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I’ve been searching for a great history of the venture capital industry since before I joined Redpoint. There are a handful of books that are pretty good. Done Deals. eBoys. Creative Capital. But there’s a great one called VC by Tom Nicholas.

Nicholas traces the history of the venture capital industry back to whaling. They weren’t called venture capitalists back then, but they serve the same role. Men would broker relationships between wealthy individuals and adventuresome captains.

I’ve gotten to know Marc Randolph as a fellow board member at Looker. Marc has helped many companies get off the ground, but the most famous is Netflix. Marc founded the business and served as its first CEO until Reed Hastings took the helm in 2003. In That Will Never Work, Marc recounts the early days of the $130B market cap company first started in Santa Cruz and it’s a remarkable story. One in which Redpoint partner Tim Haley has a cameo (Tim led the Series A.)

Last week, SaaS stocks fell by about 18% on average. The chart above shows the most recent enterprise value to forward multiple for a basket of next-generation software companies. The red line is the value and the blue line is the median over the same time frame.

As of Friday, the median forward multiple is 9.3x which is a 11% drop from the previous high of 10.5x. The current valuation level is still top decile across this time period, despite the drop. In other words, the valuation environment is still at elevated levels.

Selling based on ROI (return-on-investment) sounds great. A salesperson lays out an iron-clad case for how the customer will make 5x or 6x or 10x their initial investment in a piece of software in three years or less. The champion will use ROI math to assuage upper management and procurement’s concerns. Or so the thinking goes.

If we reflect on the most successful software companies, the very largest, very few sell based on ROI. What is the return on investment of a Salesforce or a Workday deployment? How do you calculate it? How does an AE defend it?

I’ve playing with a new mental model for early-stage startups: a pendulum. This pendulum oscillates between the limiting factors of the business at different stages. There are only two limiting factors in this mental model: product and go to market.

At the moment a startup is founded, the business is product limited. You can’t do much without a product. After the company establishes product market fit, the pendulum swings to go-to-market.

There’s a new debate about marketing efficiency recently, and it’s an important one in the era of product-led growth. If a startup has great net dollar retention (NDR), should it be willing to increase its customer acquisition spend proportionately?

I remain a believer that months-to-repay is the best metric for measuring customer acquisition efficiency for a single reason. You know the answer immediately and accurately. With the annual contract you have in your hand and the amount of money you spent in sales and marketing to acquire a set number of customers in a period, you know exactly your MTR.

Recently, we’ve seen a series of product-driven companies building huge customer bases with tremendous account expansion and terrific sales efficiency. DataDog is no exception. DataDog provides a very popular IT monitoring solution that has grown from its founding in 2010 to a huge business. During that time the product has grown from infrastructure monitoring to application performance management, logging and user experience products. The company published its S-1 Friday.

DataDog counts more than 8800 customers, 590 of which spend more than $100k, and 40 of which spend more than a $1M. No customer represents more than 5% of ARR, which means the largest customer isn’t spending more than $15M a year - still huge.

Which sectors see more startup company formation than others? The answer has changed quite a bit over the last 8 years. Some sectors have hit their apogee and are declining. Others have grown by more than 3x. Yet others are growing geometrically. Let’s take a look.

Hot Spaces

Artificial Intelligence - yes, it’s a buzzword but it’s more than that. AI or Machine Learning is a new technology that will benefit nearly every type of sector and we’re still in the very earliest innings. Eight years ago, there were nearly zero AI startups seeded. Today, that number is 400 and the chart is a classic S-curve, tapering after a period of intense growth.

Earlier this year, I read Algorithms to Live By, a book that explains how to use insights from computer science in daily life. One of the rules is the 37% rule. It’s an important rule because it’s broadly applicable. But I had forgotten about it until I listened to the author on the Software Engineering Daily podcast.

The 37% rule says that if you have a decision to make, you should spend 37% of the amount of time you have. This is called exploration period. Seek advice, learn about demands of the role, and meet some candidates.

If you must choose a long term headquarters for your startup, call an executive recruiter who focuses in that city. Ask her about each of the key roles your company will need to hire in the next 2 to 3 years. VP Engineering, VP Product, VP Sales, VP Customer Success, VP Marketing, or VP Operations.

How large is the candidate pool for each search? Which are the hardest searches to complete in this geography? Where are the best talent pools to sift through?