Zetta Venture Partners, a B2B, AI-focused venture outfit, announced a new $180 million fund. As new VC funds are anecdotally a bit thinner on the ground these days -- and because we're in the midst of economic upheaval, which is changing investing patterns and shaking up startup verticals -- I got on the phone with Zetta's Jocelyn Goldfein (a TechCrunch regular) to chat about what her firm is doing and what's up with AI investing. Zetta's new fund is about 50% larger than its preceding capital pool, which was roughly double its first fund. If you don't want to do the math, Zetta's first fund was worth $60 million, and its second $125 million. Zetta will invest in B2B-focused, AI-powered seed-stage startups like it has before, but with more capital.

The best products in the world are made by vertically integrated businesses: Apple's hardware to software; Amazon's warehouses to websites; and Carnegie's mines to mills [1]. Zetta is completely focused on investing in data and machine learning startups. We see lots of horizontal platforms and APIs that anyone can use to add some machine learning models to their application. However, machine learning has advanced to the point where customers expect better than commodity performance. We like to see startups vertically integrating their technical skills with the skills of domain experts and unique data acquisition to build applications with the level of accuracy required in commercial and industrial settings.

Iterative Lean Startup principles are so well understood today that an minimum viable product (MVP) is a prerequisite for institutional venture funding, but few startups and investors have extended these principles to their data and AI strategy. They assume that validating their assumptions about data and AI can be done at a future time with people and skills they will recruit later. But the best AI startups we've seen figured out as early as possible whether they were collecting the right data, whether there was a market for the AI models they planned to build, and whether the data was being collected appropriately. So we believe firmly that you must try to validate your data and machine learning strategy before your model reaches the minimal algorithmic performance (MAP) required by early customers. Without that validation -- the data equivalent of iterative software beta testing -- you may find that the model you spend so much time and money building is less valuable than you hoped.

Somewhere in the spare room of a home in the US or overseas, a recent high school grad and soon to be member of the Class of 2022 at a school like Stanford or Georgia Tech is gathering up the clothes, gadgets, and dorm room basics that they will need for freshman year. Four years after that traumatic (for the parents) drop-off day, that skinny but brilliant freshman will graduate and join a six-person start-up company, where he or she will play an integral part in building the game-changing technology that you and your organization will use just a few years later. We can't tell you which freshmen are on the way to cashing out their stock options or which companies they will work for, but we can get a glimpse of key information technologies that might be available in the somewhat shorter term. Some of the companies and technologies that will be in the must-have category are pulling in venture capital investments today. This is the first in an occasional series of roundups of just a few of the noteworthy startup reporting investments.

Not all applications are ready for AI, despite recent major advances in the field and enabling infrastructure. Anxiety over the prospect of being disrupted is prompting leaders in all industries to experiment with AI-powered solutions. This makes it difficult for aspiring entrepreneurs to distinguish C-suite curiosity from a long-term intention to buy. If AI startups want to move their work beyond the pilot stage toward sustainable, long-term growth, they should avoid chasing opportunities where the stakeholders are not culturally ready for AI, or where more effective technology could be applied. Before you even start working with a potential customer's data, you need to understand the ABCs of AI-readiness: Acceptance, Better Solutions, and Costs.

Ash Fontana, Managing Partner, Zetta Venture PartnersUber's cars are crashing, Microsoft's bots abusing people on twitter and U.S. judges sentencing people using biased algorithms. Machine learning models rely on probabilistic assumptions because they're trying to model things that are uncertain. Probabilistic assumptions don't always hold, so the models don't always work. We should keep this in mind when building machine learning products so that we meet the expectations of our customers, at best, and avoid the unchecked use of machine learning in high-stakes situations, at worst. As a venture capital firm focused on intelligent software for the enterprise, we at Zetta Venture Partners have developed a framework for understanding the impact of products built on machine learning.