It literally says so on the label, comrade. What do you think the C stands for?
(I guess I'm now live-tweeting my reading of this wild, wild piece)
How to become a rich VC: "give more money"
That evil VC firm of Murdoch & DeVos
Look at these shadowy VCs, pumping up the stock prices of duds like Uber and Palantir
I don't know about WeWork, but Neumann's previous ventures are gold. Rompers with kneepads: sign me up daddy!
Behold notorious drug lord Bruce Dunlevie
Why stop at jail terms professor? "Doing things that are bad for society" should be punishable by firing squad.
Three quarters of the way into the article, we have the first valid criticism of the industry. Guilty as charged, your honour.
Top 3 faults of VCs:
1. Give too much money 2. "Push" founders to make bad choices (while simultaneously being powerless to stop them from making bad choices) 3. Ask founders to grow fast
That's why companies blow up.
In all seriousness, the VC industry has plenty of problems - poor judgment of winner-take-all effects, keeping companies private for too long (and thus avoiding price discovery), funding tens of copycat models - but it would take a FAR more nuanced article to uncover those.
Meanwhile, the world's best magazine has just painted the backers of life-changing companies (like, yes, Uber) as thuggish drug lords. <end>
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Heard an incredible story about analytics and the Coronavirus.
Apparently, a top predictive analytics company offered (pro-bono) to help a state government identify upcoming Covid hotspots.
They built sophisticated models which worked reasonably well in back tests. But they started noticing a pattern in the government's response.
When local authorities were warned about an emerging hotspot, they *scaled back* testing in that area instead of ramping it up. The goalpost moved from avoiding hotspot formation to avoiding hotspot detection.
In the last two weeks, four different friends have approached me for advice on how to start angel investing. These are not HNIs or "professional angels" - just regular people with good salaries that they want to invest in this asset class.
This is what I have told them:
Don't
There are 3 reasons for my recommendation: 1) Access 2) Influence 3) Diversification
Fully agree with Nikhil's view that telemedicine is a commodity. This is why so many companies were quickly able to "pivot to telehealth" in the past few months. At its heart it's just a platform for doctors and patients to communicate over text, voice and video.
In fact, you don't even need a webRTC app to compete in this space. The largest telehealth platform in India is Whatsapp. Every doctor who wants to consult remotely starts by giving her Whatsapp number to patients. And the experience for both sides is...not unsatisfactory.
College has its problems (lack of equal access, highly variable quality), but it remains the surest means of social mobility, and a personally transformative experience for students.
The whole "skip college and become a tech entrepreneur at 17" life plan reeks of debilitating privilege. For the vast majority, skipping college means a lifetime of shuttling between low-skilled jobs at the very edge of the middle class.
Another favourite argument of the tech echo chamber is that college education should be more vocational and focus on employable skills. To me, it's a reactionary argument that seeks to perpetuate the very differences it outwardly seeks to erase.
For the past few months, I have been rethinking the "Vitamin vs Painkiller" framework in venture capital.
It sounds perfect in theory - people pay more readily to avoid pain than to experience joy.
Yet, the most successful tech companies have been built not by fixing customers' problems but by delighting them with an unimaginably better experience.
The life-altering startups of the past decade - Uber, Airbnb, Whatsapp, Instagram - didn't solve deeply felt pain for their customers. Hotels, taxis and text messages worked pretty well for most people.
Context: @jainmayank_943, @nikhilvij03 and @jainraghav17 started Talentpad in 2014 with the aim of making hiring smarter. They parsed millions of resumes to find the best fit for specific kinds of jobs.
Their algorithm could figure out that people who have worked for 5 years at IBM with a title of Senior Software Engineer made excellent Project Managers for Accenture, for example. They called it data science,but if they had called it AI they could've raised at a higher valuation