Tag: ai

Sept. 18, 2026 | VIBE-CODING PT. 2

Part 1 here.

Although I personally believe everyone should learn coding basics (even in the age of AI), I begrudgingly admit that it is NOT essential for many HR and OD professionals.

What is essential is understanding the underlying statistical concepts. Either you or someone on your team must be able to validate the output of the vibe-coded analyses.

SOMEONE has to know…

1. If the procedure was done correctly,
2. If the results are correct,
3. And even more fundamentally… What it means.

Large language models like ChatGPT and Claude can do incredible things, but they can also provide incorrect or irrelevant information (what we call “hallucinations,” although that’s a misnomer). We’ve already seen countless incidents where workers submitted or otherwise acted on AI output that turned out to be wrong, with disastrous consequences.

In these cases, the workers had no idea that the AI-generated output was incorrect—they didn’t understand the process, so they didn’t know how to verify the accuracy of the output.

And ironically that is what makes AI so alluring. It seems magical precisely because we don’t fully understand how it produces results.

But the fact that you don’t understand how AI works is NOT its value—that’s its liability.

In summary, Yes, you can rely on vibe-coding to bypass some or most of the mechanical aspects of data analysis.

But you cannot bypass statistical understanding, or else the results might as well be gibberish.

Sept. 17, 2026 | VIBE-CODING PT. 1

Question: Do I really need to understand coding and statistics to do my job effectively? Can’t I just vibe-code all of my analyses with AI?

ANSWER: Yes and no.

You do NOT necessarily need to understand coding… but this has been true since long before LLMs exploded in popularity. For a long time now, there have been tools such as MS Excel and IBM SPSS that allow users to completely bypass coding activities if desired.

The surging emphasis on coding (e.g., R, Python) over the last several years was linked to increasing employer preference for familiarity with those open-source tools, because they are free and infinitely customizable.

Now we have AI, which is quite good (and fast!) at coding, especially for straightforward and common coding tasks. This enables vibe coding, where you speak to an LLM in human language, and the LLM translates that into computer language (code).

At first, this looked like more of a back-and-forth process…

1. The human explains the goal in a chat interface and gets some response
2. The human then copy/pastes that output into their coding software, which in turn produces output
3. The human might then copy/paste that output back into the chat interface for further steps
4. Et cetera

But it’s becoming increasingly common for users to integrate the AI directly into their software.

For example, if I activate Posit AI inside RStudio, I can write the following line in my script…

# Create a random dataset of 100 scores, then show me the mean, SD, and histogram

…and then Posit AI will autocomplete something like the following starting on the next line…

data <- rnorm(100)
mean_data <- mean(data)
sd_data <- sd(data)
hist(data)

I could quibble about a few things (e.g., this doesn’t actually “show” me the mean or standard deviation; why does it assume a random distribution is Gaussian? etc.), but this is clearly pretty close to what I was asking for.

The implications are enormous. This does allow for almost completely bypassing the coding aspect of data analysis. And there’s nothing inherently wrong with that.

But the other shoe drops tomorrow…