We’ve been hearing for years how important customer experience is to business, and a whole business technology category has been built around it, with companies like Salesforce and Adobe at the forefront. But due to the economy or lack of employees (perhaps both?), 2022 was a year of poor customer service, which in turn has created poor experiences; there’s no separating the two.
No matter how great your product or service, you will ultimately be judged by how well you do when things go wrong, and your customer service team is your direct link to buyers. If you fail them in a time of need, you can lose them for good and quickly develop a bad reputation. News can spread rapidly through social media channels. That’s not the kind of talk you want about your brand.
We’re constantly being asked for feedback about how the business did, yet this thirst for information doesn’t seem to ever connect back to improving the experience.
And make no mistake: Your customer service is inexorably linked to the perceived experience of your customer. We’re constantly being asked for feedback about how the business did, yet this thirst for information doesn’t seem to ever connect back to improving the experience.
Consider the poor folks who bought tickets for Southwest Airlines flights this week. One video showed airline employees had sicced the police on their own passengers. Consider that the airline admittedly screwed up, but one representative of the same airline actually called the police on passengers for being at the gate. When it comes to abusing your customers and destroying your brand goodwill, that example takes the cake.
For too long we’ve been hearing about how data will drive better experiences, but is that data ever available to the people dealing with the customers? They don’t need data — they need help and training and guidance, and there clearly wasn’t enough of that in 2022. It seemed companies cut back on customer service to the detriment of their customers’ experience and ultimately to the reputation of the brand.
We’ve been hearing for years how important customer experience is to business, and a whole business technology category has been built around it, with companies like Salesforce and Adobe at the forefront. But due to the economy or lack of employees (perhaps both?), 2022 was a year of poor customer service, which in turn has created poor experiences; there’s no separating the two.
No matter how great your product or service, you will ultimately be judged by how well you do when things go wrong, and your customer service team is your direct link to buyers. If you fail them in a time of need, you can lose them for good and quickly develop a bad reputation. News can spread rapidly through social media channels. That’s not the kind of talk you want about your brand.
We’re constantly being asked for feedback about how the business did, yet this thirst for information doesn’t seem to ever connect back to improving the experience.
And make no mistake: Your customer service is inexorably linked to the perceived experience of your customer. We’re constantly being asked for feedback about how the business did, yet this thirst for information doesn’t seem to ever connect back to improving the experience.
Consider the poor folks who bought tickets for Southwest Airlines flights this week. One video showed airline employees had sicced the police on their own passengers. Consider that the airline admittedly screwed up, but one representative of the same airline actually called the police on passengers for being at the gate. When it comes to abusing your customers and destroying your brand goodwill, that example takes the cake.
For too long we’ve been hearing about how data will drive better experiences, but is that data ever available to the people dealing with the customers? They don’t need data — they need help and training and guidance, and there clearly wasn’t enough of that in 2022. It seemed companies cut back on customer service to the detriment of their customers’ experience and ultimately to the reputation of the brand.
The gigantic technological leap that machine learning models have shown in the last few months is getting everyone excited about the future of AI — but also nervous about its uncomfortable consequences. After text-to-image tools from Stability AI and OpenAI became the talk of the town, ChatGPT’s ability to hold intelligent conversations is the new obsession in sectors across the board.
In China, where the tech community has always watched progress in the West closely, entrepreneurs, researchers, and investors are looking for ways to make their dent in the generative AI space. Tech firms are devising tools built on open source models to attract consumer and enterprise customers. Individuals are cashing in on AI-generated content. Regulators have responded quickly to define how text, image, and video synthesis should be used. Meanwhile, U.S. tech sanctions are raising concerns about China’s ability to keep up with AI advancement.
As generative AI takes the world by storm towards the end of 2022, let’s take a look at how this explosive technology is shaking out in China.
Chinese flavors
Thanks to viral art creation platforms like Stable Diffusion and DALL-E 2, generative AI is suddenly on everyone’s lips. Halfway across the world, Chinese tech giants have also captivated the public with their equivalent products, adding a twist to suit the country’s tastes and political climate.
Baidu, which made its name in search engines and has in recent years been stepping up its game in autonomous driving, operates ERNIE-ViLG, a 10-billion parameter model trained on a data set of 145 million Chinese image-text pairs. How does it fair against its American counterpart? Below are the results from the prompt “kids eating shumai in New York Chinatown” given to Stable Diffusion, versus the same prompt in Chinese (纽约唐人街小孩吃烧卖) for ERNIE-ViLG.
Stable Diffusion
ERNIE-ViLG
As someone who grew up eating dim sum in China and Chinatowns, I’d say the results are a tie. Neither got the right shumai, which, in the dim sum context, is a type of succulent, shrimp and pork dumpling in a half-open yellow wrapping. While Stable Diffusion nails the atmosphere of a Chinatown dim sum eatery, its shumai is off (but I see where the machine is going). And while ERNIE-ViLG does generate a type of shumai, it’s a variety more commonly seen in eastern China rather than the Cantonese version.
The quick test reflects the difficulty in capturing cultural nuances when the data sets used are inherently biased — assuming Stable Diffusion would have more data on the Chinese diaspora and ERNIE-ViLG probably is trained on a greater variety of shumai images that are rarer outside China.
Another Chinese tool that has made noise is Tencent’s Different Dimension Me, which can turn photos of people into anime characters. The AI generator exhibits its own bias. Intended for Chinese users, it took off unexpectedly in other anime-loving regions like South America. But users soon realized the platform failed to identify black and plus-size individuals, groups that are noticeably missing in Japanese anime, leading to offensive AI-generated results.
