Collaborating Authors

Taming the Tail: Adventures in Improving AI Economics


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.

How to build a data architecture to drive innovation--today and tomorrow


Over the past several years, organizations have had to move quickly to deploy new data technologies alongside legacy infrastructure to drive market-driven innovations such as personalized offers, real-time alerts, and predictive maintenance. However, these technical additions--from data lakes to customer analytics platforms to stream processing--have increased the complexity of data architectures enormously, often significantly hampering an organization's ongoing ability to deliver new capabilities, maintain existing infrastructures, and ensure the integrity of artificial intelligence (AI) models. Current market dynamics don't allow for such slowdowns. Leaders such as Amazon and Google have been making use of technological innovations in AI to upend traditional business models, requiring laggards to reimagine aspects of their own business to keep up. Cloud providers have launched cutting-edge offerings, such as serverless data platforms that can be deployed instantly, enabling adopters to enjoy a faster time to market and greater agility.

The New Business of AI (and How It's Different From Traditional Software)


At a technical level, artificial intelligence seems to be the future of software. AI is showing remarkable progress on a range of difficult computer science problems, and the job of software developers – who now work with data as much as source code – is changing fundamentally in the process. Many AI companies (and investors) are betting that this relationship will extend beyond just technology – that AI businesses will resemble traditional software companies as well. Based on our experience working with AI companies, we're not so sure. We are huge believers in the power of AI to transform business: We've put our money behind that thesis, and we will continue to invest heavily in both applied AI companies and AI infrastructure. However, we have noticed in many cases that AI companies simply don't have the same economic construction as software businesses. At times, they can even look more like traditional services companies. Anecdotally, we have seen a surprisingly consistent pattern in the financial data of AI companies, with gross margins often in the 50-60% range – well below the 60-80% benchmark for comparable SaaS businesses.

Resilience and Vibrancy: The 2020 Data & AI Landscape


In a year like no other in recent memory, the data ecosystem is showing not just remarkable resilience but exciting vibrancy. When COVID hit the world a few months ago, an extended period of gloom seemed all but inevitable. Cloud and data technologies (data infrastructure, machine learning / artificial intelligence, data driven applications) are at the heart of digital transformation. As a result, many companies in the data ecosystem have not just survived, but in fact thrived, in an otherwise overall challenging political and economic context. Perhaps most emblematic of this is the blockbuster IPO of Snowflake, a data warehouse provider, which took place a couple of weeks ago and catapulted Snowflake to a $69B market cap company, at the time of writing – the biggest software IPO ever (see our S-1 teardown).