Google has announced that Google Optimize and Optimize 360 will no longer be available after September 30. All experiments will continue to run until that date.
Google launched Optimize over five years ago to help businesses test and improve their user experiences.
Many companies have widely used the tool to optimize their website, landing pages, and other online properties.
While the discontinuation of Google Optimize and Optimize 360 may disappoint, Google says it’s committed to providing a new solution in GA4.
Users are encouraged to download their data before it becomes unavailable at the end of September.
Google Optimize will go down in marketing history as a short-lived but beloved tool. Businesses that rely on it for their experimentation needs will have to find a new solution.
The History Of Google Optimize
Avid users of Google Optimize may be interested in this story from Krista Seiden, a former employee on the team since its early days.
In a 20-part Twitter thread, Seiden recounts her time on the Google Optimize team and describes how the tool came to be.
She says the idea for Google Optimize came after finding that content experiments in Google Analytics couldn’t scale to her team’s needs.
That’s when they decided to build their own server-side A/B testing solution, which eventually became Google Optimize.
11/ As the tool came to life, we got more and more excited for what it would do for the market. And in 2016, we finally launched Google Optimize as part of the GA 360 Suite. Here’s our war room launch party pic (we’d been there since 3am!) pic.twitter.com/M12jSwQNpd
Seiden stayed on the Optimize core team until she left Google in early 2019.
During her time on the team, she made dozens of educational videos and how-to’s for Google Optimize and consulted on many of its features.
Seiden’s story, worth reading in full, shows that Google Optimize was not only a valuable tool but also had a passionate team behind it.
When Google Optimize ends its service on September 30, it will leave a significant gap in the market for affordable and beginner-friendly A/B testing options.
According to Seiden, Google plans to expand A/B testing capabilities in GA4. However, it’s unlikely that the features will be available by September 30.
Lastly, she adds that Google is working on integrating with other A/B testing partners, which means that businesses who are using a third-party tool may be able to transfer their testing data to GA4.
Comment From Search Engine Journal’s Director Of Marketing
Heather Campbell, Search Engine Journal’s Director of Marketing, gives her take on the sunsetting of Google Optimize and what it means for others in the field:
I’m not surprised this day has come. It was only a matter of time before Optimize / 360 would no longer function since Google is sunsetting Universal Analytics in July.
Google is investing in GA4 and wants you to do the same.
It’s still frustrating when Google moves our marketing cheese, but don’t lose hope. This could be your opportunity to find a platform better suited to your needs.
What does this mean for now?
It would be best if you started researching alternatives. And there’s plenty out there. The first place to start, though, is with GA4.
Hopefully, you’ve already started implementing GA4, as that’s where the next iteration of Optimize will live. And if you haven’t, you should probably stop reading this and get started.
Make sure you pull down any data from past campaigns. You can still run campaigns thru September 30, but if you rely on testing and personalization (like any good marketer does), you may need a backup.
Two heads are better than one, right? That’s the idea behind social media collaborations. When brands team up with other brands and influencers to reach new audiences, it can be a win for everyone.
But to ensure your collaboration is a success, you should consider some key things before jumping in. Below is a step-by-step guide to help you pull off the perfect social media collaboration.
Bonus: Get the influencer marketing strategy template to easily plan your next campaign and choose the best social media influencer to work with.
What is social media collaboration?
Social media collaboration is when two or more brands team up to create content shared on their respective social media channels. This could include sponsored posts, giveaways, or other content that leverages each partner’s audience.
Social media collaborations can grow your reach and engagement, build brand awareness, create valuable partnerships, and drive sales. They are also a great way to foster relationships with other brands and expand your customer base.
11 tips for successful social media collaboration
If you want to capitalize on the benefits of social media collaboration, here are some tips to keep in mind.
1. Choose the collaboration type
Before you start planning your social media collaboration, deciding what kind of partnership you want is essential. Do you want a permanent alliance or a one-off event? Will the partnership involve product giveaways, joint content creation, or something else entirely?
Setting expectations helps ensure you and the other party are on the same page. Plus, it will help you clarify who’s responsible for what tasks throughout the collaboration.
We recommend building out clear contracts at the start of your collaboration. Your social media collaboration contracts should, at minimum, include the following:
A detailed list of deliverables from both parties
Deadlines for each task that must be met
Responsibilities from each side (what are you committing to?)
