“Every generation laughs at the old fashions, but follows religiously the new.”― Henry David Thoreau, Walden

In my last post, we talked about why AI automation is the new industrial revolution. At its core, such revolution represents a major landscape shift that will give birth to a new breed of tech companies — what I called the AI-first species.

Just like any emergence of new species in mother nature, AI-first companies, although share some common traits with traditional software/ SaaS counterparts, carry very different characteristics. More interestingly, the new species behave, in many ways, more aggressively than the incumbents.

In this post, we will examine a few intriguing aspects of this AI-first specie: from the gross margin, product form, business moat, to ultimately the billion-dollar question — how do we adequately measure the size (value) of these AI-first companies.

Let’s start.
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1) Unlike typical software companies, the “AI-first” companies might have a non-software-like low margin (at first). And it might be a good thing.

Investors love the 80% margin business. So do traditional software companies. Over the last decades, we have seen powerful high-margin software companies — such as Salesforce, Oracle, and SAP — created and dominated the respected fields for a long time. One of the advantages of having a high-margin product is that it allows you to stack up on your well-oiled go-to-market machine continuously. In other words, it enables the incumbents to be extremely good at doing what they have been doing. And it’s a comfortable situation.

For the new species of “AI-first” companies, they wear the cover of “low margin,” at least initially, to enter different business territories. And this is not by choice. It’s the embedded nature of AI companies at its initial phase for the following two reasons:

a) In the field of AI, it’s easy to get to 80% accuracy; it takes investment and resources to get to 95%, but it’s near impossible to get to 100% because of the diminishing returns of solving the last 5% edge cases. Because of such dynamics, AI companies still employed humans on two fronts — data labeling/training and edge-case solving. And these humans are often data scientists who are not cheap. The cost of such eats away margins.

b) Contrary to popular belief, AI computing actually costs more than the traditional one, especially as more training data accumulates *and* as your algorithm is getting more complicated. Ironically, AI companies’ cloud computing costs are likely to be much higher as the business becomes more successful. And this is not even counting the often-ignored fact that AI companies, unlike traditional software ones, will need to retrain or “refresh” their data-algorithm periodically to refresh the latest reality (e.g., mapping data, latest business wording, etc.)

It’s worth noting that both constraints above are not unsolvable, especially as we make more progress on the improvement of AI-computing infrastructure. It will just take some time. Therefore, these AI-first companies will usually carry a non-attractive low margin, at least initially.

Believe it or not, this might be a good thing, which leads me to my second point.
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2) “AI-first” species’ low margin is its deceptive camouflage. Underneath, it is often a highly attractive ROI solution or service. It should get incumbents worried.

​As Clayton Christensen — author of the classic book “Innovators’ Dilemma” — would point out, disruptive new entrants usually come in the form of “non-attractive low margin” business that’s “easy to ignore.” Such dynamics happened when Minicomputer (DEC, Wang, etc.) disrupted Mainframe Computer (mostly IBM), and then quickly Personal Computer (Microsoft, Apple, etc.) made Minicomputer absolute. In both cases, the emergence of Minicomputer and PC came with audiences that were rather niche but were proliferating nonetheless.

The nature of a “niche” market made the “customer-driven” incumbents turn the heads sideways because their main customer base didn’t demand either Minicomputer or PC at the time. More importantly, the “low margin” characteristics of the new entrants essentially stripped incumbents’ incentives to be “lean” and to be innovative again**.

Now with such framework in mind. Let’s look at what’s happening now.

AI-first companies are attacking new territories and replacing older software the same way illustrated in the PC era. Yet AI-first companies are making the moves more aggressively in a pattern we have rarely seen before. So it’s incredibly tricky and dangerous for incumbent tech companies on two folds:

1) Unlike traditional software companies that usually offer one specific value (i.e., Salesforce for CRM, Box for storage, etc.), a robust AI-first company might propose itself as a full-service company with better ROI for customers. For example, we have seen AI companies “disguise” as a virtual lead-gen / sales force service agency that could potentially replace companies’ in-house business development or sales staff. Under the hood of these “service” companies, most of the operations are automated through AI. Therefore, it results in lower monthly costs to the customers, even though the initial COGS might be higher than that of AI-first companies.

On top of that, it would be easier to match sales/lead gen performance against the cost: it’s not hard for an executive to choose $100K on sales staff vs. $20K “service” fee to this AI solution. Other territories that are being trembled are: customer service/call centers, executive assistants/ scheduling, and legal document review/ compliance, just to name a few.

The customers who are responding well and quickly are often the ones who value cost-efficiency the most. They are usually startups and SMBs. They are AI-first companies’ first “nitch market” that is often underserved (and overcharged) by incumbents. They are the beachheads.

By establishing an ultra-strong ROI case on a particular business function (i.e., sales, call center, EA, etc.), AI-first companies are quickly building up the beachheads before attacking incumbents’ primary market — fortune 500 customers.

2) On the incumbents’ side, the existing high margin software business offers little incentive to explore AI-service-like products with a lower margin. More importantly, much incumbents’ existing customer base — the stable large-cap companies — might be willing to try out some AI-first products, but they do not demand them.
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Why?

First, the mainstream market, by definition, is conservative and usually likes to wait until fully adopt a new product. Second, thanks to software incumbents’ decade-long effort in building high-switching costs to their products, the customers are even less likely to be proactively looking for new solutions.
Therefore, the “conservative” customer base, plus the high margin “lifestyle,” will immobilize incumbents to react and defend the inevitable take-over from the new breed of AI-first companies. Such is life.

