Peter Yared's Musings

This post was also published in VentureBeat. Over the holidays, I heard Donald Trump giving his stump speech in South Carolina on my car radio. It strikes me that, for all we hear about Trump’s outlandish statements in the press, there’s very little reported about his actual message. Beneath all of his bluster and meandering, Trump is advocating a cogent economic platform targeting an increasingly disenfranchised middle class. It is well understood in economic circles that a combination of globalization, technology, and government policy — trends that most members of the Republican and Democratic parties fully support — has stagnated wage growth in the middle class. Trump and Bernie Sanders are the lone presidential candidates questioning this status quo, which is why they’re both drawing so much interest. Above: Source: Congressional Budget Office, Average Federal Taxes by Income Group, “Average After-Tax Household Income,” June, 2010, http://i...

This post was also published in VentureBeat. A lot of noise has been made about the inflated valuations of Aileen Lee’s unicorns and the amount of money they have raised. There are rumors that uber-unicorn Uber is now raising an additional $1 billion, in order to continue to fuel growth financed by losing a rumored two dollars for every dollar in revenue . On the one hand, that seems irrational; but on the other hand, loss-fueled growth is how companies like Amazon became the behemoths they are today. In the broader context of Silicon Valley technology companies, the unicorns in aggregate form barely a ripple in the fabric of space-time. According to CBInsights, there are 87 Unicorns in the United States , with a combined valuation of $312 billion. Cross-referenced with Crunchbase , those 87 have raised a cumulative $48 billion, with over half of that amount invested in the top 14 unicorns. What if all of these unicorns vanished to Candy Mountain tomorrow? What would the ra...

This post was also published in VentureBeat. Unlike other category-defining Internet companies, Twitter has struggled to meet both user growth metrics and Wall Street’s expectations. There are a lot of possible explanations for Twitter’s user growth problems, but they really boil down to one simple thing: As the content shared into streams grows exponentially, the streams have to get smarter in order to remain relevant to users. Twitter presents cards in a straight reverse chronological stream that shows all content. The more people you follow and the more you use Twitter, the worse the Twitter experience becomes. Facebook took a very different tack. Back in 2008, Mark Zuckerberg established Zuckerberg’s Law of Information Sharing , which predicted that the rate people share information like status updates and photos would double every year. In 2009, Facebook acquired Friendfeed for $50 million , integrating a team that was using content shared from external sites to learn what...

This post was also published in VentureBeat. Uber’s fifth year anniversary highlights that sharing economy services have rapidly become persistent and mainstream. Uber facilitates over one million rides a day . It’s estimated Airbnb will make $6 billion in gross bookings in 2015 . Legacy regulatory environments were first ignored and then fell by the wayside or changed as people recognized that a customer-reviewed driver in a personal modern car was just as good, if not better, than a sociopath in a yellow Crown Victoria. However, Uber drivers are now congesting major cities, and neighbors of Airbnb-listed rentals don’t want to live adjacent to what is effectively a hotel . As the adage goes, bureaucracies are created to solve a real problem, actually address the problem, and then simply sustain themselves. There is no doubt that taxi commissions and hotel regulations have far exceeded that threshold. Jurisdictions like Las Vegas that forbid street-side pickups in conjunctio...

This post was also published in VentureBeat. There has been a lot of discussion about how the acceleration of technology is decimating the middle class and traditional jobs . But there has been very little discussion of an emerging trend where individuals are opting out of these same jobs people fear will disappear. Driven by a post-scarcity economic model whereby you can live very frugally if you choose to, some workers (mostly college-educated and urban) are opting out of the now traditional work structure and choosing their own path. As Chelsea Rustrum puts it in her book It’s a Shareable Life , “You can live a life dictated by choice, passion, and freedom — a life where your … experiences are of the highest value.” They are opting into alternative, passion-based professions that have gained popularity and acceptance, such as craft beer producer or yoga teacher, and that have flexible hours. Twenty years ago, if Bob, the valedictorian, showed up to his high s...

