Don’t Ask For Too Much Money

A common mistake that founders make when raising a venture round is to anchor high and ask for too much money, at too high a valuation, with the hope the VC will bid them down. This is a common failure mode that prevents people from raising money successfully when they otherwise could. Asking for too much money is driven by misunderstanding the nature of a fundraise negotiation. When fundraising, you are trying to create an auction dynamic - not a 1:1 negotiation.

In a traditional negotiation, you want to anchor high and then have people bid you down. In a venture round, you actually want to do the opposite - you want to anchor low and pull multiple VCs into an auction around the company. Once a VC is emotionally engaged in the auction they will want to win against their peers. This will drive up the dollar amount you Continue reading "Don’t Ask For Too Much Money"

Don’t Ask For Too Much Money

A common mistake that founders make when raising a venture round is to anchor high and ask for too much money, at too high a valuation, with the hope the VC will bid them down. This is a common failure mode that prevents people from raising money successfully when they otherwise could. Asking for too much money is driven by misunderstanding the nature of a fundraise negotiation. When fundraising, you are trying to create an auction dynamic - not a 1:1 negotiation.

In a traditional negotiation, you want to anchor high and then have people bid you down. In a venture round, you actually want to do the opposite - you want to anchor low and pull multiple VCs into an auction around the company. Once a VC is emotionally engaged in the auction they will want to win against their peers. This will drive up the dollar amount you Continue reading "Don’t Ask For Too Much Money"

Market Caps & The 2% Rule

One way to assess whether a startup idea is in a good market is to ask what are the market capitalizations of the biggest companies in that sector. For example in consumer internet, Google ($560 billion) and Facebook ($370 billion), and in enterprise software Microsoft ($460 billion), and Oracle, ($167 billion) are all large, high margin businesses.

Market caps in a pre-existing industry[1] tend to be proxies for the potential of the idea you are working on. There are three reasons for this:
1. The market capitalization of a set of companies reflects revenue in the market, growth rate of revenue and earnings, and the margins of the companies.
These core metrics used by wall street to value a stock are all metrics that help you understand whether a market is overall large, growing and profitable - all signs of a good market to enter.

2. Often, potential competitors Continue reading "Market Caps & The 2% Rule"

Market Caps & The 2% Rule

One way to assess whether a startup idea is in a good market is to ask what are the market capitalizations of the biggest companies in that sector. For example in consumer internet, Google ($560 billion) and Facebook ($370 billion), and in enterprise software Microsoft ($460 billion), and Oracle, ($167 billion) are all large, high margin businesses.

Market caps in a pre-existing industry[1] tend to be proxies for the potential of the idea you are working on. There are three reasons for this:
1. The market capitalization of a set of companies reflects revenue in the market, growth rate of revenue and earnings, and the margins of the companies.
These core metrics used by wall street to value a stock are all metrics that help you understand whether a market is overall large, growing and profitable - all signs of a good market to enter.

2. Often, potential competitors Continue reading "Market Caps & The 2% Rule"

Building VC Relationships

A common mistake founders make is to try to meet VCs to "build relationships" a month or two before going out for a series A or series B fundraise[1] . I explain why this is a mistake below. If you do not have strong VCs relationships and plan to fundraise in 2-3 months, wait to talk to VCs until you go out to raise. Do not do a separate "get to know you" tour. If you plan to go fundraise in 12 months, you can start to build select VC relationships early with a handful of firms.

VCs Remember Most Early Interactions As Pitches
Investors at top tier VCs are constantly deluged with a stream of companies wanting to pitch them. If an investor meets *only* 3 to 5 new companies a week, she is meeting with literally 150 to 250 companies a year. As such, it is unlikely Continue reading "Building VC Relationships"

Founder Roles

As the founder of a company, you will likely play many roles across the life of the company. The core tenet of being a founder is that you should do whatever it takes to make your company successful.

