Objectives and Key Results (OKRs) are a standard tech industry tool that teams use for quarterly goal setting. Despite the mangement-speak name, they can be a useful tool if used well — but they are often not used well. Having used them for 15 years at Google and other places, here’s my guide to doing them well.
The most important idea is that OKRs are a tool for your team to use to drive quarterly alignment. Their primary purpose is not to report to other people.
OKRs should not be a list of tasks (e.g. ‘Complete Deep Learning module’). Task…
Summarising a previous post: What is a Neural Network?
Computer programs come in many shapes and sizes, but have a few things in common. We provide them with input, and they provide us with the result as output. A simple example is the “AVERAGE” function on a spreadsheet. We provide it with a column of figures, as input. The function computes the average of those figures and outputs it in the cell where we asked for it.
A neural network is a computer program in just this sense. We provide it with input (for example, a digitised image of a handwritten number) and it returns the output (which in…
TL:DR; as we discovered addressing Ebola in West Africa in 2014, early and apparently extreme action significantly reduces the chance of much more extreme and longer lasting action later. Put differently; the actions you take today and tomorrow could save more lives than anything else you do in your life. What this means is: regardless of symptoms, self isolate as much as possible for as long as possible starting today.
In an ‘exponential’ crisis, early action by you has an exponentially positive result.
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Covid19 is an alarming crisis…
It’s election time in the UK, and a feature of the campaign this time has been a steady stream of articles from business figures warning us that moving to the left will harm our business environment, hurt jobs, raise taxes, ‘return us to the 1970s’ and so on.
I’m a Venture Capitalist; after building products for Google for many years and then DeepMind I now make my living investing in early stage tech startups. So you might imagine I’d be adding to these warnings, but in fact I don’t buy them at all, for several reasons.
I’ve been thinking about rational follow-on strategy for a long time, and how to use portfolio simulations to decide when to make follow-on bets. Abe Othman at AngelList has just published some really interesting work that moves the debate forward. Here’s my brief explainer of what he did.
Abe looked at a ‘universe’ of 1218 seed stage convertible investments from 2014 to 2017. He then imagined a pool of 10 randomly selected investments each year for a total of 40 investments in the pool, and simulated 10,000 random pools this way.
For each pool of 40, he then looked at…
In recent months I’ve had many conversations on product thinking with startups and with colleagues at Atomico and Firstminute. Last week Siraj Khaliq posted a rallying cry for European Product Management. And prompted in part by conversations with Lina Wenner I’ve been thinking about a related challenge: getting companies to think of themselves as product-first.
Since I’ve been spending most of my time investing in and advising startups, I’ve noticed a surprising pattern: many don’t deeply embrace the fact that they are product businesses.
Of course most companies talk about the importance of product, but often product thinking isn’t embedded…
TL;DR: In the last post I built some models of venture portfolios of different sizes based on the idea that venture outcomes are powerlaw distributed. The conclusion was, other things being equal, bigger portfolios should do better, with around 150 investments being table stakes. Those models assumed each investment was independent, with exactly one investor. The pooled model presented here is more realistic; overall it dampens expected returns without changing the overall pattern that more is better. Interesting implications are that dealflow and brand are key for VC.
In the last post we simulated over 45M independent outcomes (sum(x=5->300) x*1000).
This post explores some ideas about venture capital returns and portfolio size. It builds on other explorations including Jerry Neumann’s and Seth Levine’s. I wrote a simple simulator in a Google Colab notebook, which generated the graphs below. After the next post I’ll post a link to the notebook which will allow you to run the code and experiment with different parameters.
Jerry Neumann’s posts explore the consequences of the idea that venture returns (the returns to investments in early stage, high growth potential companies) are not normally distributed, as public market returns are, but follow a power law distribution.