However, this is not to say that AI-first companies don’t have shortcomings. Other than the “low margin” factor as mentioned above, AI companies will have to figure out a few things, including the sustainability of the moat. This lead to my third point.
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3) Unlike typical social network or marketplace companies, the moat for “AI-first” companies might not be deep. Therefore, the initial data advantage and an ongoing low-cost data acquisition would be the key.

Because AI algorithms are widely open-sourced, the keys to building a defensible business are proprietary data and the method of keeping acquiring quality data cheaply***.

Because of such dynamic, it’s not hard to foresee the successful breed of AI-first companies will be heavily vertical-integrated and even service-oriented (at first.) Therefore, there won’t be such a thing as horizontal AI (with the exceptions that maybe Alphabet or Microsoft can morph into horizontal AI players). Instead, we will be seeing AI companies build their data pools, users, and brands in specific verticals such as healthcare, payment, media, automotive, and manufacturing.

We are still in the early days. I don’t think we have figure out the best practices of obtaining sustainable data moat yet.

On the one hand, it could be a capital intensive endeavor as we have seen Google/ Waymo invest billions in its self-driving AI as it keeps updating the mapping and driving data on its own. On the other hand, we have seen some startups being creative by striking partnerships with hospitals, payment processors, and OEMs to get the initial data advantage started. However, is either approach sustainable and scalable? We don’t know. We haven’t figured out the formulas as much as we have on many of the SaaS calculations.

One might imagine a very different pricing model that is offered by AI companies. We have discussed AI-first companies might charge customers on a service basis (initially). But the pricing model might eventually mutate into data-discounted pricing where the customers might enjoy much of the discount in exchange for the training data they contribute. So that would be very different than the typical SaaS companies’ per-seat model.
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Parting thoughts: the next mega-giants?

​How do we, the investors, properly evaluate these pricing models and the underlying business created by this new breed of “AI-first” companies?
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I don’t have the definitive answer, but I would expect it to be different from a typical Cloud/ SaaS multiple. My guess: In the coming ten years, we will be witnessing a new breed of tech companies that are much more automated, intelligent, and ever more embedded in business functions. And this unleashes value.

The market valuation for these AI-first companies will likely be much higher than the “1-billion-dollar unicorns” over the next decade — perhaps we will see a few trillion-dollar companies across the US and China. What would be the AI-first equivalents of Amazon, Apple, and Alibaba?
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If history is of any guidance, we should be well prepared by studying the trend and then embrace it. Fully.

​** Another example would be Netflix vs. traditional media companies. Started as a DVD rental business (i.e., you actually have to mail out physical DVDs and then expect consumers to mail them back via US Postal Office), Netflix’s original business model was heavy with DVD warehouse and intense sorting and mailing labor operation, thus, relatively low margin business. Because of the initial “low margin” characteristic, many media senior executives were blindsided. The gravity of short-term quarterly profit pulled them away from the long-term threat of Netflix. Many in the media industry failed to imagine how Netflix could morph into a digital-first streaming product that left giants such as Disney, Warner to play catch-up.
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*** This is assuming everything else being equal. It’s worthwhile to point that compared to traditional software companies, AI-first companies might have to be better at sales and marketing as the customer market will experience a learning curve on its own. Within the AI companies — which are often staffed with engineers and data scientists — the ones who have the best GTM approach /team will dominate, since the end market doesn’t care about the technology but rather the ROI-proven solutions.

“Money” and “Wealth” are two simple concepts that people often use interchangeably. Growing up, I have always thought these two are the same. That turns out to be a mistake. They are two different things — i.e., money is a medium of wealth, but wealth is not all money.

Let’s talk about money first. It usually comes with a currency sign and can be in the form of paper or digital credit. A simple enough concept we typically don’t overthink about. At its essence, money is a fictional concept that we humans together have faith in, just like religions, corporation entities, and even Bitcoin. If we look at human history, it was a big breakthrough when humans collectively decide to take this concept of “money” for trades instead of real goods. Money allows convenience and enables faster trades when you don’t have to drag a sheep for shoes. Money is a medium of wealth, but it’s also an enabler for speedier wealth creation. So when we say “how to make money,” we mean “how to make wealth.”

Now let’s talk about wealth.

On a macro level, wealth represents a society’s overall living standard, wellbeings, and goods it possesses. And these tangible and intangible kinds of stuff are created by humans working collectively in a flexible fashion.

For example, living in the modern world, we don’t need to ask for or get to the know the person who makes our clothes, preps our food or build our shelter. We have this massive system called “Division of Labor” — with each member making things that other people want. More importantly, we can do it flexibly within either a small commune group or a thousand-person entity called a multinational corporation. It’s a powerful thing. In the animal world, for example, bees can have a perfect “division of labor” system, but the structure is very rigid, and there are certainly no corporations among the bees.

Humans are the only species who can create wealth on a massive, systematic level and yet in a flexible, adaptable fashion. And that’s how we create wealth for humanity as a whole.

Now let’s zoom in to a micro-level. An often-used phrase “turn lemon into lemonade” does capture the essence of creating wealth. In this case, one lemon at a time. For example, on a hot summer day, a little girl — also a future entrepreneur — bought a lemon for $0.5, squeezed it, and put into a cup. Now it’s sold for $1.5. Then that extra $1 is wealth that didn’t exist before she squeezes the lemon.

It’s important to note that the dollar sign (money) is a measurement of wealth but not wealth itself. Now let’s say if the little girl doesn’t sell the lemonade but instead drinks it herself. Does that wealth disappear? No. The wealth was still created because it had improved one’s living condition by quenching the maker’s thirst on a hot summer day. Other examples would be that if you decide to renovate your house by yourself — the end product would definitely increase the value of your home (assuming you don’t have a terrible decor taste.) But even if you don’t sell it, the renovation improves your well beings and life condition. And that’s wealth.

