Steve White He grew up as an entrepreneur in Silicon Valley in the 1970s, when it was really all about silicon, and by the 1990s he had founded or worked at four high-tech startups. He was ultimately inspired by this experience to create a customer development model inspired by the scientific method; this requires developing various hypotheses, then testing and iterating them. This model became the foundation on which the lean startup The movement was built, with its “minimum viable product,” “pivot,” and other concepts that entered the mainstream lexicon.
Now Blank, which teaches entrepreneurship at
Stanford Universitythink about how artificial intelligence The tools are poised to transform the lean startup approach, supercharging the process of testing hypotheses, developing new products, and building businesses at a speed humans could never match.
How does AI and machine learning do they affect entrepreneurship, innovation and R&D?
Empty: I was actually amazed that (Lean Startup) was not a fad, but actual fundamentals of how to build things. There have been hundreds of versions, but it comes down to hypothesis testing, minimum viable products, et cetera.
It’s not hard to imagine this being automated by AI: in the morning, I could create 100 digital customer archetypes and populate a website with 1,000 images of a product they might like. In the afternoon, it could run A/B tests with thousands of virtual tests. The fundamental ideas are the same, but when a machine operates it, versus a human being? You haven’t seen anything yet.
How do you see the collaboration between human inventors and AI evolving?
Empty: The scientific method is a 500-year-old approach that has until now been conducted by human beings. The next breakthrough could be when we hand these problem sets over to machines, and they start to have ideas about invention that humans would never have seen. We’re starting to see some of this, in everything from electronic design automation to computational fluid dynamics – ways of approaching problems that simply hadn’t been invented yet.
I always come back to AlphaFold (an AI system from Google Keep in mind that
computationally predicts protein structures). In 75 years, we have discovered 10,000 protein structures; AlphaFold calculated 200 million. If it were human, it would have won a Nobel Prize.
What advice would you give to inventors wishing to integrate AI into their creative processes?
Empty: My advice to anyone in any field of their career is this: every six months, spend three days reviewing the state of the art of tools in and around your space. The rate of delta change continues to increase and it is likely that an (advance) will cross your domain. This can be positive or negative, but you shouldn’t be surprised. Every six months won’t look like the last six months.
How should entrepreneurs and inventors think about reinventing their roles in the AI era?
Empty: If I were still an entrepreneur, I would create enterprise software, the equivalent of SAP or Salesforce that applies Lean (startup) principles from start to finish. At first it would be human-assisted machine learning, and after a while you probably wouldn’t even need a human other than someone to spit out a result to. We see (AI) automatically generating websites and code. Just imagine using it to chain Lean methodology.
I love showing people this photo from the 1920s: a room full of men reading calculators to calculate actuarial tables at an insurance company. Do you know what this room looks like now? Nothing. It doesn’t exist. However, we have not experienced mass unemployment. People’s jobs have simply changed. This is why I tend to be optimistic. Programmers will become fast engineers; protein designers will start working on more complicated things. We replaced many of these high-value jobs in the past, and the world did not end.
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