Apple has the best business model for generating cash
Apple’s $64B of operating cash flow was nearly as much as that of Alphabet ($36B), Facebook ($19B), and Amazon ($17B) combined. In essence, Amazon is doing as well financially as Facebook. Google is generating as much cash as Amazon and Facebook put together. Apple is generating nearly as much cash as Amazon, Facebook, and Google combined.
Apple’s $51B of TTM free cash flow is $3B more than the free cash flow produced by Alphabet, Facebook, and Amazon combined. In what may come as a surprise, Apple is bringing in 70% more free cash flow than Microsoft, who is still considered to possess one for the more lucrative business models in existence.
Apple is a design company focused on selling tools capable of fostering superior experiences. Scale is considered a byproduct of a properly functioning business model. Facebook and Google are service companies focused on offering free, data-capturing services to as many people as possible. The business models are dependent on achieving scale in order to access as much data as possible. Amazon is a retail platform company focused on getting you to buy more stuff over time. Scale in terms of purchase volume is needed in order for the cash flow/reinvestment cycle to continue.
Instead, we have non-hardware companies pontificate how hardware won’t matter in the future. In reality, the opposite will likely occur. Hardware will matter more going forward. The wearables industry represents a good example of this in practice. Meanwhile, the way smartphone and tablet components are mattering more now than ever to AR and AI is another hole in the “hardware won’t matter” thesis.
No one knows how much money WeChat is making, and investors are too bullish to care
…the success of Honor of Kings as an example of WeChat’s indirect influence on Tencent’s revenue growth. “When you go into the game, it becomes all about playing with your WeChat friends, and looking at their scores and achievements,” he says. Honor of Kings is currently ranked the top-grossing game in China’s iOS App Store (registration required), and four other Tencent titles fill out the top 10.
…pointing to WeChat’s low take on payments (Stripe and PayPal each charge about 3%) and its aggressive discounts, speculates it’s a loss leader. Tencent executives, meanwhile, have downplayed its role in making money for the company. “We consider payment at this point in time as to [sic] infrastructure service rather than a service that generates profit for us. And I think that status will maintain for quite some time.”
“Payments are the gateway to lending. And because you’re tracking the same consumer across so many platforms, you know the credit score of the consumer and you have very few non-performing loans.”
How Baidu will win China’s AI race—and, maybe, the world’s
But to train the algorithms that will deliver the intelligence to transform our cities, it needs data. To wit: The company with the most data wins.
Clearly, he saw more opportunity across the Pacific: In China, 731 million people—nearly twice the entire population of the United States—are online. Says Lu: “China has the structural advantage.”
We’re the first major company to clearly separate the perceptual and the cognitive layer. Perceptive capability and the cognitive are related, but they are quite different. Most of the [other] AI platforms bundle them together.
But one thing I learned is that in this race to AI, it’s actually more about having the right application scenarios and the right ecosystems.
It’s just like the phone ecosystem today. The phone ecosystem is the largest silicon software ecosystem. I believe the same thing will happen for the autonomous system. The car is going to build a larger ecosystem. And the same set of capabilities—hardware, sensors, chip sets, software—will be used to build industry robots, home robots. We want to have hundreds of companies and universities all at work on this, building a very large ecosystem. Then we can build robots, build drones, and build all those autonomous systems. So, to me, autonomy is a key.
…because China is highly, highly fragmented. There’s more than 250 car OEMs [original equipment manufacturers], unlike the United States, which is a heavily concentrated industry. None of the OEMs will have the full capabilities to build out deep R&Ds. With our code base that we released on July 5, [we will make it possible for] one person to assemble a vehicle in three days that can do autonomous driving in limited forms and start on R&Ds.
We’re competing against nobody. We enable each OEM, whether it’s Bosch, Continental, or Nvidia, to be able to do more.
Amazon has largest A.I. platform in the world, its machine learning guru boasts
Despite the lack of notoriety, “inside Amazon we’ve been doing machine learning for over twenty years,” he notes, and anyway, “We have more machine learning running on the platform than anywhere else” he claims, meaning AWS is doing more A.I. than Google or any other facility in the world.
“Today, machine learning is very technical,” he says, but overtime, and with Amazon’s help, it is going to be simpler and simpler to apply machine learning to any number of different applications, “and to do it with high accuracy.”
Wood noted another important development, the shift from just the “training” phase of A.I., where a computer deduces patterns, to the “inference” stage, where it responds to user requests based on what it’s learned.
…what he thinks of machines making machines, meaning, machine learning being able to design new algorithms for machine learning, a kind of self-reflexive moment in A.I. “Absolutely,” says Wood, “It’s already happening. There are customers on AWS who are training bots to to make algorithms.” One example is something called Bandits, where machines face off against one another, with one machine trying to deduce learning models while the other is trying to trick it with falsehoods.