Apple’s Failed Feedback Loop (Ep. 4)

For those familiar with the idea of nonlinear effects from Antifragile, learning is rooted in repetition and convexity, meaning reading a single text is more profitable than reading two different things once, provided of course that said text has some depth of content. — Nassim Nicholas Taleb

Apple has a broken feedback loop for cracked iPhones. I cracked my iPhone screen three months ago and have yet to fix it. Apple provides me six options to repair my cracked screen. There are two options to repair the screen. I can send it in for repair or bring it in for repair. Having gone through this experience before, bringing an iPhone in for repair is another way to send it in for repair. Each of the six choices below lead to sending it in for repair. From my experience, the repair process takes about five-seven business days.

I have two controversial beliefs. First individuals with cracked iPhones are more willing to use a cracked iPhone than lose their iPhone for a week. Putting the cost of repair aside, I am a victim of this short term outlook. Along with my peers, I use my iPhone for nearly every part of my life. Why is there not an option to get a temporary iPhone or a refurbished iPhone immediately upon handing over my iPhone to be repaired? Second I expect cracked iPhone screens are more ubiquitous than Apple realizes. While they can track my location, my texts, and other software related bugs, they have no way to track hardware issues, such as the number of cracked screens in the world. Apple can only track the number of iPhones brought in for repair. The feedback loop is broken. What they measured is managed. In this case, Apple may be measuring the wrong effect, repaired iPhones.

Early Marks Memos

“When I see memos from Howard Marks in my mail, they’re the first thing I open and read. I always learn something.” Would you believe Warren Buffett said this? In honor of Howard Marks’ most recent memo “Expert Opinion,” I want to highlight one of my favorite memos from 1993 “The Value of Predictions, or Where’d All This Rain Come From?”. What many are presently observing in other domains about non-consensus bets was said perfectly nearly 25 years ago.

This raises an important Catch 22. Everyone’s forecasts are, on average, consensus forecasts. If your prediction is consensus too, it won’t produce above-average performance even if it’s right. Superior performance comes from accurate non-consensus forecasts. But because most forecasters aren’t terrible, the actual results fall near the consensus most of the time — and non-consensus forecasts are usually wrong.

LinkedIn’s Series B Pitch Deck to Greylock

Y Combinator revealed at Startup School 2014 that it asks each company to read the LinkedIn Series B deck. Reid Hoffman published the deck on his blog along with context and advice for each slide. Thanks for opening up the kimono, Reid.

The general rule is one business model drives the business. It’s tempting to list multiple revenue streams because you’re trying to prove that you will be big. Yet when consumer internet companies do this, investors generally see a red flag.

Global Stock Market Valuations

As we begin the new year, I highly recommend bookmarking a page with equity market valuations broken down by country. I like StarCapital’s Shiller-CAPE market valuations page. Mean reversion is a powerful force, and there is a high likelihood that the countries most blue in the overview below are less blue at the end of 2017.

Thanks Abnormal Returns and the Collaborative Fund for the inspiration for my posts.

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