Of course also clearly not having the model adjusted properly for darker-skinned folks, sigh
Anyway Different Dimension Me is the name, but sorry they already blocked / limit overseas users as couldn’t handle the traffic pic.twitter.com/cYi6rJwTaC
Aside from ERNIE-ViLG, another large-scale Chinese text-to-image model is Taiyi, a brainchild of IDEA, a research lab led by renowned computer scientist Harry Shum, who co-founded Microsoft’s largest research branch outside the U.S., Microsoft Research Asia. The open source AI model is trained on 20 million filtered Chinese image-text pairs and has one billion parameters.
Unlike Baidu and other profit-driven tech firms, IDEA is one of a handful of institutions backed by local governments in recent years to work on cutting-edge technologies. That means the center probably enjoys more research freedom without the pressure to drive commercial success. Based in the tech hub of Shenzhen and supported by one of China’s wealthiest cities, it’s an up-and-coming outfit worth watching.
Rules of AI
China’s generative AI tools aren’t just characterized by the domestic data they learn from; they are also shaped by local laws. As MIT Technology Review pointed out, Baidu’s text-to-image model filters out politically sensitive keywords. That’s expected, given censorship has long been a universal practice on the Chinese internet.
What’s more significant to the future of the fledgling field is the new set of regulatory measures targeting what the government dubs “deep synthesis tech”, which denotes “technology that uses deep learning, virtual reality, and other synthesis algorithms to generate text, images, audio, video, and virtual scenes.”As with other types of internet services in China, from games to social media, users are asked to verify their names before using generative AI apps. The fact that prompts can be traced to one’s real identity inevitably has a restrictive impact on user behavior.
But on the bright side, these rules could lead to more responsible use of generative AI, which is already being abused elsewhere to churn out NSFW and sexist content. The Chinese regulation, for example, explicitly bans people from generating and spreading AI-created fake news. How that will be implemented, though, lies with the service providers.
“It’s interesting that China is at the forefront of trying to regulate [generative AI] as a country,” said Yoav Shoham, founder of AI21 Labs, an Israel-based OpenAI rival, in an interview. “There are various companies that are putting limits to AI… Every country I know of has efforts to regulate AI or to somehow make sure that the legal system, or the social system, is keeping up with the technology, specifically about regulating the automatic generation of content.”
But there’s no consensus as to how the fast-changing field should be governed, yet. “I think it’s an area we’re all learning together,” Shoham admitted. “It has to be a collaborative effort. It has to involve technologists who actually understand the technology and what it does and what it doesn’t do, the public sector, social scientists, and people who are impacted by the technology as well as the government, including the sort of commercial and legal aspect of the regulation.”
Monetizing AI
As artists fret over being replaced by powerful AI, many in China are leveraging machine learning algorithms to make money in a plethora of ways. They aren’t from the most tech-savvy crowd. Rather, they are opportunists or stay-home mums looking for an extra source of income. They realize that by improving their prompts, they can trick AI into making creative emojis or stunning wallpapers, which they can post on social media to drive ad revenues or directly charge for downloads. The really skilled ones are also selling their prompts to others who want to join the money-making game — or even train them for a fee.
Others in China are using AI in their formal jobs like the rest of the world. Light fiction writers, for instance, can cheaply churn out illustrations for their works, a genre that is shorter than novels and often features illustrations. An intriguing use case that can potentially disrupt realms of manufacturing is using AI to design T-shirts, press-on nails, and prints for other consumer goods. By generating large batches of prototypes quickly, manufacturers save on design costs and shorten their production cycle.
It’s too early to know how differently generative AI is developing in China and the West. But entrepreneurs have made decisions based on their early observation. A few founders told me that businesses and professionals are generally happy to pay for AI because they see a direct return on investment, so startups are eager to carve out industry use cases. One clever application came from Sequoia China-backed Surreal (later renamed to Movio) and Hillhouse-backed ZMO.ai, which discovered during the pandemic that e-commerce sellers were struggling to find foreign models as China kept its borders shut. The solution? The two companies worked on algorithms that generated fashion models of all shapes, colors, and races.
But some entrepreneurs don’t believe their AI-powered SaaS will see the type of skyrocketing valuation and meteoric growth their Western counterparts, like Jasper and Stability AI, are enjoying. Over the years, numerous Chinese startups have told me they have the same concern: China’s enterprise customers are generally less willing to pay for SaaS than those in developed economies, which is why many of them start expanding overseas.
Competition in China’s SaaS space is also dog-eat-dog. “In the U.S., you can do fairly well by building product-led software, which doesn’t rely on human services to acquire or retain users. But in China, even if you have a great product, your rival could steal your source code overnight and hire dozens of customer support staff, which don’t cost that much, to outrace you,” said a founder of a Chinese generative AI startup, requesting anonymity.
Shi Yi, founder and CEO of sales intelligence startup FlashCloud, agreed that Chinese companies often prioritize short-term returns over long-term innovation. “In regard to talent development, Chinese tech firms tend to be more focused on getting skilled at applications and generating quick money,” he said. One Shanghai-based investor, who declined to be named, said he was “a bit disappointed that major breakthroughs in generative AI this year are all happening outside China.”
Even when Chinese tech firms want to invest in training large neural networks, they might lack the best tools. In September, the U.S. government slapped China with export controls on high-end AI chips. While many Chinese AI startups are focused on the application front and don’t need high-performance semiconductors that handle seas of data, for those doing basic research, using less powerful chips means computing will take longer and cost more, said an enterprise software investor at a top Chinese VC firm, requesting anonymity. The good news is, he argued, such sanctions are pushing China to invest in advanced technologies over the long run.
As a company that bills itself as a leader in China’s AI field, Baidu believes the impact of U.S. chip sanction on its AI business is “limited” both in the short and longer term, said the firm’s executive vice president and head of AI Cloud Group, Dou Shen, on its Q3 earnings call. That’s because “a large portion” of Baidu’s AI cloud business “does not rely too much on the highly advanced chips.” And in cases where it does need high-end chips, it has “already stocked enough in hand, actually, to support our business in the near term.”