So making wealth is about making stuff people want, for oneself or others. Once we establish that, we can then dig into the topic of how to create wealth fast.

You would generally need two qualities: 1) make things not just for yourself, but for many others**; 2) make things once, but they can be used by others many times. In business terms, the first one is called “total addressable market,” and the second is “scalability or leverage.” There is a reason that investors love technology business because it usually resembles both qualities. If we look at the most valued companies and the wealthiest people today, most of them are undoubtedly in or related to the technology business (Financiers can get wealthy too because they usually master the quality of “leverage.”)  It’s not coincident.

I believe it’s important to have such a mental framework for money v.s wealth, as wealth is the one we are seeking to create, and money is often a measurement of that. So “how to make money” turns out to be a false question. What we really want to have is to create wealth. To do that, we will need to make things other people want — hopefully, many people will want it; ideally, we want to make it once but sell it many times. Simple yet tricky, for that it’s the essence of technology entrepreneurship and venture capital.

Why?

Either you are a founder or a VC; what we stand behind is the startup entity that’s going to change the world. Often time it's hard to tell if the evolving market is big enough, especially if you only get to stare at the current one. Think Airbnb vs. hotels in the early days; think Google vs. Yahoo plus thousands of other search engines; Think Salesforce vs. on-premise CRM solutions. There was no way to know if the majority of people in the future would need the stuff you are building/ funding now. Hence the inevitable uncertainty of wealth creation. Unless you can envision. Or imagine.

Albert Einstein’s famous quote goes:

“Imagination is more important than knowledge. For knowledge is limited, whereas imagination embraces the entire world, stimulating progress, giving birth to evolution.”

The longer that I am in the technology and investment business, the stronger I hold on to such belief: the ability to imagine is the key for the ultimate wealth creation and is what separates a great Founder / VC vs a good one.
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And in venture, being great is where it all matters.

For a long time, I’ve had this old poster that illustrates the mechanics of the first steam engine in my office. One might wonder what does a steam engine has to do with the “groundbreaking” technology companies a VC gets to interact with. The answer is “drastic productivity improvement.” The poster helps remind myself that with each tech breakthrough, it unleashes a tremendous amount of productivity — thus, value and wealth — within the society. Like how the industrial revolution freed up labor and accelerated production, the age of AI Automation is going to generate significant value for humanity in multiple ways.

Why AI Automation matters now
The term AI is not new — it originated in the 1960s among computer scientists. However, the real breakthroughs came within the last decade when we have developed new algorithms/ techniques set of Machine Learning (a subset of AI), Deep Learning (a subset of ML), Reinforced Learning and Transfer Learning, etc. Because of those, coupled with an abundant amount of data for training, cloud computing, and sophisticated advancements of hardware AI chips, we are seeing highly valuable tech products being created every day based on such breakthroughs.

AI automation can materialize itself into multiple forms of products:

1) New product/category. Examples include self-driving cars, voice-enabled IoT, etc. Without AI being the backbone, we wouldn’t be able to enjoy these products;

2) Help with existing products’ scalability. Chatbot, virtual executive assistants, and face/image recognition would be in this category. It’s the territory where we used to have products in place but were either a) rule-based or b) man-powered and not scalable;

3) Inject efficiency and efficacy into software products. This is the category I’m most excited about as it will have profound implications for both B2B software / SaaS and enterprise customers across multiple industries and verticals. Think high-accuracy fraud detection, robotics/ smart manufacturing, predictive maintenance, and auto business insight, etc.

​What has happened?​
Over the past five years, investors have rushed into the AI space without thinking too deeply about the hype cycle. Namely, we have funded many self-driving cars/trucks/tractors and the related sensor components, but few can claim victory just yet. Instead, the abundance of capital inevitably diminishes the upside return and typically excludes startups’ purpose in solving a real market need.

That being said, one of the benefits of re-examing the AI investment landscape is that most of the hype dust is settled and we now have a clearer view of this new macro trend. The trend is going to boost humanity’s productivity to the next level.

Characteristics of the new “AI-first” species
What the new AI automation presents is a unique landscape shift that will make many incumbents struggle to adopt. On the other hand, it will also give rises to many “new species**” companies that are AI-first.

Last few times when major landscape shifts took place, we have had “new species” rose in response to the new world. Think Industrialization (Ford Motors), Computer (IBM, Intel, Apple, Microsoft), Internet / Cloud / Mobile (Salesforce, Amazon, Google, Facebook). Iconic companies were created when they can ride major tech tides.

This time is no different. Except the unleashed productivity will likely be in the trillions of trillions of dollars, and the impact will last centries. That’s why we need to study and take a closer look at the early characteristics of these “AI-first” companies — the new type of specie that they are.
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In the next post, we will examine the interesting characteristics of these AI-first species…

**As strange as it might sound, when I was a kid, I was fascinated by Darwin’s book “On the Origin of Species.” The work not only lays out how animals adapt to a new environment, but it also paints a competitive dynamics between the old and emerging species that leads to such adoption/evolution. Even as an adult, I still love books and documentaries about nature.
It has absolutely delighted me when I find out how similar it is between nature and our business world — i.e. bio-ecosystem vs market dynamics, landscape shift vs new tech breakthroughs, startups & incumbents vs new & old species. How the business world evolves is really not that much different than our mother nature.

Burpee challenge
This started with something silly. I ran into some click-bait youtube videos with dramatic thumbnail photos (of course!). They showed the drastic differences of “before v.s after 30-day burpee challenge.” It was at the beginning of the lockdown, so I thought to myself, “why the hell not? I’ve got time..”

