4 Observations About The Valley

As we close out 2016, I wanted to share four observations of mine about Silicon Valley. This is from my perspective as a recent college graduate. Feel free to argue with me or reach out with any thoughts! I’m anaganath on Twitter.

1. Disregard Of Paper Credentials

Historically, the brand weight that your college degree carried arguably determined the number and quality of opportunities available to you. Similarly, the pedigree of prior jobs. This is still true in most sectors today, most popularly financial services and consulting. In these sectors, which offer lucrative prospects, unfortunately the only real way to “break in” as a college new grad is to go to a top tier school. Without this paper credential you are almost certainly likely to be rejected without an interview. This compounds because to advance in these sectors, you have to either originate from a top tier gig (which is only possible if you went to a top tier school) or get another paper credential such as a top tier MBA. This setup means that most graduating high school students are filtered out from the very beginning, if they weren’t admitted to certain institutions (obviously with exceptions, as with any norm).

However, Silicon Valley has been the first major industry to reject this paradigm. Nowadays, for most technical jobs in the valley, employers don’t care about pedigree. They care about your skill set. This has not only allowed for more inclusion and less elitism in parts of the industry, but also for interesting initiatives that may have wider implications in the future. For example, this facet of the culture has given birth to alternative educational opportunities like coding bootcamps that offer to equip people, regardless of background, with a tangible skill set because it translates to measurable return on investment. Similarly, startups like Triplebyte are connecting job seekers to jobs based on skill set alone and nothing else. The growing need on behalf of employers for technical talent, the ability to accurately map hard skills to success in technical roles and the success of entrepreneurs without conventional pedigree has resulted in a more fluid and inclusive labor market in the valley.

Admittedly, the market for non-technical talent in the valley stills falls prey to the conventional “pedigree” trap. Breaking into venture capital and product management is still fairly credentials driven and this is most likely because it’s relatively hard to quantify the set of skills required to be successful in these domains and to verify that a person has that skill set. However, unlike in the more traditional industries mentioned above, starting a company in the valley is accessible to all, thanks to institutions like YCombinator. And founding a successful company in the valley is the best credential one can have.

2. Desire To “Get Rich Fast”

There’s a saying that everyone in the valley is looking for his next move. According to this article, the tech industry has the lowest average employee tenure. This is not without reason. There is a “get rich fast” culture that pervades the valley and to which I attribute the following factors that interplay with each other:

1. Reward Structure: In most industries, there’s a standard and predictable ladder. As one climbs that ladder, one is rewarded with significant raises in cash — an immediately liquid asset. And so college graduates joining banks or management consulting firms, for example, know exactly what their paths will look like and consequently what their, usually very large, net worths will roughly be in the next X years. Their silicon valley counterparts, however, see a bulk of their compensation in the form of equity. If things go well and you have enough equity, there’s huge upside. Else, you risk making very little if anything at all. Since most startups fail, to make the big bucks that allow you to afford housing in the bay area and your kids’ college education, you need to make the right bet when it comes to joining an early stage company. This is why job seekers in the valley are constantly bouncing between startups — in search of their home run. They’re also hoping to do it while they’re young as the lifestyle of a startup and the fast pace at which technologies change, don’t sit well with increasing age.
2. Role Models: From what I’ve seen, ever since “The Social Network” released in 2010, millennials have sought to emulate the meteoric success that Mark Zuckerberg is portrayed to have enjoyed. The movie however, represents more than just Zuckerberg. It’s a statement about Silicon Valley — that age is only a number and that with the right innovation, you can catapult yourself to the highest echelons of society. Since 2010, we’ve seen more examples of meteoric success. Immediate examples are Evan Spiegel (Snap Inc.) and Patrick and John Collison (Stripe). The fact that these founders, under the age of 30, have become billionaires who command global respect and influence has captivated today’s generation and only fueled its conviction to follow suit. Almost every young silicon valley technologist wants to start a company with hopes of achieving similar results — because they’ve been shown, in an unprecedented way, what awaits if they get it right.
3. Easy to Startup: Over the years, the valley has organically augmented the ability of individuals to start companies. Firstly, its seen dramatic increases in the number and kinds of technologies available to entrepreneurs today. The open source movement has brought with it free technologies and abstractions at all layers of the stack that allow developers to very quickly turn concepts into working prototypes. This coupled with on demand computing platforms has meant that it’s never been easier and less capital intensive in the history of software, to build and scale a product from the comfort of your own home. Secondly, the valley has seen the advent of the accelerator model — spearheaded by YCombinator. Today, there are hundreds of mainstream and vertical-specific accelerators promising young companies quality mentorship, connections and foundational capital. They have significantly reduced the perceived risk of starting a company. Lastly, until recently, investors have shown that they’re not shy to make big bets on companies that have demonstrated product market fit. These bets have birthed the astronomical valuations that we often hear about and in general, have contributed to a liquid funding market. Collectively, these phenomenon have made it a far, far less daunting endeavor to startup.

