Well, not exactly, fellow writers. But with all the hype over the past couple of years, you’d think the robot invasion has taken place, particularly in the realm of generative art. Everywhere you look—news articles, social media posts, and so-called experts are touting the rise of machines creating content that once only humans could craft.

Amidst this buzz, a flood of misconceptions and misinformation has swirled, painting a picture that’s not entirely accurate. In the writing community, particularly, these misconceptions have been amplified by biases. I’m not here to defend the AI industry—I believe there was wrongdoing on their part, like downloading illegally from overseas pirate sites. However, I’ve seen tons of misinformation in writers’ forums and even in writers’ meetings.

My objective in this series is to illuminate the realities of present-day AI for writers. If after reading these articles you, as a writer, still hate and detest AI, that’s your choice. But at least you’ll be hating AI itself—not some distorted perception of it.

A common depiction of generative AI is that it contains a large database of images and text. It then uses some algorithms to figure out and cobble together new images or text based on a user’s prompt. A simple back-of-the-envelope calculation shows this is far from the truth. Take the stable diffusion models, trained on the LAION-5B dataset, which is estimated at around 240 TB of already compressed images (particularly in JPEG format). However, the size of a trained stable diffusion model can be downloaded at just a few gigabytes. Common sense tells us that you cannot store 240 TB of data in a space that’s four to five orders of magnitude smaller. Additionally, these models contain no algorithmic code specifically to assemble images, and very little if any algorithmic code for interpreting text.

It is surprising how ignorant people are about what LLMs actually do, even in the very sectors that actively leverage large language models. Take one step back from that—say, to businessmen and the media—and the picture gets even more distorted. No wonder so many writers end up totally misinformed about them.

I did, however, run across an article—though I wish I remembered who wrote it and where it was published—that I think was related to the stock forecasting of NVIDIA. The reporter wrote, to paraphrase: an LLM predicts the next word in a string. Period. Finally, someone who actually understands.

In a very real sense, an LLM is the opposite of machine intelligence. It is not “thinking.” It is not “reasoning.” It is simply a massive statistical model. It has no semblance of reasoning, no capacity even for basic if–then logic. At one point, I went through a phase of calling these AI systems (both text and image) BASM: big ass statistical models. It was my way of driving home that point. While the phrase is a cute encapsulation of what they are, it doesn’t really capture what they do.