So it began.

I downloaded a free app to count my daily progress. The app allows me to start at a lower count (30 burpees a day) and then gradually increase to 175 burpees a day. Truth to be told, I only made it halfway: I wrapped up 100 burpees on the 15th day, counting close to 1000 burpees finished over two weeks. The reason being that I found it increasingly difficult to combine 100 burpees a day with my regular resistant training since my major muscles were fatigued all the time. It was a big bummer since I really felt great, even having made it only halfway. It improves my strength and conditioning, and I hope I would be able to pick it up once again in the future soon.

One thing a YouTuber who was doing the challenge said had echoed in my head, which I’m paraphrasing here: “as you are doing these mindless, repetitive burpees every day, your mind is conditioned into this ‘fall and get up, fall and get up’ mode, just like life…”

Well, who knew burpees could be so zen!

Black Swans

Just when we were grappling with the global economic impacts of the US-China trade war for the better part of 2019, no one had expected it was merely a rehearsal. We had not dared to predict that anything bigger would come.
Came it did. A once-in-a-century pandemic that has, tragically taken over 120,000 lives, impacted millions in losing their jobs, is truly a Black Swan occurrence that will leave its mark on future generations.

At a time like this, it’s important to be a student of history, so that we can get a better, if not a more holistic perspective on things. One may say that our past consists of Black Swan events. To list a few from recent to more distant ones within the last century -

• 2016: Trump election;
• 2008: financial crisis;
• 2001: World Trade Center attack;
• 1991: Collapse Of The Soviet Union;
• 1983: The invention of the internet;
• 1945: US atomic bomb drop — WW2 ended;
• 1941: Pearl Harbor attack;
• 1939: WW2 started;
• 1929: the Great Depression;
• 1918: Flu Pandemic;
• 1914: WW1 started.

Over the last 100 years, the world has experienced at least 11 unpredictable and significant events — averaging one every ten years. If we stretch the time horizon even further — way further — could the extinction of Dinosauria count as one giant Black Swan occurrence that gave humanity the rise? I might go one step further to state that the creation of earth and life itself is a beautifully measured low-possibility phenomenon that happened billions of years ago.

You might be able to see where I’m going with this.

Black Swan events are not always bad — they represent infrequent, unpredictable, major changes. Sometimes changes are for the better like the invention of the internet, but often are disruptive and can be detrimental to society as a whole. Yet we have shown the resilience of the human race, each and every time. And we have always learned and adopted something new from the experience.

The COVID19 pandemic is a once-in-a-century event. One might say it’s a combination of 1918 flu (disease) + great depression / financial crisis (employment loss) + worldwide (war-like) defense. It’s nothing we have ever seen.

So what does it mean for the world going forward?

New World Order: the great decoupling accelerated by the COVID19

Our generation has enjoyed the tailwind of globalization over the past few decades. And now the pendulum seems to swing the other way. As I’m writing this, countries, including the US, China, Japan, and major European countries, have all adopted strict policies of flight travels. Noticeably, as each nation is scrambling to handle its own coronavirus crisis, we have unfortunately seen little global collaboration. Instead, we have witnessed mistrust, misinformation, scapegoating, and interfering.

It’s disheartening for sure, but not surprising. Thanks to the tone set by the trade war, the US and China have started its painful decoupling procedure, followed by the world’s search for new leadership in the global stage. For someone like myself, a Chinese immigrant to the US, it feels like you are caught in the middle of divorcing parents. It has been messy, uncomfortable, and emotional.

China and the US have very different political and societal structures. I can empathize on both sides, as a person who has spent a roughly equal amount of life in both countries. Amidst Covid19, the US and China demonstrated to the world the pros and cons of their systems — China, with the big government, showed impressive strength in containing and stopping the virus by rallying the entire nation’s resources behind one task.

The US, with the democratic system and its current white house leadership, has shown the opposite — the lack of attention from the beginning and then the chaos during. However, the US system shines its advantages during the “peacetime” when it makes sure the decision power is not overly concentrated and (ideally) quality decisions come from thoughtful deliberations.

It might be easy to fall into the heated debate on which system is better. But this post is not about that. I expect that both systems, just like Yin and Yang, can and will co-exist equally in the long foreseeable future.
The keyword at issue here is “equally.”

Future is past rolled into presence

According to the chart below, over the past 1400 years, China had been the #1 for roughly 850 years in terms of global power (starting from 600 AD to 1250, then 1400 to 1600). And it’s no surprise to anyone who studies China’s history that China is coming up and “reclaiming” its global position. On the other hand, the US has been the world’s # 1 power for less than 100 years and is very anxious in retaining its global leadership. Since Trump’s election, the America-centric view has been boosted. The Covid19 development has only accelerated such trend and hardened the viewpoint of the populists. But it is a slippery slope. Closing the border may have short-term comfort, but the long-term damages are yet to come.

China knows it fully because it had suffered from it. To the western world, China appears to be this “mysterious dragon” that just woke up and ready to take over. However, few realized that China was going through a painful nineteenth and twentieth centuries when trading prospered the rest of the world. For much of the seventeenth and eighteenth centuries, China was steadily declining, being fully ignorant of the emergence of the Netherlands and UK empires.

Until the 19th century, China continued to see itself as the center of the world, with neighboring countries bowing to its Emperor. As time progressed, China became more closed up with uprising nationalism and smug complacency for centries, even as the western world was going through a major tech breakthrough, namely the Industrial Revolution in 1760. We all know what happened afterward. China was left behind for the following 200 years, was invaded many times, and has ever since attempted to catch up.

