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Summarized by PlexPage
Last Updated: 02 July 2021

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During boom times, companies focus on growth. In tough times, they seek to improve efficiency. History shows the US that after every major economic downturn since the 1980s, businesses rely on digital technology and, specifically, innovations in software technology to return to full productivity with fewer repetitive jobs and less bloat. Years Ive spent as a VC have convinced me that this is the best time to start an AI-first enterprise, not despite the recession, but because of it. In the spring of 1993, Harvard statistics professor named Donald Rubin sat down to write a paper. The Rubin paper would go on to change the way that artificial intelligence is researched and practice, but its stated goal was more modest: analyze data from the 1990 US census, while preserving anonymity of its respondents. It wasnt feasible to simply anonymize data, because individuals could still be identified by their home address, phone number, or social security number, all of which were crucial to analysis that Rubins colleagues wanted to perform. To solve the problem, Rubin generated a set of anonymized census responses whose population statistics mirror those of the original data set. Our team at Zetta was so excited when we met Rever continuous improvement software company led by charismatic founder, Errette Dunn, that is transforming industrial businesses by digitally collecting suggestions and feedback from their frontline employees, implementing those suggestions quickly, and creating immediate and measurable return on investment. Rever is a platform that enables employees of its customers to identify, test, and implement opportunities for incremental process improvements, and cultivate continuous improvement culture in all teams. For example, workers at Grupo Bimbo, one of Revers customers, might hypothesize that switching from 1 centimeter pipe to 5 centimeter pipe would speed up the tank refilling process. Iterative Lean Startup principles are so well understood today that minimum viable product is prerequisite for institutional Venture funding, but few startups and investors have extended these principles to their Data and AI strategy. They assume that validating their assumptions about data and AI can be done in future time with people and skills they will recruit later. But the best AI startups weve seen figured out as early as possible whether they were collecting the right data, whether there was a market for AI models they planned to build, and whether data was being collected appropriately. So we believe firmly that you must try to validate your data and machine learning strategy before your model reaches minimal algorithmic performance required by early customers. Without that validation data equivalent of iterative software beta testing, you may find that the model you spent so much time and money building is less valuable than you hop. Building a B2B AI startup is hard enough between struggling to obtain training data and fighting with major tech companies to secure talent. Building B2B AI Startup hold to well-establish software-as-aservice metrics is even harder.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

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