What about the future? “When we look at it at a mid- to a longer-term, we actually have our own developed AI chip, so named Kunlun,” the executive said confidently. “By using our Kunlun chips [Inaudible] in large language models, the efficiency to perform text and image recognition tasks on our AI platform has been improved by 40% and the total cost has been reduced by 20% to 30%.”
Time will tell if Kunlun and other indigenous AI chips will give China an edge in the generative AI race.
The gigantic technological leap that machine learning models have shown in the last few months is getting everyone excited about the future of AI — but also nervous about its uncomfortable consequences. After text-to-image tools from Stability AI and OpenAI became the talk of the town, ChatGPT’s ability to hold intelligent conversations is the new obsession in sectors across the board.
In China, where the tech community has always watched progress in the West closely, entrepreneurs, researchers, and investors are looking for ways to make their dent in the generative AI space. Tech firms are devising tools built on open source models to attract consumer and enterprise customers. Individuals are cashing in on AI-generated content. Regulators have responded quickly to define how text, image, and video synthesis should be used. Meanwhile, U.S. tech sanctions are raising concerns about China’s ability to keep up with AI advancement.
As generative AI takes the world by storm towards the end of 2022, let’s take a look at how this explosive technology is shaking out in China.
Chinese flavors
Thanks to viral art creation platforms like Stable Diffusion and DALL-E 2, generative AI is suddenly on everyone’s lips. Halfway across the world, Chinese tech giants have also captivated the public with their equivalent products, adding a twist to suit the country’s tastes and political climate.
Baidu, which made its name in search engines and has in recent years been stepping up its game in autonomous driving, operates ERNIE-ViLG, a 10-billion parameter model trained on a data set of 145 million Chinese image-text pairs. How does it fair against its American counterpart? Below are the results from the prompt “kids eating shumai in New York Chinatown” given to Stable Diffusion, versus the same prompt in Chinese (纽约唐人街小孩吃烧卖) for ERNIE-ViLG.
Stable Diffusion
ERNIE-ViLG
As someone who grew up eating dim sum in China and Chinatowns, I’d say the results are a tie. Neither got the right shumai, which, in the dim sum context, is a type of succulent, shrimp and pork dumpling in a half-open yellow wrapping. While Stable Diffusion nails the atmosphere of a Chinatown dim sum eatery, its shumai is off (but I see where the machine is going). And while ERNIE-ViLG does generate a type of shumai, it’s a variety more commonly seen in eastern China rather than the Cantonese version.
The quick test reflects the difficulty in capturing cultural nuances when the data sets used are inherently biased — assuming Stable Diffusion would have more data on the Chinese diaspora and ERNIE-ViLG probably is trained on a greater variety of shumai images that are rarer outside China.
Another Chinese tool that has made noise is Tencent’s Different Dimension Me, which can turn photos of people into anime characters. The AI generator exhibits its own bias. Intended for Chinese users, it took off unexpectedly in other anime-loving regions like South America. But users soon realized the platform failed to identify black and plus-size individuals, groups that are noticeably missing in Japanese anime, leading to offensive AI-generated results.
Of course also clearly not having the model adjusted properly for darker-skinned folks, sigh
Anyway Different Dimension Me is the name, but sorry they already blocked / limit overseas users as couldn’t handle the traffic pic.twitter.com/cYi6rJwTaC
Aside from ERNIE-ViLG, another large-scale Chinese text-to-image model is Taiyi, a brainchild of IDEA, a research lab led by renowned computer scientist Harry Shum, who co-founded Microsoft’s largest research branch outside the U.S., Microsoft Research Asia. The open source AI model is trained on 20 million filtered Chinese image-text pairs and has one billion parameters.
Unlike Baidu and other profit-driven tech firms, IDEA is one of a handful of institutions backed by local governments in recent years to work on cutting-edge technologies. That means the center probably enjoys more research freedom without the pressure to drive commercial success. Based in the tech hub of Shenzhen and supported by one of China’s wealthiest cities, it’s an up-and-coming outfit worth watching.
Rules of AI
China’s generative AI tools aren’t just characterized by the domestic data they learn from; they are also shaped by local laws. As MIT Technology Review pointed out, Baidu’s text-to-image model filters out politically sensitive keywords. That’s expected, given censorship has long been a universal practice on the Chinese internet.
What’s more significant to the future of the fledgling field is the new set of regulatory measures targeting what the government dubs “deep synthesis tech”, which denotes “technology that uses deep learning, virtual reality, and other synthesis algorithms to generate text, images, audio, video, and virtual scenes.”As with other types of internet services in China, from games to social media, users are asked to verify their names before using generative AI apps. The fact that prompts can be traced to one’s real identity inevitably has a restrictive impact on user behavior.
But on the bright side, these rules could lead to more responsible use of generative AI, which is already being abused elsewhere to churn out NSFW and sexist content. The Chinese regulation, for example, explicitly bans people from generating and spreading AI-created fake news. How that will be implemented, though, lies with the service providers.
“It’s interesting that China is at the forefront of trying to regulate [generative AI] as a country,” said Yoav Shoham, founder of AI21 Labs, an Israel-based OpenAI rival, in an interview. “There are various companies that are putting limits to AI… Every country I know of has efforts to regulate AI or to somehow make sure that the legal system, or the social system, is keeping up with the technology, specifically about regulating the automatic generation of content.”
But there’s no consensus as to how the fast-changing field should be governed, yet. “I think it’s an area we’re all learning together,” Shoham admitted. “It has to be a collaborative effort. It has to involve technologists who actually understand the technology and what it does and what it doesn’t do, the public sector, social scientists, and people who are impacted by the technology as well as the government, including the sort of commercial and legal aspect of the regulation.”
