If not evident from the lack of recent content, I've found less time to write. Or, more precisely, to write here. While building product and growing a company keep me more than fulfilled creatively, I recognize that reflection remains important. So instead of writing longer form posts, until further notice, I've decided to at least provide a weekly(ish) recap of some things that I've found interesting from the days prior -- mostly articles, blog posts, podcasts, and others bits primarily focused on tech. Perhaps, but not always, they might be accompanied by the occasional commentary from yours truly. For readers, I hope it will be an interesting curation of content. For me, it will help keep track of some of the interesting things I've read from week to week in the form of a personal archive.

08.14.16 Recap

Innovating at Scale by Arun Sethi
A good commentary on (and reminder of) the Innovator's Dilemma. Basically, companies have to disrupt themselves through innovation or they will be victims of their own success. Old news, perhaps, but companies still continuously fall victim to the plight. Worth a read to refresh with some survival tips for the operators.

a16z Series on Network Effects
Network effects aren't new, but they are powerful. So much so that they've captured the attention Andreessen Horowitz (among others) who've put out a whole series aimed at what they are, why they are valuable, and how to achieve them. There is more content to the series on a16z, but the following are worth a read / listen:

All About Network Effects a slide deck by Anu Hariharan
Getting Network Effects w/ Anu Hariharan, Jeff Jordan, and Sonal Chokshi
Not All Network Effects are Created Equal w/ James Currier of NFX Guild and Anu Hariharan
Data Network Effects w/ Vijay Pande and Alex Rampell

IBM's Request for Information to the White House on Preparing the the Future of AI
The White House recently put out an RFI on AI and IBM published their response, which offers a good read.

Python's Data Science Stack by Jake VanderPlas
Reflecting on the power of Python for data science and machine learning applications. (I'm a big fan of the Python. If you don't know, now you know.)

It Takes a Surprising Amount of Seed Capital to Spoil a Startup by Jason D. Rowly
The folks at Mattermark found that the more money startups raise in Seed and Angel rounds, the higher probability they have at raising a Series A. This holds true until around the $1.5-2M mark where the effect levels off and each additional dollar of funding raised starts to become negligible.

Intuitively, this makes sense as underfunded companies will have trouble getting out the gates. Not to mention, the $1.5M ballpark has been viewed as a reasonable Seed round in light of the short and long term dangers of raising too much money -- providing enough resources to achieve validation and gain traction. However, while the size of Seed rounds continue to climb in the market (although their volume might be declining), it will be interesting to see if the data shifts. A leading indicator might be that some, like Roy Bahat at Bloomberg Beta, are beginning to advocate for startups to raise their Seed rounds for 36 months, not the typical 18-24. The cognition here being that this allows companies sufficient time for discovery absent the pressure to raise again too soon. FWIW - I think this can make sense for companies building more technical products, although it should likely change the metrics these companies pursue -- at least earlier in their runway. Moreover, more money should not mean procrastinating the business -- companies planning further out must remain close to their users and work to find a balance between exploration and exploitation to avoid getting lost in the former. Regardless, and of course, managing burn is important for early-stage companies independent of the amount of Seed capital raised.

(Speaking of Bloomberg Beta -- and ICYMI -- they recently open sourced their operating manual on GitHub in a move for increased transparency and alignment with entrepreneurs. Pretty cool move, if you ask me. Not to mention, a fascinating read.)

Google Brain AMA
Interesting to peruse from both a Q&A viewpoint.

ICYMI - Can a Neuroscientist Understand Donkey Kong, Let Alone a Brain?
Caught up with a friend last night and we revisited this study from earlier in the summer in our discussion.
(original paper) by Eric Jonas and Konrad Kording