That means that the winning headline results in 4,620 incremental opens per newsletter. Over the course of a month, that is 115,000+ incremental opens. If we apply a conservative estimate based on some of our data from 2015 of how many of those incremental opens try the CB Insights free product (0.09%) and that only 1% of those trials buy a subscription, we’re looking at nearly $625,000 of incremental revenue.
The result of billions of readers providing free content for each other is a massive network effect and a business that can serve each incremental ad at a very low marginal cost. The company’s 85% gross margin leaves a lot of meat on the bone that the company can spend on hiring new engineers and developing new technology that is needed to maintain and grow the business. But even after paying the engineers, growing the headcount, spending over $9 billion on R&D, and paying the tax bill, shareholders are still left with around 40 cents of profit for each dollar of revenue. Facebook reinvests these earnings into data centers, technology, and the occasional acquisition, but even so, the cash in the till has been piling up, and now exceeds $40 billion and counting ($14 per share). Facebook has spent $10 billion on buybacks in the last year, and this will likely accelerate in the coming years.
Facebook’s business is still growing fast (33% revenue growth last quarter), and despite the company’s large size, the runway is still long. Benedict Evans, a partner at the VC firm Andreessen Horowitz, recently pointed out that roughly $1 trillion is spent each year by businesses who are trying to reach customers who are asking the question: “What should I buy?”. Facebook has roughly 5% of this market, which I think is a share that will inevitably rise over the coming years as advertising dollars continue to shift from traditional media to the much higher ROI advertising platforms like Google, Facebook, and Amazon.
A strong network leads to Facebook grabbing a much greater share of the $1 trillion (and growing) global ad/marketing industry. Revenue from ads alone could easily be twice the current size, which means that Facebook is a $100 billion business before counting any of the possible upside from payments, messaging, or any of the other potential business lines that the company could develop on the back of its large network effect.
If the data center market as a whole were to follow in the footsteps of HPC, accelerators could displace Intel CPUs as the workhorse of enterprise computing.
If processors were to grow to 50% of total server component costs, with accelerators capturing 70% of processors, as is the case in the HPC market today, the market for accelerators could scale from $4 billion1 today to $24 billion. Currently, the size of the computer server market is $80 billion globally, according to IDC. If manufacturers take a 15% cut, then $68 billion accrues to component suppliers such as Intel, Micron, and Nvidia. Today we estimate that processors account for roughly 35% of server component costs, the balance allocated to memory, storage, and networking. With HPC and AI, servers are evolving toward higher compute configurations, pushing processors and accelerators to 80% of total component costs.
As shown by the latest IDC data above, the server market as a whole continues to grow, driven primarily by higher average selling prices (ASPs). With denser configurations, server ASPs could climb from today’s price of $8,000 to $10,000. Even at the current volume of 10 million units per year, that would grow the server market 25% from $80 to $100 billion, pushing the accelerator total addressable market to $35 billion.
Today, the accelerator market is dominated by Nvidia’s data center products, which we estimate will generate $3.2 billion of revenue in 2018. Nvidia estimates that its data center business has a total addressable market of $50 billion—a figure larger than Intel’s data center business, which seems difficult to reconcile. That said, as the latest data from IDC and Top 500 shows, the server market is undergoing a fundamental shift. The decay of Moore’s Law has increased total server spend as compute intensive workloads migrate from CPUs to specialized processors. If CPUs and accelerators were to swap places in the data center, as they did in HPC, the accelerator market would approach Nvidia’s estimate, implying a 10x increase over the next five to ten years.
In the new paper, the researchers used neural networks—the foundational software for data training—to create convincing looking digital fingerprints that performed even better than the images used in the earlier study. Not only did the fake fingerprints look real, they contained hidden properties undetectable by the human eye that could confuse some fingerprint scanners.
Julian Togelius, one of the paper’s authors and an NYU associate computer science professor, said the team created the fake fingerprints, dubbed DeepMasterPrints, using a variant of neural network technology called “generative adversarial networks (GANs),” which he said “have taken the AI world by storm for the last two years.”
There are three conditions that need to be met in order to trust one’s intuition. The first is that there has to be some regularity in the world that someone can pick up and learn. “So, chess players certainly have it. Married people certainly have it,” Kahnemen explained. However, he added, people who pick stocks in the stock market do not have it. “Because, the stock market is not sufficiently regular to support developing that kind of expert intuition,” he explained. The second condition for accurate intuition is “a lot of practice,” according to Kahneman. And the third condition is immediate feedback. Kahneman said that “you have to know almost immediately whether you got it right or got it wrong.”
Every year on Wall Street, first-year analysts who were once courted by college recruiters with lines about how they would be given meaningful, creative work and the chance to learn from top executives are shown the harsh truth: they are Excel grunts whose work is often meaningless not just in the cosmic sense, but in the sense of being seen by nobody and utilized for no productive purpose. Some of the hundred-page pitch books analysts spend their late-night hours fact-checking in painstaking detail are simply thrown away after being given a quick skim by a client. In other cases, the client doesn’t read the deliverables at all, and the analysts’ work is literally garbage.
Most days that winter, he had worked the “banker nine-to-five”—getting to work at 9:00 a.m. and staying until 5:00 a.m. the next morning, at which point he’d trudge back to his Murray Hill apartment, sleep for three or four hours, and do it all over again. Even the money he was making wasn’t cheering him up. Ricardo had once made the mistake of calculating what his hourly wage would be, given his salary and a conservative estimate of his year-end bonus. The result—which he estimated was something like $16 an hour, after taxes—was much more than minimum wage, but not nearly enough for Ricardo to be able to justify the punishment he’d been taking.
The question of in office hours versus out of office hours is a good one in thinking about a career in the finance industry. I knew basically nothing when I came into finance so a massive amount of my learning was done on my own after hours.