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Session 9: Evaluating Tech in Deep Tech

Session 9: This session was done as part of pi fellows programme by Manish Singhal to put some structure to the art of evaluating tech in deep tech startups

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Session 9: Evaluating Tech in Deep Tech

  1. 1. Tech Evaluation in Deep Tech May 2020 Manish Singhal Founding Partner, pi Ventures @manish_saarthi
  2. 2. piVentures2019AllRightsReserved Frame Work Forming a Hypothesis Validating the Hypothesis
  3. 3. Context of Tech?
  4. 4. piVentures2020AllRightsReserved Can they? Who Else? What Else? Why will they win? Why Now? Will it make a 10x impact? What is the Core? Forming a Hypothesis Inside Out Approach
  5. 5. Attempt to Simplify the world of Startups…
  6. 6. piVentures2020AllRightsReserved Understanding Demand & Supply Nature of Supply Nature of Demand Proxies Demand <-> Need Supply <-> Solution Unthought of / Unaware Aware but Latent Aware & Apparent Simpler Tech Harder Tech (Realizable) Hard Tech (Still in the Labs)
  7. 7. piVentures2019AllRightsReserved Nature of Supply Nature of Demand Proxies Demand <-> Need Supply <-> Solution Unthought of / Unaware Aware but Latent Aware & Apparent Simpler Tech Harder Tech (Realizable) Hard Tech (Still in the Labs) Zone A Zone B Zone C Zone D Zone E Zone AB Understanding Demand & Supply
  8. 8. piVentures2019AllRightsReserved Nature of Supply Nature of Demand Simpler Tech Harder Tech (Realizable) Unthought of / Unaware Aware but Latent Aware & Apparent Ripe for Disruption Possibility of building a strong moat and therefore rule the category Evangelisation Cost & Effort Lesser Players hence first mover may still win Good business can be built here Need to stand above competition Evangelisation Cost & Effort First Mover Disadvantage Hard Tech (Still in the Labs) Long lead times. Expensive. Outcomes can be very big if commercialisation can be done Zone of Experimentation, Research, Science, Fun Projects Proxies Demand <-> Need Supply <-> Solution Too early / May Never be needed A B C D E Understanding Demand & Supply
  9. 9. piVentures2019AllRightsReserved Nature of Supply Nature of Demand Simpler Tech Harder Tech (Realizable) Unthought of / Unaware Aware but Latent Aware & Apparent Ripe for Disruption Possibility of building a strong moat and therefore rule the category Evangelisation Cost & Effort Lesser Players hence first mover may still win Good business can be built here Need to stand above competition Evangelisation Cost & Effort First Mover Disadvantage Hard Tech (Still in the Labs) Long lead times. Expensive. Outcomes can be very big if commercialisation can be done Zone of Experimentation, Research, Science, Fun Projects Proxies Demand <-> Need Supply <-> Solution Too early / May Never be needed Understanding Demand & Supply
  10. 10. piVentures2019AllRightsReserved Nature of Supply Nature of Demand Simpler Tech Harder Tech (Realizable) Unthought of / Unaware Aware but Latent Aware & Apparent Ripe for Disruption Possibility of building a strong moat and therefore rule the category Evangelisation Cost & Effort Lesser Players hence first mover may still win Good business can be built here Need to stand above competition Evangelisation Cost & Effort First Mover Disadvantage Hard Tech (Still in the Labs) Long lead times. Expensive. Outcomes can be very big if commercialisation can be done Zone of Experimentation, Research, Science, Fun Projects Proxies Demand <-> Need Supply <-> Solution Too early / May Never be needed Understanding Demand & Supply - Momentums
  11. 11. piVentures2019AllRightsReserved Nature of Supply Nature of Demand Proxies Demand <-> Need Supply <-> Solution Unthought of / Unaware Aware but Latent Aware & Apparent Simpler Tech Harder Tech (Realizable) Hard Tech (Still in the Labs) A B C D E Understanding Demand & Supply
  12. 12. piVentures2019AllRightsReserved Case Study - Wysa Building the Hypothesis Core 10x Team Some players. Ability to do free text in a clinically safe way is a clear differentiator Strong “bounded” NLP engine trained on Mental Health conversations Timing Alternative / Competition / Differentiation Digital Platforms is the only way to make mental health scalable. Inhibitions friendly. At your finger tips - when you want it Stress is part of our lives. Therapist in advanced countries are back logged. Team passionate about the cause and comes with deep understanding of the technology and domain
