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Earlier this week, Zappos declared they will abandon traditional management structure for holacracy, a management ethos that eschews pyramids and hierarchy in favor of self-organizing groups, called holons. It’s not a structure without management, but one of distributed authority and management. Below is a schematic describing holacracy at a high level.

Holacracy has been adopted by a handful of other companies including David Allen’s company, the blogging platform Medium and some non-profits. Valve and 37Signals also seem to have adopted some of these ideas too. But Zappos may be the biggest company to adopt this management style yet. I’m excited to see how the transition goes because Tony Hsieh’s success could start a broad management movement.

Like many others, during my work day I fall into the firefighting trap, a time mis-allocation problem that leaves me focusing on urgent, but not necessarily important tasks. Firefighting is addictive because it’s fast-paced, nonstop and fun.

But firefighting is exhausting and leaves me feeling as if I haven’t made progress toward my goals. In addition, firefighting inhibits effective time allocation. A week can pass and I find that the non-urgent but important projects like preparing for a board meeting and researching a new sector like Bitcoin have been starved for time. Or I look up and realize I’ve been spending most of my time on urgent but not-important things, like returning huge volumes of email.

“It’s something I thought I’d never do,” one founder told me yesterday. He had hired a recruiter to help him grow his team. But at some point in time, most quickly growing startups will need help forming their teams.

We kicked off searches at two Redpoint companies in earlier this year in addition to several last year and I’ve been taking notes on each of the processes. One of my partners, Tim Haley, was a top recruiter in the valley for many years and having his experience and expertise has been invaluable. These are my notes:

Do you accept guest posts?
Sorry, I don’t.

May I use one of your charts?
Yes, if you link back. Thanks!

How do you make your charts?
With R and ggplot2. There are instructions here.

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Sorry, I don’t run ads.

May I copy your content? Feel free to use the first 2-3 sentences of a post, but please do not copy the entirety.

Even in infrastructure, many of the big data technologies driving increases in IT spending like Hadoop and Cassandra were developed by consumer internet companies (Google, Yahoo, Facebook, Amazon, etc date: 2012-10-17

The first mobile phones were purchased by corporations and given to employees. Thirty years ago, most people used computers at work but not at home. Most of the innovation flowed from the enterprise into the home. Today, it’s very much the opposite.

The big trends in enterprise trace the opposite movement both at the software layer and the device layer: consumerization of IT means using consumer channels to acquire customers and bring your own device (BYOD) means 66% of employees bring their own devices to work.

You’re walking down the hallway at work from one meeting to the next. A colleague or report stops you en route, asks for a minute and presents an important problem. It’s easy to respond with “let me think about it” and duck into the meeting. In that half-second, all the responsibility of the decision has been transferred. Unlike a minute ago, you have the monkey on your back.

The challenge with these situations is two-fold. First, you’re unlikely to have enough information to make a decision. Second, no time has been allocated to solving the problem.

One way of measuring the efficiency of a company’s revenue model is to benchmark revenue per employee. Google and Facebook, the two most efficient companies, generate $1M per revenue per employee per year.

Setting aside those exceptional cases and focusing instead on SaaS companies, the typical average revenue per employee is about $190k to $210k per year. The histogram above shows the ranges for publicly traded SaaS companies.

Financial statements are a Rosetta Stone for startups. They reveal the strategies and the tactics of how to bring a product to market. These are the ten metrics I look at when sifting through a startup’s operational model, whether when considering an investment or in a board meeting.

1. Revenue growth indicates how quickly a company can grow under the current way of doing business. The top line shows whether the market affords steady growth (SaaS) or lumpy revenue growth created by the long sales cycles of big customers (Telecom) and whether the company must sell one product or a collection of complementary products. The revenue growth projections indicate the potential of the business.

On January 8, 1966, the New Yorker profiled Buckminster Fuller for the first time. During a trip to a Maine island with the journalist Calvin Tomkins, Fuller said something tremendously prescient:

Fuller proposed a worldwide technological revolution…[that] would take place quite independently of politics or ideology; it would be carried out by what he calls “comprehensive designers” who would coordinate resources and technology on a world scale for the benefit of all mankind, and would constantly anticipate future needs while they found ever-better ways of providing more and more from less and less.

The traditional theoretical price demand curve is often drawn like this. The chart makes two points: there is some relationship between price and demand / revenue opportunity, and customer segments underpin that relationship. Each segment demands different products to satisfy different needs and presents a different revenue/profit opportunity.

Even if the details are very hazy, price demand curves are useful tools to inform product strategy and prioritization. To make PD curves useful requires marketing research. It means identifying customer segments, estimating the the demand of each segment, quantifying the investment required to build the right product, and uncovering the costs of acquiring and servicing customers within that segment. After all that, there’s also a process of testing various price points to get a better sense of price elasticity. All of these are useful in the process of establishing product market fit.