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The most overlooked path to commercialize AI is for companies to do it themselves

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编辑: 1   作者: Techcrunch   时间: 2019/4/16 13:09:45  

Editor's note: John Mannes is an investor at Basis Set Ventures, a $136 million early-stage venture capital fund focused on supporting startups using machine learning to address big problems across industries. Prior to Basis Set Ventures, John was a TechCrunch writer covering machine intelligence startups, machine learning research and major AI initiatives from big tech.

The Bessemer Process patented in 1856 by Sir Henry Bessemer is one of the inventions most closely associated with catalyzing the second industrial revolution. By reducing the impurities of iron with an innovative oxidizing air blast, the process ushered in a new wave of inexpensive, high-volume steel making.

Bessemer decided to license his patent to a handful of steel makers in an effort to quickly monetize his efforts. But contrary to expectations, technical challenges and monopolistic greed prevented large steel makers from agreeing to favorable licensing terms.

In an effort to drive adoption, Bessemer opened his own steel-making plant with the intention of undercutting competitors. The approach was so successful that each partner in the endeavor walked away from the 14-year partnership with an 81x return.

Some 162 years later, new businesses continue to struggle to convince customers to adopt new technologies — even when it’s in their best interest. Following in the footsteps of founders like Bessemer, today’s innovative startups are discovering that it often makes more sense to launch “full stack” businesses that provide a traditional service optimized with proprietary automation measures.

Chris Dixon of Andreessen Horowitz popularized the term “full-stack startup” in 2014, just before the deep-learning revolution. In his words, a full-stack startup is a company that “builds a complete, end-to-end product or service that bypasses existing companies.”

The full-stack methodology gave birth to companies like Uber and Tesla prior to the apex of the deep-learning revolution. And in today’s AI-first world of data and human labelers, full-stack startups are poised to play an even more important role in the startup ecosystem.

Going full stack comes with the advantage of being able to operate outside traditional incentive structures that limit the ability for large players in legacy industries to implement automation measures.

(Photo by Andrew Spear for The Washington Post via Getty Images.)