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You’re working on launching a new VC fund; congratulations! I’ve been a traditional equity VC for 8 years, and I’m now researching revenue-vased investing and other new approaches to VC. The question I’m asking myself: should a new VC fund use revenue-based investing, traditional equity VC, or possibly both (likely from two separate pools of capital)?
Revenue-based investing (“RBI”) is a new form of VC financing, distinct from the preferred equity structure most VCs use. RBI normally requires founders to pay back their investors with a fixed percentage of revenue until they have finished providing the investor with a fixed return on capital, which they agree upon in advance.
This guest post was written by David Teten, Venture Partner, HOF Capital. You can follow him at teten.com and @dteten. This is part of an ongoing series on Revenue-based investing VC that will hit on:
From the investors’ point of view, the advantages of the RBI models are manifold. In fact, the Kauffman Foundation has launched an initiative specifically to support VCs focused on this model. The major advantages to investors are:
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Artificial intelligence applied to information security can engender images of a benevolent Skynet, sagely analyzing more data than imaginable and making decisions at lightspeed, saving organizations from devastating attacks. In such a world, humans are barely needed to run security programs, their jobs largely automated out of existence, relegating them to a role as the button-pusher on particularly critical changes proposed by the otherwise omnipotent AI.
Such a vision is still in the realm of science fiction. AI in information security is more like an eager, callow puppy attempting to learn new tricks – minus the disappointment written on their faces when they consistently fail. No one’s job is in danger of being replaced by security AI; if anything, a larger staff is required to ensure security AI stays firmly leashed.
Arguably, AI’s highest use case currently is to add futuristic sheen to traditional security tools, rebranding timeworn approaches as trailblazing sorcery that will revolutionize enterprise cybersecurity as we know it. The current hype cycle for AI appears to be the roaring, ferocious crest at the end of a decade that began with bubbly excitement around the promise of “big data” in information security.
But what lies beneath the marketing gloss and quixotic lust for an AI revolution in security? How did AL ascend to supplant the lustrous zest around machine learning (“ML”) that dominated headlines in recent years? Where is there true potential to enrich information security strategy for the better – and where is it simply an entrancing distraction from more useful goals? And, naturally, how will attackers plot to circumvent security AI to continue their nefarious schemes?
The year AI debuted as the “It Girl” in information security was 2017. The year prior, MIT completed their study showing “human-in-the-loop” AI out-performed AI and humans individually in attack detection. Likewise, DARPA conducted the Cyber Grand Challenge, a battle testing AI systems’ offensive and defensive capabilities. Until this point, security AI was imprisoned in the contrived halls of academia and government. Yet, the history of two vendors exhibits how enthusiasm surrounding security AI was driven more by growth marketing than user needs.
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TechCrunch has learned that there has been a management shakeup at Zesty, a business catering service that’s health-minded and tech enabled. Funded by Index Ventures, Founders Fund, Forerunner Ventures and Y Combinator, Zesty has raised $20.7 million in venture funding to-date. Cofounder Chris Hollindale stepped into the CEO role in July, after the departure of his cofounding partner… Read More
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