Many multi-billion-dollar companies have been built by providing tools to make software development easier and more productive. Venture capitalists like to refer to businesses like these as “pick and shovel” opportunities, a reference to Mark Twain's famous line: “When everyone is looking for gold, it's a good time to be in the pick and shovel business.”
Atlassian, which offers a suite of software development and collaboration tools, has a public market capitalization above $30B. GitHub, a code repository, was acquired for $7.5B by Microsoft in 2018. Pivotal, which accelerates app development and deployment, was valued at $2.7B in VMWare's acquisition last year. Many more of today's hottest high-growth startups—LaunchDarkly, GitLab, HashiCorp—offer tools for software development.
Each of these companies’ tools are built for “traditional” software engineering. In recent years, an entirely new paradigm for software development has burst onto the scene: machine learning.
Building a machine learning model is radically different from building a traditional software application. It involves different activities, different workflows and different skillsets. Correspondingly, there is a need—and opportunity—to build a whole new generation of software tools. The reward for developing this next wave of “picks and shovels”: many billions of dollars of enterprise value.
How exactly do traditional software development and machine learning differ? In traditional software development, the core task is writing code. The human programmer’s job is to craft an explicit set of instructions to tell the software program what to do given different contingencies. For software programs of any sophistication, the volume of human-written code can be immense. The Internet browser Google Chrome has 6.7 million lines of code; the operating system Microsoft Windows 10 reportedly has 50 million.
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