History seems to be repeating itself on its early pattern, only this time in the US, not in China. While the US is focusing on its political fights and its close-up “America First” policies, the rest of the world, certainly China, is beaming with 5G, Clean Tech, and AI automation. This early trend ought to worry everyone.

It is my hope that, with its democratic structure, the US will not follow the suit of old China. Hopefully, the 2020 election will help slow the swing of US nationalism and place the US in a non-hostile position with the world.
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In any case, we are likely to see a world moving forward with two “parallel universes.”

1. On the political side, one is a “bottom-up” small government country with 0.3 billion population, and the other is a “top-down” big government nation with a population of 1.4 billion.
2. On the economy side, one’s GDP is $21 trillion, and the other is $14 trillion but steadily rising. It is projected that the gap would be significantly lessened by 2023 and completely closed in 2030. But I suspect it could come earlier due to the different handlings of the pandemic by the two countries.
3. On the innovation side, one has dominated fundamental tech research (semiconductor, internet, AI algorithm, etc.), and the other has been agile on the tech applications (manufacturing, mobile payment, and facial recognition, etc.).

We are certainly at a historic moment, and it would be fascinating to see how the “parallel universes” dance with each other (hopefully more peacefully) over the next decade. 

下由谷歌翻译器翻译： 

这是我长期以来打算写的文章。我在2018年写的关于中美的文章（2018年）由于2019年的贸易战而迅速过时，然后2020年爆发了大流行。形势变化如此之快，以至于全世界都在努力追赶。我觉得从来没有一个好时机写这个话题，那么就现在来写吧。

黑天鹅

就在我们为应对2019年大部分时间的中美贸易战对全球经济的影响而努力时，没有人想到这仅仅是一场排练。我们不敢预测会有更大的事情发生。

然后更大的事情发生了。一个百年一遇的大流行悲惨地夺走了12万条生命，影响了数百万人的失业，这是一次重大的黑天鹅事件，它将在后代留下印记。

在这样的时候，重要的是要学习历史，这样我们才能对事物有更好甚至更全面的认识。可以说我们历史是由黑天鹅事件组成的。列举上个世纪中从最近到较远的几个：

2016年：特朗普大选;
2008年：金融危机；
2001年：世界贸易中心袭击；
1991年：苏联解体；
1983年：互联网发明；
1945年：美国原子弹投掷- 二战结束；
1941年：珍珠港袭击；
1939年：第二次世界大战开始；
1929年：大萧条；
1918年：流感大流行；
1914年：第一次世界大战开始。

在过去的100年中，全世界至少经历了11次不可预测的重大事件-平均每10年发生一次。如果我们进一步拉长时间范围，是否可以将恐龙的灭绝算作是使人类崛起的巨型黑天鹅事件之一？我也许会更进一步地指出，地球和生命的创造本身是一种发生在数十亿年前的精美的低可能性现象。

您也许可以看到我想要说明什么。

黑天鹅事件并不总是坏事-它们代表着罕见，不可预测的重大变化。有时，更改就像互联网的发明一样好，但往往更多的时间，这些改变是有破坏性的，可能对整个社会有害。然而，我们每次都展示了人类的韧性。我们一直从经验中学到并采用新的东西。

COVID19大流行是百年一遇的事件。也许有人会说这是1918年流感（疾病）+大萧条/金融危机（就业损失）+全球（类似战争）防御的结合。这是我们从未见过的。

那么，这对世界前进意味着什么呢？

新世界秩序：COVID19促进了巨大的脱钩进程

在过去的几十年中，我们这一代人享受了全球化的动力。现在，钟摆似乎正向相反方向摆动。在我撰写本文时，包括美国，中国，日本和主要欧洲国家在内的国家都已采取严格的飞行旅行政策。值得注意的是，由于每个国家都在努力应对自己的冠状病毒危机，因此不幸的是，我们几乎没有看到全球合作。相反，我们目睹了不信任，错误信息，替罪羊和干扰。

确实令人沮丧，但不足为奇。由于贸易战的基调，美国和中国开始了痛苦的脱钩程序，随后世界在全球舞台上寻找新的领导者。对于像我这样的一样的人来说，作为一个在中国出生的新美国移民，感觉就像您陷入了与父母离异的中间。这是一个凌乱，不舒服和情绪化的过程。

中美有着不同的政治和社会结构。作为一个在两国生活时间大致相等的人，我对两边都认为可以理解。在Covid19中，美国和中国向世界展示了其系统的利弊。-中国与大政府一起，通过将整个国家的资源集中在一项任务之后，在遏制和阻止该病毒方面表现出了令人印象深刻的力量。

具有民主制度和现任白宫领导权的美国已经表现出相反的情况-从一开始就缺乏关注，然后在此期间却引起混乱。但是，美国系统在“和平时期”确保其决策权不会过于集中并且（理想情况下）质量决策来自周密的审议，从而发挥了其优势。

我们可能容易陷入关于哪种系统更好的激烈辩论。但是这篇文章不是关于这个的。我认为这两种制度就像像阴和阳一样，这两个系统在可以预见的将来可以并且将平等共存。

这里的关键字是“平等”。

未来是现在的过去叠加式

根据下面的图表，在过去的1400年中，就全球力量而言（从公元600年到1250年，然后是1400年到1600年），中国一直是世界力量的第一。对于研究中国历史的任何人来说，中国正在崛起并“夺回”其全球地位也就不足为奇了。另一方面，在过去不到100年的时间里，美国是世界第一大国，并且非常渴望保留其全球领导地位。自特朗普当选以来，以美国为中心的观点得到了提振。 Covid19的发展只是加速了这种趋势，并强化了民粹主义者的观点。但这是一个湿滑的斜坡。关闭边界可能会带来短期的舒适感，但长期的损害尚未到来。

