Welcome!

@CloudExpo Authors: Liz McMillan, Zakia Bouachraoui, Yeshim Deniz, Pat Romanski, Elizabeth White

Related Topics: @CloudExpo, Java IoT, Microservices Expo, Open Source Cloud, Containers Expo Blog, Agile Computing

@CloudExpo: Blog Feed Post

In Technology, It's About 10 Years, Not 100 Years

There's another problem with the 100 year concept - it's not realistic anymore

Silicon Valley is a strange anomaly in a big world. It's arguably the center of the technology universe, constantly cranking out new ideas, new products and new companies. And its residents create billions (or trillions) of dollars of revenue on the back on that technology. But Silicon Valley also has this self-belief that it's a romantic place, a modern day Camelot where the ills and evils of the world can be vanquished by spreading around magic silicon dust in just the right amounts. It looks to highlight the next 100 year company, which will cement it's place as a foundational pillar in the fabric of future global economies.

But there's a small problem with that romantic idea. That's not how the Silicon Valley DNA is wired. Regardless of whether or not people believe Silicon Valley is currently living in a bubble, its fabric is built on the creative desires of "what's next?" and not on "let's sustain this!".  In Silicon Valley, terms like "Serial Entrepreneur" are held in similar regard as "Doctor" or "Professor".  Nobody in Silicon Valley wants to be known as a 15yr veteran of Company X, that's not cool. They want to tell stories of their stealth start-ups and their IPOs, and how even though kids may have slowed them down a little, they are still active Angel investors and just had coffee with some Stanford kids that could be the next Google.

There's another problem with the 100 year concept - it's not realistic anymore, for almost any company, and especially in technology. The old standards of GE, General Motors, US Steel, P&G were created as a time when the opportunities were there to find, build and create "new", because major elements of society were either non-existant or lacking. Transportation infrastructure, population booms, population shifts, high-speed communications, etc. Now the next-generation of start-ups are looking to "fix the old way". Fixing things is fine, but by leveraging technology to fix it, it almost always removes segments of the value-chain that were critical to allow those 100yr companies to survive as long as they did. When you remove the barriers to entry, it's easier to deliver value to the market quicker, but it's also much easier to get disrupted by the next start-up that thinks your concepts "need to be fixed".  Your business may be one $0.99 app away from being inconsequential.

No, I think we'll find that the new standard to measure companies will not be 100yrs, but rather in 10yr increments. 10yrs is (roughly) the amount of time it now takes a new technology trend to begin, gain traction, endure criticism, rapidly grow and then hit an inflection point of either technology or business model disruption. The technology trends can be managed through shifting of R&D budgets or acquisitions, but managing through a business model shift is the real test of being a 10yr or 20yr company. 10yr alumni like Google, Facebook and VMware are trying to figure it out now, and their 30-40yr competition (Oracle, Cisco, Microsoft, EMC) are trying to figure out if they will be around as leaders or followers for the next 10yrs.

  • Proprietary vs. Open-Source.
  • Packaged vs. *as-a-Service.
  • Freemium vs. Ad-Supported.
  • Application vs. Platform.
  • Producer, Competitor or Coopetition.

We discussed some of this on a recent The Cloudcast (.net) with analyst Ben Kepes, as it related to Cloud Computing companies.

So why is any of this relevant? On one hand, as someone responsible for technology strategy (in IT, as a vendor, as an investor), it lets you plot trends against a 10yr timeline.

  • How old is a current trend? Has the next trend begun to emerge?
  • How old is the current segment leader vs. a new start-up?
  • How long has the business model of the current trend existed? Does it have a disruptive model yet?
  • How close are you to having to seriously face a business model change? Can a new technology trend help catalyze that change?
  • What does participation or lack of participation in this trend mean for your business? Is it required, nice-to-have or a or distraction?

On the other hand, even if the elaborate value-chains that created the 100yr companies of the past probably can't be repeated, it highlights that having breadth of platform does provide you with a much better opportunity for success in the current (or next) 10yr cycle. Changing technology is the easy part. Having to also change people/perception and process makes a much larger impact on either consumer or business decisions.

Subscribe the romantic novels of Silicon Valley if you wish, they make for fine tales of napkins-to-NetJets stories. But more importantly, align your strategic technology clocks to those 10yr time cycles. They'll not only tell you the pulse of tech-du-jour, but will help you decide where to place those next bets to "fix the old ways" with magic silicon dust.

Read the original blog entry...

More Stories By Brian Gracely

A 20 year technology veteran, Brian Gracely is VP of product management at Virtustream. He holds a CCIE #3077 and an MBA from Wake Forest University.

Throughout his career Brian has led Cisco, NetApp, EMC and Virtustream into emerging markets and through technology transitions. An active participant in the virtualization and cloud computing communities, his industry viewpoints and writing can also be found on Twitter @bgracely, on his blog Clouds of Change and his podcast The Cloudcast (.net). He is a VMware vExpert and was named a "Top 100" Cloud Computing blogger by Cloud Computing Journal.

CloudEXPO Stories
With more than 30 Kubernetes solutions in the marketplace, it's tempting to think Kubernetes and the vendor ecosystem has solved the problem of operationalizing containers at scale or of automatically managing the elasticity of the underlying infrastructure that these solutions need to be truly scalable. Far from it. There are at least six major pain points that companies experience when they try to deploy and run Kubernetes in their complex environments. In this presentation, the speaker will detail these pain points and explain how cloud can address them.
The deluge of IoT sensor data collected from connected devices and the powerful AI required to make that data actionable are giving rise to a hybrid ecosystem in which cloud, on-prem and edge processes become interweaved. Attendees will learn how emerging composable infrastructure solutions deliver the adaptive architecture needed to manage this new data reality. Machine learning algorithms can better anticipate data storms and automate resources to support surges, including fully scalable GPU-centric compute for the most data-intensive applications. Hyperconverged systems already in place can be revitalized with vendor-agnostic, PCIe-deployed, disaggregated approach to composable, maximizing the value of previous investments.
When building large, cloud-based applications that operate at a high scale, it's important to maintain a high availability and resilience to failures. In order to do that, you must be tolerant of failures, even in light of failures in other areas of your application. "Fly two mistakes high" is an old adage in the radio control airplane hobby. It means, fly high enough so that if you make a mistake, you can continue flying with room to still make mistakes. In his session at 18th Cloud Expo, Lee Atchison, Principal Cloud Architect and Advocate at New Relic, discussed how this same philosophy can be applied to highly scaled applications, and can dramatically increase your resilience to failure.
Machine learning has taken residence at our cities' cores and now we can finally have "smart cities." Cities are a collection of buildings made to provide the structure and safety necessary for people to function, create and survive. Buildings are a pool of ever-changing performance data from large automated systems such as heating and cooling to the people that live and work within them. Through machine learning, buildings can optimize performance, reduce costs, and improve occupant comfort by sharing information within the building and with outside city infrastructure via real time shared cloud capabilities.
As Cybric's Chief Technology Officer, Mike D. Kail is responsible for the strategic vision and technical direction of the platform. Prior to founding Cybric, Mike was Yahoo's CIO and SVP of Infrastructure, where he led the IT and Data Center functions for the company. He has more than 24 years of IT Operations experience with a focus on highly-scalable architectures.