Li Fan, a 21-year-old student, attempted suicide after posting a brief message on the Chinese Twitter-like platform Weibo just after Valentine’s Day.
“I can’t go on anymore. I’m going to give up,” he wrote.
Soon after, he lost consciousness.
He was in debt, had fallen out with his mother and was suffering from severe depression.
Some 8,000km (5,000 miles) away from his university in Nanjing, his post was detected by a program running on a computer in Amsterdam.
It flagged the message, prompting volunteers from different parts of China into action.
When they were unable to rouse Mr Li from afar, they reported their concerns to local police, who eventually saved him.
It might sound extraordinary but this was just one of many such success for the Tree Hole Rescue team.
The initiative’s founder is Huang Zhisheng, a senior artificial intelligence (AI) researcher at the Free University Amsterdam.
In the past 18 months, his program has been used by 600 volunteers across China, who in turn say they have rescued nearly 700 people.
“If you hesitate for a second, a lot of life will be lost,” Mr Huang told BBC News.
“Every week, we can save around 10 people.”
The first rescue operation was on 29 April 2018.
A 22-year-old college student, Tao Yue, in northern China’s Shandong province, wrote on Weibo she planned to kill herself two days later.
Peng Ling, a volunteer from the Chinese Academy of Sciences, and several others reacted.
Ms Peng told BBC News they had found a phone number for one of student’s friends via an earlier post and passed the information to the college.
“I tried to message her before sleep and told her that I could pick her up,” she said.
“She added me as a friend on [Chinese app] WeChat and gradually calmed down.
“Since then, I have kept check on her to see if she is eating. We also buy her a bunch of flowers through the internet once a week.”
After this success, the team rescued a man who had tried to jump off a bridge and saved a woman who had tried to kill herself after being sexually abused.
“Rescues need both luck and experience,” said Li Hong, a Beijing psychologist who has been involved for about a year.
She recalled how she and her colleagues had visited eight hotels in Chengdu, in order to locate a suicidal woman they had known had booked a room in the city.
“All the receptionists said they didn’t know the woman,” Ms Li said.
“But one of them hesitated for a moment. We assumed it must be that hotel – and it was.”
So how does the system work?
The Java-based program monitors several “tree holes” on Weibo and analyses the messages posted there.
A “tree hole” is the Chinese name for places on the net where people post secrets for others to read.
The name is inspired by an Irish tale about a man who confided his secrets to a tree.
One example is a post by Zou Fan, a 23-year old Chinese student who wrote a message on Weibo before killing herself, in 2012.
After her death, tens of thousands of other users added comments to her post, writing about their own troubles, thus turning the original message into a “tree hole”.
The AI program automatically ranks the posts it finds from one to 10.
A nine means there is a strong belief a suicide attempt will be made shortly. A 10 means it is likely to be already under way.
In these cases, volunteers try to call the police directly and/or contact the person involved’s relatives and friends.
But if the ranking is below six – meaning only negative words have been detected – the volunteers normally do not intervene.
One of the issues commonly encountered by the team is a belief among older relatives that depression is not a “big deal.”
“I knew I had depression when I was in high school but my mother told me that it was ‘absolutely impossible – don’t think about it anymore’,” Mr Li told BBC News.
The AI program also found a post from a young woman, saying: “I will kill myself when New Year comes.”
But when the volunteers contacted her mother, they said she had sneered and said: “My daughter was very happy just now. How dare you say she is planning suicide.”
Even after the volunteers showed evidence of her daughter’s depression, the mother did not take the matter seriously.
It was only after an incident in which the police had to stop the youngster jumping off a rooftop that the mother changed her mind.
Despite its successes, Mr Huang acknowledges the limits of his project.
“Because Weibo limits the use of web crawlers, we can only gather around 3,000 entries every day,” he said.
“So we can only save one or two a day on average and we choose to focus on the most urgent cases.”
Another issue is that some of those rescued require a long-term commitment.
“Most of my life now is occupied by these rescued people,” Ms Li said.
“Sometimes I get very tired.”
She said she was currently in contact with eight people who had been rescued.
“I have to reply [to] them soon after they send me a message,” she said.
Some team members also try to provide help offline.
For example, one AI professor is said to have found a data-labelling job for one person found to have a social-anxiety disorder.
There is also the issue that suicidal thoughts can return.
Ms Peng gave the example of one youngster who had “looked better each day” after being rescued but then killed herself.
“She was talking to me about getting a new photo portrait on Friday,” Ms Peng said, adding that two days later the woman was dead.
