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How should you think about designing your startup’s logo? The mark symbolizes your business. It’s destined to be ubiquitous. Business cards, ads, hoodies, mobile apps, water bottles, even custom sneakers may bear the logo.

In the most recent episode of 99% Invisible, Tom Geismar relates the creation story of the Chase logo. The Chase logo was one of the first abstract corporate marks, one without text. Since then, Geismar has created many iconic marks including Xerox, PBS, Mobil.

20 years ago, a newly minted billionaire worked in his office. Fluorescent lights, a dingy purple carpet, and a CRT monitor resting on a door. Not a desk - those were too expensive - a door supported by 4x4s, brackets and wood screws. In his 60 Minutes profile, he drives around in a Honda Civic. His rationale? “It’s a perfectly good car.”

The founder of course is Jeff Bezos.

The photograph and the interview highlight an attribute I greatly admire in Bezos: clarity of purpose. From the very first shareholder letter, Customer Obsession is the first priority.

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.

The numbers are still small. B2B crypto companies raised about $400M of ICO dollars in 2017. B2D companies commanded $1.2B. This distribution reflects the early stage of the ecosystem.

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?

1. The tax holiday for repatriation creates one of the most active M&A environments of the past ten years. The repatriation holiday is part of the new tax plan. It permit companies to bring US dollars held abroad (from software sales in other countries) back to the US at a lower tax rate than before. The scale is enormous. Apple could repatriate $252B, Cisco $65B, Google $55B. That cash could be used for dividends, share buy backs and acquisitions. Several landscape altering SaaS acquisitions will come to fruition because of cash availability from repatriation and because there are enough public SaaS companies at scale to add material revenue and market cap to buyers. Some ideas: Google buys Salesforce. Microsoft buys Workday. Oracle buys ServiceNow. There are now 5 publicly traded software companies worth more than $10B, and 19 companies worth between $2.5B and $10B.
2. The SaaS fundraising market remains ebullient through 2018 as vibrant M&A and an open IPO window trigger substantial liquidity for shareholders.
3. Machine learning fades as a buzzword. A while ago, I pulled up the Redpoint website from a decade ago, when the bios of the partners said things like “I invest in the internet.” Or five years ago, “I invest in mobile.” Just as those trends have become ubiquitous to be implicit, so will machine learning.
4. Blockchain in the enterprise takes the reign as the buzzword for 2018. Founders will look to apply the innovation of a distributed and decentralized-trust database in different parts of the ecosystem. In particular, blockchain applications will pop-up in inter-company applications or where network effects are important (payments, security, supply chain).
5. The classic open source strategy of the last fifteen years is abandoned because of the competitive threats from infrastructure-as-a-service (IaaS vendors). New licenses may emerge to create some barrier to entry or open source vendors keep proprietary a greater fraction of the code base.
6. GDPR becomes an important consideration in most SaaS companies product and sales teams as the European regulation changes lead generation practices and data retention/governance practices in Europe. Similar regulations will be adopted in other regions.
7. The industry pendulum switches from fragmentation to consolidation in products as well as companies. Businesses emerge that create opinionated stacks for different functional roles and simplify day-to-day tasks for knowledge workers.

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!