Building an AI Startup: Realities & Tactics

Managing Director at FirstMark Capital
Sep. 29, 2016

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Building an AI Startup: Realities & Tactics

  1. Matt Turck, FirstMark Capital Peter Brodsky, HyperScience Sept 27, 2016 Building An AI Startup Realities & Tactics
  2. MATT TURCK Managing Director Early stage venture capital firm based in New York City. Largest data-focused monthly event in the country
  3. HyperScience is an AI company based in New York, leveraging unique technology to solve large enterprise problems, starting with back office automation. PETER BRODSKY Co-Founder & CEO
  4. AI is hot…
  5. The technology is working…
  6. Plenty of hype!
  7. Zeitgeist
  8. VC money is pouring in A record $1.05B went into 121 private AI companies in Q2, according to CB Insights
  9. But the reality from the trenches is different
  10. How do you actually build an AI company? | Positioning | Product | Petabytes | Process | People
  12. Exciting times to build an AI startup! Just One Small Problem
  13. All the Big Tech Companies Got the Memo, Too
  14. “Artificial intelligence would be the ultimate version of Google. We’re nowhere near doing that now. However, we can get incrementally closer to that, and that is basically what we work on.” - Larry Page CEO, Google, October 2000 They’ve Been Thinking About AI For A Long Time
  15. They’re Now Betting the Farm On It “We’ve been building the best AI team and tools for years, and recent breakthroughs will allow us to do even more. We will move from mobile first to an AI first world.” - Larry Page CEO, Alphabet, April 28, 2016
  16. They Can Hire ALL THE AI ROCKSTARS Andrew NgYann LecunGeoff HintonPeter Norvig
  17. They Can Acquire The Best Teams
  18. And They Have All The Data In The World
  19. …Position away from them! So What Do You Do?
  20. V E R T I C A L However they’re not going to tackle every single vertical problem HORIZONTAL Tech giants have a formidable advantage when it comes to building broad horizontal products (image/video/voice recognition, language translation) & infrastructure (AI cloud) v s
  21. Enterprise vs. Consumer • Tech giants, on the whole, are more focused on consumer than enterprise • Plenty of opportunities to deliver deep enterprise solutions • Fortune 1000 companies have large datasets!
  22. Tools vs. Platforms • Offering broad core technology (including “strong AI”) is tricky, long-term, for any startup • Giants may impact your business just by open- sourcing some of their tech (TensorFlow) • Focus on tools that solve specific customer problems, including “last mile”
  23. The HyperScience Experience Broad AI technology that can be applied to many problems Decision #2 Back office automation as first beachhead Decision #1 Focus on the enterprise, particularly Fortune 1000
  24. | PRODUCT
  25. Should your product be all AI? Remember when I said “the technology works”? LIES It never works 100%
  26. Sudden Perception that Using Humans in AI = Failure
  27. Sometimes You Need 100% Accuracy, Sometimes You Don’t Low Product Risk High Product Risk
  28. Humans In The Loop: Avoid Disasters
  29. Or Underwhelming User Experiences
  30. Leveraging Human Users to Train the AI
  31. AI in the Enterprise: Humans Needed! • The “S Word”: Services. VCs will scream in horror! • But reality of AI is that services required for successful deployment in the enterprise
  32. Build your Product with Data Network Effects in Mind Illustration Source: Moritz Mueller-Freitag, ”10 Data Acquisition Strategies for Startups”
  33. Data Network Effects Exemplified by Industry Giants
  34. Data Network Effects Exemplified by Industry Giants But also available to startups
  36. Cold Start Problem Usually have to start the flywheel here
  37. Data Sets Licensed data setsPublic data sets
  38. Data Crawling Web Crawling Real World Crawling • While Tesla owners have driven around 100 million miles on Autopilot, Anderson reveals that the fleet Autopilot hardware-equipped cars has collectively driven 780 million miles…. • Tesla basically turned its fleet of vehicles into an incredible data gathering asset for the Autopilot program before enabling the software. Electrek, May 2016
  39. Data Capture Networks Sense360's sensor-technology is on more than 250 mobile apps and more than 1.5 million devices in the US. Our panel generates more than a terabyte of anonymous sensor data every single day and provides a detailed view of more than 100 million anonymous user visits a month.
  40. Data "Traps" • Consumer apps: Facebook, IoT products like Kinsa • Enterprise apps: Slack >> Bots • “Trojan Horse” side apps: Forevery (Clarifai) Source: Clarifai, Recode
  41. AI Training
  42. | PROCESS
  43. One Dirty Secret of AI When it comes to successfully deploying AI in the real world, half the battle has to do with expectation management and social engineering, not technological prowess
  44. AI Engineers are a Novelty in the Enterprise Companies are trying to make sense of this strange new type of vendors promising miracles WHAT YOU THINK YOU LOOK LIKE WHAT YOU ACTUALLY LOOK LIKE
  45. How NOT to sell AI to the Enterprise “Give us all your data, we’ll use it to fine tune the algorithms in our black box, and awesome things will happen.” Without real value provided upfront, that typically doesn’t work.
  46. AI Social Engineering • Help customers understand which problems can be solved with AI (and which problems cannot) • Assist customers in developing relevant testing procedures and success metrics for AI • Address any security or data privacy concern
  47. | PEOPLE
  48. One Big Misperception • TensorFlow = doesn’t (yet) mean AI is now easy to use! • Not about slapping “.ai” after your startup name • This stuff is really hard!
  49. Need Deeply Technical Teams • Need core machine learning talent – often PhD level • Also top engineers who can productize and deploy AI • Ideally, people who can do both! • In most cases, CEO needs to be deeply technical too
  50. Rare Birds • There is a very limited supply of such talent • Big tech companies will pay millions just in sign up bonus for a brand new PhD in deep learning • Hard to attract top AI talent for a startup, but even harder for a Fortune 1000 company
  51. But Talent is Globally Distributed!
  52. Recruit Globally
  53. Deep Customer Focus • Danger with very technical teams: building “tech for the sake of tech” • Focus on serving customers need to be part of the team’s DNA
  54. The HyperScience Experience • 26 team members, only one “non-technical” to handle sales (but he can code!) • Half of the team is based in Bulgaria • Secured customers very early in the life of the company and built product closely with them
  56. How do you actually build an AI company? The Five P’s of AI | Positioning | Product | Petabytes | Process | People
  57. Now is the perfect time to build an AI company! “Do not throw away your shot!”