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Co-Founder, CTO at Cloudscaling.  Since 1990, Randy has driven innovations in infrastructure, IT, operations, and 24×7 service delivery. He was the technical visionary at GoGrid and built the world’s first multi-cloud, multi-platform cloud management framework at CloudScale Networks. He led the open-licensing of GoGrid's API, which inspired Sun Microsystems, Rackspace Cloud, VMware and others to open-license their cloud APIs. Randy blogs @ Cloudscaling blog, is recognized by The Next Web as one of the 25 Most Influential People Tweeting About Cloud, is frequently interviewed and speaks at dozens of industry events annually.

6 responses to “A response to Geoffrey Moore: Manifest Disruption”

  1. Geoff Moore
  2. GIASTAR – Storie di ordinaria tecnologia » Blog Archive » Why VCs Will Continue To Invest In Big Data Startups For Many Years To Come

    […] data functionality is just part of the story. This week, Cloudscaling Founder Randy Bias wrote a post that more broadly reflects what these individual startups represent.  The market is going through a […]

  3. Why VCs Will Continue To Invest In Big Data Startups For Many Years To Come | News of Business

    […] data functionality is just part of the story. This week, Cloudscaling Founder Randy Bias wrote a post that more broadly reflects what these individual startups represent.  The market is going through a […]

  4. Geoff Moore
  5. Mark Cox

    Reading this article got me thinking. My company (Appsecute) is working specifically in the area of Platform-as-a-Service and I started thinking about whether PaaS itself is disruptive. It inspired me enough to write a blog post tonight: http://blog.appsecute.com/?p=166

  6. Why VCs will continue to invest in big data startups | Cloudscaling

    […] Bias wrote a post that more broadly reflects what these … startups represent.  The market is going through a deep disruption. It’s a model ‘pioneered by the most massive Internet players (Google, Amazon, Facebook, Twitter).  This IT model starts with datacenters built with extreme power efficiencies (PUEs reaching 1.2 or less), extends to open hardware projects (like Open Compute), and reaches into the transformation to a scale-out software architecture model (epitomized by “big data” projects like Hadoop and Cassandra).’ […]