3. Platforms Are King

The most dominant companies in the valley today (Google, Facebook, Apple, Amazon, Microsoft, Netflix etc) have built platforms. This is no coincidence. In the valley, it seems building a great product is only as good as creating a solid foundation. To really establish competitive advantage and benefit from increasing returns, you have to invest in building a platform.

The origins of this model can be seen from the early days of Apple and Microsoft. Consider this quote from the following article:

“Apple learned this the hard way in the 1980s when it created the first versions of the Macintosh. It built its own proprietary, closed, hardware, operating system, and applications. Bill Gates, on the other hand, realized that key to power and profit was the operating system and a thriving ecosystem. He designed Microsoft Windows as an open system in which other players could provide the hardware and software. The more programs that ran on Windows, the more users wanted it, and therefore more developers created applications. Windows became a near monopoly the 90s — while Apple came close to bankruptcy.

Fortunately for Apple, by 2007, Steve Jobs had figured out Microsoft’s advantage. He built the iPhone App Store and iTunes as open platforms on which other players could provide content. The top five mobile phone carriers — Nokia, Samsung, Motorola, Sony Ericsson, and LG — had owned 90 percent of the industry’s profits. Yet Apple was able to leap ahead and capture literally all of this.”

The consensus is that platforms connect producers and consumers in “high value” exchanges in the form of interactions. Furthermore, as this article points out:

“Silicon Valley doesn’t think in terms of “products,” instead embracing the unbounded economics of the platform, where connecting users and interactions is the new coin of the realm. Unlike a static product, a platform’s value is defined by the users who populate and use it; a platform can morph to adapt to their needs and continually unspool new services and innovations. Valley companies think in terms of ecosystems, networks, and sharable services — elements that are crucial to scaling very quickly. Any business needs to make money eventually, but the power of rapid scaling is a huge competitive advantage that those in the Valley understand keenly.”

In a book on the subject, titled ‘Platform Revolution: How Networked Markets are Transforming the Economy and How to Make Them Work for You’, the authors define orthodox businesses as “pipelines” that take a series of inputs and run them through a set of transformations that create marginal value. However, platforms create exponential increases in value. The shift from pipeline to platform requires 3 major changes, nicely summarized by the authors:

1. “From resource control to orchestration. In the pipeline world, the key assets are tangible — such as mines and real estate. With platforms, the value is in the intellectual property and community. The network generates the ideas and data — the most valuable of all assets in the digital economy.” Sound familiar? This is exactly how Facebook astutely positioned itself with its news feed and subsequent innovations. This has let it consistently eliminate competitors and establish a monopoly in the social media landscape.
2. “From internal optimization to external interaction. Pipeline businesses achieve efficiency by optimizing labor and processes. With platforms, the key is to facilitate greater interactions between producers and consumers. To improve effectiveness and efficiency, you must optimize the ecosystem itself.”
3. “Value the ecosystem rather than the individual. Rather than focusing on the value of a single customer as traditional businesses do, in the platform world it is all about expanding the total value of an expanding ecosystem in a circular, iterative, and feedback-driven process. This means that the metrics for measuring success must themselves change.”

I want to highlight the powers of 2 and 3 through the lens of Stripe. Stripe started out as a platform business by looking to abstract away developers’ pain, via an API, of accepting payments online. As the number of transactions began to increase and Stripe grew through word of mouth, it’s focused on honing and refining its core payments API, thus optimizing its ecosystem. To see how it has done so, look no further than this Quora answer from a user(!). Furthermore, Stripe has worked on expanding its ecosystem with more products.

This is why Stripe is valued at $9 billion and continues to grow. If the banks don’t recognize the importance of investing in the platforms they have by virtue of being incumbents, Stripe will likely monopolize the market as it leverages increasing returns from its ecosystem.

Other valuable private consumer companies like Snap, Uber and Airbnb should invest, if they haven’t already, in building platforms if they are to stay relevant in the years to come.

Aptly, as in this piece, the question people in the valley are asking is: “What is the next big platform?”

4. Artificial Intelligence: The Big Bet

Silicon Valley is known to be full of fads. From technologies and business models to people and perspectives, the valley will obsess with something for months or even years and then, in most cases, abruptly move on to the next thing. However, artificial intelligence is one of those few technologies that has remained in the purview of the valley over the years. And now, with the resurrection of deep learning and the success it has had with helping machines understand the world, artificial intelligence in the form of machine learning has become the bet that the valley has made on technology’s future.

