1. In an agrarian economy, parents make birthday cakes by themselves. 

2. In an industrial economy, parents purchase premixed ingredients from the market.

3. In a service economy, parents order cakes as a yearly service for their kids.

4. In an experience economy, there will be “experience vendors” who could create an awesome birthday party for kids. 

Their framework is also applicable to software.

1. Agrarian economy: every business needs to write software for their use cases.

2. Industrial economy: there are software vendors dedicated to building software, which could be purchased by customers to solve their needs.

3. Service Economy: The software is delivered as a service (often as a subscription) and it would still require customers to build upon the service to create an awesome experience. This is the Software-as-a-Service (SaaS) model. 

4. Experience Economy: We are entering a new era of Software-as-a-Experience (SaaE). In the SaaE model, we use software to directly orchestrate experiences for our users. Software is not considered only a utility, but a conduit of digital experiences.

SaaE is a fundamental leap forward for the SaaS business model because it significantly reduces the friction for creating awesome user experiences. According to Joseph and James, 

an experience occurs when a company intentionally uses services as the stage, and goods as props, to engage individual customers in a way that creates a memorable event. …  No two people can have the same experience, because each experience derives from the interaction between the staged event (like a theatrical play) and the individual’s state of mind.

In the SaaS model, the software service itself is rigid. There is a predetermined way, often determined during the product build stage, to use the software. Users would continue their subscription only if the problem they face could be solved by the predetermined software flow. When this is no longer the case, customers would churn.

In the SaaE model, the software would be adaptive rather than rigid. The ultimate goal is to deliver the best experience to users by staging a sequence of software services intentionally.

To reach the goal, we need two new software layers:

1. A feedback-collection layer that could continually integrate the feedback from each customer to the product.

2. A learning layer for software behaviors to adjust the software services to deliver the best customer experience.

The two layers were not practical in the software building process before because it would take an enormous amount of effort to personalize the software flow for each user.

The advancement of AI has made personalizing unique experiences for each user possible. We have already done it — YouTube’s personalized feed creates a unique video watching experience for every user, and Amazon’s product recommendation has created a unique purchase experience for every customer. 

Those systems are currently referred to as recommendation systems. But this is just the tip of the iceberg for a large paradigm shift – the advent of the “Software-as-a-Experience” age. 

Chang was born in China in 1931. Most of his early life was deeply shaped by war -- he was forced to flee three times due to the Second Sino-Japanese war and China civil war. After the wars, he came to the United States at the age of 17 to study at Harvard University, a school that primarily focused on arts and humanities education. As an underrepresented minority ethnic group, most Chinese Americans at the time worked in low-end restaurants or laundry businesses. Academic jobs were the only few alternative options that would fit with Chang's mission of achieving big societal impacts. He later transferred to MIT in the hope of becoming a scholar in engineering majors. Unfortunately, he failed the qualifying exams twice at MIT and had to quit his academic path and went to the job market after obtaining his master's degree. 

He entered the Semiconductor industry in his first job at Sylvania, an industry leader then. However, his team was dismantled three years later. He then moved to Texas Instrument, which was famous for the invention of integrated circuits (IC) and was fast-growing. After working at Texas Instrument for three years, he went back to Stanford to pursue a Ph.D. degree and then returned to Texas Instrument to continue his corporate duty. By the 1970s, Chang was already the general manager of the whole semiconductor business of Texas Instrument. 

Chang was a keen observer of the semiconductor industry. While working at Texas Instrument, he observed that a lot of brilliant people in the company were hoping to create new businesses but heavy investment requirements prevented them from getting started. As chips became more and more sophisticated, the chip manufacturing business became super capital-intensive. The cost of creating a chip manufacturing line (also known as "foundry" or "fab") could easily be over 3-4 billion US dollars. Besides, new startups cannot maintain a sustainable stream of needs to keep their manufacturing line busy all the time, which is the only way to justify the heavy investments.

In contrast, chip designing requires much less capital. It would be a win-win situation if there is a "pure-play" company that focuses on manufacturing so that startups could focus on designing. This model of chip making process, also known as Fabless manufacturing as it features the split of designing and manufacturing, is crucial for the booming of the semiconductor industry. There were a lot of chip design talents in the US, but very few were good at both chip manufacturing and cost management. Chang was one of the few talents who had the expertise.

Chang started to face career setbacks in the early 1980s. At the time, Texas Instrument shifted focus away from semiconductors and became a diversified device manufacturer. Chang disagreed with the shift and had to leave the company. After the career setback, Change decided to turn his observation into action. At the same time, Taiwan government was eager to find ways to break into high-end industries like chip manufacturing. Chang was the perfect person to lead the cause. After a short stint at another company, Chang accepted the invitation from the Taiwan government to be the first chairman of the Industrial Technology Research Institute, an institute that played a critical role in the industrial transformation of the island. With the support of the Taiwan government, Chang founded the TSMC one year later. TSMC created a whole new industry of "pure-play" chip manufacturing (a.k.a., foundry industry). By focusing on only manufacturing but not designing, TSMC assured its partners, typically US chip designing firms, that TSMC won't compete with them or share their trade secrets with their competitors. 

Now TSMC is undoubtedly the market leader in the industry and occupies 28% of the market share in a recent study. Also thanks to TSMC, Taiwan became crucial for the global semiconductor supply chain, which Bloomberg has recently published an article to illustrate.

Global semiconductor market share (image via Counterpoints Research)

In one of his recent talks, Chang gave a summary of what he thinks is the key reason for the TMSC's success. Chang attributed the success to three factors. 

The number one is Taiwanese people’s hard-working spirit. For example, during his second stint at TMSC, Chang started the "nightingale program", which included both day and night shifts to ensure there were R&D activities 24/7. This program would be unimaginable in U.S. companies. According to Chang, this nightingale program was the key reason why the TSMC could eclipse all of its competitors in technology. In chip manufacturing, the size of the device the manufacturing process could produce is a key indicator of technology level -- smaller size means more devices in the same area but also is much harder to manufacture. After losing in the competition of the 14nm manufacturing process, TSMC reached the 10nm, 7nm, and 5nm manufacturing processes one by one in just a few years. Till now, none of its competitors have reached the 10nm milestone yet.  Please refer to RISC-V, China, Nightingales for more details.

Figure. The timeline of manufacturing process in major foundry companies.

The second factor is the local professional management. This factor is crucial because chip manufacturing is operation-heavy and efficiency-driven. Chang also mentioned that managerial talent doesn't transfer well across borders because of culture and factors.

The third factor is the good infrastructure provided by the Taiwan government. It is easy to see how good infrastructure makes the transportation of goods much easier. Chang also mentioned an important point. The good high-speed railway and the small Island of Taiwan make it possible for talents to be relocated to any place within the island without the need to be separated from their families. The benefits of good infrastructure on the human management side are often ignored by governments but are crucial for businesses that require a lot of talent.

All the three points are about one thing: TSMC can attract a huge amount of disciplined and high-quality talent and can retain them through good management and providing a convenient life. The company has an envious 3-4% employee turnover rate. For those who leave for various reasons, they became the most sought-after talents in the industry. I also highly recommend this great essay by Kevin Xu based on Chang's talk.

Throughout his life, Chang overcame one challenge by another and successfully turned setbacks into new opportunities. Although he became a refugee three times in his youth, he immigrated to the US to pursue a new life. After failing in MIT Ph.D. qualifying exams and crumbling his academic pursuit, Chang entered the newborn semiconductor industry and worked tirelessly to become an expert in semiconductor manufacturing. After facing a career setback in Texas Instrument, he took the courage to leave the US, a place he had spent 36 years, to Taiwan for creating TSMC and became the godfather of the semiconductor industry of the Island. 

Chang is also a great writer. I highly recommend his Chinese autobiography that covered his early life before 33 years old (unfortunately I haven't found any translated version yet). Besides, he is working on the second half of this auto-biography, and hopefully, will publish it soon. I am very much looking forward to reading it and will share a sequel in the future.

This article shares some stories behind this incredible AI Renaissance. The sources of the stories include my first-hand observation in the field as well as Cade Metz's recent book  Genius Makers, which I highly recommend.

