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In Bias Against Creativity, a team of researchers at Cornell discuss the bias against creativity they revealed in their study. Originally published in 2010, the article resurfaced yesterday on Hacker News. It raises the question of how to evaluate creative ideas and how to engender internal incentives to support creativity.

From the Cornell paper:

People often reject creative ideas even when espousing creativity as a desired goal. To explain this paradox, we propose that people can hold a bias against creativity that is not necessarily overt, and which is activated when people experience a motivation to reduce uncertainty.

As the number of SaaS applications has exploded, the SaaS ecosystem is responding to data fragmentation with middleware. This isn’t the middleware of the early 2000s, which was focused on helping developers build software. This is middleware that is focused on helping end-users unify data from the vast numbers of data repositories now existent.

Ten years ago, sales teams captured basic data on customers and prospects. Names, addresses, budget, time frame. All of it collected over the phone.

How much revenue do you want to book for your SaaS startup next quarter? And in 12 months? It is one thing to put a number down on the financial plan. It’s another thing altogether to have the sales team staffed to close that amount of business.

The bookings capacity of the business is the amount of business a sales team should book in a certain period. Bookings capacity is calculated as:

In January, I wrote The Hardest Round to Raise which argued Series B rounds would be the most challenging early stage round in 2017. Irrespective of the annual vicissitudes of the fundraising market, Series Bs are always the most challenging rounds to raise because they are in-between rounds. The Series B is the pimpled and gangly adolescent phase of startup evolution.

If the aim of every round of venture funding is to prove a hypothesis - which I believe is true in Series Seed, A and B - then the Series B is the last milestone before proving cash is the business’ limiting factor.

When building a SaaS product for salespeople, a startup’s price will inevitably be compared to Salesforce CRM’s cost of about $150 per seat. How expensive is this new product compared to Salesforce? In diligence calls, I often hear buyers say: one-half of Salesforce’s price seems expensive; one-third might seem more reasonable. This is the price anchoring effect in the real world.

A founder emailed me last week to raise the question of whether performance pricing for SaaS companies is an effective technique. Performance pricing means explicitly pricing of product in terms of the customers’ revenue gained or cost reduced from its use.

Conceptually, performance pricing is very rational. The buyer should be willing to pay between 10 to 15% of the revenue or cost savings for the use of the product. And as classical economics instructs us, this type of pricing mechanism optimally aligns the incentives of the buyer and the seller. But the reality is more nuanced.

Five months into 2017 nine venture-backed technology companies have gone public compared to 14 in 2016. Four consumer companies and five enterprise companies have popped on average 29% since their IPO pricing. Only two, Carvana, an online used car dealer, and Netshoes, a Brazilian ecommerce company have traded down from their IPO pricing. The other seven have demonstrated the broad public investor demand for new offerings.

Earlier this week, a founder asked whether the fundraising market suffered from seasonality. Are there more prosperous months to raise than others? That’s a simple question to answer - or so I thought. Ultimately, a dinosaur proved to me the answer is more nuanced.

I plotted the mean round size of Series As from 2010-2016 in the bar chart above. You’ll notice, as I did, a spike in June and November. And a t-test shows these differences are statistically significant. The p-values for Jan/June and Jan/November are 0.002 and 0.0008. A very compelling figure to argue the difference in means is real. What logic might explain these surges?

Instead of raising an equity round, a startup might choose to borrow money - and for good reason. Venture debt dilutes founders much less than equity rounds. Low interest rates have increased the attractiveness of venture debt, because the cost to borrow is low. Venture debt is an attractive financial product. No wonder it has grown in popularity by 16x in the in the last six years.

My algorithm is better than yours. My algorithm performs better on the precision/recall tradeoffs. It surfaces fewer false positives. It converges to an answer faster. Perhaps it requires a bit less data. Those statements might all be true. But none of these advantages confer a competitive sales advantage in the market. They aren’t technology innovations leading to a go-to-market advantage.

I first observed the use of large scale machine learning at Google. In the early and mid-aughts, the advertising ecosystem bloomed. Hundreds of ad networks vied for publisher ad impressions. Each one promised a better targeting system, new algorithms, unique data, better performance, more revenue.