We’ve given in to using LLMs for writing at work. From emails and Slack comments to full-on documentation, everything is AI-generated. However, I’ve realized it only ever saves me time when I’m being lazy. That is, it’s great at converting meetings into transcripts, summarizing those transcripts into action items, and keeping information accessible.
But it’s pretty bad because the result is ridden with invisible errors. But what counts as an error if you trust the LLM and never meticulously read the output? And how much time are you really saving if you end up spending that time proofreading?
I went back to a meeting transcript after a developer struggled to turn my AI-generated summary into a coherent ticket. Looking through the source transcript, I noticed that at one point it recorded the word "embroidery." I mean, we work for a large telecom company, the chances of me using that word are slim to none. Nevertheless, the AI summarized the conversation and included "embroidery" as an action item.
In the words of Claude himself:
It's not just true, it's false.
I chuckled, but it made me think about all the BI slop we generate daily in the name of using AI technology. It feels helpful. Creating massive documents makes us look productive. Generating a pull request with changes across hundreds of files seems impressive. Asking AI to validate an idea feels instructive. Unless you take the time to scrutinize the results.
I can only imagine what the world will look like when we feed all this slop back into the machine.