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BeaconsAI engr 245 lean launchpad stanford 2019

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BeaconsAI engr 245 lean launchpad stanford 2019

  1. Beacons AI Week 1: FaceID for doors Resegmented market Now: AI to increase productivity by streamlining similar work New market 142 Interviews
  2. Rafi Holtzman Robert Locke Todd Basche David Zeng PhD, Machine learning Jesse Zhang PhD, Machine learning Neal Jean PhD, Machine learning Shing Shing Ho PhD, Applied physics Elaine Ng PhD, Bioengineering
  3. Before Lean Launchpad…
  4. Before Lean Launchpad… FaceID for doors
  5. Pencil and floss holding things up
  6. Camera Smart lock inside Raspberry Pi
  7. Customer Interviews 0 MVPs 1 Pre-LLP
  8. Customer Interviews 0 MVPs 1 Pre-LLP
  9. IDEA SPACE GOOD BAD PRODUCT-MARKET FIT
  10. IDEA SPACE GOOD BAD PRODUCT-MARKET FIT “All startups go through the drunken walk.” — Mar
  11. • Channels - Social media platforms (PTAs), online retailers, Airbnb, Thumbtack • Manufacturing - Engineering consulting firms, contract manufacturers, OEM/ODMs • Suppliers - Hardware components, cloud compute services (e.g., AWS) • Working Parents (P) - Pick up children from school • Facility Managers (O) - Small companies (~5- 30 people) - Large enterprise • Airbnb hosts (A) - Using smartlocks - Prefers not to interact w/ guests • Gig economy (G) - Value provided to homeowners who sacrifice time • Affordability: Edge computing is cheaper and right-sized (P)(A)(G) • Private: Pictures never leave the device (P)(O)(A)(G) • Convenience: One picture for registration and seamless access. (P)(A)(G) • Tracks Everyone: Tailgating and strangers (P)(O)(A)(G) • Fixed costs: Salaries, contracting hardware design, product development • Variable costs: Hardware components, manufacturing, cloud storage/compute, CAC, customer service, sales • Asset sale - One-time fee for the physical hardware (estimated $100 for consumer product) (P)(O)(A)(G) • Subscription fee - Monthly charge (~$10) for cloud services and support (P)(O)(A)(G) • Technology: ensure accuracy, stability, speed, security, privacy • Customer UIUX • Develop marketing strategies to reach core customer segments • Customer self-service (P)(A)(G) • Personal assistance (O)(A) • IP - ML pipeline • Hardware Design • Human - Marketing, sales, engineers • Computation - Cloud storage and compute (e.g. GPUs) • Direct to consumer (P)(O) • Online retail (P)(A)(G) • Word-of-mouth (P)(A)(G) • Social media (P)(G) • OEM/Systems (O) Beacons AI Week [1]
  12. IDEA SPACE GOOD BAD Week 1
  13. IDEA SPACE GOOD BAD Week 1
  14. Week 1
  15. What we thought: We can help parents make sure their kids get home safely Week 1Week 1
  16. Talked to 10 parents Week 1Week 1
  17. What we learned: We don't actually solve the problem for parents “There are so many risks that I worry about (for my son getting home safely) that making one part of the chain safer might not be enough.” — Ana Ramirez, Parent Week 1Week 1
  18. IDEA SPACE GOOD BAD Consumer Enterprise Week 2 1st Pivot: Consumer to Enterprise
  19. MVP #2 Face recognition visitor check-in on a laptop Week 2
  20. MVP #2 Face recognition visitor check-in on a laptop Week 2 What we learned: Every MVP should test a hypothesis
  21. Parents Young women Consumer Startup University Real estate HospitalMedium enterprise Large enterprise Retail What we did: Talked to all kinds of enterprise customers
  22. What we learned: One challenge for enterprise access control is preventing tailgating “The number one problem I’d like to see a solution for is tailgating.” — Jay Kohn, Director of key card services Stanford Weeks 2-3 Week 2 Week 3