To understand why, it’s important to understand what deep learning is at a high level. Here are some excerpts from this article that summarizes what’s going on:

A neural network is a “connectionist” computational system. The computational systems we write are procedural; a program starts at the first line of code, executes it, and goes on to the next, following instructions in a linear fashion. A true neural network does not follow a linear path. Rather, information is processed collectively, in parallel throughout a network of nodes (the nodes, in this case, being neurons)…One of the key elements of a neural network is its ability to learn. A neural network is not just a complex system, but a complex adaptive system, meaning it can change its internal structure based on the information flowing through it.

The neural network actually isn’t a new concept. A majority of the algorithmic breakthroughs governing neural networks were interestingly made in the 80’s and 90’s. However, it is because of recent explosions in data and computational power (eg: GPUs) that neural networks have been leveraged effectively to advance computer learning. As this article points out:

That dramatic progress has sparked a burst of activity. Equity funding of AI-focused startups reached an all-time high last quarter of more than $1 billion, according to the CB Insights research firm. There were 121 funding rounds for such startups in the second quarter of 2016, compared with 21 in the equivalent quarter of 2011, that group says. More than $7.5 billion in total investments have been made during that stretch — with more than $6 billion of that coming since 2014. (In late September, five corporate AI leaders — Amazon, Facebook, Google, IBM, and Microsoft — formed the nonprofit Partnership on AI to advance public understanding of the subject and conduct research on ethics and best practices.)

Google had two deep-learning projects underway in 2012. Today it is pursuing more than 1,000, according to a spokesperson, in all its major product sectors, including search, Android, Gmail, translation, maps, YouTube, and self-driving cars. IBM’s IBM -0.21% Watson system used AI, but not deep learning, when it beat two Jeopardy champions in 2011. Now, though, almost all of Watson’s 30 component services have been augmented by deep learning, according to Watson CTO Rob High.

Venture capitalists, who didn’t even know what deep learning was five years ago, today are wary of startups that don’t have it. “We’re now living in an age,” Chen observes, “where it’s going to be mandatory for people building sophisticated software applications.” People will soon demand, he says, “ ‘Where’s your natural-language processing version?’ ‘How do I talk to your app? Because I don’t want to have to click through menus.’ ”

As this piece further highlights, the most influential figures in the valley are backing the deep learning movement. Open AI, for example, is backed by Elon Musk, YCombinator, Reid Hoffman and other luminaries. Amazon, Google and Microsoft have rolled out near perfect speech recognition interfaces (Alexa, Google Home and Cortana respectively). Similarly Google, Tesla and Uber are investing billions in their neural-net backed self driving cars — the innovation that if successful will definitively mark the start of a new chapter in human history.

However, there are those who take an opposing viewpoint. There are some who also fervently believe that the valley’s obsession with artificial intelligence is yet another fad that will pass. Consider this piece titled ‘This AI Boom Will Also Bust’. It received a bunch of tweets and retweets on Twitter by some notable people in the valley. Here’s a snippet that illustrates their point:

“In the last few years, new “deep machine learning” prediction methods are “hot.” In some widely publicized demonstrations, they seem to allow substantially more accurate predictions from data. Since they shine more when data is plentiful, and they need more skilled personnel, these methods are most promising for the largest prediction problems. Because of this new fashion, at many firms those who don’t understand these issues well are pushing subordinates to seek local applications of these new methods. Those subordinates comply, at least in appearance, in part to help they and their organization appear more skilled.

One result of this new fashion is that a few big new applications are being explored, in places with enough data and potential prediction value to make them decent candidates. But another result is the one described in my tweet above: fashion-induced overuse of more expensive new methods on smaller problems to which they are poorly matched. We should expect this second result to produce a net loss on average. The size of this loss could be enough to outweigh all the gains from the few big new applications; after all, most value is usually achieved in many small problems.”

It certainly is true that existing enterprises do not need neural nets or complicated machine learning algorithms to augment their operations. This has been proven by the countless enterprise startups that have failed by trying to deliver deep learning solutions to existing enterprises. This, as the above author argues, is indicative of the “fact that this new tech is mainly only useful on rare big problems [suggesting that] its total impact will be limited. It just isn’t the sort of thing that can remake the world economy in two decades. To the extend that the current boom is based on such grand homes, this boom must soon bust.”

And thus, artificial intelligence has become the big bet that Silicon Valley has made on technology. What do you think?