Monetizing AI
As artists fret over being replaced by powerful AI, many in China are leveraging machine learning algorithms to make money in a plethora of ways. They aren’t from the most tech-savvy crowd. Rather, they are opportunists or stay-home mums looking for an extra source of income. They realize that by improving their prompts, they can trick AI into making creative emojis or stunning wallpapers, which they can post on social media to drive ad revenues or directly charge for downloads. The really skilled ones are also selling their prompts to others who want to join the money-making game — or even train them for a fee.
Others in China are using AI in their formal jobs like the rest of the world. Light fiction writers, for instance, can cheaply churn out illustrations for their works, a genre that is shorter than novels and often features illustrations. An intriguing use case that can potentially disrupt realms of manufacturing is using AI to design T-shirts, press-on nails, and prints for other consumer goods. By generating large batches of prototypes quickly, manufacturers save on design costs and shorten their production cycle.
It’s too early to know how differently generative AI is developing in China and the West. But entrepreneurs have made decisions based on their early observation. A few founders told me that businesses and professionals are generally happy to pay for AI because they see a direct return on investment, so startups are eager to carve out industry use cases. One clever application came from Sequoia China-backed Surreal (later renamed to Movio) and Hillhouse-backed ZMO.ai, which discovered during the pandemic that e-commerce sellers were struggling to find foreign models as China kept its borders shut. The solution? The two companies worked on algorithms that generated fashion models of all shapes, colors, and races.
But some entrepreneurs don’t believe their AI-powered SaaS will see the type of skyrocketing valuation and meteoric growth their Western counterparts, like Jasper and Stability AI, are enjoying. Over the years, numerous Chinese startups have told me they have the same concern: China’s enterprise customers are generally less willing to pay for SaaS than those in developed economies, which is why many of them start expanding overseas.
Competition in China’s SaaS space is also dog-eat-dog. “In the U.S., you can do fairly well by building product-led software, which doesn’t rely on human services to acquire or retain users. But in China, even if you have a great product, your rival could steal your source code overnight and hire dozens of customer support staff, which don’t cost that much, to outrace you,” said a founder of a Chinese generative AI startup, requesting anonymity.
Shi Yi, founder and CEO of sales intelligence startup FlashCloud, agreed that Chinese companies often prioritize short-term returns over long-term innovation. “In regard to talent development, Chinese tech firms tend to be more focused on getting skilled at applications and generating quick money,” he said. One Shanghai-based investor, who declined to be named, said he was “a bit disappointed that major breakthroughs in generative AI this year are all happening outside China.”
Even when Chinese tech firms want to invest in training large neural networks, they might lack the best tools. In September, the U.S. government slapped China with export controls on high-end AI chips. While many Chinese AI startups are focused on the application front and don’t need high-performance semiconductors that handle seas of data, for those doing basic research, using less powerful chips means computing will take longer and cost more, said an enterprise software investor at a top Chinese VC firm, requesting anonymity. The good news is, he argued, such sanctions are pushing China to invest in advanced technologies over the long run.
As a company that bills itself as a leader in China’s AI field, Baidu believes the impact of U.S. chip sanction on its AI business is “limited” both in the short and longer term, said the firm’s executive vice president and head of AI Cloud Group, Dou Shen, on its Q3 earnings call. That’s because “a large portion” of Baidu’s AI cloud business “does not rely too much on the highly advanced chips.” And in cases where it does need high-end chips, it has “already stocked enough in hand, actually, to support our business in the near term.”
What about the future? “When we look at it at a mid- to a longer-term, we actually have our own developed AI chip, so named Kunlun,” the executive said confidently. “By using our Kunlun chips [Inaudible] in large language models, the efficiency to perform text and image recognition tasks on our AI platform has been improved by 40% and the total cost has been reduced by 20% to 30%.”
Time will tell if Kunlun and other indigenous AI chips will give China an edge in the generative AI race.
Fidelity, which was among the group of outside investors that helped Elon Musk finance his $44 billion takeover of Twitter, has slashed the value of its stake in Twitter by 56%. The recalculation comes as Twitter navigates a number of challenges, most the result of chaotic management decisions — including an exodus of advertisers from the network.
Fidelity’s Blue Chip Growth Fund stake in Twitter was valued at around $8.63 million as of November, according to a monthly disclosure and Fidelity Contrafund notice first reported today by Axios. That’s down from $19.66 million as of the end of October.
Macroeconomic trends are likely to blame in part. Stripe took a 28% internal valuation cut in July, while Instacart this week reportedly suffered a 75% cut to its valuation.
But Twitter’s wishy-washy policies post-Musk clearly haven’t helped matters.
The network’s become less stable at a technical level as of late, on Wednesday suffering outages after Musk made “significant” backend server architecture changes. Twitter recently laid off employees in its public policy and engineering department, dissolving the group responsible for weighing in on content moderation and human rights-related issues such as suicide prevention. And the company’s raised the ire of regulators after banning — and then quickly reinstating — accounts belonging to prominent journalists.
Then again — as Axios business editor Dan Primack pointed out, appropriately in a tweet — Fidelity seems to rely heavily on public market performance where it concerns valuations. It’s quite possible that the firm doesn’t have any inside info on Twitter’s financial performance.
Cutbacks at Twitter abound as the company approaches $1 billion in interest payments due on $13 billion in debt, all while revenue dips. A November report from Media Matters for America estimated that half of Twitter’s top 100 advertisers, which spent almost $750 million on Twitter ads this year combined, appear to no longer be advertising on the website. Twitter’s heavily pushing its Twitter Blue plan, aiming to make it a larger profit driver. But third-party tracking data suggest it’s been slow to take off.
