The Sharing Economy + AI Assistants in China — my short trip in May, 2017
By: Lan Xuezhao
Tags: China, Tech, Artificial Intelligence, Transportation, Sharing Economy

Trips to China

I take quarterly weekend trips to China to stay in the loop of the tech scene there. It’s substantially different every time I visit.

My last trip was in Q4, 2016. Thanks to Kai-Fu’s invitation, I had the pleasure to visit the Foxconn factory in Shenzhen, and it was one of the best trips I’ve had. Needless to say, everything was confidential on that trip so I can’t really talk about it.

One takeaway is that Foxconn is one of the top robotics companies in the world, I was very impressed by the operational efficiency and scale. I’ve also decided to spend more time looking into factory AI and robotics going forward (more on that later).

Recently, I took another trip to Shanghai and Beijing; here are some observations.

The sharing economy

The biggest change this time compared to the last was that almost everyone on the streets of Beijing was using a “bike-share.” Ofo and Mobike were the most popular, followed by Bluegogo on the streets of Beijing. Even my mom uses them. In fact, she already has 3 apps. She calls them the yellow, orange and blue bikes.

The American news media has just started to pick up on this phenomenon (a little bit late). At the same time, at least 5–6 bike sharing companies have entered the U.S. market to my personal knowledge. It’s easy to think this might be the biggest thing in the U.S. as well, following the success of the Chinese biking unicorns, but the two markets are substantially different.

Here are some of the big ones for American investors to think about:

Do people’s behaviors need to change? Bikes as transportation has a long and illustrious history in China. There are bike-only lanes in most cities. In places where the bike lanes aren’t so obvious, people ride them on side walks all the time. Bikes as transportation and last mile delivery is natural and organic for Chinese citizens. How many people in the United States use bikes as transportation? Where do they ride the bikes, in lanes shared with cars?

Bikes as transportation has a long and illustrious history in China. There are bike-only lanes in most cities. In places where the bike lanes aren’t so obvious, people ride them on side walks all the time. Bikes as transportation and last mile delivery is natural and organic for Chinese citizens. How many people in the United States use bikes as transportation? Where do they ride the bikes, in lanes shared with cars? Sharing helmets? No one wears helmets in China (masks and umbrellas are in fact more common for bikers than helmets). How many cities in the United States do not require helmets? How would you feel about using a helmet dozens of others have sweated into?

No one wears helmets in China (masks and umbrellas are in fact more common for bikers than helmets). How many cities in the United States do not require helmets? How would you feel about using a helmet dozens of others have sweated into? Parking? You can practically park bikes anywhere on the street (technically, it’s not allowed, but I’ve seen bikes literally everywhere in my short trip to Beijing including in the middle of a highway). Biking companies are expected to put major work into negotiating with cities to solve the parking problems. Will US cities live with these problems — even in the short-term?

You can practically park bikes anywhere on the street (technically, it’s not allowed, but I’ve seen bikes literally everywhere in my short trip to Beijing including in the middle of a highway). Biking companies are expected to put major work into negotiating with cities to solve the parking problems. Will US cities live with these problems — even in the short-term? Chinese investors invest in biking companies for the payment entry . One of the main reasons Chinese tech giants backed the bike share companies in China is that it’s a relatively low-friction entry point for new wallet customers. Users only pay a negligible amount of money to use a bike, but the account creation and linking of bank accounts is what these payments giants sought. This makes a lot of sense, because it’s getting harder and harder to convince investors that they’ll break even based on the fees charged by the bike alone — the competition is so intense that the bike share companies are almost paying users to use the bikes. I paid zero to ride hours of bikes switching across multiple companies. Does this logic make any sense in the U.S. context?

. One of the main reasons Chinese tech giants backed the bike share companies in China is that it’s a relatively low-friction entry point for new wallet customers. Users only pay a negligible amount of money to use a bike, but the account creation and linking of bank accounts is what these payments giants sought. This makes a lot of sense, because it’s getting harder and harder to convince investors that they’ll break even based on the fees charged by the bike alone — the competition is so intense that the bike share companies are almost paying users to use the bikes. I paid zero to ride hours of bikes switching across multiple companies. Does this logic make any sense in the U.S. context? Exit strategy. Tech innovators in China expect to get acquired by one of the tech giants (all the giants want to enter the same hot market); if a company does well, the chances of them getting to a handsome exit is pretty high. This might be a positive sign for Chinese and U.S. investors looking to invest in bike share companies. Does anyone think this way here in the U.S.?

As the bike share market starts to peak in China, other kinds of sharing economy concepts have been introduced; for example — sharing phone chargers, umbrellas, gym passes, you name it.

It seems like everything is becoming cheaper and more accessible to anyone anywhere. This is a picture of a single-person KTV booth for people who need a fix on-the-go.

The powerful personal assistants

I’m a fan of human-powered AI and AI assistants. If you live in the Silicon Valley, you’ve probably heard of or have used one of the personal AI assistants (Amy, Clara, Magic or Fin).

My friend recommended a China-version called Lai Ye before this trip, backed by Sequoia China which made my life much easier (I have no relationship with Lai Ye).

Lai Ye is different from the U.S. AI assistants. It does pretty much everything I can think of: on-demand coffee, scheduling via WeChat, booking cars, finding maids, sending packages, procuring on-demand massages, booking plane and train tickets etc. etc.

I used Lai Ye 3 times and was impressed each time. Lai Ye integrates with popular services so when I asked for coffee, it showed me a menu of different coffee options from Starbucks and asked me to pick one. Lai Ye assigned the task to a worker who delivered the coffee to me in person. The whole experience end-to-end took 19 mins; the transaction on my side took less than a minute.

As a consumer, it made a big difference for me because…

It removed the friction of setting up the app and the payment method for every app I need use. I now have one interface and one payment channel and I can enjoy every service. This is particularly useful given the ever-evolving landscape of apps.

It was super-efficient and reduced the time I needed to spend on things like calling a provider, finding a delivery window that worked for me, putting it on my calendar, etc. With the app, products and services come to me.

The question, of course, is whether they’ll be able to provide consistently impressive level of services when they’re trying to run a profitable business. I only used it 3 times.

I suspect they have significant operational challenges especially on the delivery side during rush hour. Doing one task well (e.g. scheduling) is already hard, let along doing 5–10 of them well. One thing they do very well is that they integrated with many products/services and standardized a big portion of each task (the picture below shows their partners).

Much like Slack, the more integrations they have, the more useful they become.