How China’s synthetic media startup Surreal nabs funding in 3 months

What if we no longer needed cameras to make videos and can instead generate them through a few lines of coding?

Advances in machine learning are turning the idea into a reality. We’ve seen how deepfakes swap faces in family photos and turn one’s selfies into famous video clips. Now entrepreneurs with AI research background are devising tools to let people generate highly realistic photos, voices, and videos using algorithms.

One of the startups building this technology is China-based Surreal. The company is merely three months old but has already secured a seed round of $2-3 million from two prominent investors, Sequoia China and ZhenFund. Surreal received nearly ten investment offers in this round, founder and CEO Xu Zhuo told TechCrunch, as investors jostled to bet on a future shaped by AI-generated content.

Prior to founding Surreal, Xu spent six years at Snap, building its ad recommendation system, machine learning platform, and AI camera technology. The experience convinced Xu that synthetic media would become mainstream because the tool could significantly “lower the cost of content production,” Xu said in an interview from Surreal’s a-dozen-person office in Shenzhen.

Surreal has no intention, however, to replace human creators or artists. In fact, Xu doesn’t think machines can surpass human creativity in the next few decades. This belief is embodied in the company’s Chinese name, Shi Yun, or The Poetry Cloud. It is taken from the title of a novel by science fiction writer Liu Cixin, who tells the story of how technology fails to outdo the ancient Chinese poet Li Bai.

“We have an internal formula: visual storytelling equals creativity plus making,” Xu said, his eyes lit up. “We focus on the making part.”

In a way, machine video generation is like a souped-up video tool, a step up from the video filters we see today and make Douyin (TikTok’s Chinese version) and Kuaishou popular. Short video apps significantly lower the barrier to making a professional-looking video, but they still require a camera.

“The heart of short videos is definitely not the short video form itself. It lies in having better camera technology, which lowers the cost of video creation,” said Xu, who founded Surreal with Wang Liang, a veteran of TikTok parent ByteDance.

Commercializing deepfakery

Some of the world’s biggest tech firms, such as Google, Facebook, Tencent and ByteDance, also have research teams working on GAN. Xu’s strategy is not to directly confront the heavyweights, which are drawn to big-sized contracts. Rather, Surreal is going after small and medium-sized customers.

Surreal’s face swapping software for e-commerce sellers

Surreal’s software is currently only for enterprise customers, who can use it to either change faces in uploaded content or generate an entirely new image or video. Xu calls Surreal a “Google Translate for videos,” for the software can not only swap people’s faces but also translate the languages they speak accordingly and match their lips with voices.

Users are charged per video or picture. In the future, Surreal aims to not just animate faces but also people’s clothes and motions. While Surreal declined to disclose its financial performance, Xu said the company has accumulated around 10 million photo and video orders.

Much of the demand now is from Chinese e-commerce exporters who use Surreal to create Western models for their marketing material. Hiring real foreign models can be costly, and employing Asian models doesn’t prove as effective. By using Surreal “models”, some customers have been able to achieve 100% return on investment (ROI), Xu said. With the multi-million seed financing in its pocket, Surreal plans to find more use cases like online education so it can collect large volumes of data to improve its algorithm.

Uncharted territory

The technology powering Surreal, called generative adversarial networks, is relatively new. Introduced by machine learning researcher Ian Goodfellow in 2014, GANs consist of a “generator” that produces images and a “discriminator” that detects whether the image is fake or real. The pair enters a period of training with adversarial roles, hence the nomenclature, until the generator delivers a satisfactory result.

In the wrong hands, GANs can be exploited for fraud, pornography and other illegal purposes. That’s in part why Surreal starts with enterprise use rather than making it available to individual users.

Companies like Surreal are also posing new legal challenges. Who owns the machine-generated images and videos? To avoid violating copyright, Surreal requires that the client has the right to the content they upload for moderation. To track and prevent misuse, Surreal adds an encrypted and invisible watermark to each piece of the content it generates, to which it claims ownership. There’s an odd chance that the “person” Surreal produces would match someone in real life, so the company runs an algorithm that crosschecks all the faces it creates with photos it finds online.