“It’s a big shock to me that a person you got along with over a long time suddenly isn’t there.”
By contrast, Mr Li remains healthy and now works at a hotel.
“I like this job because I can communicate with many different people,” he said.
He added while he was very appreciative of the rescue team’s efforts, ultimately it was up to each individual to achieve a long-term solution.
“Different people’s joys and sorrows are not completely interlinked,” he said.
“You must redeem yourself.”
Illustration designed by Davies Surya
At the request of the interviewees, the names of the rescued people involved have been changed.
These ten enterprise M&A deals totaled over $40B in 2019
It would be hard to top the 2018 enterprise M&A total of a whopping $87 billion, and predictably this year didn’t come close. In fact, the top 10 enterprise M&A deals in 2019 were less than half last year’s, totaling $40.6 billion. This year’s biggest purchase was Salesforce buying Tableau for $15.7 billion, which would…
It would be hard to top the 2018 enterprise M&A total of a whopping $87 billion, and predictably this year didn’t come close. In fact, the top 10 enterprise M&A deals in 2019 were less than half last year’s, totaling $40.6 billion.
This year’s biggest purchase was Salesforce buying Tableau for $15.7 billion, which would have been good for third place last year behind IBM’s mega deal plucking Red Hat for $34 billion and Broadcom grabbing CA Technologies for $18.8 billion.
Contributing to this year’s quieter activity was the fact that several typically acquisitive companies — Adobe, Oracle and IBM — stayed mostly on the sidelines after big investments last year. It’s not unusual for companies to take a go-slow approach after a big expenditure year. Adobe and Oracle bought just two companies each with neither revealing the prices. IBM didn’t buy any.
Microsoft didn’t show up on this year’s list either, but still managed to pick up eight new companies. It was just that none was large enough to make the list (or even for them to publicly reveal the prices). When a publicly traded company doesn’t reveal the price, it usually means that it didn’t reach the threshold of being material to the company’s results.
As always, just because you buy it doesn’t mean it’s always going to integrate smoothly or well, and we won’t know about the success or failure of these transactions for some years to come. For now, we can only look at the deals themselves.
Jumia, DHL, and Alibaba will face off in African ecommerce 2.0
The business of selling consumer goods and services online is a relatively young endeavor across Africa, but ecommerce is set to boom. Over the last eight years, the sector has seen its first phase of big VC fundings, startup duels and attrition. To date, scaling e-commerce in Africa has straddled the line of challenge and…
The business of selling consumer goods and services online is a relatively young endeavor across Africa, but ecommerce is set to boom.
Over the last eight years, the sector has seen its first phase of big VC fundings, startup duels and attrition.
To date, scaling e-commerce in Africa has straddled the line of challenge and opportunity, perhaps more than any other market in the world. Across major African economies, many of the requisites for online retail — internet access, digital payment adoption, and 3PL delivery options — have been severely lacking.
Still, startups jumped into this market for the chance to digitize a share of Africa’s fast growing consumer spending, expected to top $2 billion by 2025.
African e-commerce 2.0 will include some old and new players, play out across more countries, place more priority on internet services, and see the entry of China.
But before highlighting several things to look out for in the future of digital-retail on the continent, a look back is beneficial.
Jumia vs. Konga
The early years for development of African online shopping largely played out in Nigeria (and to some extent South Africa). Anyone who visited Nigeria from 2012 to 2016 likely saw evidence of one of the continent’s early e-commerce showdowns. Nigeria had its own Coke vs. Pepsi-like duel — a race between ventures Konga and Jumia to out-advertise and out-discount each other in a quest to scale online shopping in Africa’s largest economy and most populous nation.
Traveling in Lagos traffic, large billboards for each startup faced off across the skyline, as their delivery motorcycles buzzed between stopped cars.
Covering each company early on, it appeared a battle of VC attrition. The challenge: who could continue to raise enough capital to absorb the losses of simultaneously capturing and creating an e-commerce market in notoriously difficult conditions.
In addition to the aforementioned challenges, Nigeria also had (and continues to have) shoddy electricity.
Both Konga — founded by Nigerian Sim Shagaya — and Jumia — originally founded by two Nigerians and two Frenchman — were forced to burn capital building fulfillment operations most e-commerce startups source to third parties.
That included their own delivery and payment services (KongaPay and JumiaPay). In addition to sales of goods from mobile-phones to diapers, both startups also began experimenting with verticals for internet based services, such as food-delivery and classifieds.
