When I woke up this morning, I asked my assistant a simple question: “Siri, is it going to rain today?”
Siri understood my intent, pulled the local weather data via an API, and answered me in less than two seconds: “There’s no rain in the forecast for today.”
In the not-too- distant past, this kind of human-computer interaction would have blown away technologists and delighted consumers but in 2016 it’s nothing special.
Conversations with Siri are commonplace, just like they are with Microsoft’s Cortana and Amazon’s Alexa.
Machine learning (ML) and narrow forms of artificial intelligence (AI) have officially reached the mainstream.
The explosion of innovation we’re seeing in AI/ML stems from a series of rapid technological advances of the last few decades: widespread Internet connectivity and proliferation of online data, faster/cheaper computers (per Moore’s Law), variable-cost cloud computing, R&D investments from large technology companies, and a vibrant open source software community.
We haven’t yet built HAL 9000, but we’re getting closer.
Challenges for Startups in a World of Mainstream AI
Like many venture capitalists, I talk to technology startups leveraging AI/ML almost every day. When I do, I’m always hunting for companies that are building something completely new; whether it’s a proprietary new data set to train machine learning models, or a radically different approach to solving big technical problems using AI.
The fundamental reason is this: if company is going to outcompete others long-term using AI/ML, it better have the best data to solve a specific problem or be playing a different game from its competitors.
Data is the fuel we feed into training machine learning models that can create powerful network effects at scale. Unfortunately for startups, big technology
companies typically have huge, proprietary data sets that span many industries. Meanwhile, the open source community’s efforts are quickly democratizing access to the most sophisticated machine learning algorithms.
It’s now nearly impossible for a startup to develop a competitive advantage around algorithm development alone.
You can’t find a big technology company in 2016 that doesn’t publicly discuss AI/ML. They heavily promote their activities in the space, and often have fantastic data upon which to train their models.
Google has built their system around search data and ad clicks; Facebook, their newsfeed and social interaction data; and
Amazon, their product purchasing and recommendation data. Google, Facebook, Amazon, and Microsoft have all open-sourced components of
their internal machine learning technologies to spur innovation in the space while building their brand as AI/MLleaders. NVIDIA is making a fortune selling chips
optimized for deep learning.
With all of this in mind, investing in the space can be tricky. Rather than fighting hand-to- hand with technology Goliaths, AI/ML Davids need to find their slingshots and stones.
Waze is one example of the first kind of startup that investors get excited about: one that builds a proprietary data set through its product and uses that data to deepen its competitive advantage as it scales. [Chris Dixon, General Partner at Andreessen Horowitz, mentions this example in a blog post that’s worth reading, “What’s Next in Computing.”] Waze has a tangible network effect, where the number of users and quality of their data set drives the predictive power of the platform and user experience.