  23. “Tailgating is the top problem or a very important problem for me.” Microsoft: Brian Tuskan, Chief Security Officer; Joe Fairchild, Lead for Real Estate Technology GRAIL: Lisa Peloquin, Facilities Coordinator; Arlito Legaspi, Facilities Manager Kilroy Realty: Rob Paratte, VP Business Development; Chris Johannsen, Director of Security and Safety Stanford: Jay Kohn, Director of Card Services; Fred Vasquez, Building Access and Security Manager Weeks 4-6 Week 3
  24. IDEA SPACE GOOD BAD Consumer Enterprise Tailgating Week 4
  25. We solved tailgating, will you buy our product? No. Weeks 4-6 Week 4
  26. We solved tailgating, will you buy our product? “No.” — multiple customers Weeks 4-6 Week 4
  27. IDEA SPACE GOOD BAD Consumer Enterprise Tailgating Week 4
  28. We solved tailgating, will you buy our product? “No.” — multiple customers Weeks 4-6 Week 4 What we learned: Our solution detects tailgating, but doesn't address the risk they represent after getting in… That requires surveillance
  29. Perimeter Access Volume Surveillance Weeks 4-6 Week 5 2nd Pivot: Access Control to Surveillance
  30. IDEA SPACE GOOD BAD Consumer Enterprise Tailgating Surveillance Week 5
  31. IDEA SPACE GOOD BAD Consumer Enterprise Tailgating Surveillance Week 5
  32. IDEA SPACE GOOD BAD Consumer Enterprise Tailgating Surveillance Week 5
  33. IDEA SPACE GOOD BAD Consumer Enterprise Tailgating Surveillance Week 5 Week 6
  34. IDEA SPACE GOOD BAD Consumer Enterprise Tailgating Surveillance Week 5 PRODUCT-MARKET FIT Week 6
  35. IDEA SPACE GOOD BAD Consumer Enterprise Tailgating Surveillance Week 5 PRODUCT-MARKET FIT PROBLEM-TEAM FIT What we learned: Problem-team fit is important Week 6
  36. "Find something that you really like… you have my blessing." — Jeff (loosely paraphrased) Week 7 Week 6
  37. Thinking about problems that we have...
  38. Thinking about problems that we have... Working in teams is #*^&ing hard
  39. Week 7 We use a lot of tools
  40. Week 7 We use a lot of “dumb” toolsWhat if we could make them smarter?
  41. IDEA SPACE GOOD BAD Workplace productivity tools Week 7 We did a complete restart
  42. Existing tools: Knowledge is archived (i.e., lost forever) Surveillance project Archive Week 7 Week 7
  43. Existing tools: Knowledge is archived (i.e., lost forever) Surveillance project Week 7 Week 7 New project started from scratch! Same old mistakes, redundant work, etc.
  44. New task Week 7 Week 8 MVP #3
  45. New task Week 7 Week 8 What happens: AI returns most relevant tasks from history MVP #3
  46. New task Week 7 Week 8 What happens: AI returns most relevant tasks from history MVP #3 The user gets: 1. A warm start on their task 2. Know who to ask for help
  47. Customers want the product! “If you can give my engineers boilerplate starter code, I would buy it immediately.” Eric Xiao, PM, Facebook Week 8 “If you can surface relevant documentation, I would buy it in a heartbeat.” Kevin Bao, EM, Karat “I would use this, and I would pay for it today.” Aref Erfani, Enterprise Architect, DC Water
  48. Customer Segments • ML researchers - Industry research groups - Academic research groups - PhD students • ML engineers - AI/DL/RL engineer - SW engineer • Data scientists - Data engineers - Data analysts Week 9 Start where we understand the problems
  49. Customer Segments • ML researchers - Industry research groups - Academic research groups - PhD students • ML engineers - AI/DL/RL engineer - SW engineer • Data scientists - Data engineers - Data analysts Week 9 Value Propositions • Dataset access: Standardize preparation of common datasets “I have 10 copies of the MNIST dataset on my laptop.” Kristy Choi, CS PhD, Stanford “...version control for data would be cool. Right now I just save different versions.” Sherrie Wang, ICME PhD, Stanford