“I don’t think ethics is something that Surreal itself can address, but we are willing to explore the issue,” said Xu. “Fundamentally, I think [synthetic media] provides a disruptive infrastructure. It increases productivity, and on a macro level, it’s inexorable, because productivity is the key determinant of issues like this.”

AWS reorganizes DeepRacer League to encourage more newbies

AWS launched the DeepRacer League in 2018 as a fun way to teach developers machine learning, and it’s been building on the idea ever since. Today, it announced the latest league season with two divisions: Open and Pro.

As Marcia Villalba wrote in a blog post announcing the new league, “AWS DeepRacer is an autonomous 1/18th scale race car designed to test [reinforcement learning] models by racing virtually in the AWS DeepRacer console or physically on a track at AWS and customer events. AWS DeepRacer is for developers of all skill levels, even if you don’t have any ML experience. When learning RL using AWS DeepRacer, you can take part in the AWS DeepRacer League where you get experience with machine learning in a fun and competitive environment.”

While the company started these as in-person races with physical cars, the pandemic has forced them to make it a virtual event over the last year, but the new format seemed to be blocking out newcomers. Because the goal is to teach people about machine learning, getting new people involved is crucial to the company.

That’s why it created the Open League, which as the name suggests is open to anyone. You can test your skills and if you’re good enough, finishing in the top 10%, you can compete in the Pro division. Everyone competes for prizes, as well, such as vehicle customizations.

The top 16 in the Pro League each month race for a chance to go to the finals at AWS re:Invent in 2021, an event that may or may not be virtual, depending on where we are in the pandemic recovery.

Brandwatch is acquired by Cision for $450M, creating a PR, marketing and social listening giant

Online consumer intelligence and social media listening platform Brandwatch has been acquired by Cision, best known for its media monitoring and media contact database services, for $450 million, in a combined cash and shares deal. TechCrunch understands Brandwatch’s key executive team will be staying on. The move combines two large players to offer a broad range of services from PR to marketing and online customer engagement. The deal is expected to close in the second quarter of 2021.

Cision has a media contact database of approximately 1 million journalists and media outlets and claims to have over 75,000 customers. Brandwatch applies AI and machine learning the practice known as ‘social listening’.

Along the way, Brandwatch raised a total of around $65 million. It was Series A-funded by Nauta Capital, followed by Highland Europe and then Partech.

IN a statement, Giles Palmer, founder, and CEO of Brandwatch said: “We have always built Brandwatch with ambition… Now is the time to take the next step – joining a company of significant scale to create a business and a suite of products that can have an important global impact.”

Abel Clark, CEO of Cision said: “The continued digital shift and widespread adoption of social media is rapidly and fundamentally changing how brands and organizations engage with their customers. This is driving the imperative that PR, marketing, social, and customer care teams fully incorporate the unique insights now available into consumer-led strategies. Together, Cision and Brandwatch will help our clients to more deeply understand, connect and engage with their customers at scale across every channel.”

Brandwatch has been on an almost case-study of a journey from fundraising to acquisition to a merger, but less characteristically for a well-funded tech company, it did much of it from its home-town of Brighton, on the southern coast of England.

The financing journey began for Giles Palmer, with Angel funding in 2006. In 2010 Brandwatch raised $1.5m from Durrants, a marketing and PR firm, and Nauta Capital. In 2014 it raised $22 million in funding in a Series B round led by Highland Capital. That was followed by a $33M Series C financing led by Partech Ventures in 2015.

With the war chest, it went on to acquire BuzzSumo in 2017, a content marketing and influencer identification platform, for an undisclosed sum. And in 2019 Brandwatch merged with a similar business, Crimson Hexagon, creating a business with around $100 million in ARR. It also acquired the London-based SaaS research platform Qriously.

Brandwatch was recently named a leader in Forrester’s guide for buyers of social listening solutions.

