Much of the conversation today about crypto is about Bitcoin and currency. But that’s myopic. Soon, we will be talking about how crypto will change the software world. In fact, many founders have already started that pursuit. More than 30% of the initial coin offerings (ICO) in 2017 target developers and businesspeople with their products.
When a startup takes form, the first weeks and months and years are spent furiously. The team assembles itself. The lightbulb illuminates. It is formed and reformed again and again as customers supply feedback. Eventually the team hews the right product. The startup raises capital. Then the team returns focus to hiring, evolving the product, and closing customers. However, continuing this way isn’t the path to huge scale. There’s a critical step missing.
This is one of the best pieces of advice I’ve collected about writing.
Put each sentence on a separate line when you write.
When we read, we hear a rhythm.
There is a rhythm to the words.
And when we read, we look to find that rhythm, which pushes us through the end of a sentence, so if I write a really long sentence, and the reader isn’t expecting it – like this one – the reader will find it really hard to follow and understand where I am going.
And if I keep writing really long sentences, I’m going to exhaust the reader, because there’s no place to stop and take a breath and think about what it is that I have written.
Whew!
In SaaS, machine learning has become an essential component to many different products. Whether it’s automating responses to inbound sales queries, identifying expense reports for audit, or surfacing anomalies in data, machine learning improves workflow software. To date, most software imbued with machine learning reduces costs rather than increase revenues.
Why is this the case? Because machine learning is focused on efficiency gains.
First, to train a machine learning model requires large amounts of data. Repetitive workflow processes produce this data. With enough data, a machine learning model can be applied to automate the workflow. For example, it’s straightforward to build machine learning models to automatically answer password reset questions.
“My goal is to make you love rowing.” That’s how my first rowing started. I had never picked up an oar or stepped into a racing shell before. With 100 other freshman men, I had assembled at the boathouse along the maple and conifer lined Connecticut River. I’ll never forget those words.
I had played many sports before. In every other case, the focus, the mission, the motivation had always been to win. This was different.
Last week, I shared a presentation with an executive team at a large public SaaS company on everything I’ve learned about pricing. Here’s a summary of the frameworks and theory that I’ve aggregated over a decade of investing in startups.
Why do we set prices? Setting aside the important reasons of generating revenue and maintaining solvency for a business, there are many other reasons to set price. Price reinforces brand because price telegraphs whether a product is a premium product or a value product. Price differentiates products in the market and can be used as a go-to-market strategy. Underprice the competition to gain share. There are many others too.
In December 2017, the amount raised in ICOs nearly equaled the amount raised by Series A investments globally. The technology innovation catalyzed by Bitcoin and Blockchain is creating many multibillion dollar economies quickly. The ICO market today bears many similarities to the dotcom era. Startups can raise hundreds of millions of dollars on an idea. Twenty years ago, the idea had to be sketched on a napkin. Today, the protocol must be detailed in a white paper. But the ability to access huge amounts of capital at a similarly nascent stage is identical.
Below are 7 predictions about the startup software ecosystem. How many of them do you agree with?
I like writing these lists because they are very likely to be proven wrong in the fullness of time and looking back at them a few years later gives you a sense of what the world felt like back then. Have a great new year, everyone!
There’s a crisis in the scientific academic world. It’s called the Replication Crisis. Scientists have found that they cannot replicate the results published by many scientific studies. The same thing is happening in the world of business.
Over the last 15 years I’ve read several hundred business books, and I’ve written one. Across those 15 years, one of the most interesting is a book called The Management Myth, which traces the history of management science back to its less than solid origins. The book illuminates the history of scientific management and Taylorism, and supplies evidence that much the original data underpinning these theories was fabricated. I was curious if this was a broader trend, and it may be. I found other cases.
When negotiating your next fundraising round, should you talk valuation in premoney terms or postmoney terms? Premoney is the valuation before the investment, employee stock option pool (ESOP) expansion, debt-to-equity conversion and investment. Postmoney is the value of the business after all that.
As an investor, postmoney is simpler. Despite the improved simplicity, I don’t think the industry is going to move to postmoney anytime soon.
Why are postmoney conversations simpler? Because the valuation of the business is fixed. The value of the business doesn’t float from variables like pro-rata participation, ESOP expansion and debt-to-equity conversion.