ensure that we don’t box ourselves in with outdated thinking. Because when we stop dreaming and exploring, even the most powerful AI can’t help us break new ground. About Me: 25+ year IT veteran combining data, AI, risk management, strategy, and education. 4x hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here: https://docligot.com About Me: 25+ year IT veteran combining data, AI, risk management, strategy, and education. 4x hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here: https://docligot.com

The is an opinion piece based on the author’s POV and does not necessarily reflect the views of HackerNoon.

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