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AI/ML development is like reining in the natural world, more like physics and even metaphysics, where data and models are fluid. But this not just a philosophical observation; it has real implications for the margins, organizational structures, and building of such businesses. Especially as we’re in a tricky time of transition, where customers don’t even know what they’re asking for, yet are looking for AI/ML help or know it’s the future. So what does this all mean for the software value chain; for open source collaboration and commodification; for a new type of AI/ML company; and for the future of software businesses?
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In 2019, long before the outbreak of COVID-19, many lower gross margin tech companies were not being well-received by the public markets, and an excessive spotlight was cast by many on company gross margins. In the prese...
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Should I take my company public? Why? If so, when and how? In this post, I’ll demystify these questions and provide a framework for answering them.
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From hardware and hardwires to smartphones and social, technology wants to connect. It’s almost a native property of technology and especially software businesses, which is why network effects matter. “It wa...
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watch time: 21 minutes
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One of the biggest misconceptions around network effects (which are one of the key dynamics behind many successful and highly defensible software companies) is confusing growth with engagement. So how does one tell th...
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If network effects are one of the most important concepts for software-based businesses, then that may be especially true of data network effects — a network effect that results from data. Particularly given the p...