I Joined Airbnb at 52, and Here’s What I Learned About Age, Wisdom, and the Tech Industry

A growing number of people feel like an old carton of milk, with an expiration date stamped on their wrinkled foreheads. One paradox of our time is that Baby Boomers enjoy better health than ever, remain young and stay in the workplace longer, but feel less and less relevant. They worry, justifiably, that bosses or potential employers may see their age more as liability than asset. Especially in the tech industry.

And yet we workers “of a certain age” are less like a carton of milk and more like a bottle of fine wine — especially now, in the digital era. The tech sector, which has become as famous for toxic company cultures as for innovation, and as well-known for human resource headaches as for hoodie-wearing CEOs, could use a little of the mellowness and wisdom that comes with age.

I started a boutique hotel company when I was 26 and, after 24 years as CEO, sold it at the bottom of the Great Recession, not knowing what was next. That’s when Airbnb came calling. In early 2013 cofounder and CEO Brian Chesky approached me after reading my book Peak: How Great Companies Get Their Mojo from Maslow. He and his two Millennial cofounders wanted me to help turn their growing tech startup into an international giant, as their Head of Global Hospitality and Strategy. Sounded good. But I was an “old-school” hotel guy and had never used Airbnb. I didn’t even have the Uber app on my phone. I was 52 years old, I’d never worked in a tech company, I didn’t code, I was twice the age of the average Airbnb employee, and, after running my own company for well over two decades, I’d be reporting to a smart guy 21 years my junior. I was a little intimidated. But I took the job.

On my first day I heard an existential tech question in a meeting and didn’t know how to answer it: “If you shipped a feature and no one used it, did it really ship?” Bewildered, I realized I was in deep “ship,” as I didn’t even know what it meant to ship product. Brian had asked me to be his mentor, but I also felt like an intern.

I realized I’d have to figure out a way to be both.


What is it like to work in industry versus working in academia at Stanford? – by Andrew Ng

When people ask me if they should work in industry or in academia, I usually advise them to first figure out what they want to do. I.e., what is the mission that you’re on? What is the change you want to make happen in the world? When you have an answer to that, you can then figure out if a company or a university is a better place for you to execute your mission.

Here’re some things I like about working in the business world:

  • Access to significant resources to do big projects. AI research is increasingly capital intensive, requiring huge data and computational resources. These are easier to get in a company.
  • Strong sense of teamwork. When you don’t need to worry as much about authorship order or making sure this work counts toward your PhD thesis, you can better execute with a strong sense of teamwork and go after team goals and celebrate team successes.
  • Rapid decision making (depending on the company). I love working in a nimble environment where we can rapidly direct resources to where there’re needed, ranging from quickly building a new compute cluster, to buying a large amount of data, etc.
  • The ability to directly help huge numbers of people, through launching novel products and services.

Here’re some things I like about the academic environment:

  • Ability to explore almost any topic under the sun. For example, at Stanford I started recording educational videos. Initially no one considered this “real” Stanford work; but this turned into Coursera (and benefitted Stanford too). At Stanford, when my students and I like felt like building an open-source robotics platform, we could also just do it without justifying it to anyone. This led to the creation of ROS, a very successful open-source platform.
  • The freedom to spend 100% of your time learning and not have any direct output even for years. Companies like Baidu are very supportive of employee growth and often have people spend months just learning/studying; but it would be harder to have employees do this for years.
  • The ability to earn a degree. Even today, having an advanced degree is helpful. Universities and companies can both be very good at developing talent, and society is getting really good at recognizing ability, regardless of where you gained that ability.

For myself, one of the missions I’ve been excited about is creating universal access to the world’s best education, and I thought a company (Coursera) would execute best on that mission. More recently, I wanted to develop AI technologies that let us help hundreds of millions of people, and I thought a company (Baidu) would be best for that mission. But there’re plenty of other worthy missions, such as teaching students, and certain areas of investigation, that would be better to execute in a university.


A Programmer’s Introduction to Unicode

Unicode! 🅤🅝🅘🅒🅞🅓🅔‽ 🇺‌🇳‌🇮‌🇨‌🇴‌🇩‌🇪! 😄 The very name strikes fear and awe into the hearts of programmers worldwide. We all know we ought to “support Unicode” in our software (whatever that means—like using wchar_t for all the strings, right?). But Unicode can be abstruse, and diving into the thousand-page Unicode Standard plus its dozens of supplementary annexes, reports, and notescan be more than a little intimidating. I don’t blame programmers for still finding the whole thing mysterious, even 30 years after Unicode’s inception.

A few months ago, I got interested in Unicode and decided to spend some time learning more about it in detail. In this article, I’ll give an introduction to it from a programmer’s point of view.

I’m going to focus on the character set and what’s involved in working with strings and files of Unicode text. However, in this article I’m not going to talk about fonts, text layout/shaping/rendering, or localization in detail—those are separate issues, beyond my scope (and knowledge) here.


Computer Science video courses

List of Computer Science courses with video lectures.

  • Please note:
    • Focus would be to keep the list to the point so that it is readable and usable. To access syllabus/notes/assignments, please visit link to the course or use Google search with course number/name.
    • Only MOOCs with comprehensive lecture material which may be equivalent to a standard University course will be added.
    • NPTEL contains large number of good Computer Science courses. To check courses by Indian IIT’s, please refer nptel site.


Why software sucks

“No one makes bad software on purpose. No benevolent programmer has ever sat down, planning out weeks of work, with the intention of frustrating people and making them cry. Bad software, or bad anything, happens because making things is hard, making good things doubly so.

The three things that make it difficult are:

  1. Possessing the diverse skills needed not to suck.
  2. Understanding who you’re making the thing for.
  3. Orchestrating the interplay of skills, egos and constraints over the course of the time required to make the thing.

Individually these challenges are significant, but combined they create a wall of suck so high that few people can see the top, much less throw anything over to the other side…”