A Sputnik Moment

A key reason for the current revolution is the re-invention of deep learning, a technology that simulates human brains as complex network architectures through computers.

The idea is not new. Scientists have been searching for the truth of human intelligence for a long time, and a natural starting point is our brain -- the only intelligent machinery built by our mother nature. Artificial neural networks, predecessors of deep learning, were very popular between the 1950s and 1980s but lost their popularity because data was not enough and the computers then were too weak to solve any interesting problems.

It would take another two decades before its revival. In the late 2000s, a group of young scientists started to connect the power of the booming Internet with artificial intelligence research. In 2009, an assistance professor of Princeton Dr. Fei-fei Li1 compiled a vast database of Internet images (a.k.a. ImageNet dataset). The ImageNet dataset soon became the benchmark for Computer Vision, a subfield of artificial intelligence. In 2012, Geoffroy Hinton and his team significantly improved the metrics by more than ten percent, a jaw-dropping achievement that was a magnitude higher than any previous improvement.

This was a "Sputnik moment" for the artificial intelligence research community, at the time the mainstream research direction was for scientists to figure out the solution and to program the software based on the solution. Hinton's success in the ImageNet challenge showed that an alternative approach -- letting the neural networks learn a solution without prescription from humans -- would work better. 

Godfather is Heading to Industry

Huge amounts of data and computer resources were essential to this success. No one knew this better than Geoffroy Hinton himself, who is also known as the Godfather of deep learning. Hinton was one of the early persons that popularized back-propagation, a fundamental algorithm used to train neural networks. When the field entered into a winter between the 1990s and early 2000s, most researchers switched to other research directions due to scarce funding sources. However, Hinton was still a stubborn proponent of the idea and was trying to revive it.

Hinton knew that his research would need resources from elsewhere, and only the big Internet companies had the pocket deep enough and data big enough to make the idea work. In addition to his academic achievement, Hinton also had great business savvy. With his two students, Hinton founded the DNNResearch company in 2012 and soon decided to sell it to big Internet companies. The book Genius Makers gave a vivid description of how Hinton orchestrated the auction in a Lake Tahoe hotel and how tech companies all over the world wooed him. DNNResearch was eventually acquired by Google for 44 million US dollars.

More important than the price tag is the precedence that Hinton created. To lure Hinton, Google allowed him to keep his position on both sides — but he had to be an "intern" in Google to work around the company's rules. Before Hinton, it was rare for eminent researchers to work for tech companies because of the fear of losing their tenured positions in universities. Soon after the purchase, a lot of AI researchers followed Hinton's example to join technology companies, including Yann LeCun,  another deep learning pioneer who later led Facebook's AI lab, Andrew Ng, who led the research lab in Baidu. What's more, following their advisors, students from various research labs flocked into big technology companies.

Among the technology companies, Google (and its parent company Alphabet) stood out for its unparalleled role in this wave of AI Renaissance. Its research divisions -- Google Brain and DeepMind -- are the driving force behind a lot of the greatest breakthroughs. What's more, the fact that it could use AI to create so many profitable applications has a demonstration effect on all other companies.

One important person behind this is Jeff Dean, a legendary engineer that laid the foundation of Google's infrastructure. In 2011, Andrew Ng introduced the deep learning concept to Jeff and he was intrigued immediately.  Jeff was looking for his next application and deep learning was a perfect one.  Andrew, Jeff, and another researcher Greg Corrado founded the Google Brain team. As a founding engineer of Google, Jeff has a great influence on Google's management team and also enjoys enormous popularity within its engineering and research organizations (so much so that people made fun of him by creating the "Jeff Dean facts"). Jeff created an umbrella where the Google Brain team could operate without worrying about anything else.

In Google Brain, Andrew Ng and his colleagues helped create the system that could learn the "cat" concept from millions of YouTube videos, which drew a lot of media attention and publicized the field.  Andrew Ng eventually left the Brain team to work on his own startup2. But he recommended Geoffroy Hinton as his replacement, which triggered the DNNResearch acquisition. Under the leadership of Jeff and Hinton, Google Brain significantly contributed to the field by both pushing the research frontiers and publishing the TensorFlow framework that makes the technology accessible to outside communities.

New World Champion

One limitation of the techniques Hinton was trying (a.k.a. supervised deep learning) was that it requires datasets labeled by humans. Demis Hassabis co-founded DeepMind to address this limitation and explore other applications. He wanted to build a system that doesn't depend on human supervision and could perform better than humans. A child prodigy in Chess, Hassabis believes that games are the best starting points. Although games had been a proving ground for AI since the 50s, no one has been more committed and successful than Hassabis in this direction.

Hassabis and his DeepMind team combined deep learning with reinforcement learning, a technology that allows computers to adapt their behaviors through trial and error (the same way we humans learn). With this new technology, DeepMind built a system that could learn the nuances that were never found by humans before in popular video games like Breakout and published their results in Nature. This publication drew the attention of  Google's management. In 2014, Google purchased DeepMind for more than $500M -- this time both Hinton and Jeff are on the buyer side. With the resources from Google, DeepMind doubled down on its mission. In May 2017, DeepMind's AlphaGo AI beat the world champion Ke Jie. Since then, it has kept beating humans in one field after another.  In addition to DeepMind, Google's other AI division also released the BERT system that significantly improved performance in natural language tasks.

There were also a lot of breakthroughs outside Google and DeepMind. For example, OpenAI, which was co-founded by some of Hinton's students and Silicon Valley elites like Elon Musk and YC CEO Sam Altman, tackled many other games and robotics applications through reinforcement learning and they released the language models that achieved amazing results. The successes of Google/DeepMind/OpenAI and other AI research teams have brought the public interest in AI to an unprecedented level.

What's ahead?

A keen observer would find that the current AI Renaissance consists of many small cycles. Each cycle starts when a difficult yet well-defined benchmark problem is solved. Thanks to the huge public attention, the research team that solved the problem would be able to get a huge amount of resources to continue their research. The team then tackles the next more challenging benchmark problem with a larger model.  The cycles were started by academics and their students and were reinforced by big technology companies. People knew, either consciously or unconsciously, that it was the best way of attracting attention, funding, and talents.

Notwithstanding, ImageNet and Go games are still not real-world problems. In addition, there have been increasing concerns that this type of AI research pattern has caused enormous resource consumption and has made the AI models to be overly complex. For example, the GPT-3 language model related by OpenAI includes 175 billion parameters and each train takes around 4.6 million dollars.  In addition, many AIs that overfit man-made tasks turn out to perform poorly in real-world applications.

We should and would break such cycles. Building cost-effective AI and making it really work in real-world applications is crucial to keep the movement going. In the next decade, there will be a lot more exciting stories ahead of us.

Disclaim: All opinions are mine and not endorsed by my current or previous employers.

Thanks a lot for all of your feedback on the past issues and I highly appreciate that! We are exploring a new format in which each issue focuses on original and in-depth discussions on one topic and will contain references and links for follow-up reading.

Besides, the focus of this newsletter is always quality rather than quantity, and I hope every issue will be useful for you. As a result, I will decrease the email frequency to bi-monthly to give myself more time to research topics and to prepare high-quality essays (I will add one-off issues if bandwidth allows).

Please see below the first issue of the new format. Thanks again for your readership and please feel free to let me know if you have any suggestions or feedback. :-)

Best,

Conan

In Search of Memory

Eric Kandel won the Nobel prize in 2000 for his contribution to the understanding of memory at the molecular level. His autobiography, In Search of Memory, describes both his experience of escaping from Austria to America and his inquiry into the science behind our memory throughout his career. This essay is created based on the autobiography and extensive researches on the web.

Escape from Holocaust

Eric was born in a Jewish family in Vienna. As a Jew, Eric's childhood was dominated by the Nazi's growing influence in Austria. From its very beginning of the 1920s, the Nazi party aimed to merge all German-speaking people into a Greater Germany. In 1937, Hilter forced the Austrian chancellor Schuschnigg to resign and sent his troops to occupy the country, which was the largest German-speaking state outside Germany. The event, known as Anschluss, was welcomed by the Austrian Germans because a lot of them felt Austria was not fairly treated in the Treaty of Saint-Germain signed after WWI. After the Nazis took the power in Austria, a lot of Austrian Jews were forced to leave the country due to the violence targeting them (e.g., Kristallnacht). Thanks to the help of a local Jewish organization Kultusgemeinde, Eric's family was able to emigrate to the United States of America in 1939. The young Eric was only Nine by then.