While Jumia and Konga were competing in Nigeria, there was another VC driven race for e-commerce playing out in South Africa — the continent’s second largest and most advanced economy.
E-tailers Takealot and Kalahari had been jockeying for market share since 2011 after raising capital in the hundreds of millions of dollars from investors Naspers and U.S. fund Tiger Global Management.
So how did things turn out in West and Southern Africa? In 2014, the lead investor of a flailing Kalahari — Naspers — facilitated a merger with Takealot (that was more of an acquisition). They nixed the Kalahari brand in 2016 and bought out Takelot’s largest investor, Tiger Global, in 2018. Takealot is now South Africa’s leading e-commerce site by market share, but only operates in one country.
In Nigeria, by 2016 Jumia had outpaced its rival Konga in Alexa ratings (6 vs 14), while out-raising Konga (with backing of Goldman Sachs) to become Africa’s first VC backed, startup unicorn. By early 2018, Konga was purchased in a distressed acquisition and faded away as a competitor to Jumia.
Jumia went on to expand online goods and services verticals into 14 Africa countries (though it recently exited a few) and in April 2019 raised over $200 million in an NYSE IPO — the first on a major exchange for a VC-backed startup operating in Africa.
Jumia’s had bumpy road since going public — losing significant share-value after a short-sell attack earlier in 2019 — but the continent’s leading e-commerce company still has heap of capital and generates $100 million in revenues (even with losses).
Airbnb’s New Year’s Eve guest volume shows its falling growth rate
Hello and welcome back to our regular morning look at private companies, public markets and the gray space in between. It’s finally 2020, the year that should bring us a direct listing from home-sharing giant Airbnb, a technology company valued at tens of billions of dollars. The company’s flotation will be a key event in…
Hello and welcome back to our regular morning look at private companies, public markets and the gray space in between.
It’s finally 2020, the year that should bring us a direct listing from home-sharing giant Airbnb, a technology company valued at tens of billions of dollars. The company’s flotation will be a key event in this coming year’s technology exit market. Expect the NYSE and Nasdaq to compete for the listing, bankers to queue to take part, and endless media coverage.
Given that that’s ahead, we’re going to take periodic looks at Airbnb as we tick closer to its eventual public market debut. And that means that this morning we’re looking back through time to see how fast the company has grown by using a quirky data point.
Airbnb releases a regular tally of its expected “guest stays” for New Year’s Eve each year, including 2019. We can therefore look back in time, tracking how quickly (or not) Airbnb’s New Year Eve guest tally has risen. This exercise will provide a loose, but fun proxy for the company’s growth as a whole.
Before we look into the figures themselves, keep in mind that we are looking at a guest figure which is at best a proxy for revenue. We don’t know the revenue mix of the guest stays, for example, meaning that Airbnb could have seen a 10% drop in per-guest revenue this New Year’s Eve — even with more guest stays — and we’d have no idea.
So, the cliche about grains of salt and taking, please.
But as more guests tends to mean more rentals which points towards more revenue, the New Year’s Eve figures are useful as we work to understand how quickly Airbnb is growing now compared to how fast it grew in the past. The faster the company is expanding today, the more it’s worth. And given recent news that the company has ditched profitability in favor of boosting its sales and marketing spend (leading to sharp, regular deficits in its quarterly results), how fast Airbnb can grow through higher spend is a key question for the highly-backed, San Francisco-based private company.
- 2009: 1,400
- 2010: 6,000 (+329%)
- 2011: 3,1000 (+417%)
- 2012: 108,000 (248%)
- 2013: 250,000 (+131%)
- 2014: 540,000 (+116%)
- 2015: 1,100,000 (+104%)
- 2016: 2,000,000 (+82%)
- 2017: 3,000,000 (+50%)
- 2018: 3,700,000 (+23%)
- 2019: 4,500,000 (+22%)
In chart form, that looks like this:
Let’s talk about a few things that stand out. First is that the company’s growth rate managed to stay over 100% for as long as it did. In case you’re a SaaS fan, what Airbnb pulled off in its early years (again, using this fun proxy for revenue growth) was far better than a triple-triple-double-double-double.
Next, the company’s growth rate in percentage terms has slowed dramatically, including in 2019. At the same time the firm managed to re-accelerate its gross guest growth in 2019. In numerical terms, Airbnb added 1,000,000 New Year’s Eve guest stays in 2017, 700,000 in 2018, and 800,000 in 2019. So 2019’s gross adds was not a record, but it was a better result than its year-ago tally.
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