Ride-Hailing Startups: Why Do They Get Such Crazy Valuations?

It is common knowledge that Uber valuations have reached the sky – Uber is now worth more than Nissan motor. Then there is this recent news that Didi Kuaidi is now valued at about $15 billion. Of course, India’s Ola is valued in the billions as well – $2.5 Billion, to be precise.

Some numbers now. This is from Forbes –

“If you assume a normalized long-term free cash flow margin of about 35% (yes, this is quite high, but Uber’s business model is very efficient), Uber’s $50 billion valuation means that they will need to generate about $35.7 billion dollars of gross revenue and about $7.1 billion dollars of net revenue to justify the recent valuation. Perhaps more interestingly, the company will have to have an annual growth rate of about 286% each year over the next five years to hit these numbers. To put those numbers into perspective for a moment, it means that Uber is currently valued at 125x trailing annual net revenue.” Source

Which slide in these ride-hailing companies’ pitch decks are we missing?

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Algorithms Are Eating The World

Marc Andreessen wrote this in 2011 – in his WSJ piece aptly titled Why Software Is Eating The World:

“More and more major businesses and industries are being run on software and delivered as online services—from movies to agriculture to national defense. Many of the winners are Silicon Valley-style entrepreneurial technology companies that are invading and overturning established industry structures. Over the next 10 years, I expect many more industries to be disrupted by software, with new world-beating Silicon Valley companies doing the disruption in more cases than not.”

It is probably not software that is eating the world – it is the algorithms that sit behind such software, that are eating the world. Algorithms are everywhere, starting with what you receive in your mail, to the ads that follow you online. Recommendation engines are just small evidences of algorithms at work – if you look around, you will realize that a complete transition of the world’s functioning is in full swing, from humans to algorithms.

What you read, what you watch, what you listen and what you eat have all been driven by algorithms for so long now, it just seems natural. Where you go, who you meet and what you think about – these are areas that are starting to see the impact of algorithms. Targeted marketing messages, price prediction engines and behavior simulation machines are just the tip of the iceberg. Algorithms are here, and are taking over huge parts of our lives.

Make no mistake, algorithms are eating the world.

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The Revenue Model Will Take Care Of Itself

When you try to think about revenue models for your concept, here is one shortcut – just focus on improving the efficiencies in any area related to the concept – as long as you make something – something – better than how it was earlier, someone is bound to pay for that efficiency. There are very rare cases you could get this wrong, but in most cases it works. Try to come up with an example of a concept that had improved the efficiency of a process but did not work as a business. There are very few – you might not be able to name one.

Pick a process and build something to make it more efficient. The revenue model will just take care of itself.

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‘Look At What Is Getting Shut Down’

Obviously, this post is triggered by the recent Google Reader shut down episode. While this might not be a great heuristic for ideas – it is definitely a good one. Keep your eyes and ears open for what is getting shut down – actually you can be a little broader than that, and watch out for any of the following:

1. A popular (or) once popular service shutting down (e.g. Google Reader)
2. A popular free service moving to a premium-only model (e.g. Ning, StackExchange)
3. A popular service getting acquired, with a good possibility of change in direction (e.g. posterous, dodgeball)

The common theme in each of the above sunset scenarios is this: ‘popular’ – and that means customers. In many cases, contrary to what you might expect, these would be profitable customers.Remember, the world loves status-quo. With very few exceptions.

A solid substitute and ease of migration is all that is needed to land these customers. If you have ever tried getting an initial set of customers for a new product – you know how big a jackpot that is.

If you can’t build a substitute, figure out who is emerging as the most popular substitute, and build something to make the migration less painful. People ask for, and buy painkillers generally. And building painkillers is a better idea than building vitamins. If you can “identify a painkiller, that is.

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The $1 Trillion Coin : A Programmer’s Advice

If you follow economics / politics (even at a high level), in the last one week, you must have heard of a solution with the phrase ‘$1 Trillion Coin’ in it. Here is our attempt to get a programming equivalent for the same.

The context is very simple. The Government needs money. Generally the mechanism is this: The Treasury issues debt. The Fed buys that debt and sends money to the Treasury. The government can then spend that money. The catch is, when that happens, debt goes up. and everyone hates that.

So how can the Government get money from the Fed without issuing debt? A legal loophole allows the Treasury to mine coins (meant to enable it to issue commemorative coins) to issue two $1 trillion platinum coins, and then deposit them with the Federal Reserve. The Fed could then deposit funds with the Treasury in return. Debt does not go up, Government got the money, and everybody is happy. (okay, obviously not everyone is happy)

What is the programming analogy for this? We thought that swapping two variables without using a temporary variable could be a close analogy:

Normal process:

swap(int *a, int *b)
int temp = *a;
*a = *b;
*b = temp;

Without a temporary variable:

swap(int *a, int *b)
*a = *a + *b;
*b = *a – *b;
*a = *a – *b;

The temporary variable is the debt that must have been issued. And the authority to print coins, is the ability to add the value of b to the value of a.

The issue with the second approach is this: The temp variable is not tracked, and three/four such transactions in, nobody has any idea of how we got there. Not just that – in the case of the government raising money, bypassing the temporary variable and ‘managing’ with existing debt misleads everyone by painting a wrong picture of the number of temporary variables that need to be cleared later.

The $1 Trillion Coin sounds like a feasible solution. It is not.

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BRIC helping US Exports:Geithner

From The Economic Times:

Rapid growth of emerging economies like India, China and Brazil is boosting American exports and raising incomes and jobs across the US, Treasury Secretary Timothy Geithner has said.

“Emerging economies like China, Brazil and India are growing very rapidly. That growth is helping to support rapid growth in US exports which in turn is raising income and employment across the United States in manufacturing and high tech and agriculture,” he told the Senate Foreign Relations Committee Thursday.

This is Ludic Fallacy, at best. This is too simplistic a view to take on such a complicated issue.

From Wikipedia:

The ludic fallacy is a term coined by Nassim Nicholas Taleb in his 2007 book The Black Swan. “Ludic” is from the Latin ludus, meaning “play, game, sport, pastime.”[1] It is summarized as “the misuse of games to model real-life situations.”[2] Taleb explains the fallacy as “basing studies of chance on the narrow world of games and dice.”[3]

It is a central argument in the book and a rebuttal of the predictive mathematical models used to predict the future – as well as an attack on the idea of applying naïve and simplified statistical models in complex domains. According to Taleb, statistics only work in some domains like casinos in which the odds are visible and defined. Taleb’s argument centers on the idea that predictive models are based on platonified forms, gravitating towards mathematical purity and failing to take some key ideas into account:
it is impossible to be in possession of all the information.
very small unknown variations in the data could have a huge impact. Taleb does differentiate his idea from that of mathematical notions in chaos theory, e.g. the butterfly effect.
theories/models based on empirical data are flawed, as events that have not taken place before cannot be accounted for.

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