Month: January 2020

Fred Wilson the NY based VC, as part of a look back at the 2010s:

The massive experiment in using capital as a moat to build startups into sustainable businesses has now played out and we can call it a failure for the most part…Uber has not yet proven that it can build a profitable business… WeWork was a fast follower with this strategy and failed to get to the public markets… much of [VC capital] was wasted chasing the “capital as a moat” model.

What is notable to me about this is not whether or not it is true – I personally think it has yet to play out fully. To me it’s notable how new the model is of raising massive amounts of capital quickly up front in a bid to capture a market. Paytm is the pioneer and poster child for this in India though there are now many others too. The startup landscape seems to now think this is the norm, and so the average initial funding round gets larger and larger.

It probably started with Softbank, and the founder Son using his massive fund size as a weapon to get into deals:

“Anthony-san, you take my money. It’s good for you. It’s good for me. If you don’t take my money, not so good for you.” Like so many others before and since, Tan took Son’s money.

Regardless, if, as Fred Wilson implies, that model has played out, it’s going to lead to more sustainable business models because the alternative then is product and innovation as a moat, with profitability as a top metric.

What a beautiful essay on gardening. Talks about flow, the connection to nature, and something called attention restoration therapy.

Gardening is a relaxing and rewarding hobby. It provides an opportunity to escape and reflect away from our daily routines, and to relish the intensity of fascination. But it is more than that. The psychological power of gardening derives from the garden’s reach beyond the here and now. My contention is that different and complex forms of time are continuously interacting through the garden and the gardener. Past, present and future collide in a flowerbed, enticing the gardener to lose themselves in the pleasure of ‘flow’. Let someone else worry about lunch.

Today I spent a whole morning and some part of the afternoon on maintenance in my apartment garden, and it hardly felt like any substantial time had passed.

UPI is a great, easy-to-use platform for P2P and person-to-business digital payments in India. Its like Venmo, but open and interoperable.

Given this, it’s a travesty that Google Pay, Whatsapp and the rest are creating the impression of closed systems above UPI.

At a petrol pump in Hyderabad late at night, the person in the car ahead of me wanted to pay via Paytm/UPI but the pump only accepted cards. The guy asked me if he could have my card swiped after he transferred money via Google Pay.

He asked for my mobile number and couldn’t find it on Google Pay (I’ve never used that particular app). I asked him to send it to my UPI handle, but he had no idea what it was or how to enter it. I’m not sure if Google Pay also actively obscures payment to UPI handles.

I realized for him BHIM, BharatPe, Amazon P2P, Google Pay, PhonePe, Paytm, Whatsapp Payments are all unconnected systems, not unlike digital prepaid wallets.

Several local businesses now accept UPI payments (maybe even prefer them?), but have different QR code stickers per app. This should be unnecessary. This BharatPe sticker does it correctly:

Received this for a panel I participated in in New York a couple of months ago. What a fine, well-designed, well-constructed thing.

I’ve used it nearly every day since as my morning coffee mug and it’s been transformative. I once left some coffee in the Yeti in my car and took a 45 minute meeting. When I returned the coffee was still very warm.

Used to be that by the time I was done with my morning reading routine my coffee would be tepid. Now it’s hot even twenty minutes after pouring into the Yeti. A side effect seems to be that it also holds in the flavours of the brew much better than a regular ceramic mug.

Buy the Yeti Rambler Lowball from Amazon India here (no affiliate link).

Once you wake up, everything starts competing for your attention: every single channel on your TV, every single application on your phone, that device that you plan to use today needs special attention because is not charged and has full memory, the light bulb is blinking, the heating unit stopped working and you have notifications over notifications on your phone. 

How Focus Became More Important Than Intelligence

From a profile of Robert Cottrell, who reads over 1000 articles a day for his newsletter:

Robert has built an AI to help him read even more articles.

As I said before, I’m currently constrained by my physical capacity to read so many articles in a day.

Last year, I began working with a computer scientist named Jeremy Davies to build a machine learning algorithm to read articles for me.

Basically, what we’ve done is trained the algorithm on all of my past issues of The Browser. And we can set it to read thousands of posts that I don’t have time to read, and judge them.

This ML is essentially learning to be me. It’s basically an output from the virtual Robert Cottrell. 

It’s still very early, but if we set it to read 1,000 articles it will return roughly 50 articles as being high quality. Of those, roughly half are going to be false positives and I’m going to look at them and think, “What’s going on here?” The other half of them are going to be real. I’m going to say,“Yes — exactly, I can see why it has chosen this.”

When it becomes better trained and a little bit better overall (when it’s finding three or four slam dunk pieces a day), I won’t have it fully substitute for me, but I’m going to use it to read more things. So, if I’m able to cope with a thousand feeds already, I can send the ML to go read 10,000 feeds. 

While Cottrell is doing this for extreme productivity, the idea of a personal AI reminds me of the human editors that Neal Stephenson describes in his book Fall.

In the rural parts of the country, there’s “Ameristan,” where the poor and uneducated live steeped in misinformation, hoarding ammunition for a war that will never come and crucifying those they deem to be heathens.

But the coasts and the cities are full of wealthy elites who pay to have professional editors weed out fake news from their digital feeds, and regard the rest of the country with a kind of disgusted anthropological fascination.

We already live in an age of misinformation, and an AI to help readers sift through data to glean credible knowledge is already necessary and would be hugely valuable. Whether it’s made broadly, cheaply available versus commercially distributed will determine whether we end up with a schismatic future of the sort Stephenson sketched, or we understand and collaborate with each other. At this moment, information discovery services like Twitter, Facebook (.com/Whatsapp) and Google (search/Now/Youtube) are doing a pretty pedestrian job.

Growth investors invest in companies that exhibit signs of above-average growth, even if the share price appears expensive. Value investing involves buying securities that appear underpriced by some form of fundamental analysis.

Speculation and some data on why growth investing has beaten value investing for a while now in the US:

… it makes sense that computers are winning in arbitrage-style hedge fund strategies, where an asset worth 95 cents is bought in one market and a quasi-identical one is sold in another market for $1.00. Most of those traditional hedge fund strategies are rules-based, and if anything, data ubiquity means computers have a huge advantage in parsing ever-increasing amounts of data not only to find that often temporary discount, but also to step in front of slower investors for a smaller spread if necessary, essentially beating them to the punch… even if the specific metric used for implementation of value (say, a price-to-book or price-to-earnings ratio) changes because of market conditions, competitive dynamics, or the financial reporting tendencies of corporate executives, computers are far better positioned to identify the shift faster than we are.

On the other hand, growth is perhaps harder to spot. It’s not just that doing so involves making simple forecasts about the future — computers generally beat humans at that, too, by the way — but it requires something more than that. In my experience, successful growth investors have an ability to synthesize unstructured data across multiple, often seemingly unrelated, domains.

Since inception in 2010, the AI index has absolutely crushed the average hedge fund, with the machines annualizing at 12.8 percent per annum versus just 5.0 percent for the humans. If efficiency is the enemy of alpha, then computers are its worst nightmare, hulked out on steroids.

Given this, there should still be a window (months? years?) to create high alpha value based funds that use technology in India. There has never been more compute power available for cheaper. Machine learning’s also a relatively new field and hasn’t been applied widely to Indian data sets. This means you can make failures (cost) free and successes real. The race is then for the best data.