Docyt raises $1.5M for its ML-based accounting automation platform

Accounting isn’t a topic that most people can get excited about — probably not even most accountants. But if you’re running any kind of business, there’s just no way around it. Santa Clara-based Docyt wants to make the life of small and medium business owners (and their accounting firms) a bit easier by using machine learning to handle a lot of the routine tasks around collecting financial data, digitizing receipts, categorization and — maybe most importantly — reconciliation.

The company today announced that it has raised a $1.5 million seed-extension round led by First Rays Venture Partners with participation from Morado Ventures and a group of angel investors. Docyt (pronounced “docket”) had previously raised a $2.2 million seed round from Morado Ventures, AME Cloud Ventures, Westwave Capital, Xplorer Capital, Tuesday and angel investors. The company plans to use the new investment to accelerate its customer growth.

At first glance, it may seem like Docyt competes with the likes of QuickBooks, which is pretty much the de facto standard for small business accounting. But Docyt co-founder and CTO Sugam Pandey tells me that he thinks of the service as a partner to the likes of QuickBooks.

Image Credits: Docyt

“Docyt is a product for the small business owners who find accounting very complex, who are very experienced on how to run and grow their business, but not really an expert in accounting. At the same time, businesses who are graduating out of QuickBooks — small business owners sometimes become midsized enterprises as well — [ … ] they start growing out of their accounting systems like QuickBooks and looking for more sophisticated systems like NetSuite and Sage. And Docyt fits in in that space as well, extending the life of QuickBooks for such business owners so they don’t have to change their systems.”

In its earliest days, Docyt was a secure document sharing platform with a focus on mobile. Some of this is still in the company’s DNA, with its focus on being able to pull in financial documents and then reconciling that with a business’ bank transactions. While other systems may put the emphasis on transaction data, Docyt’s emphasis is on documents. That means you can forward an emailed receipt to the service, for example, and it can automatically attach this to a financial transaction from your credit card or bank statement (the service uses Plaid to pull in this data).

Image Credits: Docyt

For new transactions, you sometimes have to train the system by entering some of this information by hand, but over time, Docyt should be able to do most of this automatically and then sync your data with QuickBooks.

“Docyt is the first company to apply AI across the entire accounting stack,” said Amit Sridharan, founding general partner at First Rays Venture Partners. “Docyt software’s AI-powered data extraction, auto categorization and auto reconciliation is unparalleled. It’s an enterprise-level, powerful solution that’s affordable and accessible to small and medium businesses.”

Foresite Capital raises $969 million fund to invest in healthcare startups across all stages of growth

Health and life science specialist investment firm Foresite Capital has raised a new fund, its fifth to date, totally $969 million in commitments from LPs. This is the firm’s largest fund to date, and was oversubscribed relative to its original target according to fund CEO and founder Dr. Jim Tananbaum, who told me that while the fundraising process started out slow in the early months of the pandemic, it gained steam quickly starting around last fall and ultimately exceeded expectations.

This latest fund actually makes up two separate investment vehicles, Foresite Capital Fund V, and Foresite Capital Opportunity Fund V, but Tananbaum says that the money will be used to fuel investments in line with its existing approach, which includes companies ranging from early- to late-stage, and everything in between. Foresite’s approach is designed to help it be uniquely positioned to shepherd companies from founding (they also have a company-building incubator) all the way to public market exit – and even beyond. Tananbaum said that they’re also very interested in coming in later to startups they have have missed out on at earlier stages of their growth, however.

Image Credits: Foresite Capital

“We can also come into a later situation that’s competitive with a number of hedge funds, and bring something unique to the table, because we have all these value added resources that we used to start companies,” Tananbaum said. “So we have a competitive advantage for later stage deals, and we have a competitive advantage for early stage deals, by virtue of being able to function at a high level in the capital markets.”

Foresite’s other advantage, according to Tananbaum, is that it has long focused on the intersection of traditional tech business mechanics and biotech. That approach has especially paid off in recent years, he says, since the gap between the two continues to narrow.

“We’ve just had this enormous believe that technology, and tools and data science, machine learning, biotechnology, biology, and genetics – they are going to come together,” he told me. “There hasn’t been an organization out there that really speaks both languages well for entrepreneurs, and knows how to bring that diverse set of people together. So that’s what we specialized i,n and we have a lot of resources and a lot of cross-lingual resources, so that techies that can talk to biotechies, and biotechies can talk to techies.”

