What Professionals Really Think About AI

By Karen Spinner

This is a weird time in history to be a writer. Professionally, I've been working to identify the most helpful (and most dangerous) AI writing workflows for my tech industry clients. At the same time, I've been seeing a lot of headlines about how writers will be obsolete and how most office jobs won't be far behind. The news Google is suggesting for me isn't exactly reassuring, either.

A random screenshot of my Google news feed. Scary!

Perhaps not surprisingly, AI is all over my LinkedIn feed. Posts that are strongly pro- and anti- AI both seem to get a lot of traction. My own most popular post is basically a lengthy complaint about the ongoing need for both AI writing detection and "copy humanizer" models.

But research across multiple domains suggests that emotional, polarizing content performs well, whether or not it reflects how most people actually feel. What looks like an existential battle between AI boosters and detractors could just be the algorithm's bias for engagement.

All this left me wondering: what do the vast majority of professionals, who aren't selling AI tools or consulting services, think and feel about AI?

EVERYONE IS WONDERING ABOUT AI

According to Google's Keyword Planner, there are 1,500,000 search queries every month for "artificial intelligence" and another 1,500,000 for "artificial general intelligence."

💡 Use Reddit Data for a "Vibes Check"

To answer this question, I turned to Reddit, which is relatively anonymous and actively discourages sales and personal brand building. My idea was to collect and then analyze a statistically valid sample of posts in marketing and creative subbreddits. Because my data science background is limited to Google's machine learning crash course, I used ChatGPT and Claude to help me write the code necessary to gather the posts and suggest models to make sense of them.

Ultimately, I ended up scraping and analyzing 2,082 Reddit posts mentioning AI across professional communities like r/marketing, r/copywriting, r/entrepreneur, and r/technology. Spoiler alert: The results surprised me and revealed patterns that don't fit neatly into either the "AI will save us" or "AI will destroy us" narratives.

How I Chose the Right Subreddits

For this project, I focused on Reddit communities where professionals actively discuss AI primarily in the context of their work and creative pursuits. I specifically limited the dataset to posts from the past six months to capture how feelings have evolved in response to recent developments like big company announcements, new model releases, and AI's large-scale introduction into the workplace.

Rather than casting a wide net across all of Reddit, I selected subreddits that each offer a distinct, professionally skewed perspective, such as founders testing AI in startups, engineers evaluating tooling, and marketers and writers adapting their workflows. Some of these communities are niche but specialized; others are large and fast-moving.

The table below outlines why each subreddit was chosen:

How I Gathered the Data (Without a Data Science Degree)

Once I'd identified key subreddits, the next step was to collect the posts. I started by asking ChatGPT how to scrape Reddit (post content, timestamps, and URLs included) and to summarize Reddit’s API rules and best practices. After cross-referencing with Reddit's docs, I was ready to go.

I used the PRAW library (Python Reddit API Wrapper) to build a script that searched for posts across relevant subreddits using keywords like “ChatGPT,” “Claude,” and “generative AI.” The script collected each post’s title, content, date, and some engagement metrics like upvotes and comment count. I limited the scope to six months of posts, yielding 2,082 high-signal discussions.

The whole thing took about five minutes, a great reminder of how powerful AI-assisted workflows can be, even for non-experts.

import praw 
import pandas as pd 
from datetime import datetime, timedelta, UTC 
# ✅ Your Reddit credentials 
reddit = praw.Reddit( 
 client_id="YOUR_ID_HERE", 
 client_secret="YOUR_SECRET_HERE", 
 user_agent="YOUR_AGENT_NAME_HERE" 
) 
# 🔍 Search setup 
subreddits = [ 
 "marketing", "copywriting", "advertising", 
"FreelanceWriters", 
 "Entrepreneur", "technology", "artificial", 
"digital_marketing", 
 "smallbusiness", "dataisbeautiful", "productivity", 
"UXDesign", 
 "ChatGPT", "OpenAI", "ClaudeAI", "QualityAssurance", 
"worldbuilding" 
] 
query = ( 
 '"artificial intelligence" OR ChatGPT OR GPT-4 OR Gemini OR 
Claude ' 
 'OR Midjourney OR DALL-E OR "generative AI" OR "AI tools"' 
) 
limit_per_subreddit = 200 # Adjust as needed 
# Time filter: last 6 months 
after_timestamp = int((datetime.now(UTC) - 
timedelta(days=180)).timestamp()) 
# Scrape posts 
posts = [] 
for subreddit_name in subreddits: 
 print(f"\n🔍 Searching r/{subreddit_name}...") 
 subreddit = reddit.subreddit(subreddit_name) 
 for post in subreddit.search(query, sort="new", 
limit=limit_per_subreddit, time_filter="all"): 
 if post.created_utc >= after_timestamp:  
 posts.append({ 
 "id": post.id, 
 "subreddit": subreddit_name, 
 "title": post.title, 
 "text": post.selftext, 
 "created": 
datetime.fromtimestamp(post.created_utc), 
 "score": post.score, 
 "num_comments": post.num_comments, 
 "author": str(post.author), 
 "url": post.url 
 }) 
print(f"\n✅ Collected {len(posts)} posts.") 
df = pd.DataFrame(posts) 
df.to_csv("praw_ai_sentiment_posts.csv", index=False) 
print("📝 Saved as praw_ai_sentiment_posts.csv in this folder.")