This post was also published in ReadWriteWeb. Since its inception in the 1960s, the modern computer has offered humans the same “pull computing” paradigm: make a query, get a response. Or, as we often experience it: Go to the haystack, try to find the needle. But that’s quickly changing. As software grows more intelligent and learns more about our preferences and behavior, it seemingly gets to know us. That knowledge makes software more valuable because it means that it can deliver things to us, perhaps even before we know we want it. We are at the start of the era of push computing. Pushmi-Pullyu With push computing, a computer is no longer just a question-and-answer service; it’s expected to proactively figure out what’s interesting to you and deliver that data. On mobile, that’s often an actionable stream of cards and timely notifications of important items. Push computing represents a major shift in architecture from the pull relationship computers have long maintained wi...

This post was also published in VentureBeat. When Salesforce’s Marc Benioff first started pitching on-demand CRM software, people thought he was insane and were convinced software-as-a-service would never work. Although we are now living in a SaaS heaven with all of the benefits of software that is always available and up-to-date, we are also beginning to see the SaaS hell naysayers were warning us about. When selling Salesforce to a mid to large organization, Salesforce expects multi-year contracts with pre-negotiated user counts, exactly like the on-premise predecessors it ridiculed during its early days. The whole idea of “pay for what you use” has been subsumed by the realities of the sweet cash flow dynamics of a traditional enterprise sale, which ends up as shelfware when customers over-provision. Compounding this issue is that the expense is accounted as an operating expense that affects EBITDA, a key Wall Street metric, while on-premise software was accounted as a m...

This post was also published in VentureBeat. One of the biggest problems in software is feature bloat. Even sprightly new companies that base their competitive strategy on minimalism and ease of use can eventually fall prey to the same force that bogged down their predecessors. For example, one could argue that thanks to feature bloat, Salesforce is more complicated today than Siebel was when Salesforce first promised a simple, cloud-based solution. The promise of simplicity must quickly meet the business reality of closing deals or improving productivity — and for many software vendors, the promise of simplicity gets lost as they add customers, all of whom have feature requests. Feature bloat happens because as software vendors add diverse customers, they end up adding features that are rarely used or only applicable to fringe use cases — many of the same ones that plague their incumbent competitors — ultimately slowing down the software and adding unnecessary complexity. ...

This post was also published on TechCrunch. It seems as though everyone in tech today is infatuated with the full-stack developer. Full stack may have been possible in the Web 2.0 era, but a new generation of startups is emerging, pushing the limits of virtually all areas of software. From machine intelligence to predictive push computing to data analytics to mobile/wearable and more, it’s becoming virtually impossible for a single developer to program across the modern full stack. When I first started programming computers as a kid in the pre-mobile, pre-web late 1970s/early 1980s, a single person typically wrote a complete software program from start to finish, and there weren’t many other layers of software between the programmer and the hardware. Using assembly language was the norm for programmers trying to squeeze more performance and space out of machines with 8-bit processors and very limited memory. Programming applications quickly evolved into a team sport with the ...

This post was also published on TechCrunch. When iPads first came out, they were hailed as the undoing of the PC. Finally, a cheap and reliable computing device for the average user instead of the complicated, quirky PC. After a few years of strong growth for iOS and Android tablets and a corresponding decrease in PC sales, the inverse is suddenly true: PC sales are up and tablet sales are “crashing.” What happened? The tablet slowdown shouldn’t be a surprise given that tablets have hardly improved beyond relatively superficial changes in size, screen resolution, and processor speed. The initial market for tablets is now saturated: grandparents and kids have them, people bought them as Sonos controllers and such, and numerous households have them around for reading. People that want tablets have them, and there’s just no need to upgrade because they more than adequately perform their assigned tasks. Businesses and consumers alike are again purchasing PCs , and Mac sales are on t...