Although my title at Color for the last ~4 years was CEO, at different times I played the role of recruiter, supply chain lead, product manager, office manager, and head of sales. Similarly, my 3 other co-founders played a variety of roles across engineering, design, PR, genetics, and other areas. As Color has grown we have hired smart experienced people to take over these areas. It is always a magical moment (and a relief) when you find a smart, experienced person to take over a function from you who does the job much better then you ever will. The role of the CEO is, in part, to find amazing talent so that Continue reading "Founder Roles"

Facebook Must Really Suck At Machine Learning

Facebook recently claimed it is hard[1] to differentiate between fake news[2] and real news. Given how similar fake news detection is to related problems such as search index spam, ads landing page spam, social networking bots, and porn detection this suggests one of two things: (1) Facebook really sucks at machine learning or (2) Facebook does not want to address the problem. Lets look at each of these:

1. Facebook Sucks At Machine Learning?
Over the course of my career I worked on, amongst other things, Google mobile products (including mobile search index and looking at items like porting Google News to mobile), Google ads targeting to pages across the web, and Twitter Search (I was Director of Search Product for a time). At both Google and Twitter, the companies had to deal with large number of ambiguous signals including:

Startups in Machine Learning & AI

Artificial intelligence is going to have a massive impact on multiple business verticals over time. The displacement of both blue collar and white collar work by machine learning is going to cause major societal displacements in the next 10-20 years[0].

While there is a lot of discussion in the popular press about general purpose AI (aka AGI - which is defined as a machine that can perform any intellectual task a person can), much less emphasis has been placed on near-term specific vertical markets or areas that AI and machine learning (ML) are likely to transform in the coming 5 years. In short, I think AGI is still 10+ years away, but vertical products driven by AI will be transformative in the coming years.

The areas listed below are underinvested by entrepreneurs and VCs. I believe some of the largest AI companies in the near term will emerge from Continue reading "Startups in Machine Learning & AI"

Investor Update Emails

Entrepreneurs often spend an enormous amount of time raising and optimizing who is involved in a financing round.  However too few founders tap into their investor and advisor pool sufficiently after all that hard work.  One key way to keep investors involved and excited about the company is to send a monthly update.

Below are two formats for communicating with investors.  Note that once you get to a certain stage/size, you may discontinue or limit broad investor outreach.  At some point investors can start to become a source of leaks about the company.

LONG FORMAT INVESTOR UPDATES (FOR SERIES A OR REVENUE/USER GROWTH GENERATING COS)
This slightly longer email format (1-2 pages in an email) for companies with a launched product and some ongoing growth. Investor "Asks" should always come first (as some investors won't read much past the first section in the email).  This way if you need help, Continue reading "Investor Update Emails"

Hot Markets For 2015

Being in rapidly growing markets (or ones perceived as hot) increases likelihood of success of a company dramatically[1].  Being in a hot market increases the ability to hire great people, get press and awareness, raise money, and eventually exit via M&A or IPO.

The average startup exit takes 7 years.  Market hotness increases the likelihood of a fast exit dramatically.  In the late 1990's the average time to acquisition or IPO was just 2-3 years due to Internet mania.  The fastest exits usually come via M&A.  Markets with the most natural acquirers will lead to the most exits in a segment.

To successfully IPO you usually need ~$50 million in revenue and a few quarters of profitability behind you.  If you are in a hot market, the profitability constraint may lesson and you can even loose money for a while (see e.g. Hortonworks IPO and big data hotness).

Hot Market Sustainability.

Caveat emptor - about 50% of the markets that are considered hot at any given point turn out to be false alarms.   Examples of past hot markets that turned out to largely be duds include First Wave AI (in the 1980s),  Nanotech (as an industry in the early 2000s), CleanTech (early to mid 2000s) and Geo (smaller scale in late 2000s).

Hot markets that yielded huge companies and large exits include social networking (mid 2000s -Facebook, Twitter, LinkedIn), and mobile social (early 2010s - WhatsApp, Instagram).  Some large market trends are still playing themselves out as per below including big data, sharing economy and other segments.

Hot Markets For 2015

Below is my view of both what markets are hot in 2015, as well as the likelihood of these market segments being medium term duds[2].