Most of the Jews who didn't escape Nazi Austria became the victims of the Holocaust. The experience of escaping the Holocaust greatly influenced Eric throughout his whole life. Later after Eric won the Nobel prize in 2000, he used his influence to press the Austrian government to recognize the misfortune of the Jew community during Anschluss, which was largely ignored post-WWII, and to advocate the rights of Jews community in the country.

America

After emigrating to the US, Eric finished his education first in a Jewish school and then attended Harvard College. There Eric was attracted to psychoanalysis because it was imaginative, comprehensive, and empirically grounded. His attraction to psychoanalysis was further enhanced by the fact that its founder Freud was Viennese and Jewish and had been forced to leave Vienna. He later enrolled in New York University and aspired to become a psychoanalyst.  In the fall of 1955, Eric decided to take an elective at Columbia University with the neurophysiologist Harry Grundfest. Since then, Eric's research career gradually shifted to find the biological basis of mental function.

Eric is particularly interested in the formation of memory. In 1890, William James concluded that memory must have at least two different processes: a short-term process and a long-term process. The basic units of the brain are the neurons, which are connected through synapses. Signals of one neuron are passed to the next neuron through chemical neurotransmitters that are available in synapse. One common hypothesis is that short-term memory is stored as the distribution of neurotransmitters across different synapses. A stimulus would activate a spatial pattern of activity across neurons in a brain region,  which will deplete the neurotransmitters. The distribution of neurotransmitters will form a trace of the stimuli, which is the short-term memory.

The short-term memory trace decays over time as neurotransmitters are re-generated. As a result, short-term memories need to be consolidated to long-term storage. Behavioral experiments suggest it happens through repetition --- what is well known as "Practice makes perfect".

Scientists also realized the importance of the hippocampus in turning short-term memory into long-term memory thanks to the extensive research on Henry Molaison (H.M.), who is probably the most famous patient in the history of memory research. After a treatment operation in which his hippocampus was removed, H.M.'s intelligence was intact, yet he lost the ability to form new memories. Other than this vague picture, Scientists had very little knowledge of the exact biochemical process of memory. It was under such a background that Eric entered the domain of memory research.

Aplysia

The first question Eric needs to answer is how neurons could adjust their connections based on environmental stimuli. Unfortunately, Human brains are too complex for any thorough analysis, each human brain has about 100 billion neurons. As a result, Eric experimented on Aplysia instead, whose brain has only about 20,000 cells, making it a perfect model animal to analyze how neurons work. In 1962,  Eric joined the lab of a French scientist Ladislav Tauc, one of the few scientists who worked on Aplysia then, as a post-doc to learn about this interesting sea slug. Eric's work on Aplysia has laid the foundation for understanding the mechanism of memory -- so much so that Eric presented a picture of Aplysia wearing a Nobel medal during his Nobel prize ceremony,

In Search of Memory

Eric and his team realized that long-term memories are formed as anatomical changes of the neurons. A single neuron has approximately 1300 presynaptic terminals (only 40% of which are active) with which it contacts about 25 different target cells. Through the consolidation process, the creates long-term memory, both the percentage of active presynaptic terminals and their total number. The number of synapses changes during learning. Memory is recalled when a certain sensory stimulus triggers the "reads out" of the new state of the synapse, which has been altered by learning.

In 1953, Waston and Crick proposed the famous Double Helix model of DNA, which opened the new world of molecular biology.  In the memory-research field, Louis Flexner from the University of Pennsylvania discovered that applying a drug that inhibits the synthesis of proteins would disrupt long-term memory. Eric realized that the same process also applies to Aplysia and that long-term memory storage requires the synthesis of new proteins.

One revolutionary breakthrough in molecular biology was the realization that gene function can be regulated up and down in response to environmental signals. Inspired by this breakthrough, Eric continued to investigate genes' role in learning and memory formation.  Through researching Aplysia, Eric and his team realized that long-term memory is formed through switching on and off certain genes that increase or inhibit the growth of certain synapses.

For decades, Kandel has been studying how we create short-term and long-term memories at the molecular level. His work helps reveal the full picture of the memory-forming mechanism:

1. The memory storage takes place in at least two stages: A short-term memory lasting minutes is converted -- by a process of consolidation that requires the synthesis of new protein -- into stable, long-term memory lasting days, weeks, or even longer. 

2. A single stimulus strengthens the synapse through the depletion of neurotransmitters, which form the short-term memory.

3. Repeated stimulation causes certain genes to be switched on and the growth of new synapses, which creates long-term memory.

Eric's journey from a refugee from Austria to a Nobel Laureate is a great example of how the tolerant and open environment of America could release boundless energy from immigrants like Eric and inspire them to think in new ways. In contrast, the city of Vienna, once a center of art and science, lost its glory under the suppressive occupation by the Nazis. His experience is still important for us after a hundred years.

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Happy weekend! This week I recommend The Box: How the Shipping Container Made the World Smaller and the World Economy Bigger. It is a great book about how containerships affect the world.

I knew this book by Bill Gates's book review. One interesting point of Bill Gates is that containerization provided a common standard for shipping. He is the right person to call that out --- Microsoft drove a similar movement in the software Industry. 

I also wrote a book review myself after finishing the first pass. The focus of my article was more on globalization and government regulations.

Another interesting thing I learned this week is that the Shanghai Containerized Freight Index has significantly increased in 2020. Because the COVID-19 pandemic has completely disrupted global trade, there are not enough containers in China port, which drove up the index.

The author of The Box Marc Levinson also did an interview about this topic recently: Why the Cost of Shipping Goods From China Is Suddenly Soaring. In this interview, Marc talks about where all this transport disruption is coming from, what it means for global trade, and whether it will lead to a big rethink of the shipping industry.

One new thing Marc mentioned in this interview but not in his book is the downside of containerization. Containerships have made the long-distance supply chain reliable and made it possible for American companies to leverage the supply chain in east Asia (One example is Nike that I mentioned in Newsletter No. 16). At the same time, containerships became super big and inflexible in the past few decades. Each containership is typically built for a specific route and could dock only in certain ports due to its big size. The containerized shipping industry has been optimized for a predictable demand. However, global trade demand is highly unpredictable in 2020, so the whole industry struggles to adapt to it.

Another impact of the COVID-19 pandemic is on people’s life. For many people (myself included), life has become mundane since they are constrained at home and it feels like nothing memorable has happened in the past 2020. How to expand subjective time during the lockdown (~30 mins) is a great article about expanding "subjective" time during the lockdown. Hope the framework in this article will be useful to you!

Best,

Conan

Happy weekend! This week I will share an article written by myself. Hope it is useful for you. Your like/share or any feedback would be greatly appreciated.

What modern business leaders could learn from Genghis Khan?

Genghis Khan created the Mongol Empire, the biggest empire in human history. At its height, the Mongol Empire covered a land area of more than 9.15 million square miles and a population of more than 100 million. Another surprising fact is that the population of Mongols was only a few million.

Why were Mongols able to conquer the world with such a tiny population? One important reason is that Genghis Khan created a specialized organization that could leverage the most advanced technology at the time (Mongolian horses) to solve the most ambitious problem (conquering the world). 

Despite the small number of human soldiers, there were a huge number of horses in the Mongol army. Each Mongol soldier has 3-4 Mongolian horses at his disposal at any time. Mongolian horses had very great endurance and were the most advanced military technologies during the cold-weapon era. In contrast, their enemies either didn’t have any horse or could only use inferior horses. 

More importantly, Genghis Khan organized his Mongol soldiers in a way that could leverage the advantages of those Mongolian horses to the full extent. The command structure of the Mongol army was much more flexible than other armies during the period. Lower-level leaders have significant license to execute orders in the way they considered best. The super flexible organization allowed Mogol armies to attack en masse, divide into smaller groups to encircle and lead enemies into an ambush, or divide into small groups to mop up a fleeing and broken army. Because they could fully leverage the mobility of horses, a few Mongolian cavalry soldiers could easily defeat hundreds of foot soldiers.