Compensation package and terms of payment
Ownership of intellectual property and content
Contingency plans in case the project falls through
A social media collaboration aims to expand your reach and acquire new customers. That’s why it’s crucial to partner with a brand that will expose you to a new audience, but one who will care about your product or service.
Finding the right influencer for your social media campaign is key. Look for someone who speaks to the target audience you want to reach and has the credibility necessary to persuade them.
The influencer should also have a following that’s large enough to make a difference in your reach but not so big that you’ll be lost in the crowd.
Smart collaborations might include:
A shoe brand collaborating with a fashion blogger
A health food company partnering with a nutritionist or dietician
An athletic apparel brand teaming up with an athlete or fitness enthusiast
A video game company partnering with a popular YouTuber or Twitch streamer
The key is to find creative ways to collaborate that will get your brand noticed and give followers something truly valuable—like exclusive discount codes, content, or a uniquely entertaining experience.
Goals are essential to any social media strategy, but they’re even more critical for collaborations.
Before you and your collaborator start brainstorming ideas and creating content, you must agree on a primary goal that can be tracked and measured. It should be something tangible—like driving traffic to a specific product page or increasing sales of the product featured in your collaboration.
Try to avoid less quantifiable goals like “get more followers.” It’s easy to mistake increased follower count for success, but there’s no way to measure how much value these additional followers add.
Use social media collaboration tools to ensure you meet your goals. Social media and online collaboration tools are invaluable for keeping projects on track, staying organized, and tracking progress. With the right tool, you can keep all your assets in one place for easy reference, create metrics-driven reports about how your collaborations have performed, and share your progress and results with your collaborators.
A special @elfyeah radiance report: It’s an E.L.F.ING GLOW STORM! Please exercise ✨extreme iridescence!✨ (and thank you @weatherchannel for inspiring the glowcast!)🤍 #elfpartner
Setting firm deadlines, clear expectations, and explicit objectives should be prioritized when pursuing social media collaborations. The more direct and detailed you are, the more effective and successful your collaboration will be. This is especially important when working with multiple partners or over longer periods of time.
To maintain the success of collaborations, checking in regularly and maintaining good communication is essential. Schedule regular meetings with both sides to discuss progress and objectives, resolve any problems that come up and set new goals.
5. Choose social media collaboration tools
On top of a solid contract, you should consider investing in social media collaboration tools to make the process smooth for all involved parties.
These tools simplify the management of your campaign by allowing multiple social media team members to access and contribute at any time. It also helps streamline communication between all stakeholders (including influencers and partners) for a seamless workflow.
Tools like Hootsuite for social media scheduling, Trello or Monday for project management, and Asana for task management can make collaboration among team members much easier. They also provide a centralized platform to monitor progress and store all the necessary assets in an accessible place.
As well, consider how you plan to track the success of your social media collaboration. Using UTM tracking codes and hashtags for campaigns or events and sharing analytics and insights can help you see what’s working in your collaboration. Tools like Hootsuite Analytics can help you to track the success of each element of your collaboration campaigns.
Finally, ensure that everyone involved in the collaboration understands their roles and responsibilities. Using tools like Slack or Microsoft Teams can streamline communication and ensure everyone is on the same page.
6. Share your social media style guide
Your social media style guide is the essence of your brand. Not only does it explain how you want your brand represented in its interactions, but it also sets the stage for how your collaboration will be executed.
Before your campaign begins, share and explain your style guide to any brand collaborators and partners. You want to ensure that any new content you produce together is in line with your brand’s standards.
Keep in mind that your collaborator will also have their own style guide and expectations for the content produced. Review these together and devise a unified approach for how the two brands will work together regarding visuals, content, and the like.
The best social media collaborations combine the best of both brands, so don’t be afraid to make adjustments and compromises to create a cohesive look and feel.
7. Consider the emotional angle of your campaign
Once you and your collaborator have agreed on the terms, it’s time to get creative. Think of ways to use your social collaboration to create an emotional connection with your audience.
This could include donating some portion of the profits to a charity, highlighting a cause that you both care about, or simply creating content that will make people smile and connect with it on a deeper level.
If you’re working with an influencer, ask them to create content that speaks to their values and resonates with their audience. Similarly, if the collaboration involves creating a product or service together, focus on how it can solve a problem for your customers in a unique way.