Some Twitter employees are bringing their own toilet paper to work after the company cut back on janitorial services, the New York Times recently reported, and Twitter has stopped paying rent for several of its offices including its San Francisco headquarters.
Musk has attempted to save around $500 million in costs unrelated to labor, according to the aforementioned Times report, over the past few weeks shutting down a data center and launching a fire sale after putting office items up for auction in a bid to recoup costs.
Separately, Musk’s team has reached out to investors for potential fresh investment for Twitter at the same price as the original $44 billion acquisition, according to The Wall Street Journal.
A poll put up by Musk asking if he should step down as head of the company closed December 19 with users voting resoundingly in favor of him leaving. Musk responded several days afterward, saying he’d resign as CEO “as soon as [he found] someone foolish enough to take the job” and after that “just run the software and servers teams.”
To get a roundup of TechCrunch’s biggest and most important stories delivered to your inbox every day at 3 p.m. PDT,subscribe here.
Welcome back to your daily digest of TechCrunch goodness. It is my last day with you (you’re welcome!), so Christine will be back in the Daily Crunch seat on Tuesday. Haje will not be back just yet because he is heading to Vegas as part of the team covering CES. Speaking of CES, Brian raised the curtain on what we can expect from its first full-fledged production since before COVID.
Bye for now, folks. Safe and Happy New Year to you all. — Henry
At the top
Into the Matrix: No, not that Matrix. We’re talking about the open standards-based comms protocol called Matrix that Paul went deep on. Its network doubled thanks in part to increased use by enterprises and government. Reddit is also having a go, experimenting with it for its chat feature.
For the fusion: Tim took a look at five startups primed to benefit from the recent breakthroughs in fusion. [TC+]
Alt-ChatGPT: In the wake of the response to OpenAI’s ChatGPT comes an open source equivalent. It’s called PaLM + RLHF (rolls right off the tongue, eh?), but Kyle writes that it isn’t pre-trained, which means good luck running it.
The Meta eyes have it: Amanda writes that Meta is getting into the eyewear business with its purchase of the Netherlands-based, smart eyewear company Luxexcel.
Book tracking: Aisha rounded up a list of five apps that you can use to track all that reading you’re planning to do once the clock strikes 2023.
Netflix vs. Hulu: Perhaps you’ve decided to cut a streaming service or two from your lineup in light of their continued price hikes. Lauren took a look at the features of Netflix and Hulu to help you make a decision.
What to look for in a term sheet as a first-time founder
TechCrunch+is our membership program that helps founders and startup teams get ahead of the pack.You can sign up here. Use code “DC” for a 15% discount on an annual subscription!
Looking back and looking ahead
We rounded up TC+ venture capital stories from a year that unfortunately saw a lot of downs. And here are a few more favorites for good measure:
Indian startups were flush with cash with record investments. Now, Manish writes, the ecosystem is struggling with tightening funding purses, layoffs and disappointing public debuts.
To get a roundup of TechCrunch’s biggest and most important stories delivered to your inbox every day at 3 p.m. PDT,subscribe here.
Welcome back to your daily digest of TechCrunch goodness. It is my last day with you (you’re welcome!), so Christine will be back in the Daily Crunch seat on Tuesday. Haje will not be back just yet because he is heading to Vegas as part of the team covering CES. Speaking of CES, Brian raised the curtain on what we can expect from its first full-fledged production since before COVID.
Bye for now, folks. Safe and Happy New Year to you all. — Henry
At the top
Into the Matrix: No, not that Matrix. We’re talking about the open standards-based comms protocol called Matrix that Paul went deep on. Its network doubled thanks in part to increased use by enterprises and government. Reddit is also having a go, experimenting with it for its chat feature.
For the fusion: Tim took a look at five startups primed to benefit from the recent breakthroughs in fusion. [TC+]
Alt-ChatGPT: In the wake of the response to OpenAI’s ChatGPT comes an open source equivalent. It’s called PaLM + RLHF (rolls right off the tongue, eh?), but Kyle writes that it isn’t pre-trained, which means good luck running it.
The Meta eyes have it: Amanda writes that Meta is getting into the eyewear business with its purchase of the Netherlands-based, smart eyewear company Luxexcel.
Book tracking: Aisha rounded up a list of five apps that you can use to track all that reading you’re planning to do once the clock strikes 2023.
Netflix vs. Hulu: Perhaps you’ve decided to cut a streaming service or two from your lineup in light of their continued price hikes. Lauren took a look at the features of Netflix and Hulu to help you make a decision.
What to look for in a term sheet as a first-time founder
TechCrunch+is our membership program that helps founders and startup teams get ahead of the pack.You can sign up here. Use code “DC” for a 15% discount on an annual subscription!
Looking back and looking ahead
We rounded up TC+ venture capital stories from a year that unfortunately saw a lot of downs. And here are a few more favorites for good measure:
Indian startups were flush with cash with record investments. Now, Manish writes, the ecosystem is struggling with tightening funding purses, layoffs and disappointing public debuts.
Interoperability and decentralization have been major themes in tech this year, driven in large part by mounting regulation, societal and industrial pressure, and the hype trains that are crypto and web3. That rising tide is lifting other boats: an open standards-based communication protocol called Matrix — which is playing a part in bringing interoperability to another proprietary part of our digital lives: messaging.
The number of people on the Matrix network doubled in size this year, according to Matthew Hodgson, one of Matrix’s co-creators — a notable, if modest, boost to 80.3 million users (that number may be higher: not all Matrix deployments “phone home” stats to Matrix.org).
While the bulk of all this activity has been in enterprise communications, it looks like mainstream consumer platforms might now also be taking notice.
