The Rise And Fall Of The Big Telecom, Networking Startups

from the less-publicity,-more-money dept

While the consumer-focused Webvans and failures got all of the interest as the dot com bubble popped, a lot more money went into a number of telecom and networking startups that actually could make more reasonable claims for needing large capital investments to get their business plans on track. However, despite all that money, they’ve mostly struggled. The San Jose Mercury News looks at the “big 10” of networking and telecom startups that all raised upwards of $150 million in venture capital during the boom years. Most are mere shells of what they once were, with a good percentage filing for bankruptcy at some point. While some are still trying to turn whatever cash they have left over into a viable business plan, many don’t look long for this world. Of course, all of this is part of the risk of venture investing. Not every investment is going to be a success, and plenty will end up as complete failures. The only thing here, was that many of these companies clearly had half-baked business plans. But, in the go-go boom years, many VCs were falling all over themselves to invest in companies that had “buzz”. Also, with so much money invested, I’d bet that these companies weren’t the most conservative in setting up their plans, and thus, were slower to react when reality set in.

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Comments on “The Rise And Fall Of The Big Telecom, Networking Startups”

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Anonymous Coward says:

Imaginary numbers

In engineering courses, we are told to ignore solutions to math equations that have imaginary numbers, because they are not a part of our world.

But hmm, in an alternative universe where there are “minus $1” bills, VCs are rewarded for losing money, and telecoms are rewarded for slowing down the speed of light so it takes a month for an e-mail to travel around the world….

It could be that if we look at all technology investment, a coordinated treatment of linear and generalized linear models and their application. Linear regression, analysis of variance and covariance, random effects, design and analysis of experiments, quality improvement, log-linear models for discrete multivariate data, model selection, robustness, graphical techniques, productive use of computers, in-depth case studies.

If 9 out of 10 tech companies lose, and the 10th company turns a profit, we could still have zero or net negative return on investment. If we find a way to express that benefit as a square root, then it turns out that the benefits of technology are imaginary, belonging to a parallel universe.

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