Originally published in a16z.com, Aug 12, 2020.
AI has enormous potential to disrupt markets that have traditionally been out of reach for software. These markets – which have relied on humans to navigate natural language, images, and physical space – represent a huge opportunity, potentially worth trillions of dollars globally.
However, as we discussed in our previous post The New Business of AI, building AI companies that have the same attractive economic properties as traditional software can be a challenge. AI companies often have lower gross margins, can be harder to scale, and don’t always have strong defensive moats. From our experience, many of these challenges seem to be endemic to the problem space, and we’ve yet to uncover a simple playbook that guarantees traditional software economics in all cases.
That said, many experienced AI company builders have made tremendous progress in improving the financial profiles of their companies relative to a naive approach. They do this with a range of methods spanning data engineering, model development, cloud operations, organizational design, product management, and many other areas. The common thread that often guides them is a deep, practical understanding of the problem to be solved.
So while our previous post outlined the challenges facing AI businesses, the goal of this post is to provide some guidance on how to tackle them. We share some of the lessons, best practices, and earned secrets we learned through formal and informal conversations with dozens of leading machine learning teams. For the most part, these are their words – not ours. In the first part, we’ll explain why problem understanding is so important – particularly in the presence of long-tailed data distributions – and link it to the economic challenges raised in our last post. In the second part, we’ll share some strategies that can help ML teams build more performant applications and more profitable AI businesses.
We are hugely bullish on the business potential of AI and continue to invest heavily in this area. We hope this work will continue to spark discussion and support founders in building enduring AI companies.
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social experiment by Livio Acerbo #greengroundit #thisisnotapost #thisisart