Thanks to horses, the Mongolian army could cover up to 100 miles (160 km) per day, which was unheard of by other armies of the time. Mongolian soldiers were able to travel thousands of miles without stopping by rotating horses during the trip. Because of such great mobility, the Mongol empire could allocate resources on a global scale to defeat every local enemy. For example, the Mongols were able to fight with both the Muslim world and China at the same time. After Mongols conquered Muslims, they were able to leverage the technology they got from Muslims (like the counterweight trebuchet) to destroy the Song dynasty.

Genghis Khan and his Mongolian armies have taught us two things:

1. New technology requires a new form of human organization to fully leverage its power. 

2. An organization that could leverage the new power would be able to unlock even more new opportunities.

In the past few decades, we are creating new technologies to extend our brains. One notable new technology is artificial intelligence (AI), which allows machines to make predictions and decisions autonomously.  The relationship between the new AI tools and humans is similar to horses and Mongolian soldiers.

A business would need to transform its organizational structure to fully leverage the power of AI tools.

1. For a lot of traditional businesses, the bottom of the organizational chart is a huge number of employees who work on operational tasks. As a result, management is based on carrots and sticks.

2. In AI-first organizations, even junior employees will have hundreds of AI tools at his/her disposal and their influence on the organization is equivalent to a much higher-level person in those traditional organizations. Organizational management needs to be more motivation-driven throughout the organization.  The organization also (is able to ) and needs to be leaner and flatter, which encourages innovation.

   

Proactively leveraging AI tools not only reduces cost but also unleashes new powers (like horses do to Genghis Khan's troops).  

1. The natural way of organizational growth is by hiring humans. However, more people would create a communication burden and operational overhead. As an organization grows, the Return-On-Investment (ROI) of extra hiring will eventually decrease to be below 1, which prevents the company to scale further. 

2. "Hiring" AI systems, in contrast, would not incur extra overhead. What's more, AI systems typically get smarter as more people use them. As a result, the ROI will increase as the usage of the AI system increases.

The only ceiling floor for the scaling of an AI system is from the technical side. Currently, most of the commercial-viable AI system is only designed for a single problem. And for most of the problems, AI systems haven't reached human-level capability yet. This will be a bottleneck in the foreseeable future but more and more AI systems will be invented as time goes by. Human + AI collaboration would be a strong disruptive power for industries in which AI solutions are availiable. 

Hiring more people doesn't make the manager's job redundant. Instead, it makes their jobs more important. Similarly, the adoption of AI systems doesn't make their users redundant. They will increase the scope of their users and the whole organization. Humans are tremendously flexible and could always find creative new usage of new capabilities.  For example, AI may be able to help doctors to diagnose basic medical conditions, but it won't be able to replace doctors. Instead, doctors would be able to focus on more complicated medical problems. As long as humans haven't reached immortality, there are always new problems for doctors to solve.

We don't want another Mongol empire that causes deaths, but we do need business growth that could make human life better.  In addition to scaling the human part of the organization, every business leader should also consider where their "horses" are and how to provide organizational support to enable employees to use them.

Reference

1. Mongol military tactics and organization

2. Wikipedia: Mongol military tactics and organization.

3. "The Mongol Empire’s Best Weapon: The Mongolian Horse" History on the Net © 2000-2021, Salem Media.

I hope you've had a great week! Here are some of my recommendations for this coming week:

Books

1. The Shallows is an excellent book by Nicholas Carr that discusses the impact of the Internet on our minds. Although the "Internet" is a boon for searching and accessing information, the media's interruptable nature deprives us of the ability of deep thinking, empathy, and compassion. This book also discusses the evolution of our intelligence. It is a good read for us to be more conscious in this digital world.

Productivity & Organization

1. How to Stop Endless Discussions (8 min). The article describes a process called Request For Comments (RFC). The basic idea is that there should be an owner to write a detailed document for proposal, request comments from others, and make judgment calls for every decision. The process accelerates decision-making and also brings accountability to a team environment. 

2. Write five, then synthesize: good engineering strategy is boring (15 min). It is an interesting article about creating an engineering strategy. The author mentions that durably useful engineering strategy and vision are the output of iterative, bottoms-up organizational learning. To create a good strategy, you need to follow three steps. 1) Write five design documents, 2) Synthesize those five design docs into a strategy. 3) Extrapolate five strategies into a vision.

3. The Complete Guide to Effective Reading (45 min). This is a great tutorial about active reading. It is a very great framework if you would like to improve your return on investment on your reading hours. Besides, I intentionally did not use such frameworks for some leisure readings (as fun is more important in those contexts).

Startup

1. The Mike Speiser Incubation Playbook (40 min). This is another good essay from Kevin Kwok on startup repeatability (see his other easy in No. 19). Mike Speiser has shown the repeatable ability to creating successful companies from scratch. He is the person who makes snowflake possible. His approach is very different from other VCs. Instead of investing in existing companies, Speiser stays solely focused on one thing: starting and building companies. The core of his model is to find 2-3 co-founders and be the founding investor. Often he takes on the interim CEO role himself for the first year or two. In this model, he could attract excellent technical co-founders who are generally not good at the business part. He could also offer a better package to bring new people as CEO after he steps down as interim CEO.

Interesting Facts

1. Why It Took So Long to Invent the Wheel? (9 min). Wheels feel like a primitive technology but only appeared pretty late in the human history of intention. This article gives some interesting opinions on why that's the case.

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Happy New Year! The eventful year 2020 has finally passed, and I wish everyone a prosperous and healthy 2021! Here are some articles for this week:

1. Making Uncommon Knowledge Common (20 minutes). Rich Barton has founded three $1B+ consumer companies (Expedia, Zillow, and Glassdoor). The key to Barton's repeated successes is owning demand, whose importance has been popularized by Ben Thompson's many essays (e.g., Demand Aggregation Theory). This blog summarizes the 4-stage playbook that Barton used:

   1. Stage 1: Kick-off Data Content Loops of 1) Create high-quality data source → 2) Attract consumers to use the data → 3) Turn consumers to contributors to enrich data → back to 1). For example, Zillow makes the house data that was only available to real estate brokers to be accessible to anyone. The high-quality data attracts many potential buyers/sellers to Zillow, and they eventually become data contributors. 

   2. Stage 2: Search Engine Optimization (SEO). All of Rich Barton's companies have primarily used Search (and word of mouth) as their acquisition channel. Barton makes his websites huge SEO land grabs by creating high-quality content from authoritative sources and user-generated data.

   3. Stage 3: Becoming a Trusted Brand.  Becoming a trusted brand builds its own network effects. Consistently building this reputation increases people's trust in them and makes them a go-to destination.

   4. Stage 4: Saturation and Sequencing. When the demand for Data Content loops saturates, you need to weigh in other scalable demand generation loops to carry the torch forward.

2. From sales-led to product-led and how it changed everything (20 minutes). Ashton Rankin from Vidyard shared the company's journey from sales-led growth to product-led growth.  

   1. To make the switch, they first need to answer the following two questions.: 1) freemium or trial? 2) limit features or usage in the freemium offer? 

   2. Their challenges: 

      1. They faced pushback from the sales team because they already had some notable success selling to larger enterprises with the sales-driven model. 

      2. Besides, they have to redesign products and transform engineering team structure to focus more on self-serve features.

   3. Their discoveries:

      1. Large enterprise organizations want to try products before making their decision.

      2. A product that allows users to learn how to use them on their own is a requirement for product-led growth.

3. How Visa makes money (16 minutes) is a great article that describes Visa’s revenue sources, which could be summarized as: 

   1. A) Service fees. Visa charges a percentage of the transaction amount as service fees. In 2018, the service fee was, on average, about 0.11% of every transaction ($8.9 billion service fees on $8.1 trillion of network spend).

   2. B) Data processing fees. It is a fixed/flat fee per transaction for settling transactions and transferring funds between banks. In 2018, the data processing fee was $0.07 per transaction ($9 billion data processing fees on 124 billion transactions)

   3. C) Cross-border fees. Visa charges additional fees for cross-border transactions. In 2018, Visa's cross-border revenue was $7.2 billion. 