A Quick “Vibes Check” with ChatGPT

From a time and efficiency standpoint, manually reviewing all 2,082 posts was out of the question. So I started with ChatGPT’s 4o model to run a first-pass sentiment analysis, using a CSV file and a detailed prompt to classify each post as positive, negative, or neutral and which keywords (e.g., curiosity, frustration, excitement) appeared most frequently.

ChatGPT also used keyword analysis to guess the emotional tone of each post.

A word cloud based on text from 2,082 Reddit posts

A Second Opinion from Better Models

Before taking the results at face value, I manually reviewed the first 100 or so posts, and I’m glad I did. While ChatGPT’s keyword analysis picked up some interesting findings, like a large number of people building and testing AI tools, it also misread sarcasm and often mislabeled more nuanced posts as “neutral.”

To get more accurate results, I re-ran the analysis using two Hugging Face models:

• cardiffnlp/twitter-roberta-base-sentiment — a RoBERTa model trained on tweets to classify posts as positive, neutral, or negative. It's more attuned to informal, emotionally charged text.
• SamLowe/roberta-base-go_emotions — a multi-label emotion classifier based on the GoEmotions dataset, which tags up to 28 emotions per post (including admiration, nervousness, realization, remorse, and confusion). It’s particularly effective at teasing out emotional content in longer-form text.

These models, which are generally 75-85% accurate depending on the dataset, did better. Sarcasm was recognized more frequently, mixed emotions were surfaced more clearly, and emotional overlap (e.g., curiosity + anxiety) was handled more realistically.

What I Found: We're Sure We're Feeling Unsure

Overall results suggested that people are not extremely pro- or anti-AI despite the prevalence of emotionally charged news and AI-related engagement bait. But that doesn't mean everything's OK. While the sentiment model showed about half of posts falling into the neutral category, the emotion model revealed a persistent undercurrent of uncertainty as today's dominant vibe.

Sentiment Results: Reserving Judgement

I ran CardiffNLP's RoBERTa sentiment classifier first.

That heavy tilt toward neutral suggests a lot of professionals are reserving judgment. Many posts describe AI tools or workflows without strong emotion, or include both praise and concern in the same thread. Also, the relatively large percentage of negative posts suggests some level of confusion, frustration, or fear.

Emotion Classifier Results: Curiosity, Uncertainty, and Concern

To dig deeper, I used the SamLowe/roberta-base-go_emotions model, which tags posts with up to 28 emotional labels. These include obvious ones like fear or joy, but also subtler ones like realization, confusion, or admiration.

To make the data easier to interpret, I grouped the emotions into four broad categories: positive, negative, neutral, and uncertain.

This analysis told a somewhat different story than the sentiment chart alone.

Uncertainty was the most common emotional theme, showing up in over 30% of posts. That includes people exploring new tools, asking questions, or reflecting on how AI might affect their work.

Meanwhile, only 8% of posts reflected fear, sadness, or frustration. This result appears to contradict the sentiment analysis, which identified 25% of posts as having a negative sentiment. Some of the difference may be explained by my decision to include confusion as an uncertain emotion rather than a negative one. Overall, these results suggest that while concerns exist, outright pessimism is not dominant.

Top Emotions by Count

Here are the most frequently detected emotions (across both top and secondary tags):

Emotion by Community

While curiosity was the most common emotion across nearly all communities, secondary emotional tones varied by context and focus:

How Professionals Use AI Today: Testing, Tinkering, and Therapy

If mainstream media coverage—and corporate press releases—suggest that AI is being rapidly integrated across organizations with clear workflows and guidelines, Reddit tells a different story. The data reveals a more organic, exploratory phase that's happening at the individual level first.

In fact, most experiments had nothing to do with team efficiency or production at scale. Instead, they focused on individuals testing what is possible with AI and sometimes finding unexpected applications. This pattern suggests that contrary to top-down implementation narratives, AI adoption is happening through grassroots experimentation by professionals, each finding their own path through trial and error.

The diversity of these experiments also contradicts the notion that AI will simply automate existing workflows. Instead, we're seeing new use cases emerge organically, often in areas that weren't initially targeted by AI developers.