Stumbled across this. So interesting.

Goes way back and and surfaces some new names for me.

Enjoy!

https://www.slideshare.net/lebret/a-history-of-venture-capital

Unpacking Alpha in Venture Capital — Chapter 1: Setting the Context

Pretty interesting deep dive into thoughts around VC.

Tend to agree heavily with 2, 4, 6, 7, & 8.

Here is the top 10:

1. VC is a cottage industry but done scrupulously and systematically it can deliver strong, uncorrelated returns. Alpha generation is very poorly attributed.
2. Dollars should be focused into capacity constrained strategies that are attacking the early stages. VC does not scale.
3. I see no obvious warning signs that this is a poor time to enter the asset class. Technology-led innovation is pervasive and cumulative.
4. Whilst Silicon Valley has undoubtedly been the epicentre of technology innovation, other hubs of ideation, innovation and global problem solving are developing fast.
5. VC is a human capital business, driven by prescient GPs and outlier founders. There is limited evidence to support long-term consistent firm-level performance, in fact persistence of performance is declining.
6. Investing with more metrics = less alpha. The best investors are comfortable investing at the edges but do so on the basis of a scientific and rigorous process that appreciate the risk. A quick summary of a rigorous methodology is inspired by a recent book from P. Tetlock (Superforecasting).
7. The best early stage investors are foxes — they are curious polymaths, with broad peripheral vision. LPs should test for and allocate to investors with the optimal attributes versus making their own editorial about where the tech next wave will come from.
8. Technology KPIs have evolved but I believe most public market investors still don’t understand the pervasiveness of technology. Every listed asset is potentially impaired.
9. LPs have not challenged their GPs to innovate nor gone deeper on GP level data. I consider the industry must mature faster and both sides must do better.
10. Most early stage investors waste the informational alpha generated by VC — it provides a lens into what will work in the future but in nearly every scenario tells you what is not working within the incumbents. Cross-pollinate this information to unlock more alpha in you public portfolios.

Predictions for 2019 • Tomasz Tunguz

Let’s dissect some:

The M&A market slows meaningfully, especially at the multi-billion dollar level. The recent seasickness in the public markets forces most CEOs adopt a more conservative approach to acquisitions. Facing large swings in valuation, these leaders may struggle to advocate and articulate that large acquisitions are accretive and will be immediately rewarded by share appreciation after an acquisition.

Could be true – there has been a pretty feverish pace of some big acquisitions. I guess all depends how the wind blows in the public markets and the global economy. As far as SEA goes – there has not really been a flurry of acquisitions by global players yet – maybe that is one place come companies will look for growth?

Blockchain technology finds its second killer application. After all the hype and ICO-mania in 2017, the flurry of startups attempting to solve every startup with a distributed ledger and the collapse of currencies in 2018, one startup emerges in 2019 with the next killer use case; Bitcoin being the first. I suspect the second killer application will not be currency based, but a consumer product.

Let’s see how it goes. I am slightly dubious due to all the hype not being washed out yet. I suspect deeper bottoms and a shifting of talent before blockchain emerges a winner.

Startups begin to siphon off important but underserved segments of SaaS incumbent’s customer bases. 1% of Salesforce’s revenue makes a unicorn. This will occur in all major SaaS categories, products serving VPs of Marketing, Sales, Engineering, and Customer Support.

Niche might be the new black – I am a believer in category killers but the category still needs to be big enough for some serious revenues but I think some people are sick of “big company” stale product lines.

Data engineering is the new Customer Success. A decade ago Customer Success wasn’t on anyone’s lips. There were no VPs of CS in most software businesses. Data engineering, the discipline of moving data well, will have a breakout moment, complete with conferences, thought leaders and a company that becomes the symbol of the category.

I think this has been happening for a while and will continue to be important especially as privacy issues keep growing and customers wanting something in return for all this “data”. I still feel like the big players know too much about me but apart from ads – don’t really do anything useful with it.

Onward ho!