Some sleuthing from engineer and app researcher Jane Manchun Wong unearthed evidence that Reddit is experimenting with Matrix for its Chat feature — a move more or less confirmed to TechCrunch by Reddit. A spokesperson said that it’s “looking at a number ways to improve conversations on Reddit” and was “testing a number of options,” though they stopped short of name-checking Matrix specifically.
Given the bigger swing in support of interoperability — it’s happening also in digital wallets and maps — a closer look at Matrix gives some insight into how we got here.
In the beginning
View from above hands holding mobile phones Image Credits: Malte Mueller / Getty
Anyone who has ever sent an SMS or email won’t have considered for a second what network, service provider, or messaging client their intended recipient used. The main reason is that it doesn’t really matter — T-Mobile and Verizon customers can text each other just fine, while Gmail and Outlook users have no problems emailing each other.
But that wasn’t always the case. In the earliest days of electronic mail, you could only message users on the same network. And as mobile phones proliferated throughout the 1990s, people initially couldn’t message their friends if they were on a different mobile network. Europe and Asia led the charge on interoperability, and by the start of the millennium the big North American telcos also realized they could unlock a veritable goldmine if they allowed consumers to message their friends on rival networks. It was a win-win for everyone.
Fast forward to the modern smartphone age, and while email hasn’t exactly gone the way of the dodo and SMS is still stuttering along, the preeminent communication tools of today aren’t nearly as friendly with each other. Those looking to embrace independent privacy-focused messaging apps such as Signal will hit a brick wall when they realize that literally all their pals are using WhatsApp. Or iMessage. Or Telegram. Or Viber… you get the picture.
This trend permeates the enterprise realm, too. If your work uses Slack, good luck sending a message to your buddy across town forced to use Microsoft Teams, while those in human resources shoehorned onto Meta’s Workplace can think again about DM-ing their sales’ colleagues along the corridor using Salesforce Chatter.
This is nothing new, of course, but the issue of interoperability in the online messaging sphere has come sharply into focus in 2022. Europe is pushing ahead with rules to force interoperability and portability between online platforms via the Digital Markets Act (DMA), while the U.S. has similar plans via the ACCESS Act.
Meanwhile, Elon Musk’s arrival at Twitter has driven awareness of alternatives such as Mastodon, the so-called “open source Twitter alternative” that shot past 2 million users off the back of the chaos at Twitter. Mastodon is powered by the open ActivityPub protocol and is built around the concept of the fediverse: a decentralized network of interconnected servers that allow different ActivityPub-powered services to communicate with each other. Tumblr recently revealed that it intends to support the ActivityPub protocol in the future, while Flickr CEO Don MacAskill polled his Twitter followers on whether the photo-hosting platform and community should also adopt ActivityPub.
But despite all the hullaballoo and hype around interoperability spurred by the Twitter circus in recent weeks, there was already a quiet-but-growing movement in this direction, a movement driven by enterprises and governments seeking to avoid vendor lock-in and garner greater control of their data stack.
Enter the Matrix
Element founders and Matrix co-creators Matthew Hodgson and Amandine Le Pape Image Credits: Element
Matrix was developed inside software and services companyAmdocsback in 2014, spearheaded by Hodgson and Amandine Le Pape who later left the company to focus entirely on growing Matrix as an independent open source project. They also sought to commercialize Matrix through acompany called New Vector, which developed a Matrix hosting service and a Slack alternativeapp called Riot. In 2018, Hodgson and Le Pape launched the Matrix.org Foundationto serve as a legal entity and guardian for all-things Matrix, including protecting its intellectual property, managing donations, and pushing the protocol forward.
The flagship commercial implementation of Matrix was rebranded as Element a little more than two years ago, and today Element — backed by Automattic, Dawn Capital, Notion, Protocol Labs and others — is used by a host of organizations looking for a federated alternative to the big-name incumbents sold by U.S. tech giants.
Element itself is open source and promises end-to-end encryption, while its customers can access the usual cross-platform features most would expect from a team collaboration product, including group messaging and voice and video chat.
Element in action Image Credits: Element
Element can also be hosted on companies’ own infrastructure, circumventing concerns about how their data may be (mis)used on third-party servers, ensuring they remain in control of their full data stack — a deal maker or breaker for entities that host sensitive data.
A growing array of regulations, particularly in Europe, are forcing Big Tech to pay attention to data sovereignty, with the likes of Google partnering with Deutsche Telekom’s IT services and consulting subsidiary T-Systems last year to offer German companies a “sovereign cloud” for their sensitive data.
This regulatory push, alongside growing expectations around data sovereignty, has been a boon for the Matrix protocol. Last year, the agency responsible for digitalizing Germany’s health care system revealed that it was transitioning to Matrix, ensuring that the 150,000 individual entities that constitute the health care industry such as hospitals, clinics, and insurance companies, could communicate with each other regardless of what Matrix-based app they used.
This builds on existing Matrix implementations elsewhere, including inside the French government via the Tchap team collaboration platform, as well as the German armed forces Bundeswehr.
“The pendulum has been clearly swinging towards decentralization for quite a while,” Hodgson explained to TechCrunch. “We’re now seeing serious use of Matrix-based decentralized communications across or within the French, German, U.K, Swedish, Finnish and U.S governments, as well as the likes of NATO and adjacent organisations.”
Back in May, open source enterprise messaging platform Rocket.Chat revealed that it would be transitioning to the Matrix protocol. While this process is still ongoing, this represented a major coup for the Matrix movement, given that Rocket.Chat claims some 12 million users across major organizations such as Audi, Continental, and Germany’s national railway company, The Deutsche Bahn.
“We believe that the value of any messaging platform grows based on its ability to connect with other platforms,” a Rocket.Chat spokesperson told TechCrunch. “We put a lot of effort into connecting Rocket.Chat with other platforms. We don’t have to worry about what client we use when emailing each other, and the same should be true when we’re messaging each other.”
