037 – A VC Perspective on AI and Building New Businesses Using Machine Intelligence featuring Rob May of PJC

Rob May is a general partner at PJC, a leading venture capital firm. He was previously CEO of Talla, a platform for AI and automation, as well as co-founder and CEO of Backupify. Rob is an angel investor who has invested in numerous companies, and author of InsideAI which is said to be one of the most widely-read AI newsletters on the planet. In this episode, Rob and I discuss AI from a VC perspective. We look into the current state of AI, service as a software, and what Rob looks for in his startup investments and portfolio companies. We also investigate why so many companies are struggling to push their AI projects forward to completion, and how this can be improved. Finally, we outline some important things that founders can do to make products based on machine intelligence (machine learning) attractive to investors. In our chat, we covered: The emergence of service as a software, which can be understood as a logical extension of “software eating the world” and the 2 hard things to get right (Yes, you read it correctly and Rob will explain what this new SAAS acronym means!) ! How automation can enable workers to complete tasks more efficiently and focus on bigger problems machines aren’t as good at solving Why AI will become ubiquitous in business—but not for 10-15 years Rob’s Predict, Automate, and Classify (PAC) framework for deploying AI for business value, and how it can help achieve maximum economic impact Economic and societal considerations that people should be thinking about when developing AI – and what we aren’t ready for yet as a society Dealing with biases and stereotypes in data, and the ethical issues they can create when training models How using synthetic data in certain situations can improve AI models and facilitate usage of the technology Concepts product managers of AI and ML solutions should be thinking about Training, UX and classification issues when designing experiences around AI The importance of model-market fit. In other words, whether a model satisfies a market demand, and whether it will actually make a difference after being deployed. Resources and Links: Email [email protected] PJC Talla SmartBid The PAC Framework for Deploying AI Twitter: @robmay  Sign up for Rob’s Newsletter Quotes from Today’s Episode “[Service as a software] is a logical extension of software eating the world. Software eats industry after industry, and now it’s eating industries using machine learning that are primarily human labor focused.” — Rob “It doesn’t have to be all digital. You could also think about it in terms of restaurant automation, and some of those things where if you keep the interface the same to the customer—the service you’re providing—you strip it out, and everything behind that, if it’s digital it’s an algorithm and if it’s physical, then you use a robot.” — Rob, on service as a software. “[When designing for] AI you really want to find some way to convey to the user that the tool is getting smarter and learning.”— Rob “There’s a gap right now between the business use cases of AI and the places it’s getting adopted in organizations,” — Rob “The reason that AI’s so interesting is because what you effectively have now is software models that don’t just execute a task, but they can learn from that execution process and change how they execute.” — Rob “If you are changing things and your business is changing, which is most businesses these days, then it’s going to help to have models around that can learn and grow and adapt. I think as we get better with different data types—not just text and images, but more and more types of data types—I think every business is going to deploy AI at some stage.” — Rob “The general sense I get is that overall, putting these models and AI solutions is pretty difficult still.” — Brian “They’re not looking at what’s the actual best use of AI for their business, [and thinking] ‘Where could you really apply to have the most economic impact?’ There aren’t a lot of peop