   4. Visa’s business model has the following characteristics:

      1.  High fixed costs create a high entry barrier for later comers, so Visa could grow with very little investment.

      2. Because the incremental cost of processing a transaction is virtually zero, the operating margin keeps increasing as the company processes more transactions (the margin was 65% in 2018). 

      3. The revenue grows as long as the economy grows, making it a de-facto tax.

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The pandemic-incurred e-commerce boom had vastly different impacts on the existing market places: 

1. Amazon holds its place in the last year, 

2. Walmart/Etsy are big winners, and

3. Google Shopping/Target/Wish/eBay was caught unprepared to react.

Amazon

There are several interesting trends about Amazon.

1. Trend 1: Third-party merchants grow faster. Now third-party merchants contribute to 62% of Amazon's worldwide GMV, up from 60% in 2019 and 58% in 2018.

2. Trend 2: Fulfillment operations become a bottleneck. Amazon's fulfillment struggles caused a surge of negative reviews during May due to the COVID order influx. Merchants reacted by taking more fulfillments on their own from late February to early August.

3. Trend 3: Top sellers represent a shrinking percent, and new sellers are bringing incremental growth. More of the sales come from a broader set of sellers than the top sellers outpacing the rest. In addition, new sellers could find incremental opportunities to grow, which means the Amazon marketplace is not saturated yet.

4. Trend 4: More native brands. There are three generations of Amazon sellers: 1) resellers who buy products and sell them on Amazon, 2) private label brands, 3) sellers who build Amazon-native brands. This year there are more and more success stories of Amazon-native brands. For example, Anker went public this year with an 11 billion market cap.

5. Trend 5: More sellers from China.  The share of China-based top sellers has increased from 11% to 42% since May 2015.

6. Trend 6: Big bands like Nike are leaving Amazon and focusing on their own channel. Nike has longspun issues with Amazon, and it realized that it could instead build a channel on its own thanks to the COVID boost to e-commerce. The direct e-commerce jumped to 30% of Nike's sales in the last year, a mark it had previously expected to hit only in 2023. Nike is not the only big brand that chose to double down its own channel. It is also the reason I am super excited about using AI to optimize websites, which become critical for those brands (BTW, I am working at a great startup called Intellimize in this space, and we are hiring).

Winners

Both Walmart and Etsy are big winners in the e-commerce boost in the last year. 

1. Walmart's marketplace GMV more than doubled in 2020. In the third quarter, marketplace sales grew "triple-digits" (over 100%) while overall e-commerce sales were up 79%. It also announced a partnership with Shopify to add Shopify stores to the Walmart marketplace. 

2. Etsy has used surging demand for face masks to accelerate growth for all handmade and vintage goods.

Losers 

1. Target is still very hesitant in embracing the online marketplace. Target's marketplace (Target+) is invite-only, meaning merchants and brands on it are selected by the company instead of more open marketplaces of other retailers. Target continued to put Target stores as the center of both online and offline user experience.  

2. Although eBay was benefited from the surge in online shopping in 2020, it is unlikely to retain the momentum because its operations have been slim down under activist investors' push in the past years.

3. Wish heavily relied on China sellers and suffered from the COVID outbreak in China early in the year and a global logistic freeze later. The average "time-to-door" was 62 days in the second quarter of 2020 for US customers. This slow delivery time is a significant roadblock for the growth of Wish.

4. Google shopping should focus on making itself more attractive to shoppers instead of focusing on the seller side. To attract more sellers, Google Shopping reduced commission fees to zero in July. However, the report thinks the most valuable asset is shoppers, not the supply of products. According to Ben Thompson of Stratechery, In a world of abundance, being able to aggregate demand is more valuable than being able to create supply. It is shoppers who determine the demand for e-commerce.

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Tech Trend & Entrepreneurship

1. The "$20M to $500M" Question: Adding Top Down Sales. It is an interesting essay from the a16z team about sales models in enterprise startups. One challenge for enterprise startups is long sell-cycles, and many (e.g., Zoom and Slack) have adopted a bottom-up "growth + sales" model. However, the essay argues that a bottom-up strategy could be a bottleneck for startups when they reach a particular stage. The authors advise that enterprise startups should consider switching to top-down sales when they get $20M to $30M ARR.

2. What comes after smartphones?  It is an excellent article from Benedict Evans to predict the next big tech wave after smartphones. Benedict presents three different mental models to understand industry trends and make predictions based on each mental model.

   1. Mental model 1 is the evolution of the devices (i.e., from the mainframe, PC, web, and then the smartphone). Following this logic, VR/AR could be a logical candidate for the next big thing. Of course, it is just one of the possibilities. Benedict himself is very doubtful about VR and wrote another in-depth essay about The VR winter.

   2. Mental model 2 is the evolution of the software paradigm (i.e., from databases, client/server, open-source, and then cloud). Based on this logic flow. Machine-learning would be the next big software paradigm, and there have been many pieces of evidence for that. Benedict also mentions that only around a quarter of large enterprise workflows have moved to the cloud. As a result, there is still a lot of business that needs to move to the cloud.

   3. Mental model 3 is about the derivative industry that smartphones could enable. Benedict mentions an interesting analogy between the Smartphone industry and the Car industry. The second fifty years after the car invention is about the new applications caused by people's increased mobility, such as McDonald's and Walmart's development, suburbs. Similar things may happen for the smartphone industry as well.

Product Development

1. The CTO Journey at a Small Startup - The Zapier Engineering Blog. In this great article, Zapier's CTO shares about how he scaled himself as Zapier grew.

2. This tweet is a good summary of the hidden cost of an unuseful feature, which is much larger than the initial development cost.

Interesting Stuff

1. Hugo is a fantasy movie about how a boy helped an early film pioneer to find the soul of himself. This award-winning movie satisfies every imagination I have about the early 20th century and is based on a real historical person Georges Méliès, a French illusionist, actor, and film director who led many technical and narrative developments in the earliest days of cinema. One of his most famous movies is A trip to the Moon (1902), and the scene in which the spaceship hits the Moon's eye would become one of the most iconic images in cinematic history.

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Book of the Week: Shoe Dog

I typically recommend a book only if it's worth reading multiple times. Shoe Dog by Nike co-founder is one of such books. I finished reading this book a few months back when my manager recommended it and completed another pass when I was on a road trip to Death Valley. Every read gives me fresh thoughts because Phil is a great storyteller, and Shoe Dog is not a typical memoir.

The book starts with a vivid description of Phil's world trip right before he founded Nike. In the 1960s, German products dominated the American sports shoe market. As a former track runner at college, Phil envisioned that Japanese running shoes would become significant competitors to German shoes. During his stop in Japan, he made contact with a Japanese shoemaker Onikusa. When Phil returned from his world trip with a contract to distribute Onitsuka shoes in the U.S., he started his legendary Nike journey.

In the early days of Nike, Cashflow has always been a bottleneck for its growth. Although the American venture capital was booming then, most of them were in Silicon Valley, far away from Nike's headquarter in Portland. Besides, the Shoe business is not the high-growth field V.C.s were looking for. As a result, Nike had to grow from bootstrapping and from bank loans. For the first five years, Phil had to keep a day job to earn Nike cash and work on Nike in the evenings and weekends. Nike was continuously groaned by its bankers and almost fell into bankruptcy in 1975.  

Nike rides the tide of Globalization. From Nike, you could see how Globalization (particularly Japan) have profoundly influenced the United States in the 1970s. Nike was initially just a distributor of Onikusa in the USA. Later, when it started selling shoes, it relied on loans from Nissho, a Japanese trade company.

Flying geese paradigm

The history of Nike is also a history of supply chain outsourcing from the U.S. to east Asia. Despite that it is the most famous sports shoe brand globally, Nike itself manufactures nothing and entirely relies on a global supply chain, which gave Nike the edge over Adidas. Nike's supply chain was first in Japan, then was moved to Taiwan and later to China. Nike is not alone. A lot of other American companies (such as Apple and Tesla) followed the pattern.

Japanese scholar Akamatsu's came up with a concept of Flying geese paradigm for the phenomenon that the Asian countries would catch up with the West like flying geese because the production of commoditized goods would continuously move from the more advanced countries to the less advanced ones in the region hierarchy. 