  13. 13. piVentures2019AllRightsReserved Resources Next 6 bn people AI can bridge Supply & Demand at Scale Models learn from Experts knowledge / experience Platforms help to make the “experience” accessible to all
  14. 14. piVentures2019AllRightsReserved Mapping Nature of Supply Nature of Demand Proxies Demand <-> Need Supply <-> Solution Unthought of / Unaware Aware but Latent Aware & Apparent Simpler Tech Harder Tech (Realizable) Hard Tech (Still in the Labs) A B C D E
  15. 15. Case Study - Wysa Validating the Hypothesis Core 10x Team Some players. Ability to do free text in a clinically safe way is a clear differentiator Strong “bounded” clinically safe NLP engine trained on Mental Health conversations Timing Alternative / Competition / Differentiation Digital Platforms is the only way to make mental health scalable. Inhibitions friendly. At your finger tips - when you want it Stress is part of our lives. Therapist in advanced countries are back logged. Team passionate about the cause and comes with deep understanding of the technology and domain In depth deep dive on the product and tech architecture. Played around with the product We felt that the NLP engine was not an incremental innovation but significantly departure from what else is out there Market Need was very compelling. Question on timing of the monetisation Looked at other products. Felt Wysa was overall performing better. In any case, only 3-4 players were there. Market is quite huge Series of conversations convinced us about the team’s ability and vision.
  16. 16. Case Study - Agnikul Building the Hypothesis Core 10x Team Around 25+ such efforts in progress. No- one has been able to make the engine fully 3D printable 3D printable rocket engines Timing Alternative / Competition / Differentiation Can make the small rockets in similar price range Demand for small rockets going up. Supply is in higher payloads Strong understanding of the tech and supply chain
  17. 17. piVentures2020AllRightsReserved Payload launch capacity of small to mid satellite launchers <60 kg 100 kg 150 kg 200 kg 300 kg 500 kg 500kg-1T 1T-1.5T Interstellar tech >1.5T
  18. 18. piVentures2020AllRightsReserved # of Satellites launched from 2015-19 wrt Satellite Weight Interstellar tech 0 150 300 450 600 <60 kg 60-100 kg 100-150 kg 150-200 kg 200-300 kg 300-500 kg 500 to 1 T 1-1.5T >1.5T 167281552317122731542 Launch Capacity Peak (Supply) Launch Weight Peak (Demand)
  19. 19. piVentures2019AllRightsReserved Mapping Nature of Supply Nature of Demand Proxies Demand <-> Need Supply <-> Solution Unthought of / Unaware Aware but Latent Aware & Apparent Simpler Tech Harder Tech (Realizable) Hard Tech (Still in the Labs) A B C D E
  20. 20. Case Study - Agnikul Validating the Hypothesis Core 10x Team Around 25+ such efforts in progress. No- one has been able to make the engine fully 3D printable 3D printable rocket engines Timing Alternative / Competition / Differentiation Can make the small rockets in similar price range Demand for small rockets going up. Supply is in higher payloads Strong understanding of the tech and supply chain Visited the labs to see the internal design of engines Demand Supply Curve mismatch proves the dire need and the difference Agnikul can make Market Research showed that the small satellite launch need is growing massively Based on our analysis, team was uniquely positioned to stand out in a very capital efficient manner Many in-depth conversations and reference checks
  21. 21. Validation Techniques for Core Tech Deep Dive on tech / product Break it down - understand the architecture, technology construct, adjacencies Best “hack” I have found is to pick a “tech nerve” in the first / second pitch and build a discussion around it Talk to Experts with specific questions Look for IP (Trade Secrets / Patents…) Evaluate team’s inherent understanding on tech / product / domain Assess Residual Value
  22. 22. AI Companies Concept of AI First and Second Understanding the Data Strategy Proprietary vs Public Cost of acquiring Data Half Life of Data Undestanding Algorithms
  23. 23. Biases Incumbent Bias Familiarity Bias Innovators’ Bias Confirmation Bias Challenges your ability to hold two opposing thoughts at the same time!
  24. 24. Thank You! *Thanks to Prof Shivaram from IIT Mumbai for the phrase J AI HIND*

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  • alekshendra

    Jul. 22, 2020

Session 9: This session was done as part of pi fellows programme by Manish Singhal to put some structure to the art of evaluating tech in deep tech startups

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