Rocket.chat Image Credits: Rocket.chat
What’s perhaps most interesting about all this is that it runs contrary to the path that traditional consumer and enterprise social networks, and team collaboration tools, have taken.
Slack, Facebook, Microsoft Teams, WhatsApp, Twitter, and all the rest are all about harnessing the network effect, where a product’s value is intrinsically linked to the number of users on it. People, ultimately, want to be where their friends and work colleagues are, which inevitably means sticking with a social network they don’t particularly like, or using multiple different apps simultaneously.
Open and interoperable protocols support a new breed of business that’s cognizant of the growing demand for something that doesn’t lock users in.
“Our goal is not to force people to use Rocket.Chat in order to communicate with each other,” Rocket.Chat’s spokesperson continued. “Rather, our goal is to enable organizations to collaborate securely and connect with other organizations and individuals across the platforms of their choosing.”
Bridging the divide
The Matrix protocol also supports non-native interoperability through a technique called “bridging,” which ushers in support for non-Matrix apps, including WhatsApp, Telegram, and Signal. Element itself offers bridging as part of a consumer-focused subscription product called Element One, where users pay $5 per month to bring all their friends together into a single interface — irrespective of what app they use.
Element One subscribers can bring different messaging apps together Image Credits: The Matrix Foundation
This is enabled through publicly available APIs created by the tech companies themselves. However, terms of use are typically restrictive with regards to how they can be used by competing apps, while they may also enforce rate-limits or usage costs.
Bridging as it stands sits somewhere in a grey area from a “is this allowed?” perspective. But with the world’s regulatory eyes laser-focused on Big Tech’s stranglehold on online communications, the companies perhaps don’t enforce all their T&Cs too rigorously.
The DMA came into force in Europe last month — though it won’t officially become applicable until next May — and it has specific provisions for interoperability and data portability. At that point, we’ll perhaps start to see how the Big Tech “gatekeepers” of the world plan to support the new regulations. In reality, what we’re talking about are open APIs that “formally” permit smaller third-parties to integrate and communicate with their Big Tech brethren. This doesn’t necessarily mean that such APIs will be slick and easy-to-use with clear documentation though, and we can probably expect some deliberate heel-dragging and hurdles along the way.
Compliance
WhatsApp and Facebook application displayed on a iPhone Image Credits: Justin Sullivan/Getty Images
Popular messaging apps such as WhatsApp, while offering end-to-end encryption, weren’t designed for enterprise or governmental use-cases as they don’t allow organizations to easily manage any of their messaging data — yet such apps are widely used in such scenarios. Back in July, the U.K.’s Information Commissioner’s Office (ICO) called for a government review into the risks around “private correspondence channels” such as personal email accounts and WhatsApp, noting that such usage lacked “clear controls” and could lead to the loss of key information being “lost or insecurely handled.”
“I understand the value of instant communication that something like WhatsApp can bring, particularly during the pandemic where officials were forced to make quick decisions and work to meet varying demands,” U.K. information commissioner John Edwards said in a statement at the time. “However, the price of using these methods, although not against the law, must not result in a lack of transparency and inadequate data security. Public officials should be able to show their workings, for both record keeping purposes and to maintain public confidence. That is how trust in those decisions is secured and lessons are learnt for the future.”
In the business realm, meanwhile, the U.S. Securities and Exchange Commission (SEC) recently settled with 16 Wall Street firms for $1.1 billion over “widespread recordkeeping failures” related to their use of private messaging apps such as WhatsApp.
“Finance, ultimately, depends on trust,” SEC Chair Gary Gensler said at the time. “Since the 1930s, such record keeping has been vital to preserve market integrity. As technology changes, it’s even more important that registrants appropriately conduct their communications about business matters within only official channels, and they must maintain and preserve those communications.”
Maintaining an accurate paper trail, and ensuring that politicians and businesses are accountable for their actions, is the name of the game — a level of control that something like the Matrix protocol promises. However, mandating that every company over a certain size — as the DMA regulation does — has to make their software interoperable with others raises a bunch of questions around privacy, security, and the broader user experience.
The encryption elephant in the room
Concept illustration of “elephant in the room” Image Credits: Klyaksun / Getty Images
As Casey Newton has noted over at The Platformer on more than one occasion, Europe’s new interoperability regulations come with several pitfalls, chief among them, perhaps, being the hurdles they will create for end-to-end encryption — that is, ensuring that data remains encrypted and impossible to decode while in transit.
End-to-end encryption is a huge selling point for the big technology companies of today, one that WhatsApp hollers from the rooftops. But making this work between different platforms built by different companies is not exactly easy, and many — if not most — experts on the subject say that it’s not possible to enforce a truly secure, interoperable messaging infrastructure that doesn’t compromise encryption in some way.
WhatsApp can control — and therefore promise — end-to-end encryption on its own platform. But if billions of messages are flying between WhatsApp and countless other applications run by other companies, WhatsApp can’t really know what’s happening to these messages once they leave WhatsApp.
Ultimately, no two services deploy their encryption identically, a challenge that Hodgson acknowledges. “End-to-end encrypted platforms have to speak the same language from end-to-end,” he said.
In a blog post published earlier this year to address encryption concerns, the Matrix Foundation suggested some workarounds, including having all the big gatekeepers switch to the same “decentralized end-to-end protocol” (i.e. Matrix, unsurprisingly) which, by the Foundation’s own admission, would be a large undertaken — but one “we shouldn’t rule out,” it said.
To illustrate this point, Hodgson pointed to Element’s 2020 acquisition of Gitter, a developer-focused community and chat platform purchased from GitLab and used by big-name companies including Google, Microsoft, and Amazon. Within two months of closing the deal, Element had introduced native Matrix connectivity to Gitter.