Foresite extended this approach to company formation with the creation of Foresite Labs, an incubation platform that it spun up in October 2019 to leverage this experience at the earliest possible stage of startup founding. It’s run by Dr. Vik Bajaj, who was previously co-founder and Chief Science Officer of Alphabet’s Verily health sciences enterprise.

“What’s going on, or last couple decades, is that the innovation cycles are getting faster and faster,” Tananbaum said. “So and then at some point, the people that are having the really big wins on the public side are saying, ‘Well, these really big wins are being driven by innovation, and by quality science, so let’s go a little bit more upstream on the quality science.'”

That has combined with shorter and shorter healthcare product development cycles, he added, aided by general improvements in technology. Tananbaum pointed out that when he began Foresite in 2011, even, the time horizons for returns on healthcare investments were significantly longer, and at the outside edge of the tolerances of venture economics. Now, however, they’re much closer to those found in the general tech startup ecosystem, even in the case of fundamental scientific breakthroughs.

CAMBRIDGE – DECEMBER 1: Stephanie Chandler, Relay Therapeutics Office Manager, demonstrates how she and her fellow co-workers at the company administer their own COVID tests inside the COVID testing room at Relay Therapeutics in Cambridge, MA on Dec. 1, 2021. The cancer treatment development company converted its coat room into a room where employees get tested once a week. All 100+employees have been back in the office as a result of regular testing. Relay is a Foresite portfolio company. (Photo by Jessica Rinaldi/The Boston Globe via Getty Images)

“Basically, you’re seeing people now really look at biotech in general, in the same kind of way that you would look at a tech company,” he said. “There are these tech metrics that now also apply in biotech, about adoption velocity, other other things that may not exactly equate to immediate revenue, but give you all the core material that usually works over time.”

Overall, Foresite’s investment thesis focuses on funding companies in three areas – therapeutics at the clinical stage, infrastructure focused on automation and data generation, and what Tananbaum calls “individualized care.” All three are part of a continuum in the tech-enabled healthcare end state that he envisions, ultimately resulting “a world where we’re able to, at the individual level, help someone understand what their predispositions are to disease development.” That, Tananbaum suggests, will result in a transformation of this kind of targeted care into an everyday consumer experience – in the same way tech in general has taken previously specialist functions and abilities, and made them generally available to the public at large.

DataJoy raises $6M seed to help SaaS companies track key business metrics

Every business needs to track fundamental financial information, but the data typically lives in a variety of silos making it a constant challenge to understand a company’s overall financial health. DataJoy, an early stage startup, wants to solve that issue. The company announced a $6 million seed round today led by Foundation Capital with help from Quarry VC, Partech Partners, IGSB, Bow Capital and SVB.

Like many startup founders, CEO Jon Lee has experienced the frustration first hand of trying to gather this financial data, and he decided to start a company to deal with it once and for all. “The reason why I started this company was that I was really frustrated at Copper, my last company because it was really hard just to find the answers to simple business questions in my data,” he told me.

These include basic questions like how the business is doing this quarter, if there are any surprises that could throw the company off track and where are the best places to invest in the business to accelerate more quickly.

The company has decided to concentrate its efforts for starters on SaaS companies and their requirements. “We basically focus on taking the work out of revenue intelligence, and just give you the insights that successful companies in the SaaS vertical depend on to be the largest and fastest growing in the market,” Lee explained.

The idea is to build a product with a way to connect to key business systems, pull the data and answer a very specific set of business questions, while using machine learning to provide more proactive advice.

While the company is still in the process of building the product and is pre-revenue, it has begun developing the pieces to ultimately help companies answer these questions. Eventually it will have a set of connectors to various key systems like Salesforce for CRM, HubSpot and Marketo for marketing, Netsuite for ERP, Gainsight for customer experience and Amplitude for product intelligence.