中国完全了解它，因为它遭受过其带来的痛苦。在西方世界，中国似乎是刚刚醒来并准备接手的“神秘巨龙”。然而，很少有人意识到，当贸易繁荣到世界其他地区时，中国正经历一个痛苦的十九世纪和二十世纪。在十七，十八世纪的大部分时间里，中国一直在不断下降，完全不了解荷兰和英国帝国的崛起。

直到19世纪，中国一直将自己视为世界中心，周边国家向其皇帝鞠躬。随着时间的流逝，即使西方世界正在经历一项重大的技术突破，即1760年的工业革命，中国也变得更加封闭起来，内部更是有民族主义和自满自大。在随后的200年中，中国被抛在后面，被多次入侵，从那时起就一直试图追赶。

历史似乎正在以其早期模式重演，仅这次是在美国，而不是在中国。尽管美国将重点放在政治斗争和特写的“美国优先”政策上，但世界其他国家（尤其中国）也正在着手5G，清洁能源和AI自动化。这种早期的趋势应该让每个人都担心。

我希望美国凭借其民主结构不会仿效旧中国的做法。希望2020年大选将有助于减缓美国民族主义的步伐，并使美国与世界保持不敌对的立场。

无论如何，我们很可能会看到一个带有两个“平行宇宙”的世界。

1）在政治方面，一个是“自下而上”的小政府国家，人口为3亿，另一个是“自上而下”的大政府国家，人口为14亿。
2）在经济方面，一个国家的GDP为21万亿美元，另一个为14万亿美元，但仍在稳定增长。预计到2023年，这一差距将大大缩小，并在2030年完全消除。但是，我怀疑由于两国对流感大流行的不同处理，这种差距可能会更早地出现。
3）在创新方面，一个主导了基础技术研究（半导体，互联网，AI算法等），另一个则在技术应用（制造，移动支付和面部识别等）上保持敏捷。

我们无疑处于历史性时刻，在接下来的十年中看到“平行宇宙”如何彼此（希望更加和平地）共舞会非常有意思。

Like everyone else, I’ve been spending a fair amount of time trying to understand the length and impacts of this pandemic. (I will try to write COVID19’s impact on the new world order in a separate post)

The “hammer and dance”
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First, let’s talk about duration. I will be the first to admit that a lot of us were in the phase of denial, thinking that “oh, it’s an outbreak in China” and “oh, it will last only a few months, and things will be back to normal soon.” But in reality, we will likely be living in this “hammer and dance” dynamics for the next couple of years.

I will start with something simple — things that won’t change, perhaps never will — the basic human needs. People still need to eat, shelter, work, socialize, feel worthy, love, and be loved.

However, a lot will change, mainly how we deliver/resolve those needs, if not entirely reimagined in the post-COVID world. Whether you agree with it or not, the pandemic is forcing humanity to overhaul our status quo and to reexamine our priorities.

In a way that’s not trying to predict the future, I’m convinced that we are likely to see a few megatrends playing out in the few decades owing to COVID19. I will list the following three as examples.

1. The “stay-at-home economy” would further penetrate our society, taking more values away from the conventional offline economy.

The “stay-at-home economy” category is not new — it consists mostly of the internet companies we see today. Netflix, Zoom, Amazon, Doordash, Facebook in the US, along with Meituan, ByteDance, Alibaba, Tencent in China, were all founded before the pandemics and are all taking the tailwinds.

But that’s not all. Some of the “tougher” categories would be cracked: Grocery, higher education, healthcare, mobile payment, cloud-kitchen, fitness, etc. In a post-pandemic world, how people buy, learn, care, pay, eat, and work out will be changed in an accelerated fashion.

A months-long window of habit-forming is a powerful thing. Just when the global B2C opportunities were nearing to the tail end in 2018 /2019, I expect we might see a new breed of “stay-at-home” companies that will be created in the next few years and completely reimagine the way that we live.

The “losers” owing to the pandemics are the traditional economy consisting of restaurants, shopping malls, gyms, office spaces, and so on. Their values are being squeezed out and will have to find a new place to go. What this means is that the “offline economy” will either have to embrace the digitalization more aggressively or compete harder with new offerings. I don’t think restaurants, theaters, airlines would go away, despite popular beliefs. Instead, they would be “upgraded,” perhaps much more “personalized,” catering for a new type of experience. That takes me to my second prediction.

2. Social norms will never be the same. In-person time will be “luxurious.”

Many years following the potentially two-year-long pandemic, we will continue to keep our distance with each other. Handshaking is likely not going to happen in most social settings. We will start to see in-person meetings much like a “luxurious” experience, reserving it only to those we love, care for, and consider important.

By “luxurious,” I don’t mean the service will be expensive; it may very well be affordable and minimalist — it just means that psychologically, people are going to set aside a special place for the in-person time. And they would want it to be unique, well planned, and about who they are.

I don’t know what format and at what price this new type of service will be offered. And I don’t know how technology would be leveraged to achieve a reasonable scale such that more people will be able to enjoy it. But I know there will be needs for it. And people will pay for it.

3. Digitalization and AI automation will be accelerated

During (and after) this pandemic, most enterprises worldwide are (and will continue to be) pressured on two ends.

First, the frontend. Following consumers’ demand for digital experience, companies will be replacing a more considerable portion of their “fat infrastructure” (e.g., physical space, human-interacting staff) with leaner tech stacks (e.g., cloud, bots). For instance, certain restaurants might not need to rent a full venue to serve their customers. Instead, they would open shops on delivery apps. Physicians might only see patients in person on a very selected basis, with most people comfortable with telehealth service going forward.