  50. Customer Segments • ML researchers - Industry research groups - Academic research groups - PhD students • ML engineers - AI/DL/RL engineer - SW engineer • Data scientists - Data engineers - Data analysts Week 9 Value Propositions • Dataset access: Standardize preparation of common datasets • Data processing: Templates for data pipelines “...you feel bad that your whole day was spent figuring out how to load and preprocess your data. And it happens every time you have a new dataset.” Aditya Grover, CS PhD, Stanford
  51. Customer Segments • ML researchers - Industry research groups - Academic research groups - PhD students • ML engineers - AI/DL/RL engineer - SW engineer • Data scientists - Data engineers - Data analysts Week 9 Value Propositions • Dataset access: Standardize preparation of common datasets • Data processing: Templates for data pipelines • Improved communication: Increase transparency and reduce barriers “I would love to talk to other researchers who are working in the same area, even if they’re not well-known.” Rui Shu, CS PhD, Stanford “It can be hard to ask for help if you’re not sure who can answer your question.” Geet Sethi, CS PhD, Stanford
  52. Week 9 A proven business model… freemium Saas
  53. Week 9 A proven business model… freemium Saas $100 per user/year x 500,000 ML + data science workers in 2024 = $50M ARR
  54. Week 9 A proven business model… freemium Saas $100 per user/year x 500,000 ML engineers + data scientists in 2024 = $50M ARR Source: The age of analytics: Competing in a data-driven world, McKinsey Global Institute.
  55. Week 9 A proven business model… freemium Saas $100 per user/year x 500,000 ML engineers + data scientists in 2024 = $50M SAM Source: The age of analytics: Competing in a data-driven world, McKinsey Global Institute.
  56. Week 9 A proven business model… freemium Saas $100 per user/year x 500,000 ML engineers + data scientists in 2024 = $50M SAM Source: The age of analytics: Competing in a data-driven world, McKinsey Global Institute. Eventually: Expand to adjacent markets like software engineering 25M+ workers => $2.5B market
  57. “...as I start my own lab, I need a tool like this.” — Lisa Wedding, Associate Professor, Oxford University “I would use this today!” — Kristy Choi, ML PhD, Stanford University “Let me know when I can beta test it.” — Neeraja Ravi, BioE PhD, Stanford University Week 9 “I want to be your first customer!” — Surabhi Sharma, Product operations, Twitter Lining up beta testers
  58. IDEA SPACE GOOD BAD Now Workplace productivity tools Where we are today
  59. The rest of our team Rafi Holtzman Robert Locke Todd Basche
  60. David Zeng PhD, Machine learning Jesse Zhang PhD, Machine learning Neal Jean PhD, Machine learning Shing Shing Ho PhD, Applied physics Elaine Ng PhD, Bioengineering Beacons AI
  61. AI for medical diagnostics & imaging David Zeng PhD, Machine learning Jesse Zhang PhD, Machine learning Neal Jean PhD, Machine learning Shing Shing Ho PhD, Applied physics Elaine Ng PhD, Bioengineering Beacons AI
  62. AI for medical diagnostics & imaging David Zeng PhD, Machine learning Jesse Zhang PhD, Machine learning Neal Jean PhD, Machine learning Shing Shing Ho PhD, Applied physics Elaine Ng PhD, Bioengineering Beacons AI
  63. AI for medical diagnostics & imaging Summer: Beacons AI Fall: Stanford postdoc David Zeng PhD, Machine learning Jesse Zhang PhD, Machine learning Neal Jean PhD, Machine learning Shing Shing Ho PhD, Applied physics Elaine Ng PhD, Bioengineering Beacons AI
  64. Appendix
  65. Now
  66. Graveyard