Lee says the set of connectors will be as specific as the questions themselves and based on their research with potential customers and what they are using to track this information. Ashu Garg, general partner at lead investor Foundation Capital says that he was attracted to the founding team’s experience, but also to the fact they were solving a problem he sees all the time sitting on the boards of various SaaS startups.

“I spend my life in the board meetings. It’s what I do, and every CEO, every board is looking for straight answers for what should be obvious questions, but they require this intersection of data,” Garg said. He says to an extent, it’s only possible now due to the evolution of technology to pull this all together in a way that simplifies this process.

The company currently has 11 employees with plans to double that by the middle of this year. As a long-time entrepreneur, Lee says that he has found that building a diverse workforce is essential to building a successful company. “People have found diversity usually [results in a company that is] more productive, more creative and works faster,” Lee said. He said that that’s why it’s important to focus on diversity from the earliest days of the company, while being proactive to make that happen. For example, ensuring you have a diverse set of candidates to choose from when you are reviewing resumes.

For now, the company is 100% remote. In fact, Lee and his co-founder Chief Product Officer Ken Lee, who was previously at Tableau, have yet to meet in person, but they are hoping that changes soon. The company will eventually have a presence in Vancouver and San Mateo whenever offices start to open.

Berlin’s MorphAIs hopes its AI algorithms will put its early-stage VC fund ahead of the pack

MorphAIs is a new VC out of Berlin, aiming to leverage AI algorithms to boost its investment decisions in early-stage startups. But there’s a catch: it hasn’t raised a fund yet.

The firm was founded by Eva-Valérie Gfrerer who was previously head of Growth Marketing at FinTech startup OptioPay and her background is in Behavioural Science and Advanced Information Systems.

Gfrerer says she started MorphAIs to be a tech company, using AI to assess venture investments and then selling that as a service. But after a while, she realized the platform could be applied an in-house fund, hence the drive to now raise a fund.

MorphAIs has already received financing from some serial entrepreneurs, including: Max Laemmle, CEO & Founder Fraugster, previously Better Payment and SumUp; Marc-Alexander Christ, Co-Founder SumUp, previously Groupon (CityDeal) and JP Morgan Chase; Charles Fraenkl, CEO SmartFrog, previously CEO at Gigaset and AOL; Andreas Winiarski, Chairman & Founder awesome capital Group.

She says: “It’s been decades since there has been any meaningful innovation in the processes by which venture capital is allocated. We have built technology to re-invent those processes and push the industry towards more accurate allocation of capital and a less-biased and more inclusive start-up ecosystem.”

She points out that over 80% of early-stage VC funds don’t deliver the minimum expected return rate to their investors. This is true, but admittedly, the VC industry is almost built to throw a lot of money away, in the hope that it will pick the winner that makes up for all the losses.

She now plans to aim for a pre-seed/seed fund, backed by a team consisting of machine learning scientists, mathematicians, and behavioral scientists, and claims that MorphAIs is modeling consistent 16x return rates, after running real-time predictions based on market data.

Her co-founder is Jan Saputra Müller, CTO and Co-Founder, who co-founded and served as CTO for several machine learning companies, including askby.ai.

There’s one problem: Gfrerer’s approach is not unique. For instance, London-based Inreach Ventures has made a big play of using data to hunt down startups. And every other VC in Europe does something similar, more or less.

Will Gfrerer manage to pull off something spectacular? We shall have to wait and find out.

Aquarium scores $2.6M seed to refine machine learning model data

Aquarium, a startup from two former Cruise employees, wants to help companies refine their machine learning model data more easily and move the models into production faster. Today the company announced a $2.6 million seed led by Sequoia with participation from Y Combinator and a bunch of angel investors including Cruise co-founders Kyle Vogt and Dan Kan.

When the two co-founders CEO Peter Gao and head of engineering Quinn Johnson, were at Cruise they learned that finding areas of weakness in the model data was often the problem that prevented it from getting into production. Aquarium aims to solve this issue.

“Aquarium is a machine learning data management system that helps people improve model performance by improving the data that it’s trained on, which is usually the most important part of making the model work in production,” Gao told me.

