On the employer side: Pretty tight, hard to find talent.
On the employee side: It's good. Salary are OK considering the decent cost of living. The AI boom is helping bringing more options for smart and qualified people.
Feel free to email me if you have more questions, know some companies that are looking ;)
I'd say anticipated exit, comparable companies, competitive pressure and desired ownership play the role of social proof. For revenue, most likely related to the business model.
The interesting distinction in the report was between deal flow and deal selection. VCs care more about deal selection. For deal flow most of it is in their own network or through syndicates.
For the 90% fail 10% success you can look at exit multiples coupled with type of exit (M&A, IPO, failure).
My hypothesis is that most of them dont really have a proprietary deal flow and are unable to make great deal selection apart from the top VCs..
The way the data was structured is they were asked what is the MOST important factor (you can only chose one) whereas other parts of the survey were what are the important factorS.. Might create the confusion.
Agreed, I look at about 10 business paper a week and quality of statistical analysis is frequently missing.
What I liked about this one is that they are pretty transparent on the methodology so you can see the surveyed VCs biases. They also cross check the sources with other data..
yes most likely confirmation bias which is in sync with their reported ability to outperform but they dont usually.. this was the shadiest part of the survey for me.. failure data looks similar to
Marc Andreessen wrote a good article on the layers of risks that VCs are looking into [1].
Most of them want to maximize the upside while minimizing the downside..
Yes factors do interact. However it was not really part of the survey.
One thing they had was the difference between most important factors and important factors
In the summary, I reported only the most important factors. If you look at the important factors here are the results, which show a bit the factors interacting:
Team 96% for early, 93% for late
Business model 84% for early, 86% for late
Product 81% for early, 60% for late
Market 74% for early, 69% for late
Industry 30% for early, 37% for late
Valuation 47% for early, 74% for late
Ability to add value 44% for early, 54% for late
Fit with the fund 48% for early, 54% for late
There are also other factors that will impact the companies valuation such as anticipated exit, comparable companies, competitive pressure and desired ownership.
For more a deeper analysis I think it would be interesting to see the survey applied to a scenario planning model like the team at the OS Fund did [1].
Other people that used that quote is Emanuel Derman. He's an interesting character (PHD in theoretical physics, Ex-Goldman Sachs Partner). He is the author of Models.Behaving.Badly: Why Confusing Illusion with Reality Can Lead to Disaster, on Wall Street and in Life [1] and wrote a great paper on Metaphors, Models & Theories. I did a review of his paper [2], but some highlights:
Theories: Tell us what something is. According to Derman, theories “deal with the world on its own terms, absolutely.”
Models: Tell us what something is partially like. According to Derman, models are “reductions in dimensionality that always simplify and sweep dirt under the rug.”
Metaphors: Models can be compared to Metaphors. Metaphors are relative descriptions that compare it to something similar, but better understood through theories or real life applications.
"A model is a metaphor, not the thing itself. Good metaphors compare something we don’t understand, to something we think we do. Based on this, a model is simple and of limited applicability when compared to the real thing as it focuses on some parts rather than the whole. It is a caricature which overemphasizes some features at the expense of others."
Hey! Thanks for reading and/or upvoting.
This article is part of a series I started where I summarize an interesting research paper in the fields of business, management and strategy.
This is inspired by by the morning paper [1] which is produced each weekday by Adrian Colyer, a venture partner with Accel Partners, and covers the field of computer science.
I try to do it every week, I don't know Adrian does, but it's challenging/time consuming.
If you have any feedback or want to see a paper featured, feel free to reach out at [email protected]
Hey i'm Antoine the author. Let me know if you have questions/comments. Happy to hear your thought since the format is a work in progress.
Here's two good papers to follow up: 1- From MIT, Ricardo J. Caballero which covers the process of creative destruction in depth: https://economics.mit.edu/files/1785 2- From Yale, William D. Nordhaus: Schumpeterian Profits and the Alchemist Fallacy which states that most "profit" of new innovations go to consumer surplus: http://economics.yale.edu/sites/default/files/files/Working-...
Agreed on your comment (ex software dev, now management consultant)..
In MC, the delivery of the case is extremely important (how you structure the solution, how you speak, how you present yourself, etc.). Your point on people optimizing is spot on. Some students spend 10-20 hours a week practising before a McKinsey, Bain, BCG interview..
When I'm building cases and interviewing people, I try to structure it around a real life problem we have (e.g. a wood/fiber B2B company wanting to go B2C) which makes it less awkward than solving random algorithms.
However, I'm still not satisfied with the process, so wondering if people have ideas ?
I thought about giving a case to solve at home, but we're missing the part of how an applicant will react with a lot of pressure and in front of clients :/
Also really like the Milestones of a Jazz Legend - Jim Hall on Guitar Vol. 2 on Spotify https://open.spotify.com/album/5bwrg1BJvmtGEVxXmBvM2r?si=szA...