1. Gold Rush.
Markets That Will Yield Large Stand Alone Companies and Many Acquisitions
Big Data.
"Big data" as termed in the press has 4 subsegments[3]:
(1) Dealing with large amounts of data (Hadoop, Spark, etc.)
(2) Smart data.  I.e. doing something intelligent with the data you have regardless of the number of petabytes.  This is more analytical tools or tools for data scientists.
(3) Data center infrastructure (sometimes this gets clustered into "big data", sometimes not).  Mesos (and Mesosphere) would be an example of this.
(4) Verticalized data apps (e.g. data store and analytics for medical insurance claims).

In general this market segment has a lot of legs and will continue to create both stand alone public companies, as well as has a large number of natural acquirers.  Potential acquirers include the traditional enterprise companies (HP, IBM, etc.) as well as the earliest companies in the space who support liquid public Continue reading "Hot Markets For 2015"

The 3 Types of "Platform" Companies

People use the word "platform" to describe products with fundamentally different characteristics.  OSs (e.g. Android), infrastructure products (e.g Twilio), and platforms (Facebook APIs e.g. Connect) may all be called "platform".  However, the distribution approaches and product strategy for each differs. Conflating what makes a platform work versus e.g. an infrastructure product can backfire and cause a team to have the wrong strategy for building a product or getting customers.  These startups tend to fail.

Below I attempt to define and differentiate between these different types of companies and their products.

1. Infrastructure.
Infrastructure products are ones that multiple companies have to build over and over again.  Eventually some smart entrepreneur realizes this and builds the common infrastructure product that other companies will pay to use.  An example of this is the founders of Mailgun, who built versions of the same email server for multiple employers until they realized they could build this as a general service for all developers.

Infrastructure products are often necessary for a product to function (every ecommerce site needs Stripe for payments) but are not often a "strategic" differentiating buy for their customer (although Stripe has managed to differentiate strategically based on its fast iteration on new features and its simplicity as a product).  Early on, many users of Twilio didn't care if they were using Twilio or another telephony provider - they just want it to work quickly, simply, at a good price (which ultimately meant using Twilio due to its ease of use).

The best infrastructure companies have clear economies of scale or network effects.  Twilio is probably able to negotiate better and better deals with carriers on pricing the more volume it aggregates from its customers.  Similarly, large amounts of payment data can provide scale effects for fraud or risk management.

An infrastructure company's success often boils down to a handful of factors:
-Ease of use and integration.
-Cost.
-Up time.
-Differentiated features or historical customer data.  This helps you lock in your customer base.
-Economies of scale.  This can lead to network effects on costs (pricing power of the infrastructure provider relative to its own suppliers) or features (fraud detection).
-Developers or sales channel.  In some cases a developer ecosystem emerges around an infrastructure product (note: this is different from developers using or adopting a product).  This is less common for infrastructure then people think, and is more common for a true "platform" (see below).

In rare cases, an infrastructure company can move up to become a "platform" in its own right.  This only works if the infrastructure company is able to collect and re-position unique end user data, or build direct brand recognition with its customer's customers. Continue reading "The 3 Types of "Platform" Companies"

The 3 Types of "Platform" Companies

People use the word "platform" to describe products with fundamentally different characteristics.  OSs (e.g. Android), infrastructure products (e.g Twilio), and platforms (Facebook APIs e.g. Connect) may all be called "platform".  However, the distribution approaches and product strategy for each differs. Conflating what makes a platform work versus e.g. an infrastructure product can backfire and cause a team to have the wrong strategy for building a product or getting customers.  These startups tend to fail.

Below I attempt to define and differentiate between these different types of companies and their products.

1. Infrastructure.
Infrastructure products are ones that multiple companies have to build over and over again.  Eventually some smart entrepreneur realizes this and builds the common infrastructure product that other companies will pay to use.  An example of this is the founders of Mailgun, who built versions of the same email server for multiple Continue reading "The 3 Types of "Platform" Companies"