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Machine learning meets the lean startup

Steve Blank, lecturer, Haas School of Business | November 18, 2016

We just finished our Lean LaunchPad class at UC Berkeley’s engineering school, where many of the teams embedded machine learning technology into their products.

What struck me, as I watched the teams try to find how their technology would solve real customer problems, is that machine learning is following a similar pattern of previous technical infrastructure innovations. Early entrants get sold to corporate acquirers at inflated prices for their teams, their technology and their tools. Later entrants who miss that wave have to build real products that people want to buy.
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I’ve lived through several technology infrastructure waves; the Unix business, the first AI and VR waves in the 1980’s, the workstation wave, multimedia wave, the first internet wave. Each of those had a set of common characteristics that the Gartner Group characterizes as the Hype Cycle.

hype-cycle-gartner

The five stages of the hype cycle are:

Stage 1: The Technology Trigger: A potential technology breakthrough kicks things off. Early proof-of-concept stories and media interest trigger significant publicity. Often no usable products exist and commercial viability is unproven.

Stage 2: Peak of Inflated Expectations: Early publicity produces a number of success stories — often accompanied by scores of failures. Some companies take action; most don’t.

Stage 3: Trough of Disillusionment: Interest wanes as experiments and implementations fail to deliver. Producers of the technology shake out or fail. Investments continue only if the surviving providers improve their products to the satisfaction of early adopters.

Stage 4: Slope of Enlightenment: More instances of how the technology can benefit the enterprise start to crystallize and become more widely understood. Second- and third-generation products appear from technology providers. More enterprises fund pilots; conservative companies remain cautious.

Stage 5: Plateau of Productivity: Mainstream adoption starts to take off. Criteria for assessing provider viability are more clearly defined. The technology’s broad market applicability and relevance are clearly paying off.

Shiny object meets first mover advantage

What’s become apparent in the last few technology hype cycles is that for startups and their investors there is a short multi-year window of opportunity (at the Peak of Inflated Expectations) to sell a startup at an inflated price. This occurs because large technology companies (Google, Facebook, IBM, Microsoft, Twitter, Apple, Salesforce, Intel, et al,) and increasingly other non-tech firms, are in an arms race to stay relevant. For example, according to CBInsights nearly 140 machine intelligence have been acquired since 2011, with over 40 being bought so far in 2016.

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Most often the first acquisitions in a hype cycle are for the “shiny objects” – the technology, the team and the tools. The acquired technical teams usually start up or complement the company’s research group in a specific new technology area.

hype-cycle

If you’re a startup (or their investors) getting acquired at this point in the hype cycle is exactly where you want to be – short time in business, large acquisition price, value based on a frenzy, perceived scarcity of expertise, and fear of a competitor getting the key talent.

History shows that the acquirers often overpay buying this expertise early. While these acquisitions have teams of great researchers, they rarely contribute actual revenue generating products (because most never reached that stage when they were acquired.) The irony is that the acquisitions made later in the hype cycle – when companies have built real products that customers want, are the ones that generate revenue and profit for the acquirer.

I had all that in mind as we watched our teams present.

Machine learning meets Lean – Berkeley Lean LaunchPad class

Each of our teams in this class followed the canonical Lean model:

  • Articulate your hypotheses using the business model canvas
  • Get of the building and test those hypotheses using customer development
  • Validate learning by building minimal viable products and getting them in front of customers

Each week the teams got out of the classroom and talked to 10-15 customers, testing a new part of the business model canvas. And after week two, they had to build and then update their minimal viable product weekly. And present what they learned each week in an 8-minute presentations.

The presentations below are their final Lessons Learned presentations, along with a 2-minute video summary.

SalesStash
Three Berkeley PhD computer science students and an MBA working on machine learning. How can you not hit out of the park on day one?

This team epitomized rapid learning. Once their initial assumptions ran into the wall of actual customer feedback they rapidly built multiple minimum viable products (MVPs) and kept pivoting until they found product/market fit (i.e. a customer segment that was grabbing the product out of their hands.)

If you can’t see the video click here

If you can’t see the presentation click here

Delphi
Before this class this team had spent three months in an incubator building the product after talking to only one customer. After week two of the class they realized they had wasted three months building something no one actually wanted. What they next learned was pretty amazing.

If you can’t see the video click here

If you can’t see the presentation click here

Homeslice
Homeslice had a great journey. They came together over a personal pain – the inability to afford a house in Silicon Valley. Their initial plan was to provide fractional ownership to solve that problem. But they found that first serving an adjacent market – slices of investment properties – could serve as a launchpad for their initial idea of fractional home ownership.

If you can’t see the video click here

If you can’t see the presentation click here

Exit Strategy
Exit Strategy was building the penultimate planning tool. This teams learning that this wasn’t a business was as important as finding one that is. Really impressive process.

If you can’t see the video click here

If you can’t see the presentation click here

This class this was a team effort. Professor Kurt Keutzer and Errol Arkilic (former program director for the National Science Foundation Innovation Corps (NSF I-Corps), now founder of M34 Capital) were the lead instructors. Steve Weinstein (CEO of MovieLabs) and I assisted. Thanks to our TA Kathryn Crimmins and all the team mentors: Lev Mass, Kanu Gulati, Ewald Detjens, James Cham, Kanu Gulati, Patrick Chung, Rick Lazansky, Ashmeet Sidhana, Mike Olson, Michael Borrus, Fabrizo De Pasquale, Amit Kumar, Rob Rodick, Mar Hershenson.

Steve Blank’s blog: www.steveblank.com

Comments to “Machine learning meets the lean startup

  1. Enjoyed reading your article. Your point ” Later entrants who miss that wave have to build real products that people want to buy.” is so true. Funny those late comers to the party will probably be the most successful because they will use this new technology to the age-old adage “give them what they want”.

    Your second point “Get of the building and test those hypotheses using customer development” is also key. Again while technology is developing rapidly -humans are still the same and “want what they want”.

    Your article does a good job of combining knowledge about Machine Learning and Market Research. A killer combination. Thank You

  2. Great article Steve. Really impressive and thoughtful and it makes me think more that the future of computers is the Artificial Intelligence. I know a good friend of mine who works for Code for America and spent tremendous months in building an online platform called SkyHub http://skyhub.me that wants to use machine learning and AI to connect people based on their interests. He is in a beta version with his work, but I believe that after he finish his work the world will never be the same.

    AI will disrupt the startups and the world.

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