He says that they are seeing a lot of different models being built across a variety of industries, but teams are getting stuck because iterating on the data set and continually finding relevant data is a hard problem to solve. That’s why Aquarium’s founders decided to focus on this.

“It turns out that most of the improvement to your model, and most of the work that it takes to get it into production is about deciding, ‘Here’s what I need to go and collect next. Here’s what I need to go label. Here’s what I need to go and retrain my model on and analyze it for errors and repeat that iteration cycle,” Gao explained.

The idea is to get a model into production that outperforms humans. One customer Sterblue offers a good example. They provide drone inspection services for wind turbines. Their customers used to send out humans to inspect the turbines for damage, but with a set of drone data, they were able to train a machine learning model to find issues. Using Aquarium, they refined their model and improved accuracy by 13%, while cutting the cost of human reviews in half, Gao said.

The 7 person Aquarium startup team.

The Aquarium team. Image: Aquarium

Aquarium currently has 7 employees including the founders, of which three are women. Gao says that they are being diverse by design. He understands the issues of bias inherent in machine learning model creation, and creating a diverse team for this kind of tooling is one way to help mitigate that bias.

The company launched last February and spent part of the year participating in the Y Combinator Summer 2020 cohort. They worked on refining the product throughout 2020, and recently opened it up from beta to generally available.

Aquarium scores $2.6M seed to refine machine learning model data

Aquarium, a startup from two former Cruise employees, wants to help companies refine their machine learning model data more easily and move the models into production faster. Today the company announced a $2.6 million seed led by Sequoia with participation from Y Combinator and a bunch of angel investors including Cruise co-founders Kyle Vogt and Dan Kan.

When the two co-founders CEO Peter Gao and head of engineering Quinn Johnson, were at Cruise they learned that finding areas of weakness in the model data was often the problem that prevented it from getting into production. Aquarium aims to solve this issue.

“Aquarium is a machine learning data management system that helps people improve model performance by improving the data that it’s trained on, which is usually the most important part of making the model work in production,” Gao told me.

He says that they are seeing a lot of different models being built across a variety of industries, but teams are getting stuck because iterating on the data set and continually finding relevant data is a hard problem to solve. That’s why Aquarium’s founders decided to focus on this.

“It turns out that most of the improvement to your model, and most of the work that it takes to get it into production is about deciding, ‘Here’s what I need to go and collect next. Here’s what I need to go label. Here’s what I need to go and retrain my model on and analyze it for errors and repeat that iteration cycle,” Gao explained.

The idea is to get a model into production that outperforms humans. One customer Sterblue offers a good example. They provide drone inspection services for wind turbines. Their customers used to send out humans to inspect the turbines for damage, but with a set of drone data, they were able to train a machine learning model to find issues. Using Aquarium, they refined their model and improved accuracy by 13%, while cutting the cost of human reviews in half, Gao said.

The 7 person Aquarium startup team.

The Aquarium team. Image: Aquarium

Aquarium currently has 7 employees including the founders, of which three are women. Gao says that they are being diverse by design. He understands the issues of bias inherent in machine learning model creation, and creating a diverse team for this kind of tooling is one way to help mitigate that bias.

The company launched last February and spent part of the year participating in the Y Combinator Summer 2020 cohort. They worked on refining the product throughout 2020, and recently opened it up from beta to generally available.

YouTube to launch parental control features for families with tweens and teens

YouTube announced this morning it will soon introduce a new experience designed for teens and tweens who are now too old for the schoolager-focused YouTube Kids app, but who may not be ready to explore all of YouTube. The company says it’s preparing to launch a beta test of new features that will give parents the ability to grant kids more limited access to YouTube through a “supervised” Google Account. This setup will restrict what tweens and teens can watch on the platform, as well as what they can do — like create videos or leave comments, for example.

Many parents may have already set up a supervised Google Account for their child through Google’s Family Link parental control app. This app allows parents to restrict access across a range of products and services, control screen time, filter websites and more. Other parents may have created a supervised Google Account for their child when they first set up the child’s account on a new Android device or Chromebook.

If not, parents can take a few minutes to create the child’s supervised account when they’re ready to begin testing the new features. (Unfortunately, Google Edu accounts — like those kids now use for online school — aren’t supported at launch.)