Coordinating such a transition on a Facebook, Google, or Apple scale would be an entirely different proposition, of course, one that could cause all manner of knock-on chaos. In a blog post earlier this year, cryptography and security expert Alec Muffett suggested that messaging apps and social networks adhering to the same standard protocol would lead to “no practical differentiation” between different services.
“Imagine a world where Signal and Snapchat would have to interoperate — what would that look like?” Muffett asked TechCrunch rhetorically in a Q&A for this story. “Specifically, which features from one need to be presented on the other, and what are the educators which surround those features? And how would conflict in functionality be reconciled?”
This is why the Matrix Foundation proposed other potential solutions, such as adopting a TLS certificate-style warning, where the user is alerted to the fact that their cross-service conversation is not fully protected. This is perhaps comparable to how Apple’s Messages app supports both encrypted iMessage texts, and (unencrypted) SMS. But according to Muffett, it would bring unnecessary complexity to the mix.
“Apart from any other reason that I could cite, there is any amount of user interface research which explains that security-pop-up-warnings are generally not understood and not heeded,” Muffett said. “There is tons of research to back this up — popup warnings are an ‘anti-pattern‘.”
The Matrix Foundation also proposed converting communication traffic between encryption languages in a “bridge,” though this would effectively mean having to break the encryption and re-encrypt the traffic safely somewhere.
“These bridges could be run client-side — for example, the Matrix iMessage bridge runs client-side on iPhone or Mac — or by using client-side open APIs to bridge between the apps locally within the phone itself,” Hodgson said. “Alternatively, they could be run server-side on hardware controlled by the user in a decentralized fashion, ensuring that the re-encryption happens in as secure an environment as possible, rather than on a vulnerable centralized server.”
There’s no escaping the fact that breaking encryption is far from ideal, irrespective of how a solution proposes to reconcile this. But perhaps more importantly, a robust solution for addressing the real encryption issues introduced by enforced interoperability doesn’t truly exist yet.
Despite that, Hodgson has said in the past that the upsides of the new EU regulations are greater than the downsides.
“On balance, we think that the benefits of mandating open APIs outweigh the risks that someone is going to run a vulnerable large-scale bridge and undermine everyone’s E2EE,” he wrote in May. “It’s better to have the option to be able to get at your data in the first place, than be held hostage in a walled garden.”
Tip of the iceberg
It’s worth noting that the Matrix protocol, while chiefly known for its presence in the messaging realm today, has other potential applications too. The Matrix Foundation recently announced Third Room, a decentralized and interoperable metaverse platform built on Matrix. This runs contrary to a potential future metaverse controlled by a handful of gatekeepers such as Facebook’s parent company Meta.
For now, Element remains the flagship poster-child of what a Matrix-powered world could look like. The company has secured some big-name customers already such as Mozilla, which is using Element as a fully-managed service, while Element said that it signed a $18 million four-year deal with another (unnamed) company this year. Meanwhile, it also has strategic backers, among them WordPress.com parent Automattic, which first invested $4.6 million in Element back in 2020, before returning for its $30 million Series B last year.
In many ways, the ground has never been so fertile for Matrix to flourish: it’s in the right place at the right time, as the world seeks an exit route from Big Tech’s clutches backed by at least a little regulation. And Twitter, too, has played more than a bit part in highlighting the downsides of centralized control, playing into the hands of all the companies banging the interoperability drum.
“The situation at Twitter has been absolutely amazing in terms of building awareness of the perils of centralization, providing a pivotal moment in helping users discover that we are entering a golden age of decentralization,” Hodgson said. “Just as many users have discovered that Mastodon is an increasingly viable decentralized alternative to Twitter, we’ve seen a massive halo effect of users discovering Matrix as a way to reclaim their independence over real-time communications such as messaging and VoIP — our long-term user base in particular is growing at its fastest ever rate.”
Lukas Gentele is the CEO of Loft Labs, which builds open source developer tooling for Kubernetes.
Kubernetes is a wonderful but complex software that can present significant “Day Two” challenges when put into production.
Developers who are new to Kubernetes — and most are — face a large knowledge gap when they look to sustain and optimize Kubernetes clusters.
In this piece, I will share several ways to address problems as they arise.
Optimize your Kubernetes cluster for cost
As adoption of Kubernetes rises, the need for applications and engineers to access clusters is also growing. However, it is neither feasible nor cost-efficient to always use entire physical clusters to achieve this goal.
Virtual clusters are a great way to reduce costs. In a scenario of 100 developers, we calculated up to 78% savings by using open source virtual clusters.
Leveraging virtual clusters with open source software such as VirtualCluster or vcluster lets Kubernetes operators can run multiple virtual clusters within a single physical cluster, thereby increasing the tenancy of each. By utilizing computing resources via this more communal method, organizations can save on computing costs as opposed to operating entirely separate Kubernetes clusters.
Increase tenant isolation
By leveraging policy engines, it’s possible to implement software security guardrails on your cloud-native Kubernetes infrastructure.
Another great benefit of virtual clusters is that they are isolated from other users on the cluster. This gives each user their own workspace that looks and feels exactly like a physical Kubernetes cluster.
In addition, virtual clusters enable a stricter form of multitenancy compared to namespace-based multitenancy. One of the main concerns with namespace-based multitenancy is that it cannot contain cluster-scoped resources. Many applications must create, or at least access, cluster-scoped resources like nodes, cluster roles, persistent volumes and storage classes.
Virtual clusters also provide security benefits by increasing the isolation in multitenancy clusters via:
Full control-plane isolation.
Domain Name System (DNS) isolation.
Resources created on a single namespace.
Organizations seeking a solution for multitenant applications that provide greater isolation for resources shared among their clusters should consider virtual clusters as an option. On top of saving costs and being simpler to deploy, they are also easier to manage than physical clusters.