The flying geese paradigm is the reason why there are so many economic miracles in east Asian in the past few decades (Japanese economic miracle, Miracle on the Han River, Taiwan Miracle)

Then why Asia? Some crucial reasons are the region's social and cultural characteristics: hard-working ethic, Collectivism, and low labor cost (initially), which are somewhat very different and complementary with the Western culture. You could get more context for the difference in this excellent documentary American Factory.

However, the social and cultural characteristics that help the countries catch up with the West are a double-edged sword. Although the flying geese paradigm created economic miracles in those countries, it also makes them prone to the "technology snapshot trap,"--- a phenomenon in which a society develops involutely in a "snapshot" of the outdated technology because it fails to learn from outside or generate innovation innately continuously.

For example, Japan developed an advanced automobile and Electronics industry in the 1970s but failed to lead the personal computer revolution. Korea and Taiwan picked up the semiconductor industry but missed the Internet. Recently, "Involution" also became a hot topic in China social media. More and more people complain that society starts to stagnate and more and people have to face more fierce competition on limited resources.

In east Asia, the working population suffers from severe over-working (e.g., 996, Karoshi) in the catchup process. Over-work culture will prevent people from learning new things and reduce fertility rates, which will drive up labor costs and reduce the competitive advantage of society in the long run. The obedient culture in the region also reduces the diversity of ideas and disruptive innovation within itself.

Companies like Nike combine both ends' advantages by leveraging the West's marketing and sales creativity and delivering high-quality yet cheap products using the Asia supply chain. However, this fundamentally drives the tension on both ends and is the inherent reason for a couple of trade wars. 

We are facing a dilemma for Globalization. American people complain about the loss of manufacturing jobs, and Asian countries complain that the West captures most profits. We are at the crossroad of deglobalization, and the pandemic adds fuel to the process. A healthy society needs to strive for the right balance between the two cultures. Both ends should learn more from each other and take the opportunity to transform their culture and industry structures.

One hot topic recently is that Apple released its new ARM chips -- M1. It is not the first time Apple designs chips -- Apple has successfully designed chips for its iPhone and IPads. It is also not the first time Apple uses non-Intel chips in its Mac products -- Mac had Intel cores only since 2006.

Then why is it important? In short, this is a declaration of war from Apple to Intel and a game-changer for Reduced Instruction Set Computer (RISC) in performance-sensitive applications.

What are Instruction Sets?

Developers use chip instruction sets to communicate with computer chips. Metaphorically chip instruction sets are similar to the alphabets of human languages.

There are only twenty-six characters in English, but more than three thousand in Chinese. Similarly, the size of chip instruction sets also varies. Reduced Instruction Set Computer (RISC) refers to building chips using a small instruction set. In contrast, Complex Instruction Set Computer (CISC) refers to the option that uses an extensive instruction set. (Please see here for more descriptions)

A little history

Early computer chips were all CISC and mostly were designed by Intel. In the 1980s, there was a movement of reducing the instruction set. The ARM technology was founded in this period, and Apple–IBM–Motorola alliance built the PowerPC chips for Macintosh computers.

On the other side of the table, the Windows-Intel alliance (a.k.a "WinTel") kept investing heavily in CISC. The rest is history; WinTel crushed Apple computers in personal computing. Apple had to switch to Intel chips in 2006. ARM survived only in a then niche market of IoT devices thanks to its energy efficiency.

Then the mobile Internet era came, thanks to Apple's iPhone release. ARM is appealing for those applications because people care about the battery life of smartphones. As a result, ARM captured 90% of the market share for mobile processors. Intel lost the mobile war because it suffered from the Innovator's Dilemma and wasn't willing to risk upsetting its existing CISC business.

Despite ARM's success in mobile phones, Intel still holds the crown for applications that require high-performance. Many people think this is due to CISC's inherent superiority in high-performance computation, and Intel is safe in those fields.

Apple declared this is wrong through the release of M1. Intel maintained CISC's advantage in the high-performance applications through massive investment, and previously there was no significant player who could compete.

Except for Apple. Some early users mention the performance of M1 could be comparable to NVIDIA's popular 1080Ti GPU. The TensorFlow team also shows new M1 chips could outperform many workstations for AI applications, which have the highest computation requirements. 

What's more, Apple has a great track record for disrupting industries. A lot of ARM manufacturers will follow Apple's path to optimize ARM for high-performance applications, and they are eager to do so, given that the mobile phone market is saturating.

Besides, NVIDIA now owns ARM. The merger gives both edges in the age of AI. The road ahead for Intel is not rosy. Would the aging Titan be able to hold its position? It's hard to say. But one thing is sure. More competition in the field is a great thing for companies in downstream areas like Cloud and AI, which could benefit from increased computation powers and reduced cost.

Note: There is an interesting podcast from A16z about Apple Silicon. 16 Minutes #46: Apple Silicon — A Long Game, Changing the Game

Note: Although it is very promising. If you are ML researchers, please still wait for a few months before you decide to upgrade to Big Sur or M1 chip. A lot of the libraries are not compatible with the new system yet (tweet)

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Business

1. The Market Curve. Mike Vernal from Sequoia points out that a great product is not sufficient for a great business -- a great market is needed too. In this essay, Mike introduces a concept called "Market Curve," a  long-tail curve for the relationship between the number of customers and revenue per customer variables for a given Market size. Mike divides companies into five categories (Enterprise, SMB, Prosumer, Commerce + Marketplaces, and Consumer Apps) and offers some great examples for each type.

2. Moving upmarket and the ascent of SMB SaaS. Adam Fisher from Bessemer Venture Partner shares his thoughts on how SMB SaaS companies could move to the left side in the Market Curve. There are two broad types of go-to-market strategies. One is the Customer-pull strategy, which relies on the growth of customers. The other is the Bottom-up strategy, which targets individual employees as entry points within an organization or targeting specific types of employees. Adam then shared ten best practices for SMB-focused SaaS vendors to move to the upmarket.

3. To own or not to own delivery? Grocers reassess the Instacart dilemma. This article discusses the dilemma the food retailers need to face in dealing with eCommerce platforms like Instacart. I have been relying on service like Instacart since the start of the pandemic. This weekend is the first time I do grocery shopping in a physical store, and it feels very strange (and inefficient) to me. I think grocery eCommerce will continue to be a thing after the pandemic ends.

Interesting Facts

1. How could plankton in the Cretaceous influence modern American politics? This fascinating tweet stream summarizes the formation of a "swoosh" of counties in red states that consistently vote for the democratic party in the past decades. In the Cretaceous, the area was the coastal shore where millions of plankton live. As the planet cools down, the oceans recede. But the dead bodies of plankton make the soil extra organic and more suitable for cotton to grow. As a result, many African-Americans whose ancestors worked in cotton plantations live nowadays in the region, and they vote for democrats. You could also read this essay for more descriptions of this interesting link between ancient history and modern politics.

Fun

1. Lee Trott on Twitter: "Going to any website in 2020:… ". A sarcastic tweet about website popups. Browsing websites nowadays definitely feel like playing Super Mario games.

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This is an overwhelming week for everyone, so I will only recommend one book -- a book about a president. Nothing more and nothing less.

The book is Destiny and Power: The American Odyssey of George Herbert Walker Bush. The 41st President's biography also gives a great view of American politics from the sixties to the end of the 20th century. Is there a better time than now to reminisce about American traditions?

Bush's road to Whitehouse was by no means rosy. He experienced much more failure than success throughout his career. He was defeated twice for his Senate bid (1960 and 1970), lost to Regan in the 1980 republican primary, and failed to get a second term. The loss of the 1992 presidential election to Bill Clinton was exceedingly hurtful for him. He called the pain "ghastly" many years after he left the Whitehouse.

No matter what happened, the 41st president held on American values and traditions to his heart. He had rivals but virtually no enemies, and he had proven himself an attractive and reliable man to those who know him. 

Nothing is more exemplar than his handling of the government transition after the agonizing defeat. In a Whitehouse tour after the election, Bush told Clinton: "I want to tell you something when I leave here; you are going to have no trouble for me. The campaign is over. It was tough, but I'm out of here, and I will do nothing to complicate your work, and I just want you to know that."  He also left a well-wish letter for Bill Clinton when he left the Whitehouse -- a beautiful symbol for American value. Even without his other outstanding achievements, this letter alone would make George H.W. Bush a revered and memorable president.

