Automation Tool Use Deviation from AI-Related Tools Confirms Possible AI Hype Cycle Focus on Automation; Trend Now Reversing
Source: tai_blog

TLDR:Automation tools (Zapier as an example) API public development declined (-13.1% y/y) until last month  while AI-related APIs have experienced steady growth (+12.0% y/y) during same timeframe.Zapiers recent spike may indicate strategic adaptation or solution to AI trends  with highest correlation to UIPaths free tools  but correlation doesnt equal causation either way.Caveat on public developer activity  so not accounting for private trends (which could be substantially different).Question this quick analysis answers:Did AI hype-infused solutions to workflow automation affect trends with Zapiers workflow automation solutions  and could that be shaking out differently at an inflection point in the hype cycle?   Lets start by importing the necessary libraries and loading our data (see my previous blog post for public development trend query out of GCP). Note this code is on my github repo in the form of a notebook.   # imports import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from scipy import stats import numpy as np # Load the data - in this case sourced from same query over weekend data = pd.read_csv('ff.csv')Long table format  so transformations are called for   # Convert 'month' to datetime data['month'] = pd.to_datetime(data['month']) # Filter out September 2024 - incomplete month data = data[data['month'] < '2024-09-01'] # Filter data for the complete years (2023 and 2024) data = data[data['month'].dt.year.isin([2023  2024])] # Separate Zapier data zapier_data = data[data['keyword_category'] == 'zapier'].set_index('month') # Aggregate all other categories as 'AI-related APIs' ai_apis_data = data[data['keyword_category'] != 'zapier'].groupby('month')['new_repo_count'].sum().reset_index() ai_apis_data = ai_apis_data.set_index('month') # Calculate 7-day rolling average for smoothing zapier_data['rolling_avg'] = zapier_data['new_repo_count'].rolling(window=7).mean() ai_apis_data['rolling_avg'] = ai_apis_data['new_repo_count'].rolling(window=7).mean()Zapier data Id queried is so small (!) so the month-over-month variation isnt going to lend to anything stat sig  by month  but in aggregate its likely going to help support a hypothesis (plotted with and without the below CI  ended up removing for legibility).   # Calculate 95% confidence intervals def calculate_ci(data): confidence = 0.95 degrees_of_freedom = len(data) - 1 sample_mean = np.mean(data) sample_standard_error = stats.sem(data) ci = stats.t.interval(confidence=confidence  df=degrees_of_freedom  loc=sample_mean  scale=sample_standard_error) return ci zapier_ci = calculate_ci(zapier_data['new_repo_count']) ai_apis_ci = calculate_ci(ai_apis_data['new_repo_count'])And just so I mentioned it  quick aggregate to compare Y/Y   def calculate_yoy_growth(data  year1  year2): jan_jul_year1 = data[(data.index.year == year1) & (data.index.month.isin(range(1  8)))]['new_repo_count'].sum() jan_jul_year2 = data[(data.index.year == year2) & (data.index.month.isin(range(1  8)))]['new_repo_count'].sum() return (jan_jul_year2 - jan_jul_year1) / jan_jul_year1 * 100 zapier_yoy = calculate_yoy_growth(zapier_data  2023  2024) ai_apis_yoy = calculate_yoy_growth(ai_apis_data  2023  2024)Plotting this result  its easy to see the divergence during the AI hype cycle timeframe.   # Create the plot fig  ax1 = plt.subplots(figsize=(12  7)) # Plot Zapier data on the left y-axis ax1.plot(zapier_data.index  zapier_data['rolling_avg']  color='blue'  label='Zapier') # Set up the right y-axis for AI-related APIs ax2 = ax1.twinx() ax2.plot(ai_apis_data.index  ai_apis_data['rolling_avg']  color='red'  label='AI-related APIs') # Customize the plot ax1.set_xlabel('Date') ax1.set_ylabel('New Repo Count (Zapier)'  color='blue') ax2.set_ylabel('New Repo Count (AI-related APIs)'  color='red') ax1.tick_params(axis='y'  labelcolor='blue') ax2.tick_params(axis='y'  labelcolor='red') # Add legend lines1  labels1 = ax1.get_legend_handles_labels() lines2  labels2 = ax2.get_legend_handles_labels() ax1.legend(lines1 + lines2  labels1 + labels2  loc='upper left') # Set title and subtitle plt.title(Public API Usage Trends Y/Y  fontsize=16  pad=20) plt.figtext(0.7  0.80  fZapier Y/Y Growth: {zapier_yoy:.1f}%  AI-related APIs Y/Y Growth: {ai_apis_yoy:.1f}%\n f(Based on Jan-Jul trends) * not statistically significant at 95% CI  fontsize=10  ha='center') # Adjust layout plt.tight_layout() plt.subplots_adjust(top=0.85) # Adjust top margin to accommodate subtitle # Show the plot plt.show()Does this correlate to any specific packages? The plot below shows UIPath correlation  while this doesnt equal causation  messaging from this company became aggressive in recent months towards the scholastic communities (free tools)  C3.ai data is dirty but also worth noting some correlation to Oracle AI and Google Vertex tools.   # Create a pivot table with months as index and keyword categories as columns pivot_data = data.pivot_table(values='new_repo_count'  index='month'  columns='keyword_category'  aggfunc='sum') # Calculate correlation between Zapier and other categories correlations = pivot_data.corrwith(pivot_data['zapier']).sort_values(ascending=False) # Remove Zapier's self-correlation and any NaN values correlations = correlations.drop('zapier').dropna() # Get the top 5 correlated categories top_5_correlations = correlations.head(5) print(Top 5 dimensions correlated with Zapier:) for category  correlation in top_5_correlations.items(): print(f{category}: {correlation:.4f}) # Plot the correlation results for top 5 plt.figure(figsize=(12  6)) top_5_correlations.plot(kind='bar') plt.title(Top 5 Correlations (again  sans CI): Developer Usage of Zapier vs Other Categories) plt.xlabel(Categories) plt.ylabel(Correlation Coefficient) plt.xticks(rotation=45  ha='right') plt.tight_layout() plt.show()Synthesizing  what could this suggest?1. Shift in Developer Focus in Past Year:   The declining trend for Zapier activity could indicate a shift in developer focus away from traditional automation platforms towards AI-centric technologies that were attempting to accomplish similar goals.   2. Recent Upturn for Zapier   The sharp increase in Zapiers trend recently could be attributed to:   Introduction of AI-related Features: Zapier may have introduced new AI-centric capabilities or integrations  sparking renewed interest among developers.AI hype may not have automated what developers were trying to do: There is no data to suggest this  since AI APIs are still increasing in usage.Synergy with AI Technologies: The rise could reflect Zapiers efforts to incorporate AI into its platform  possibly something involving free tools or UIPath  and also potentially offering new ways for developers to leverage both automation and AI capabilities together.Caveats: Its important to note that these trends may not capture the full complexity of the API ecosystem. Factors such as changes in Zapiers business strategy  shifts in the broader tech landscape  and the emergence of new competitors could also play roles in shaping these trends (in theory).   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