We have seen this consumer digitization trend taking place in China over the past ten years in areas like food delivery, remote health, and mobile payment. However, it would be fascinating to see the US is finally getting on the transformation. Tremendous values will be generated as companies in various verticals are switching to a new “interface.”

Second — probably a more important point — we would expect businesses worldwide to be challenged on the backend- the company operation side: e.g., logistics, manufacturing, warehousing, sales, marketing, recruiting, planning, etc. What it means is that as the economy takes on further downward pressure over the next few years, companies will have to work out a more efficient, automated way to operate on the backend, *while* many of them are forced to adopt a more digital frontend.

This represents a significant challenge and opportunity for enterprise software companies. For one, it will push B2B companies to demonstrate their ROI even more. The nice-to-have “vitamin” B2B solutions would be washed out, quickly. And two, just like a sick patient in need of good medicine, corporate customers are more eager to experience and try new solutions, particularly solutions that can solve their transformation dilemma. But the catch here is that you will have to show results quickly.

On the backend, a mere “digitalization” or “change of the interface” is not enough. For example, with manufacturing, it has never been about making digitalized records but rather how to use that digitalized information to make intelligent decisions, or better yet to automate certain tasks so that efficiency can be created. We are seeing startups like PlusOne Robotics, Kubit.ai that are taking an AI-first approach to demonstrate such ROI through automation, and will expect to see more.

In all honesty, the solutions will continue to come and evolve, but in the enterprise space, market readiness is the key. In a post-pandemic world, most enterprise customers worldwide will not only be ready but will demand a new form of software or solutions that will help with the automation of business tasks. It goes beyond “digitalizing” or “moving to the cloud,” it will push us into a new era of enterprise tech that is enabled by AI and automation.

Parting Thoughts

Like every generation ahead of us who have gone through famine, wars, and the Great Depression, we are experiencing our own crisis. In Chinese, crisis (危机）is written as “danger and opportunity” in a single word. It cannot describe what we are going through any better. It is a tragic occurrence worldwide, but it’s also a forceful catalyst for our society to evolve, improve, and adopt a new, more reflective norm. When we look at humanity’s history, we have always come out the other end, stronger and more unified than ever before.
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And this time is no different.

以下由谷歌翻译器翻译： 

像其他所有人一样，我一直在花大量的时间试图了解这种流行病的持续时间和影响。 （我将在另一篇文章中尝试写出COVID19对新世界秩序的影响）

“锤子与舞蹈”

首先，让我们谈谈持续时间。 我将是第一个承认我们很多人处于否认状态的人，认为“哦，这是中国的爆发”和“哦，它只会持续几个月，而一切很快就会恢复正常” 。” 但实际上，未来几年我们可能会生活在这种“锤子和舞蹈”的动态中。
从较高的层次上讲，研究人员在这里的展现的是，我们的社会活动将根据病毒的周期性而出现和后退。 由于我们不能完全包含或去除COVID19，这种情况将一直持续到疫苗问世，并在未来两年内分发给全球数十亿人。

我知道这是一个非常黑暗模糊的愿景。 但是如果这是事实，我们应该保持理性的头脑并做好准备。 具体来说，作为创始人和投资者，我们应该考虑未来，我们的社会需要什么，什么将会改变，什么不会改变。

我将从简单的事情开始-人类基本需求不会改变，甚至永远不会改变。 人们仍然需要吃饭，居住，工作，社交，感到值得，被爱和被爱。

但是，很多事情将会改变，主要是我们如何交付/解决这些需求，即使在后COVID世界中并未完全重新构想。无论您是否同意，这种流行病都在迫使人类彻底改变我们的现状并重新审查我们的优先事项。

我坚信，，由于COVID19，我们可能会在几十年内看到一些大趋势。我将列出以下三个示例。

1.“家庭经济”将进一步渗透到我们的社会，从传统的离线经济中获取更多价值。

“家庭经济”类别并不是新事物，它主要由我们今天看到的互联网公司组成。美国的Netflix，Zoom，亚马逊，Doordash，Facebook，以及美团，ByteDance，阿里巴巴，中国的腾讯，都在大流行之前就成立了，并且都在顺风顺水。

但这还不是全部。某些“艰难”类别将被破解：杂货，高等教育，医疗保健，移动支付，云厨房，健身等。在大流行后的世界中，人们如何购买，学习，护理，支付，饮食和工作将会以更快的方式改变。

一个月的习惯养成之窗是非常有力的。就在2018/2019年全球B2C机会接近尾声之际，我希望我们会看到新一代的“在家”公司，这些公司将在未来几年内创建，并完全重新构想我们的方式生活。

大流行造成的“失败者”是传统经济，包括餐馆，购物中心，体育馆，办公场所等。他们的社会价值被挤出，必须寻找一个新的地方。这意味着“离线经济”要么必须更积极地拥抱数字化，要么与新产品竞争更加激烈。尽管人们普遍相信，我认为餐馆，剧院，航空公司不会消失。取而代之的是，他们将被“升级”，也许会更加“个性化”，以迎合一种新型的体验。这使我想到了第二个预测。

2.社交规范将会永远改变。未来面对面的时间将是“奢侈的”。

在可能长达两年的大流行之后的许多年里，我们将继续保持彼此的距离。在大多数社交环境中，握手可能不会发生。我们将开始看到面对面的会议，就像“豪华”的体验，只保留给我们所爱，关心和认为重要的人。

我所说的“豪华”并不意味着服务会很昂贵。它很可能是负担得起且极简的-这只是意味着从心理上讲，人们将为现场时间留出特殊的位置。他们希望它是独特的，精心计划的并且关于他们是谁。

