Awesome, not awesome.
“…Today Iron Ox is opening its first production facility in San Carlos, near San Francisco. The 8,000-square-foot indoor hydroponic facility — which is attached to the startup’s offices — will be producing leafy greens at a rate of roughly 26,000 heads a year. That’s the production level of a typical outdoor farm that might be five times bigger. The opening is the next big step toward fulfilling the company’s grand vision: a fully autonomous farm where software and robotics fill the place of human agricultural workers, which are currently in short supply…bringing automation to both indoor and outdoor farming is necessary to help a wider swath of the agricultural industry solve the long-standing labor shortage.” — Erin Winick, Editor Learn More from MIT Technology Review >
“Kai-Fu Lee, a prominent investor and entrepreneur based in Beijing, has been talking up China’s artificial intelligence potential for a while. Now he’s got a message for the United States. The real threat to American preeminence in AI isn’t China’s rise, he says — it’s the US government’s complacency…Rather than competition from China, Lee says, the real risk for the US is in failing to invest in and prioritize fundamental AI research — a problem that’s being exacerbated as big US companies suck up much of the top talent in the field. In general, tech firms focus less on fundamental breakthroughs than does academia, which struggles to compete with the private sector in retaining researchers.” — Will Knight, Editor Learn More from MIT Technology Review >
Where we’re going.
The AI-first SaaS Funding Napkin by Louis Coppey
What does it take to raise seed funding as an AI-first SaaS startup?
At Point Nine, we have been focused on investing in SaaS companies and have been fortunate to work with several generations of successful businesses in this segment over the years. Since joining the firm 24 months ago, I’ve spent a significant share of my time looking at SaaS companies using machine learning to change industries.
From my vantage point, it appears that we’re now at a turning point when it comes to the use of AI in B2B applications. I published this post to that effect 18 months ago and I thought it was time to dive deeper into the topic with the benefit of hindsight and data. To that end, I’ve attempted to consolidate our thoughts on investing in AI-first SaaS businesses by creating a customary Point Nine napkin! Why? Because this is the format we typically use at Point Nine to consolidate our thoughts.
This post consists of 3 parts:
1. First, I will share a quick historical perspective on the broader SaaS industry and use it to explain some of the key success factors of each of these generations at a (very) high level,
2. Second, I define what I call an “AI-first SaaS business” and outline a work-in-progress investment thesis for seed stage startups in this category,
3. Third, I try to explain why AI-first SaaS is an exciting category based on the analysis of some of their intrinsic characteristics.
What we’re reading.
1/ The Department of Transportation expands the definition of drivers and operations to refer not just to humans, but also automated systems — a major regulatory step that brings us closer to seeing self-driving cars on the road. Learn More from WIRED >
2/ Using data derived from virtual photorealistic worlds could be the key that leads to major advancements in artificial intelligence because the technologies developed would no longer be “bottlenecked by data.” Learn More from Medium >
3/ A new legal argument makes the case for artificial intelligence systems to be considered people under the law — one professor argues this would harm human rights and dignity for the rest of us. Learn More from PBS News Hour >
4/ . Using a combination of neural networks, diverse sources of data, and a couple of sensors a former, an Obama Whitehouse staffer turned his car into an intelligent author that creates a narrative about its travels. Learn More from The Atlantic >
5/ Major automakers (like Honda and GM) that you wouldn’t normally expect to forge new partnerships are doing just that to ensure they aren’t left in the dust by the changes that self-driving cars will bring. Learn More from WIRED >
6/ Researchers from MIT and other top institutions race to create new machine learning models that detect fake news — all of them are still a long way from finding a great solution. Learn More from MIT Technology Review >
7/ As machine learning continues to transform organizations, organizations themselves need to transform processes that had been tuned to a world of traditional engineering — one place to start is with the ML engineering hiring loop. Learn More from Insight Data Science >