Nerdy SPAC Presentation Deck

1. 2. Matching Layer Selecting the best expert We optimize across a high dimensional set of features to identify the learner-to-expert matches with the highest projected probability of a successful interaction. 100+ Features per learner and instructor >800K Learner and expert matches¹ >80M Usable data points for personalization² Al-Powered Learner/Expert Matching Both learners, struggling with the same subject, are intelligently matched to the instructor who best fits their specific needs. Introvert Low motivation. (100+ total attributes) Extrovert 3.9 GPA High motivation (100+ total attributes) 4.9 ** Avg. Rating Sources: Average session rating on customer feedback (all time thru December 2020, also 4.9/5 on Apple App Store 18.5K ratings) 2012 thru December 2020. Amounts exclude Legacy Businesses. Defined as data points generated from student attributes, instructor attributes, past matching, learning interactions from online platform, website and marketing event interactions, and self study interaction. Amounts exclude Legacy Businesses. 22 TPG nerdy TECH OPPORTUNITIES Ⓒ Nerdy / TPG Pace Tech Opportunities Corp. 2021 PACE

View entire presentation