Advice for Investing in Startups

Una version de este post aparece en español con el titulo Lo Que Debes Saber Antes de Invertir en Startups. También fue una charla (video).

As startups and tech investing has gone mainstream with things like AngelList, Shark Tank, The Social Network, the proliferation of accelerators and incubators, and the celebritization of founders and VCs, more money has flowed into the industry.

Most non professional investors interested in startups are starting to dabble in early stage companies as angel investors. Friends often send me deals they’re think about investing in, asking for advice.

Here’s a concrete example of a typical question from a successful entrepreneur friend looking to invest:

I am starting to look at some investments in startups that have proven initial product-market fit and revenue traction with positive gross margins. I have found some great opportunities [and] I am only targeting businesses that meet my personal criteria. My biggest concern is how I should evaluate the businesses from a valuation perspective. Is there any guide suggested on this?

I personally am finding companies that claim to have a higher valuation than my own company which has greater revenues and faster growth rates, etc. So, that puts off a bit of an alarm in my head but I realize it’s normal in the VC world. I don’t really know how to value a VC backed start up.

I decided to share my answer: Continue reading…

My 2016


Ever since I started writing here, I’ve done a year end post summarizing what I’ve done in the past year. These posts are mostly for me, so that I can look back and remember what I did, what I was thinking and what was important to me each year. Previous versions (2000s200920102011201220132014, 2015). Here’s what I did in 2016.

2016 followed on from 2015’s main two themes: focus and growth. In 2015, I started the process of eliminating distractions from Magma Partners and Andes Property and in 2016 I focused even more. I took Derek Sivers’ mantra of Hell Yeah! or No! that I started to implement at the end of 2015 to heart and said no to things that I wasn’t 100% excited about.

I not only implemented this framework for deciding to invest in new Magma portfolio companies, but also for speaking engagements, events, press opportunities, writing opportunities and more. Along the same lines, Tim Urban’s Your Life in Weeks helped me revalidate that time is my most precious resource. Thanks Derek and Tim.

I spent ~5 months in Chile, ~1 in other Latin American countries and the rest in the US. 6 months is the most I’ve spent in the US since 2010. It was good to be back more than a few months per year and I really enjoyed getting back to doing more business in the US. It was also great to see my family and friends more than I have for the past few years. My Mom finished a book project she’d been working on for multiple years and I was happy to be able to help her get it designed, edited and printed. Continue reading…

Tech Will Allow a Family to Live Well on $3500 per Year

So far, I’ve talked about some of the downsides of the massive change from AI and path dependency:

I wrote about how being compensated for our data might be a way out. But what about some other potential good news?

“Competing Without Software Is Like Competing Without Electricity” – Naval Ravikant

As technology impacts every industry and becomes as ubiquitous as electricity, we will see the vast majority of industries behave like the computer and software industries do: getting better each year, while deflating in price.

As Sam Altman, the head of YCombinator puts it: Continue reading…

Should We Be Paid to Train the AI Algorithms?

If it’s free, you’re the product. If it’s extremely subsidized, you’re probably the product, too. Facebook is free. You’re the product. Google and Gmail are free. You’re the product. Mechanical Turk is cheap, you’re the product. Uber is cheap, you’re the product. Tesla self driving cars are add on features. You’re the product. Snapchat is free. You’re helping them build the best facial recognition database out there. They’re “paying” you with access to use their service.

Tesla needs a few billion miles of driving data to train its computer program to react to all situations. How does Tesla get this data? By tracking all car trips and adding it to the database. Once they have enough data, cars can react to nearly all situations. They’ve used massive amounts of each persons’ data to train the program.

All of the companies I listed above are using free or highly subsidized products to train their algorthims to further automate away humans. Is this bargain fair?  That everyone who uses free and subsidized services are contributing to training the AI? The AI that will later run that market and create massive benefits for the company that captured all of this data that people freely gave it? Continue reading…