Remember, social media collaborations can be more than just marketing. They’re also a great way to do something meaningful while strengthening the connections you make with your customers. With the right combination of content, creativity, and collaboration tools, you can turn this into an excellent opportunity for everyone involved.
#SpotifyEQUAL artist #SilvanaEstrada values the company of fellow women musicians and is inspired by her peers and predecessors 💚 Listen to her music on the EQUAL playlist #womeninmusic
Timing is everything when it comes to social media content. Not only do you need to decide on a platform and content type, but you need to time your posts correctly.
Consider any holidays, trending topics, or special events that could coincide with your post. Also, pick a time when you know your followers are most likely to see your content–and optimize this per platform.
You can also use platform insights to figure out what content your audience is interested in right now and how they want you to communicate with them. Your followers may be more interested in Instagram stories than static images. Or they are more active on TikTok these days. Your research will help create a content calendar that resonates with your audience.
It’s also a good idea to ask your collaborator for their input. You’re not just speaking to your audience but also theirs. Compare your insights and find a middle ground that benefits both parties equally.
9. Disclose all partnerships
Disclosure of partnerships is a must, especially when it comes to social media collaborations. Your audiences should always know when a post has been sponsored or created in collaboration with someone else.
Not only is this legally required by the Federal Trade Commission (FTC) and other regulating entities worldwide, but it also helps build trust between you, your collaborator, and your audience. Users tend to appreciate when companies are transparent about their collaborations.
Be sure to label all sponsored posts accurately so that users know that a collaboration has taken place. Use hashtags such as #ad and #sponsored, or if you’re on Instagram or TikTok, use the “Paid Partnership” tag in the post header. And make sure to include both written and verbal disclosure of the partnership within the video itself—not just the description.
One of the biggest benefits of social media collaborations is the ability to tap into existing audiences. But keep in mind you’ll both need to promote the content on your own channels to be successful.
This means you’ll need an effective plan for cross-promotion. Coordinate with the other partners to determine who will post what content and when each post should go live. Don’t forget to add links to each other’s social media profiles, websites, and other content you’ve created for the collaboration.
It’s also smart to announce your collaboration beforehand, so your audiences can get excited about what’s to come.
Wondering how to announce a social media collaboration?
Social media users often appreciate a personal touch. If you have multiple influencers and brands involved, consider having them post on their accounts to announce their partnership.
You can also create a unique hashtag that incorporates the names of both parties involved. This will help make your collaboration more visible.
11. Share results with your collaborator
Finally, share the results of your collaboration with any collaborators or partners involved. The data you collect can be invaluable to both your digital marketing efforts and those of your collaborators. It will also help inform future social media collaborations and strategies.
The best way to share this information is through a comprehensive report that outlines how the collaboration performed, what key metrics were achieved, and how the content resonated with audiences. Metrics like reach, engagement, conversion rates, and sentiment can all be tracked and reported to give a more holistic view of the collaboration’s success.
Social media collaboration tools can help you create detailed reports, track performance, and identify key insights.
Examples of social media collaborations
Let these social media collaboration examples inspire you to create and launch your own collab campaigns.
Charli D’amelio x Takis
Charli D’amelio is one of the biggest names on TikTok, with over 149 million followers. For this social media marketing collaboration, she teamed up with snack company Takis to create a short TikTok video of how she spends her movie nights. The video saw more than 2.6 million views and 10.5k shares.
Sometimes brand partnerships happen offline, too (crazy, we know). When TikTok influencer Bella Poarch was asked to walk in the Hugo Boss fashion show, both brands knew it was the perfect opportunity to create buzz on TikTok. This social media collaboration is the perfect example of how online and offline tactics can work together.
Creative hashtags are a great way to increase the reach of collaborative posts organically.
NBA legend Shaquille O’Neal and pizza chain Papa John’s created the #Shaqaroni challenge on TikTok, which asked users to video themselves dancing to the song “Shaq-a-Roni” by Shaq, and use the #Shaqaroni hashtag. Shaq then responded to entries in a series of TikTok duets that are funny and heartwarming.
This campaign is a great example of how humor and creativity can go a long way in expanding the reach of your message.
Social collaboration happens between brands, too. This year, the outdoor brands Yeti and Traeger Grills partnered to launch a unique giveaway. The two companies asked fans to share images of their barbeque setup with the hashtag #YETIxTraegerBBQ to win a collaborative prize package. This partnership was a success, and the contest had over 1,000 entries.