The new features will allow parents to select between three different levels of YouTube access for their tween or teen. Initially, YouTube will test the features with parents with children under the age of consent for online services — age 13 in the U.S., but different in other countries — before expanding to older groups.

Image Credits: YouTube

For tweens who have more recently graduated out of the YouTube Kids app, an “Explore” mode will allow them to view a broad range of videos generally suited for viewers age 9 and up — including vlogs, tutorials, gaming videos, music clips, news, and educational content. This would allow the kids to watch things like their favorite gaming streamer with kid-friendly content, but would prevent them (in theory) from finding their way over to more sensitive content.

The next step up is an “Explore More” mode, where videos are generally suitable for kids 13 and up — like a PG-13 version of YouTube. This expands the set of videos kids can access and allows them access to live streams in the same categories as “Explore.”

For older teens, there is the “Most of YouTube” mode, which includes almost all YouTube videos except those that include age-restricted content that isn’t appropriate for viewers under 18.

Image Credits: YouTube

YouTube says it will use a combination of user input, machine learning, and human review to curate which videos are included in each of the three different content settings.

Of course, much like YouTube Kids, that means this will not be a perfect system — it’s a heavily machine-automated attempt at curation where users will still have to flag videos that were improperly filtered. In other words, helicopter parents who closely supervise their child’s access to internet content will probably still want to use some other system — like a third-party parental control solution, perhaps — to lock down YouTube further.

The supervised access to YouTube comes with other restrictions, as well, the company says.

Parents will be able to manage the child’s watch and search history from within the child’s account settings. And certain features on YouTube will be disabled, depending on the level of access the child has.

For example, YouTube will disable in-app purchases, video creation, and commenting features at launch. The company says that, over time, it wants to work with parents to add some of these features back through some sort of parent-controlled approach.

Also key is that personalized ads won’t be served on supervised experiences, even if that content isn’t designated as “made for kids” — which would normally allow for personalized ads to run. Instead, all ads will be contextual, as they are on YouTube Kids. In addition, all ads will have to comply with kids advertising policies, YouTube’s general ad policies, and will be subject to the same category and ad content restrictions as on Made for Kids content.

That said, when parents establish the supervised account for their child, they’ll be providing consent for COPPA compliance — the U.S. children’s privacy law that requires parents to be notified and agree to the collection and use personal data from the kids’ account. So there’s a trade-off here.

However, the new experience may still make sense for families where kids have outgrown apps designed for younger children — or even in some cases, for younger kids who covet their big brother or sister’s version of “real YouTube.” Plus, at some point, forcing an older child to use the “Kids” app makes them feel like they’re behind their peers, too. And since not all parents use the YouTube Kids app or parental controls, there’s always the complaint that “everyone else has it, so why can’t I?” (It never ends.)

Image Credits: YouTube Kids app

This slightly more locked down experience lets parents give the child access to “real YouTube” with restrictions on what that actually means, in terms of content and features.

YouTube, in an announcement, shared several endorsements for the new product from a few individual youth experts, including Leslie Boggs, president of National PTA; Dr. Yalda Uhls, Center for Scholars & Storytellers, UCLA – Author of Media Moms & Digital Dads; Thiago Tavares, Founder and President of SaferNet Brazil; and Professor Sun Sun Lim, Singapore University of Technology & Design – Author of Transcendent Parenting.

YouTube’s news, notably, follows several product updates from fast-growing social video app and YouTube rival TikTok, which has rolled out a number of features aimed at better protecting its younger users.

The company in April 2020 launched a “family pairing” mode that lets a parent link their child’s account to their own in order to also lock down what the child can do and what content they can see. (TikTok offers a curated experience for the under-13 crowd called Restricted Mode, which can be switched on here, too.) And in January of this year, TikTok changed the privacy setting defaults for users under 18 to more proactively restrict what they do on the app.

YouTube says its new product will launch in beta in the “coming months” in over 80 countries worldwide. It also notes that it will continue to invest in YouTube Kids for parents with younger children.