Monthly Archives

August 2013

Thoughts on quant approaches to early stage investing

By | Venture Capital | 8 Comments

‘Quant VC’ is a hot topic at the moment. Techcrunch and VCs have been writing about it, VC funds have been hiring data scientists, and Kleiner Perkins has taken the step of branding its proprietary data mining tool as Dragnet. There is even a company called Mattermark with the tagline ‘Big data meets venture capital’.

All of this activity is happening for the same reason as in many other industries: there is now a lot of data that can be used to support decisions. Data sources to be mined include AngelList, Crunchbase, VentureSource, the App Store, the Google Play Store, the Facebook Platform,  Klout scores, Twitter mentions, LinkedIn.

There’s a lot of good insight to be drawn from this data, but nobody (with the possible exception of Mattermark) is saying it’s possible to build a predictive model that estimates a startup’s chances of success. That’s mostly because the world is changing too fast. Startup success generally takes five to seven years but technology and markets have moved on too far for repeating a formula that worked five years ago to make sense. 2006-2008 was a mini-bubble period where gen 1 social networks Bebo, Myspace and Facebook were the big story and investor interest in business models was at an all time low.

So rather than predicting success the data is used to support decision making by surfacing trends and prospective investments and for analysing competition.

Eight startup lessons you don’t hear very often

By | Startup general interest | One Comment

This morning everyone has been retweeting Slava Akhmechet’s 57 Startup Lessons. It’s a great list (although there are a couple of lessons I don’t agree with), but if you don’t have time to read through 57 lessons you can find my top eight here. I’ve picked the ones that a) resonate with me, and b) you might not have heard before.

  1. People: Your authority as CEO is earned. You start with a non-zero baseline. It grows if you have victories and dwindles if you don’t. Don’t try to use authority you didn’t earn.
  2. Fundraising: If you haven’t earned people’s respect yet, fundraising on traction is an order of magnitude easier than fundraising on a story. If you have to raise on a story but don’t have the reputation, something’s wrong.
  3. Markets: Starting with the right idea matters. Empirically, you can only pivot so far.
  4. Markets: Pick new ideas because they’ve been made possible by other social or technological change. Get on the train as early as possible, but make sure the technology is there to make the product be enough better that it matters.
  5. Products: Product sense is everything. Learn it as quickly as you can. Being good at engineering has nothing to do with being good at product management.
  6. Products: Build a product people want to buy in spite of rough edges, not because there are no rough edges. The former is pleasant and highly paid, the latter is unpleasant and takes forever.
  7. Marketing: Don’t say things if your competitors can’t say the opposite. For example, your competitors can’t say their product is slow, so saying yours is fast is sloppy marketing. On the other hand, your competitors can say their software is for Python programmers, so saying yours is for Ruby programmers is good marketing. Apple can get away with breaking this rule, you can’t.
  8. Sales: Qualify ruthlessly. Spending time with a user who’s unlikely to buy is equivalent to doing no work at all.

Success metrics for new startups

By | Startup general interest | One Comment

This article is a good reminder of how to set metrics at startups. The choice of metric will vary depending on the business but this piece of advice is universal:

My number one tip is to start simple, by developing high level success metrics that, based on your best assumptions, are the key drivers of your business. Document these and make them a part of your team and investor meetings, and focus maniacally on optimizing them one at a time.

There’s actually three pieces of advice in here:

  1. Think carefully about what your metrics should be
  2. Once identified use them in all aspects of your business (bonus tip: if you are using different metrics in your team and investor meetings something is wrong)
  3. Focus your improvement efforts on one metric at a time

The difference between brains and computers

By | Identity, Ray Kurzweil, Startup general interest | 4 Comments

I believe that within my lifetime processors will get powerful enough and software good enough that we will see computers that emulate human brains and pass the Turing Test (for more detail see my earlier posts Kurzweil predicts we will reverse engineer the human brain using software, Kurzweil predicts computers with the power of the human brain by 2025, Scientists create artificial brain with 2.3m simulated neurons).

Today I read a good Economist article which explains the challenges of building ‘human computers’. Brains currently have three key characteristics that computers do not:

These are: low power consumption (human brains use about 20 watts, whereas the supercomputers currently used to try to simulate them need megawatts); fault tolerance (losing just one transistor can wreck a microprocessor, but brains lose neurons all the time); and a lack of need to be programmed (brains learn and change spontaneously as they interact with the world, instead of following the fixed paths and branches of a predetermined algorithm).

Having identified these characteristics scientists are now working to design around them, as detailed in the rest of the Economist article. In summary progress is being made but it is very early days. The third characteristic of not needing to be programmed runs contrary to our current notions of development, and is perhaps the most challenging.

The other issue at play here is consciousness. ‘Would human computers be conscious or not?’ and ‘What is consciousness anyway?’ are unresolved questions with inherently unknowable answers. My view is that consciousness arises from the mind and human computers would be to all intents and purposes be conscious. Any other answer creates more questions than it answers and hence falls foul of Occam’s razor. This question was debated earlier today here and on Hacker News.


The way to do really big things is to do really small things and grow them bigger

By | Startup general interest | 3 Comments

I just read this in a post about learning to be an entrepreneur:

Paul Graham promotes the view that it’s a bad idea to begin with big ambitions, because the bigger they are, the longer they are going to take to realize, and the longer you are projecting into the future, then the more likely you are going to be wrong. “The way to do really big things,” he says, “is to do really small things, and grow them bigger.”

This important advice is often difficult to get across because it can sound unambitious, but that misses the point. The safest way to get big is to build momentum by getting early scores on the doors. Success begets success and the business model gets validated with less time and money.