3 social media collaboration tools
Ready to get started? Learn how to use social media platforms for collaboration and engagement.
1. Hootsuite Analytics
Hootsuite Analytics is a great tool for managing and analyzing social media collaboration efforts. It offers detailed insights into the performance of campaigns, including impressions, engagement, and reach. You can even benchmark your performance against industry standards.
You’ll also have access to a full suite of reporting tools, which can be shared with influencers throughout your campaign. Use it to track conversations, monitor engagement on your and your influencers’ posts, and optimize your campaigns for maximum success.
2. Instagram collab posts
Instagram collab posts allow you to reach an even wider audience by combining the power of two accounts. One user creates the post and invites another to be listed as a collaborator. Once accepted, the post appears under both users’ accounts.
This feature can help you gain more exposure for your brand or campaign by appearing in two places simultaneously. As a bonus, both accounts will receive the same comments, likes, and number of shares. Social media collab posts are a great way to boost your reach, engagement, and followers.
3. Facebook Brand Collabs Manager
Facebook Brand Collabs Manager is a tool that makes it easy for brands to connect with the right creators on Facebook and Instagram. This platform allows companies to search for users with the most relevant followers, interests, and content. Brands can then reach out directly to those they believe would be a great fit for their campaigns.
The tool also helps ensure that any messages sent from the brands to creators don’t get lost or ignored. Brand Collabs Manager provides an easy way for brands and creators to come together, eliminating the need to spend hours searching for collaborations one by one.
Make social media collaborations easier with Hootsuite. Schedule posts, research and engage with influencers in your industry, and measure the success of your campaigns. Try it free today.
Twitter is rolling out a new ad unit called Search Keywords Ads that allows users to promote sponsored tweets in search results.
Advertisers will now be able to pay for their tweets to appear at the top of search results for specific keywords.
The feature is similar to the promoted tweets that appear in users’ timelines but with the added benefit of appearing in search results.
This will allow advertisers to reach a wider audience, as users searching for specific keywords will now be exposed to sponsored tweets.
Advertisers can find Search Keywords Ads as a new campaign objective within the Twitter Ads interface.
Search Keywords Ads are a new Conversion ‘campaign objective’ in the Twitter Ads interface.
For now, these campaigns will optimize only for conversions to advertiser websites, and they require the integration of the Twitter Pixel or Conversion API (CAPI) to maximize relevance. pic.twitter.com/xCowp8yvfL
As the company states in the tweet above, the new Search Keywords Ads objective is designed to drive conversions to advertiser websites.
Twitter’s Search Keywords Ads are unique in that they only target users actively searching for specific keywords, which provides a more accurate signal of user intent.
For maximum relevance, you’ll need to utilize the Twitter Pixel or Conversion API on your website.
Twitter intends to expand Search Keywords Ads to other advertising objectives in the future.
Like other promoted tweets, search ads will be clearly labeled as “promoted,” which will help differentiate advertisements from organic tweets.
Search Keywords Ads can potentially be a significant revenue generator for Twitter, which the company needs right now.
Reuters reports that in December, ad spend on Twitter dropped by 71%.
This decline is attributed to top advertisers reducing their spending on the social media platform following the takeover by Elon Musk.
Will the introduction of search ads help lure advertisers back?
We may learn more about Twitter’s plans to boost revenue later this week when the company holds its earnings call.
During a recent earnings call with shareholders, the CEO of Microsoft, Satya Nadella, stated that artificial intelligence would eventually be included in all of the company’s applications.
After discussing Microsoft’s financial performance for the last quarter, Nadella answered questions about AI and its expected integration into more products.
Given Microsoft’s commitment to expanding its partnership with OpenAI, it makes sense that shareholders would have questions about when Microsoft plans to use the technology it’s investing in.
Although Nadella wasn’t willing to give a specific date, he stated that AI would be integrated into all of Microsoft’s applications in the future.
In this article, we’ll take a closer look at Nadella’s statements regarding AI and briefly summarize the relevant highlights from Microsoft’s earnings report.
Nadella Says Every Microsoft App Will Be An AI App
Tyler Radke, the Lead Analyst at Citi, asked Nadella about the extent to which AI will be employed throughout Microsoft Azure, which is the cloud computing platform that runs its applications.
