Download as PDF, PPTX
Uploaded byMatt Turck
Building an AI Startup: Realities & Tactics
The document discusses the challenges and strategies for building an AI startup, emphasizing the competitive landscape dominated by large tech companies. It highlights the importance of focusing on enterprise solutions, leveraging data, and managing customer expectations during AI deployment. The key takeaways include the need for deep technical teams, understanding user needs, and the significance of positioning and product development in AI entrepreneurship.
More Related Content
Similar to Building an AI Startup: Realities & Tactics
![Coded Agents – with UiPath SDK + LangGraph [Virtual Hands-on Workshop]](https://cdn.slidesharecdn.com/ss_thumbnails/codedagentsdeck-251215155422-5497c599-thumbnail.jpg?width=640&height=640&fit=bounds)
Building an AI Startup: Realities & Tactics
- 1. Matt Turck, FirstMarkCapital Peter Brodsky, HyperScience Sept 27, 2016 Building An AI Startup Realities & Tactics
- 2. MATT TURCK Managing Director Earlystage venture capital firm based in New York City. FirstMarkCap.com Largest data-focused monthly event in the country DataDrivenNYC.com
- 3. HyperScience is anAI company based in New York, leveraging unique technology to solve large enterprise problems, starting with back office automation. HyperScience.com PETER BRODSKY Co-Founder & CEO
- 4.
- 5.
- 6.
- 7.
- 8. VC money ispouring in A record $1.05B went into 121 private AI companies in Q2, according to CB Insights
- 9. But the reality fromthe trenches is different
- 11. How do youactually build an AI company? | Positioning | Product | Petabytes | Process | People
- 12.
- 13. Exciting times tobuild an AI startup! Just One Small Problem
- 14. All the BigTech Companies Got the Memo, Too
- 15. “Artificial intelligence wouldbe 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
- 16. They’re Now Bettingthe 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
- 17. They Can Hire ALLTHE AI ROCKSTARS Andrew NgYann LecunGeoff HintonPeter Norvig
- 18. They Can AcquireThe Best Teams
- 19. And They HaveAll The Data In The World
- 20. …Position away from them! So WhatDo You Do?
- 21. V E R T I C A L However they’re not goingto 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
- 22. 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!
- 23. 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”
- 24. The HyperScience Experience BroadAI 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
- 25.
- 26. Should your productbe all AI? Remember when I said “the technology works”? LIES It never works 100%
- 27. Sudden Perception that UsingHumans in AI = Failure
- 28. Sometimes You Need100% Accuracy, Sometimes You Don’t Low Product Risk High Product Risk
- 29. Humans In TheLoop: Avoid Disasters
- 30. Or Underwhelming UserExperiences
- 31. Leveraging Human Usersto Train the AI
- 32. AI in theEnterprise: 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
- 33. Build your Productwith Data Network Effects in Mind Illustration Source: Moritz Mueller-Freitag, ”10 Data Acquisition Strategies for Startups”
- 34. Data Network Effects Exemplifiedby Industry Giants
- 35. Data Network Effects Exemplifiedby Industry Giants But also available to startups
- 36.
- 37. Cold Start Problem Usuallyhave to start the flywheel here
- 38. Data Sets Licensed datasetsPublic data sets
- 39. Data Crawling Web CrawlingReal 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
- 40. Data Capture Networks Sense360'ssensor-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.
- 41. Data "Traps" • Consumerapps: Facebook, IoT products like Kinsa • Enterprise apps: Slack >> Bots • “Trojan Horse” side apps: Forevery (Clarifai) Source: Clarifai, Recode
- 42.
- 43.
- 44. One Dirty Secretof 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
- 45. AI Engineers area 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
- 46. How NOT tosell 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.
- 47. 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
- 48.
- 49. 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!
- 50. Need Deeply TechnicalTeams • 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
- 51. Rare Birds • Thereis 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
- 52. But Talent isGlobally Distributed!
- 53.
- 54. 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
- 55. 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.
- 57. How do youactually build an AI company? The Five P’s of AI | Positioning | Product | Petabytes | Process | People
- 58. Now is theperfect time to build an AI company! “Do not throw away your shot!”