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Book of the Week

That will Never Work

This memoir from the less famous Netflix co-founder Marc Randolf is an excellent read if you are interested in good stories about startups. Startup-founding stories are often about a group of genius, with a eureka moment, creates a terrific product to change the world. Those stories are beautiful but unfortunately less useful for us -- the real world doesn't work in that romantic way. As a result, I am always interested in the stories of either invisible founding members or companies that are not home-run hit. Successful people are more hesitant to tell us the real story, and even if they do, they may have survivorship bias.

Marc Randolph fits into that type: he is the founder of Netflix and its CEO for the first year, but he later departed and was outside the limelight. This book reveals many details about Netflix's early days, including conceiving the idea and building initial prototypes. Marc also described a lot of his personal life when he was working on Netflix ideas.

The book also includes some more drastic moments, e.g., when Reed Hastings -- the more famous founder -- sidelined Marc using a PowerPoint presentation and Marc's eventual decision to depart Netflix. Many of the moments are personal and emotional, but Marc describes what happened from his perspective objectively. He is honest about his limitation and was in full respect of Reed Hasting and all his decisions. He sets his contribution to Netflix straight but gives Reed the most credit for what Netflix had achieved.

Productivity

GOTO 2012 • Scaling Yourself • Scott Hanselman. This video is a great and fun tutorial from Scott Hanselman about improving your focus and productivity. Although I already know many concepts, I still watched it end-to-end because Scott presents those concepts delightfully and exactly.

One tip I find interesting is here. "Conserving your keystrokes is important … You should never write a long email to someone, anything longer than three sentences should be in Blogs/Wiki/Product document/Knowledge database, anywhere but in your email. Email is where your keystrokes die".

Technology

1. How to price your SaaS product. This excellent article from Patrick Campbell discusses the pricing of SaaS products. Here are my takeaways from the report:

   1. There are three critical steps for reasonable pricing. 1) Understand and quantify what value you bring to your customers. 2) Understand what your ideal customer profiles are. 3) Do user research and experiment frequently.

   2. Patrick also offers ten rapid-fire bonus tips. Her some interesting ones to me:

      1. Revenue per customer is 30% higher when you use the proper currency symbol.

      2. In B2B, value propositions can swing the willingness to pay ±20%. In DTC, it's ±15%

      3. Don't discount over 20%. Large discounts get people to convert, but they don't stick around.

      4. Social proof is important. Case studies can boost willingness to pay by 10-15% in both B2B and in DTC

      5. Design helps boost the willingness to pay by 20%.

2. Measuring the engagement of an open-source software community. This study from Bessemer Venture Partners discusses the metrics that are useful to measure open-source communities' engagement. My takeaways:

   1. The authors think the North Star metric for a project is its unique monthly contributor activity. A contributor is any user that has created a Github Issue or Issue Comment, or logged a Pull Request or Commit in a given month.

   2. Out of the top 10,000 projects, only 2% have reached 250 monthly contributors in 6 or more months. One hundred contributors per month is a substantial milestone.

   3. The authors expect more and more companies will open-source their core technologies—for the mutual benefit that open source provides to both the community and the company—and focus on monetizing only a small portion of their user base.

Other Stuff

Cecilia Chiang, an S.F. legend and the matriarch of Chinese food in America, dies at 100. Chiang's incredible life goes beyond food and encapsulates the 20th-century history of Chinese culture in San Francisco. Chiang fled from China in 1949, first went to Japan, and then came to the US to found the groundbreaking Mandarin restaurant. She changed the course of Chinese restaurants in America. She introduced many dishes that become the canon for Chinese food in the United States: potstickers, hot-and-sour soup, sizzling rice soup, beggar's chicken, and the bestseller, smoked tea duck.

Chiang is also the subject of a documentary: Soul Of A Banquet (amazon prime video, Youtube trailer)

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Technology

1. Emerging Architectures for Modern Data Infrastructure. This excellent essay by the a16z team summarizes emerging best practices and draw up a common vocabulary around data infrastructure. Several takeaways from the study:

   1. Data infrastructure serves two purposes at a high level: 1) to help business leaders make better decisions through the use of data (analytic use cases) and 2) to build data intelligence into customer-facing applications, including via machine learning (operational use cases).

   2. Two parallel ecosystems have grown up around these broad use cases. The essay mentioned there hasn’t been consensus on the two ecosystems will converge eventually.

   3. My take for that is that most of the two ecosystems’ infrastructure will converge because there is no inherent boundary between the two use cases. For example, interactive data analysis is essential to build new ML features or inspect models’ quality. Building interpretable models are also super helpful to get business insights.

2. Interview of Kevin Systrom and Mike Krieger, the two co-founders of Instagram. In this insightful interview, Kevin and Mike shared some principles behind building the Instagram product and their future view. 

   1. Instagram is a phenomenal product of the mobile Internet. One interesting thing from this interview is how tools and technologies made Instagram possible. In particular, two key factors enabled Instagram. 1) The popularity of mobile phones with good cameras, and 2) the availability of open-source tools and scalable AWS cloud infrastructure. The former created the mobile phone-sharing market of billions of users, and the latter made it possible for Instagram to scale to serve a massive amount of users with a small team.

   2. Both Kevin and Mike are very excited about what is happening in the data & machine learning field. Many data infrastructures are being made accessible for the general audience, and the cost of setting up a machine learning (ML) system has reduced significantly in the past years. Open-source tools like TensorFlow and many clouds AI infrastructures affect the tech community, similar to what the AWS had influenced mobile development. As the machine learning infrastructure matures, what matters more for a company is what problem you use ML to solve and how you tailor your system to solve those problems.

3. Roadmap: Consumer Earthquakes. In this video series, BVP Partner Kent Bennett details what makes a consumer earthquake startup and the keys to a viable business model and long-term defensibility.

Leadership & Productivity

1. Establishing a Product Organization Structure | by Jens-Fabian Goetzmann. (you could switch the guest mode if you hit a paywall). In this article, the Head of Product @ 8fit Jens-Fabian Goetzmann gives many insightful tips for organizational structures that could empower people to build great products. Here are takeaways from the article:

   1. An innovative product organization’s core building blocks should be “pizza-teams,” which are small teams that are often 5-8 people and formed by people of different functional groups. Pizza teams could be either permanent or temporary depends on the nature of the problems that need to be solved.

   2. Each pizza team needs to be fully aligned with the organization’s direction. However, day-to-day decisions should be made autonomous instead of flowing through the management reporting chain, which slows down the feedback loops and often yields suboptimal results.

   3. Functional managers should engage in coaching and empowering instead of telling their reports what to do. Managers must step back to create this space while stepping in to remove impediments, clarify context, and provide guidance.

2. Creative serendipity via Zoom. Is it possible? Or is creative… | by Bret Waters. In the post-pandemic new norm, Zoom is everything. However, many people miss the serendipitous nature of face-to-face interactions, which still couldn’t be entirely replaced in Zoom yet. This excellent article from Bret includes some useful tips about increasing the serendipity in Zoom meetings. 

   1. Besides, I took Bret’s course in Standford Continuing study a few years ago. It is super helpful, and I highly recommend it.

   2. As with Zoom, this is another good article from Benedict Evans about Zoom and what will happen next. 

3. The days are long but the decades are short. This article includes some useful life advice from Sam Altman, former president of Y Combinator, and now the CEO of OpenAI.

Interesting Stuff

1. A talented guy makes live clay sculptures on TikTok like it’s nothing

2. Singapore Air sold tickets to eat airline meals on a grounded A380. Sold out in 30 minutes. Link

3. Pitney Bowes global parcel shipping index. China accounts the 62% of global parcel volume, with 2002 parcels shipped every second.

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The three challenges of modern work life. Credit: http://www.markwk.com/productive-calendar-usage.html

A good framework for managing your time is important for personal productivity. One interesting framework is the Tripartite Productivity System. The key is to handle three challenges: managing information, managing tasks, and managing time.