This was Nadella’s response:
“I think it’s too early to sort of start somehow separating out AI from the rest of the workload. I mean, even the workloads themselves, AI is just going to be a core part of a workload in Azure versus just AI alone. In other words, if you have an application that’s using a bunch of inference, let’s say, it’s also going to have a bunch of storage, and it’s going to have a bunch of other compute beyond GPU inferencing, if you will. I think over time, obviously, I think every app is going to be an AI app. That’s, I think, the best way to think about this transformation.”
Nadella is saying it’s too early to separate AI into its own category because it’s becoming a core part of all products.
He believes AI will be integrated into all apps over time and will also be integrated with storage and other forms of computing beyond just GPU inferencing.
In response to a question from Keith Weiss of Morgan Stanley, Nadella expressed a similar sentiment, saying investors should expect AI to be included in everything.
“I think the way for our investors to see this is we fundamentally believe that the next big platform wave, as I said, is going to be AI. And we strongly also believe a lot of the enterprise value gets created by just being able to catch these waves, and then have those waves impact every part of our tech stack, and also create new solutions and new opportunities…
And so, we fully expect us to sort of incorporate AI in every layer of the stack, whether it’s in productivity, whether it’s in our consumer services.”
Other Highlights From Microsoft’s Earnings Call
Microsoft announced that in the last quarter of 2022, it made $52.7 billion in revenue, which is 2% more than the year before.
However, the company’s profit is down due to decreases in operating income, net income, and earnings per share.
Advertising spending declined slightly more than expected, impacting search and news advertising and LinkedIn Marketing Solutions.
One of the most notable highlights is the increase in LinkedIn revenue by 10%. This indicates the professional networking platform is performing well and that businesses can feel confident investing their time in it.
Another highlight from the report is the increase in revenue for server products and cloud services by 20%. This growth was driven by the revenue of Azure and other cloud services, which increased by 31%.
This indicates that Microsoft’s cloud services are becoming increasingly popular in the market, which bodes well for the future of the company’s partnership with OpenAI.
Despite a slight decrease in profit, the company’s overall financial performance remains strong.
Following the curtails of a 12,000-employee layoff on January 20th, the U.S. Department of Justice officially filed an antitrust lawsuit against Google this week.
With rumors swirling about a possible lawsuit for quite a while, this move from the DOJ is not unexpected.
The lawsuit has allegations concerning the tech company’s monopoly on the current digital advertising ecosystem.
8 States Join New Lawsuit
Eight states so far have joined forces with the DOJ on the lawsuit. They include:
Virginia
California
Colorado
Connecticut
New Jersey
New York
Rhode Island
Tennessee
Remember that this lawsuit is separate from the first lawsuit from the DOJ back in 2020 against Google.
In the 153-page document, the DOJ argues that Google has created an advertising environment that favors its Alphabet-owned products unfairly.
Going into further detail, the DOJ states:
Google, a single company with pervasive conflicts of interest, now controls:(1) the technology used by nearly every major website publisher to offer advertising space for sale; (2) the leading tools used by advertisers to buy that advertising space; and (3) the largest ad exchange that matches publishers with advertisers each time that ad space is sold.
Speaking to the monopoly accusation, the complaint further states:
Google abuses its monopoly power to disadvantage website publishers and advertisers who dare to use competing ad tech products in a search for higher quality, or lower cost, matches. Google uses its dominion over digital advertising technology to funnel more transactions to its own ad tech products where it extracts inflated fees to line its own pockets at the expense of the advertisers and publishers it purportedly serves.
Google Publicly Reacts To Allegations
After the news broke, Google released its official statement on the matter.
Their main counterpoints to the lawsuit focus on the following:
Government’s control within a competitive industry
Rewriting history and reversing innovation
Dan Taylor, Vice President, Global Ads of Google, stated:
We are one of hundreds of companies that enable the placement of ads across the Internet. And it’s been well reported that competition is increasing as more and more companies enter and invest in building their advertising businesses.
He provided examples of the increased competition over the past few years, such as Apple and Amazon’s increased investment in their advertising platforms and other media companies like Comcast and Disney.
Where Does This Leave The Advertising Industry?
If Google is found guilty in this lawsuit, that would likely mean the reversal of 15-year acquisitions such as AdMeld and DoubleClick.
If those are shut down, it’s difficult to say how much it would directly affect advertising technologies within the Google Ads platform, amongst others that marketers use.
Google also states that a ruling against Google would harm the broader advertising sector, “making it harder for Google to offer efficient advertising tools that benefit publishers, advertisers and the wider U.S. economy.”
