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Beacons AI
Week 1:
FaceID for doors
Resegmented market
Now:
AI to increase productivity by
streamlining similar work
New m...
Rafi Holtzman Robert Locke Todd Basche
David Zeng
PhD, Machine learning
Jesse Zhang
PhD, Machine learning
Neal Jean
PhD, M...
Before Lean Launchpad…
Before Lean Launchpad… FaceID for doors
Pencil and floss
holding things up
Camera
Smart lock
inside
Raspberry
Pi
Customer
Interviews
0
MVPs
1
Pre-LLP
Customer
Interviews
0
MVPs
1
Pre-LLP
IDEA SPACE
GOOD
BAD
PRODUCT-MARKET FIT
IDEA SPACE
GOOD
BAD
PRODUCT-MARKET FIT
“All startups go through
the drunken walk.”
— Mar
• Channels - Social
media platforms
(PTAs), online retailers,
Airbnb, Thumbtack
• Manufacturing -
Engineering consulting
f...
IDEA SPACE
GOOD
BAD
Week 1
IDEA SPACE
GOOD
BAD
Week 1
Week 1
What we thought:
We can help parents
make sure their kids
get home safely
Week 1Week 1
Talked to
10 parents
Week 1Week 1
What we learned: We don't actually solve the problem for parents
“There are so many risks that I worry
about (for my son g...
IDEA SPACE
GOOD
BAD
Consumer
Enterprise
Week 2
1st Pivot: Consumer to Enterprise
MVP #2 Face recognition visitor check-in on a laptop
Week 2
MVP #2 Face recognition visitor check-in on a laptop
Week 2
What we learned:
Every MVP should test a
hypothesis
Parents
Young women
Consumer
Startup
University
Real estate
HospitalMedium
enterprise
Large enterprise Retail
What we did:...
What we learned: One challenge for enterprise
access control is preventing tailgating
“The number one problem I’d like to ...
“Tailgating is the top problem or a
very important problem for me.”
Microsoft: Brian Tuskan, Chief Security Officer; Joe
F...
IDEA SPACE
GOOD
BAD
Consumer
Enterprise
Tailgating
Week 4
We solved tailgating, will you buy
our product?
No.
Weeks 4-6 Week 4
We solved tailgating, will you buy
our product?
“No.” — multiple customers
Weeks 4-6 Week 4
IDEA SPACE
GOOD
BAD
Consumer
Enterprise
Tailgating
Week 4
We solved tailgating, will you buy
our product?
“No.” — multiple customers
Weeks 4-6 Week 4
What we learned:
Our solution ...
Perimeter Access Volume Surveillance
Weeks 4-6 Week 5
2nd Pivot: Access Control to Surveillance
IDEA SPACE
GOOD
BAD
Consumer
Enterprise
Tailgating Surveillance
Week 5
IDEA SPACE
GOOD
BAD
Consumer
Enterprise
Tailgating Surveillance
Week 5
IDEA SPACE
GOOD
BAD
Consumer
Enterprise
Tailgating Surveillance
Week 5
IDEA SPACE
GOOD
BAD
Consumer
Enterprise
Tailgating Surveillance
Week 5 Week 6
IDEA SPACE
GOOD
BAD
Consumer
Enterprise
Tailgating Surveillance
Week 5
PRODUCT-MARKET FIT
Week 6
IDEA SPACE
GOOD
BAD
Consumer
Enterprise
Tailgating Surveillance
Week 5
PRODUCT-MARKET FIT
PROBLEM-TEAM FIT
What we learned...
"Find something that
you really like…
you have my blessing."
— Jeff (loosely paraphrased)
Week 7 Week 6
Thinking about problems that we have...
Thinking about problems that we have...
Working in teams is #*^&ing hard
Week 7
We use a lot of tools
Week 7
We use a lot of “dumb” toolsWhat if we could
make them smarter?
IDEA SPACE
GOOD
BAD
Workplace
productivity
tools
Week 7
We did a complete
restart
Existing tools: Knowledge is archived (i.e., lost forever)
Surveillance
project
Archive
Week 7 Week 7
Existing tools: Knowledge is archived (i.e., lost forever)
Surveillance
project
Week 7 Week 7
New project started
from scr...
New task
Week 7 Week 8
MVP #3
New task
Week 7 Week 8
What happens:
AI returns most
relevant tasks from
history
MVP #3
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 ...
Customers want the product!
“If you can give my
engineers boilerplate
starter code, I would buy
it immediately.”
Eric Xiao...
Customer Segments
• ML researchers
- Industry research
groups
- Academic research
groups
- PhD students
• ML engineers
- A...
Customer Segments
• ML researchers
- Industry research
groups
- Academic research
groups
- PhD students
• ML engineers
- A...
Customer Segments
• ML researchers
- Industry research
groups
- Academic research
groups
- PhD students
• ML engineers
- A...
Customer Segments
• ML researchers
- Industry research
groups
- Academic research
groups
- PhD students
• ML engineers
- A...
Week 9
A proven business model… freemium Saas
Week 9
A proven business model… freemium Saas
$100
per user/year
x
500,000
ML + data science workers
in 2024
=
$50M ARR
Week 9
A proven business model… freemium Saas
$100
per user/year
x
500,000
ML engineers + data
scientists in 2024
=
$50M A...
Week 9
A proven business model… freemium Saas
$100
per user/year
x
500,000
ML engineers + data
scientists in 2024
=
$50M S...
Week 9
A proven business model… freemium Saas
$100
per user/year
x
500,000
ML engineers + data
scientists in 2024
=
$50M S...
“...as I start my own
lab, I need a tool like
this.”
— Lisa Wedding, Associate
Professor, Oxford University
“I would use t...
IDEA SPACE
GOOD
BAD
Now
Workplace
productivity
tools
Where we are today
The rest of
our team 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, App...
AI for medical
diagnostics & imaging
David Zeng
PhD, Machine learning
Jesse Zhang
PhD, Machine learning
Neal Jean
PhD, Mac...
AI for medical
diagnostics & imaging
David Zeng
PhD, Machine learning
Jesse Zhang
PhD, Machine learning
Neal Jean
PhD, Mac...
AI for medical
diagnostics & imaging
Summer:
Beacons AI
Fall:
Stanford postdoc
David Zeng
PhD, Machine learning
Jesse Zhan...
Appendix
Now
Graveyard
BeaconsAI engr 245 lean launchpad stanford 2019
BeaconsAI engr 245 lean launchpad stanford 2019
BeaconsAI engr 245 lean launchpad stanford 2019
BeaconsAI engr 245 lean launchpad stanford 2019
BeaconsAI engr 245 lean launchpad stanford 2019
BeaconsAI engr 245 lean launchpad stanford 2019
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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

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