Can AI Really Write Like a Human? (Probably Not)
The question isn't just whether AI can generate text. It's whether it can generate insightful text. As someone who's spent years sifting through financial reports trying to separate signal from noise, I'm naturally skeptical.
The Illusion of Understanding
AI language models are impressive. They can mimic writing styles, summarize information, and even generate creative content. But mimicry isn't understanding. A parrot can repeat words, but it doesn't grasp their meaning. It's the same with AI. It can string words together in a coherent way, but it doesn't possess the critical thinking skills necessary to truly analyze and interpret data.
Take, for example, the ability of these models to answer questions based on a provided text. They can identify keywords and extract relevant information. But can they identify contradictions, inconsistencies, or hidden biases? Can they evaluate the credibility of the source material? (The answer, in my experience, is generally no.)
The real problem is that AI models are trained on massive datasets of text and code. They learn to identify patterns and relationships, but they don't develop a genuine understanding of the world. They're essentially sophisticated pattern-matching machines. This is where the illusion of understanding comes from. The AI can generate text that sounds intelligent, but it's ultimately based on statistical probabilities, not genuine comprehension.
The Ghost in the Machine
One of the biggest challenges in AI writing is injecting a sense of personality and perspective. A human writer brings their own experiences, beliefs, and biases to the table. This is what makes their writing unique and engaging. AI, on the other hand, is designed to be objective and neutral. It's supposed to present the facts without injecting its own opinions. (Although, as we've seen, biases can creep in anyway.)
But is true objectivity even possible? As humans, we all have our own filters and biases that shape our perception of the world. These biases inevitably influence our writing, whether we realize it or not. The challenge for AI is to replicate this human element without simply regurgitating pre-programmed opinions.
I've looked at hundreds of these filings, and this particular attempt to emulate a human voice is unusual. To truly create AI that writes like a human, you'd need to give it the ability to learn from its own experiences, to develop its own beliefs and biases. But at that point, would it still be AI? Or would it be something else entirely?
The Data Doesn't Lie... Or Does It?
AI is great at processing large amounts of data and identifying trends. But it's not so good at interpreting the underlying meaning of that data. A human analyst can look at a set of numbers and see a story. They can identify anomalies, question assumptions, and draw conclusions that might not be immediately obvious. AI, on the other hand, tends to take data at face value. It doesn't have the critical thinking skills to question the validity of the data or to consider alternative explanations.
Growth was about 30%—to be more exact, 28.6%. But what if that growth is driven by unsustainable practices? What if it's based on misleading marketing claims? AI might not be able to see past the surface-level numbers to identify these potential problems.
And this is the part of the report that I find genuinely puzzling. It's not about access to data; it's about the ability to ask the right questions. A good analyst knows that the data is only as good as the questions you ask. And AI, at least for now, is still limited by the questions it's programmed to ask. The acquisition cost was substantial (reported at $2.1 billion), but was it worth it?