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I’ve been reading Robert Greene’s book Mastery. Greene has amassed biographies of tens of great people from Henry Ford to Paul Graham, from Alexander the Great to Larry Page. Across many of them, he identifies two common paths to mastery: mentorship and grit.

The story of Leonardo da Vinci captures both ideas. Da Vinci was born out of wedlock and was prohibited from attending school. His father, a notary, had access to a large supply of paper which was a rare commodity at the time. With all of his free time, Leonardo would walk through the forests of Vinci and draw.

LinkedIn. Twitter. Facebook. Asana. RelateIQ. Slack. 3 Email Accounts. Each of those is an inbox, an implicit or explicit to do list. If I count the feed and the messaging products as separate inboxes, I have a grand total of 12 inboxes for work. 12 things to check first thing and last thing. How many do you have?

The rate of new software company formation seems to have declined materially in the past few years. In 2011-2013, about 1450 software companies were founded each year on average. In 2014, that figure fell to 1186 and in 2015, we count 481.

Why does Crunchbase data indicate this decline? First, there might be a few data issues in the most recent years. Perhaps software companies remain in stealth longer. Perhaps there a substantial delay between when the business is founded and when it appears in Crunchbase. However, Pitchbook analysis corroborates this trend.

What is the optimal pricing strategy for a start up? That depends. It depends on at least three other variables: the product, the placement, the positioning. Combined with price, these are the four 4Ps of marketing created by Dr. E. Jerome McCarthy, former professor of marketing at Michigan State and Notre Dame. Also called the marketing mix, these four variables need to be aligned when determining pricing for your startup.

Apple serves as a great example of a company whose marketing mix is aligned and consistent. The company positions itself in the market as a premium brand. It builds products of the highest quality often with innovative and novel manufacturing techniques. Apple sells its products in glorious, well lit and modern stores. And it’s price points top the market.

As a SaaS startup scales from finding initial product market fit to building its go to market organization, one of the most important goals in building that go to market organization is developing a multifaceted marketing team. Marketing’s role in SaaS sales has expanded to the success of SaaS companies as customers prefer to educate themselves more than they have in the past.

The acquisition frenzy continues in SaaS. This morning Microsoft announced it would acquire LinkedIn for $26.2 billion, which prices the business at 8 .1x trailing 12 month revenues and 6.7x next 12 month revenues. In addition, Symantec announced that it would acquire Blue Coat for $4.7 billion, a 7.7x trailing multiple. In the 2016 acquisition market, the median acquisition multiple is 7.8x trailing 12 month revenue and 6.4x next 12 month revenue.

Which are the ripest areas for startups to disrupt using machine learning? At the core, machine learning/artificial intelligence relies on two key ingredients: advanced algorithms and data sets to train those algorithms. Novel algorithms are increasingly making their way into the public domain in the form of open-source libraries. So, the key differentiator for startups and ultimately long-term competitive advantage is access to proprietary data sets.

In the consumer world, there are natural and intrinsic monopolies at play. Google’s search has a natural feedback mechanism of users clicking on the best search results that results in Google continuing to have the best search. Facebook’s network effects of it social network, in combination with its strategic acquisitions of other fast-growing social networks like Instagram and WhatsApp, reinforce its natural monopoly.

How does a chat bot company transform a technology advantage into a distribution advantage? This is the key question facing startups in this part of the ecosystem.

Deep learning is advancing at an incredible rate. Google has released TensorFlow and Parsey McParseface. These advances will improve natural language understanding, enabling chat interfaces to amaze and delight users.

So let’s assume that the technology works. Let’s take for granted that despite the myriad ways a human can ask a question, the computer will understand the query. And let’s also assume that on top of the messaging platforms, these chat services flourish. Distribution resembles that of the early days of the iTunes Store, when users were rapt to try out new products and startups aggregated massive user basis overnight. Then what?

When is the right time to increase sales headcount for a SaaS startup? It is one of the most strategic decisions for early-stage business to make given the amount of effort and expense involved in building, managing and scaling a sales team.

While there is no single absolute sign, neither qualitative nor quantitative, these are some of the signals I have found indicate it might be a good time to scale sales.

There are two main ways of building a SaaS sales team: top down or bottoms up. Top down SaaS sales organizations start with a VP of Sales who often hires senior account executives. Bottoms up sales teams hire the first account manager and promote from within. Which is better?

Let’s create a framework to compare the bookings capacity, the ramp period and the cost for three levels of sales hires. These figures will vary by company and sales team.