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Y Combinator CMU-Affiliated AI Startups Analysis

Overview

This notebook analyzes a dataset of 34 AI startups affiliated with Carnegie Mellon University (CMU) and other universities that have gone through Y Combinator. The analysis explores funding patterns, geographic distribution, and university affiliations to understand the landscape of university-affiliated AI ventures.

Key Findings

Geographic Distribution

USA dominance: The vast majority of startups are based in the United States, which has also raised the highest total

[12]

Dataset shape: (34, 6)

First few rows:
   Number                     Startup  \
0       1                    Solyx AI   
1       2               Perforated AI   
2       3                  Impulse AI   
3       4                        Alph   
4       5                      Strata   
5       6                     CAMB.AI   
6       7                        App0   
7       8                 Namecard.ai   
8       9                   Lyceum AI   
9      10  The Lexsee Reading Company   

                                       Brief Summary  Raised  Country  \
0           Decentralized AI compute infrastructure.   $200K      USA   
1  Perforated AI empowers ML engineers to build A...      $0      USA   
2  AI Machine Learning Engineer that goes from da...     $1M      USA   
3  AI Infra platform for Jupyter notebooks. Think...   $290K      USA   
4  Strata is an embedded integration platform tha...      $0      USA   
5  CAMB.AI is the localization infrastructure for...  $18.5M      USA   
6  App0 is the AI Agent Platform built for eComme...   $3.8M      USA   
7  Warm intros convert at 35%—cold calls at 1%—bu...      $0      USA   
8  We have built an AI-powered corporate training...   $900K      USA   
9  Lexsee tackles the $2.2T literacy crisis—1 in ...   $250K  Denmark   

  University  
0        CMU  
1        CMU  
2        CMU  
3        CMU  
4        CMU  
5        CMU  
6        CMU  
7        CMU  
8        CMU  
9        CMU  

Data types:
Number            int64
Startup          object
Brief Summary    object
Raised           object
Country          object
University       object
dtype: object

Basic statistics:
          Number
count  34.000000
mean   17.500000
std     9.958246
min     1.000000
25%     9.250000
50%    17.500000
75%    25.750000
max    34.000000

[13]

============================================================
SUMMARY STATISTICS
============================================================

Total Startups: 34
Total Funding Raised: $119.47M
Average Funding: $3.51M
Median Funding: $1.30M
Startups with Funding: 24 (70.6%)
Startups with No Funding: 10 (29.4%)

Countries represented: 6
Universities represented: 5

[15]

/tmp/ipykernel_596/2705667948.py:36: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(data=df_multi_country, x='Country', y='Raised_Numeric', ax=axes[1, 0], palette='Set2')

============================================================
ADDITIONAL INSIGHTS
============================================================

Top 5 Countries by Total Funding:
  USA: $104.22M
  Colombia: $15.00M
  Denmark: $0.25M
  $1.7M: $0.00M
  $122K: $0.00M

Funding Concentration:
  Top 5 startups hold 67.4% of total funding

Funding by Category:
  No Funding: 10 startups (29.4%)
  <$500K: 4 startups (11.8%)
  $500K-$2M: 4 startups (11.8%)
  $2M-$5M: 10 startups (29.4%)
  >$5M: 6 startups (17.6%)

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