1. Information: We need a place to retrieve information, to process them, and to store the distilled knowledge. Generally, any document processing or note-taking software (i.e., “Filing Cabinets“) should work.

2. Task: This is to track the context of each task to allow attention to be focused on taking action instead of recalling contexts. There are plenty of tools for this. I personally use Trello as a personal Kanban system.

3. Time: We should align our day-to-day tasks to a larger picture by tracking and reviewing our time usage.

   1. Tracking: We could use any time-tracking or calendar app to track times. I generally use Google Calendar and export my events to a spreadsheet to be reviewed manually. Besides, This is a good article that describes more tips on using Calendar as a self-tracking tool.

   2. Reviewing: It is also important to do a weekly review during which you could reflect on what you have done well and what could be improved in the past week.

Remote Team

How to debug remote work, as suggested by new research. With the pandemic, a lot of startups have to operate in the full-remote mode for an extended period of time. This great article by Atlassian summarizes the ways that could improve the productivity of WFH teams. Here are several highlights from the article:

• We have an opportunity to embrace the fact that hours worked (or cold-calls made, or lines of code written) was never a meaningful performance metric in the first place. Now is an ideal moment to shift our attention away from outputs of effort and focus on outcomes achieved instead.

  • Employee motivation is much more critical in the WFH setting. You have to make sure employees have intrinsic motivations in order for them to keep staying focused and delivering results. This is also where innovation would come.

• Handle the inequality caused by working from home. Different people have different expectations of working from home. Adjust accordingly. 1) Household complexity. 2) Role complexity 3) Network quality.

  • WFH removes the isolation between work and life and causes a lot of extra challenges. Be mindful of the different situations employees need to face at home and make sure it doesn’t create systematic inequality.

Entrepreneurship

1. In this blog, the former CircleUp CEO Ryan shared his journey of creating the CircleUp and his decision to step down as CEO of the company. This is a super honest reflection with a lot of details. A good read for anyone who is curious about the startup world.

2. In this tweet, the Okta CEO Todd shared his decision framework. One interesting thing about Todd is that he wrote a pitch deck for his wife when he decided to leave a senior executive position of Salesforce to co-found Okta.

Interesting Facts

The following video is an awesome visualization of the Gartner Hype Cycle of technologies in the past 25 years. This is a great way to see how technology has evolved in the past two and a half decades

The following tweet shows the tremendous progress we have made to learn our solar system in the past three decades.

This tweet shows what a Rocket launch looks like from space. Hope that it won’t be too long before we could trip by rockets could be as normal as flights nowadays.

Book of the Week

The Great Influenza. Credits: www.gatesnotes.com

The current COVID-19 pandemic is not the first pandemic, and it won't be the last one. The book The Great Influenza by John M Barry may help you envision how the future years or even decades look like. I learned this book from Bill Gates's article in May but only finished it recently. Despite that a whole century has passed, a lot of things in this pandemic have happened in the same way as the 1918 one so the book is still very relevant.

The book is not just a chronicle, it illustrates how American society reacts to a challenge like this and provides many interesting observations. In Chapter thirty-two, the author speculates that it was influenza that caused the bad judgments of the US President Woodrow Wilson in the Paris Peace Conference and caused the even deadlier World War II. In early 1919, the representatives of all nations gathered in Paris to discuss the new world order after World War I ended in 1918 partially due to the pandemic itself. For weeks and then months, heads of the US, Britain, and France were negotiating on the terms, and the sessions often went brutal. On April 3, Woodrow Wilson suffered a health attack, which Barry thinks was influenza, and had a fever of over 103 degrees. Partially because of the deteriorated health, Wilson lost the grit and ceded the extremely harsh terms insisted by the French prime minister Clemenceau, which created chaos in Germany and caused the rise of Hitler. Wilson also agreed to Japan's insistence that it takes over German concession in China, which triggered the May Fourth Movement in China and catalyzed the spread of communism throughout the country.

It is incredible that the aftermath of the 1918 pandemic could be felt for the whole century. Had the Wilson not cede in the Paris Peace Conference, the rest of the 20th century will be quite different. Now that the current President and many white house officials have also contracted COVID-19 recently and an election is on the horizon, there is a chance that the aftermath of this pandemic may be equally long.

Personal Improvement

This week I highly recommend this interview by Spotify Cofounder and CEO Daniel Ek. In this interview, Daniel shared his lessons on managing Spotify and his personal life.

A great meeting has three key elements: the desired outcome of the meeting is clear ahead of time; the various options are clear, ideally ahead of time; and the roles of the participants are clear at the time.

One thing I find interesting is how Daniel's perspective on time management and meetings. According to Daniel, a meeting would be a waste of time if one or more of the elements are missing. The make-up meetings are the single largest source of optimization for a company.

"..learning resembles a tree: you see the trunk, you see the branches, and you see the leaves...".

Another interesting thing from the interview is his comments on learning. Learning is all about the abstraction of the world. As you keep trying things, you will figure out what's important and what's not and build an abstract model yourself.

There are many tools to help with the process. One thing I find very useful is to organize knowledge in a graph and materialize the graph in some places (i.e. build your personal wiki). Previously I have been using Google docs to manage my notes and add cross-reference among them. Recently I started to use Notion, which I highly recommend. This video from Notion is a good tutorial for building a personal wiki using Notion.

Interesting Facts

The following tweet from Joaquim shows the first snowball fight in 1896 that happened in France. This is probably the most well-dressed snowball fight I've ever seen.

This cool tweet from Simon shows the trajectory of all active satellites. One interesting thing is that you could see the trains of StarLink satellites very clearly in the animation.

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Social Dilemma

Tristan Harris, a former design ethicist at Google, as seen in “The Social Dilemma.”Credit...Netflix, via Associated Press

I highly recommend the Netflix hit The Social Dilemma and here is a good review from the New York Times. This documentary from Jeff Orlowski explores how addiction and privacy breaches are features, not bugs, of a lot of Internet products. Although the title calls out social networks, this problem exists for many other Internet products built on advertising business models, which sell users' attention to advertisers.

The main benefit of advertising is that it allows free access to information. Without the income from advertising, creators would have to hide their content behind paywalls, which makes them less accessible to lower-income people and widens the information inequality in society. However, advertising also misaligns the interests of the companies that build products and their users. Without enough counter-balance, companies will inevitably put the interests of their real customers -- advertisers -- before their users.

One important counter-balance factor is competition -- companies that put less emphasis on users will lose market share, which reduces their values to advertisers. However, when the market share is concentrated on the top players, companies will tend to optimize for short-term interests and cause problems both for their users and the whole industry.

This is not the first time we face such a situation. Newspaper, Radio, and TV Broadcasting are all built on the same premise as the Internet. History tells us that the public and the government need to play a more active role in regulating the market when it becomes mature. If you are interested in this topic, I highly recommend you read the book The Attention Merchants: The Epic Scramble to Get Inside Our Heads by Tim Wu.

One difference between the Internet and its predecessors is that it has unprecedented power to influence users thanks to the big data and advance of machine learning. TikTok is an example of this and this podcast by Eugene Wei and Sonal Chokshi covers Tiktok from algorithmic and product design perspectives. 

Netflix

Let us switch the topic to the platform that created the hit. I highly recommend you to read this book to No Rules Rules: Netflix and the Culture of Reinvention. Netflix has been famous for its innovative working environment. In the past two decades, it has done two successful transformations -- first from DVD rentals to streaming, and then to original contents. In this book, the co-founder of Netflix tries to decode the culture genes of Netflix and how he made Nextflix an innovation hub by increasing talent density and removing processes relentlessly. Here is the podcast of Reed Hasting in a16z about the book. Another interesting content is the dialog between Reed and Chris Anderson.

Reed is not the only founder of Netflix. This video is by another co-founder Marc Randolph about the early history of Netflix.

Interesting Facts

Tabby's Star. In 2016, astronomer Tabetha Boyajian announced the discovery of weird behavior coming. The star exhibited odd dips in light, which are very irregular and unpredictable. As scientists were not able to figure out the cause, some speculate it is a Dyson sphere created by an alien civilization. Although there are some new pieces of evidence suggesting this is unlikely, people couldn't stop speculating before the science community could fully understand what happens.

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