我不知道这种新型服务将以何种格式和价格提供。而且我不知道如何利用技术来达到合理的规模，从而使更多的人能够享受它。但我知道会有需求。人们并会为此付费。

3.数字化和人工智能自动化将加速

在这种大流行期间（及之后），全球大多数企业（而且将继续）受到两个方面的压力。

首先，前端。随着消费者对数字体验的需求，公司将用更精简的技术堆栈（例如云，机器人）替换其“肥胖的基础设施”（例如物理空间，与人互动的员工）中的较大部分。例如，某些餐厅可能不需要租用完整的场地来为其顾客提供服务。相反，他们会在送货应用程序上开设商店。医师可能只会在非常有选择的基础上亲自看病人，并且大多数人对远程医疗服务感到满意。

在过去的十年中，我们已经看到这种消费者数字化趋势正在中国发生，例如送餐，远程医疗和移动支付。但是，看到美国终于开始进行转型将是一件令人着迷的事情。随着各行各业的公司切换到新的“互动界面”，将产生巨大的价值。

第二点，也许是更重要的一点，我们希望全世界的企业在后端-公司运营方面都面临挑战：例如，物流，制造，仓储，销售，市场营销，招聘，计划等。这意味着在接下来的几年中，经济将进一步承受下行压力，公司将不得不设计出一种更高效，自动化的方式来在后端进行操作，而同时其中许多公司被迫采用更数字化的前端。

对于企业软件公司而言，这是一个巨大的挑战和机遇。首先，它将推动B2B公司更多地展示其投资回报率。很好的“维生素” B2B解决方案将很快被洗掉。第二，就像病患者需要优质药物一样，公司客户更渴望体验和尝试新的解决方案，尤其是可以解决其转型难题的解决方案。但是这里要注意的是，您必须快速显示结果。

在后端，仅“数字化”或“接口更改”是不够的。例如，对于制造业来说，从来就不是制作数字化记录，而是如何使用该数字化信息来做出明智的决定，或者更好地实现某些任务的自动化以提高效率。我们看到像PlusOne Robotics，Kubit.ai这样的初创公司正在采用AI优先方法来通过自动化展示这种ROI，并且期望看到更多。

老实说，解决方案将继续发展，但是在企业领域，市场准备就绪是关键。在大流行后的世界中，全球大多数企业客户不仅准备就绪，而且需要一种新形式的软件或解决方案，以帮助实现业务任务的自动化。它超越了“数字化”或“移动到云”的范畴，它将使我们进入由AI和自动化支持的企业技术新时代。

最后一点的想法

像我们在我们之前的经历过饥荒，战争和大萧条的每一代人一样，我们正在经历属于我们这一代的危机。“危机”一词被形容为“危险和机遇”，非常完美地描绘了我们正在体验的经历。这是世界范围内的悲剧性事件，但它也是我们社会发展的有力催化剂， 改进并采用新的，更具反思性的规范当我们回顾人类历史时，我们总是走到另一端，比以往任何时候都更强大，更统一。

这一次并没有什么不同。

​As I'm writing this, I just finished a long email with a startup founder. This startup has a stellar team, and they quickly realized what didn't work. The company is going through a pivot. The CEO and I have been on the phone, on the email over the past few months, during which we have discussed, tested, and debated no less than five ideas. 

I will be the first one to admit that, at times, as a VC/ advisor, it's tempting to jump in and offer one's own idea. 

But that's not the right way to do it. 

For one, VCs are generally good at observing the macro trends and betting on it - but terrible at originating great ideas. If we look at the iconic consumer tech companies over the past decades - Netflix, Uber, Airbnb, Twitter, Snap, TikTok, etc - none of those startup ideas come from VCs. Almost 100% of the time, these ideas were products of founders' own life experience and insights, which could be very different from a typical Sand Hill VC. That's why when the majority of great consumer companies get started, they had the most challenging time to convince others to invest. On the contrary, consumer startups who had an effortless time to raise money early on - thanks to VC hype - often turn out to be living short of the expectation and running of gas in the long term. 

Secondly, VCs do play an important role and add value to a company's early life. Investors usually have the privilege to observe the macro market and study the ever-evolving dynamics by comparing multiple companies and tech. But VCs have to be very thoughtful about how an investor's insights can be translated into a company's actions. Over the past 10+ years as an investor, I've shared boards with both excellent and mediocre VCs. I learned that a good VC tends to ask great questions to provoke new thinkings from founders, whereas a bad one tends to force-feed his / her thoughts into the company. 

During a startup pivot, the dynamics could be confusing and sometimes frustrating. And that's when a great founder / VC partnership is needed. 

Started about last year, I began working out. Although I went to the gym in college now and then, I completely dropped it after I started working professionally. I was so busy with everything else that I forgot to take care of my body. So around mid last year, I was changing to a new platform at the time, and it came with the stress of further adjustment. 
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I felt that I needed to take care of my body better. I needed a change. 

At first, my weight-lift routine started with only 15 minutes. No more, no less. I told myself that that’s all I’ve got per day. As you can imagine, 15 minutes pass very fast and it sort of makes it fun because I WANTED to work out more. Little did I know, with a small habit I could easily stick to, I was building strength and resilience. And I got better too. From 15 minutes, I finally allowed myself to work out for 30 minutes. And now I find myself easily spend 1.5 - 2 hours in the gym while listening to the music/podcast that I enjoyed. 

Not only the 15-minutes starting point empowered me to take care of my body. I now eat better too. And I stress less. Looking back, I’m grateful for the small initiative I made last year, and modest progress does matter.