Editor's Notes

  • Come up with a better slogan

    Title slide must include:
    Team name
    Succinct description of what your company does
    # of interviews done this week
    # of interviews in total
    Team members (names, pictures, roles)
    Market type
    New market
    Re-segmenting existing market as low cost
    Re-segmenting existing market as niche entrant
    Cloning a successful business model from another country
    Hacker = Engineer
    Hustler = Customer Development
    Designer = Product
    Picker = Visionary
  • Ended at: 0:50
  • Ended at: 1:23
  • Ended at: 1:23
  • Explain our idea so that audience knows why we bought a door
  • Ended at: 1:39
  • TODO:
    1. Add enterprise and Airbnb to Customer Segments.
    2. Add shortened value prop for each customer segment

    *Update weekly
    Each customer segment needs a matching value prop. Use a different color for each customer segment.

    Order of Validation:
    1. Customer Segments
    2. Value Propositions
    3. Channels
    4. Customer Relationships
    5. Revenue Streams
    6. Key Activities
    7. Key Resources
    8. Key Partners
    9. Cost Structure
  • Ended at: 2:20
  • What we thought: Parents with young children would love this
    Some ways we were dumb: Which of these value props is important to working parents? Hypotheses from value props and customer segments should be linked
  • What we thought: Parents with young children would love this
    Some ways we were dumb: Which of these value props is important to working parents? Hypotheses from value props and customer segments should be linked
  • Ended at: 2:43
  • Draft
  • Ended at: 4:00
  • Ended at: 4:00
  • Expected start-ups to care more about security because of IP, but they actually didn’t
    Middle to Large companies very much cared about tailgating for IP reasons
  • Ended at: 4:25
  • Ended at: 5:51

    Tailgating requires 1. detecting tailgating and 2. deterring tailgating (which requires knowing what's going on inside)
  • Ended at: 5:51

    Tailgating requires 1. detecting tailgating and 2. deterring tailgating (which requires knowing what's going on inside)
  • Ended at: 5:51

    Tailgating requires 1. detecting tailgating and 2. deterring tailgating (which requires knowing what's going on inside)
  • Ended at: 6:05
  • Ended at: 7:50
    Highlight our flip from tech searching for problem to thinking directly about the problems
  • Ended at: 8:19
  • Ended at: 8:19
  • Ended at: 8:19
  • Ended at: 8:42
  • Ended at: 10:21
  • TODO: Add mentor photos

    Ended at: 12:35
  • TODO: Elaine will edit this slide - move Shing Shing to left
    Ended at: 12:15
  • TODO: Elaine will edit this slide - move Shing Shing to left
    Ended at: 12:15
  • TODO: Elaine will edit this slide - move Shing Shing to left
    Ended at: 12:15
  • TODO: Elaine will edit this slide - move Shing Shing to left
    Ended at: 12:15
  • Ended at 11:25
    Video demo of Trello system

    "Know it when we see it"
  • Ended at 9:00
  • Ended at 9:00
  • Today: A diligent assistant
    Tomorrow: A powerful collaborator


  • TODO:
    1. Add enterprise and Airbnb to Customer Segments.
    2. Add shortened value prop for each customer segment

    *Update weekly
    Each customer segment needs a matching value prop. Use a different color for each customer segment.

    Order of Validation:
    1. Customer Segments
    2. Value Propositions
    3. Channels
    4. Customer Relationships
    5. Revenue Streams
    6. Key Activities
    7. Key Resources
    8. Key Partners
    9. Cost Structure
  • Identify ​market ​size ​(TAM/SAM/Target/Year ​1-3)

    Productivity software stats: https://www.mindsettlers.com/guide/4JTuifFzyM6IeowC0u4CEc

    Sources: 3.6M engineers: https://www.computerworld.com/article/2483690/india-to-overtake-u-s--on-number-of-developers-by-2017.html
    TAM: https://www.omnicoreagency.com/linkedin-statistics/

    Extra references: https://www.prnewswire.com/news-releases/digital-workplace-market---global-forecast-to-2023-use-of-advanced-technologies-tools-and-employees-demanding-a-greater-work-life-balance-is-going-to-drive-the-digital-workplace-market-300800273.html
    https://www.quora.com/How-many-people-use-Jira
    # Trello users: 25M
  • 30 seconds
  • Ended at: 3:16

    Maybe remove
  • Ended at: 3:16

    Maybe remove
  • Ended at: 4:41
  • Ended at: 5:15
  • How did we get to schools? By talking to many different customer segments

    Need to explain that we dropped enterprise due to privacy concerns
  • They way this presents is that parents are against us, but parents are
    Founder-product fit was not so good
  • Ended at: 7:17
  • Ended at: 9:18
  • “I’m sure I’ve written the same code many times” -George

    Also add quotes from our interviews!
  • George: don’t really understand the image

    The way you explain your value props matter: we were describing it as streamlineing repeated tasts, but people didn’t respond positively even though they described - they ddidn’t like to think of themselves as doing repeatable work. People don’t like to think of themselves - it’s very easy to blame others for the problem
  • What should you test over the next week?
    See hypothesis validation sequence in the slide note on the Business Model Canvas slide.
    Rank your hypotheses by High Impact + Low Confidence/Certainty

    Using this slide:
    Before running the test, complete left side boxes and leave right side boxes blank.
    After running the test, leave original left side boxes untouched and complete right side boxes to compare predicted to actual
    Feel free to add subsequent slides with screenshots, photos, or other visuals showing your test setup and results
  • Stronger close - team still friends, learned a lot, excited to keep working on it
  • Ended at: 11:10

    Great space but big, where should we focus? Start with where we have domain expertise
  • Ended at: 10:21
  • ×