Summary
The lawsuit filing against Google is still in its early stages. It’s unlikely that any drastic changes will occur in the immediate future.
We’ll continue to update as more information is provided.
RISC-V is one of MIT Technology Review’s 10 Breakthrough Technologies of 2023. Explore the rest of the list here.
Python, Java, C++, R. In the seven decades or so since the computer was invented, humans have devised many programming languages—largely mishmashes of English words and mathematical symbols—to command transistors to do our bidding.
But the silicon switches in your laptop’s central processor don’t inherently understand the word “for” or the symbol “=.” For a chip to execute your Python code, software must translate these words and symbols into instructions a chip can use.
Engineers designate specific binary sequences to prompt the hardware to perform certain actions. The code “100000,” for example, could order a chip to add two numbers, while the code “100100” could ask it to copy a piece of data. These binary sequences form the chip’s fundamental vocabulary, known as the computer’s instruction set.
For years, the chip industry has relied on a variety of proprietary instruction sets. Two major types dominate the market today: x86, which is used by Intel and AMD, and Arm, made by the company of the same name. Companies must license these instruction sets—which can cost millions of dollars for a single design. And because x86 and Arm chips speak different languages, software developers must make a version of the same app to suit each instruction set.
Lately, though, many hardware and software companies worldwide have begun to converge around a publicly available instruction set known as RISC-V. It’s a shift that could radically change the chip industry. RISC-V proponents say that this instruction set makes computer chip design more accessible to smaller companies and budding entrepreneurs by liberating them from costly licensing fees.
“There are already billions of RISC-V-based cores out there, in everything from earbuds all the way up to cloud servers,” says Mark Himelstein, the CTO of RISC-V International, a nonprofit supporting the technology.
In February 2022, Intel itself pledged $1 billion to develop the RISC-V ecosystem, along with other priorities. While Himelstein predicts it will take a few years before RISC-V chips are widespread among personal computers, the first laptop with a RISC-V chip, the Roma by Xcalibyte and DeepComputing, became available in June for pre-order.
What is RISC-V?
You can think of RISC-V (pronounced “risk five”) as a set of design norms, like Bluetooth, for computer chips. It’s known as an “open standard.” That means anyone—you, me, Intel—can participate in the development of those standards. In addition, anyone can design a computer chip based on RISC-V’s instruction set. Those chips would then be able to execute any software designed for RISC-V. (Note that technology based on an “open standard” differs from “open-source” technology. An open standard typically designates technology specifications, whereas “open source” generally refers to software whose source code is freely available for reference and use.)
A group of computer scientists at UC Berkeley developed the basis for RISC-V in 2010 as a teaching tool for chip design. Proprietary central processing units (CPUs) were too complicated and opaque for students to learn from. RISC-V’s creators made the instruction set public and soon found themselves fielding questions about it. By 2015, a group of academic institutions and companies, including Google and IBM, founded RISC-V International to standardize the instruction set.
The most basic version of RISC-V consists of just 47 instructions, such as commands to load a number from memory and to add numbers together. However, RISC-V also offers more instructions, known as extensions, making it possible to add features such as vector math for running AI algorithms.
With RISC-V, you can design a chip’s instruction set to fit your needs, which “gives the freedom to do custom, application-driven hardware,” says Eric Mejdrich of Imec, a research institute in Belgium that focuses on nanoelectronics.
Previously, companies seeking CPUs generally bought off-the-shelf chips because it was too expensive and time-consuming to design them from scratch. Particularly for simpler devices such as alarms or kitchen appliances, these chips often had extra features, which could slow the appliance’s function or waste power.
Himelstein touts Bluetrum, an earbud company based in China, as a RISC-V success story. Earbuds don’t require much computing capability, and the company found it could design simple chips that use RISC-V instructions. “If they had not used RISC-V, either they would have had to buy a commercial chip with a lot more [capability] than they wanted, or they would have had to design their own chip or instruction set,” says Himelstein. “They didn’t want either of those.”
RISC-V helps to “lower the barrier of entry” to chip design, says Mejdrich. RISC-V proponents offer public workshops on how to build a CPU based on RISC-V. And people who design their own RISC-V chips can now submit those designs to be manufactured free of cost via a partnership between Google, semiconductor manufacturer SkyWater, and chip design platform Efabless.
What’s next for RISC-V
Balaji Baktha, the CEO of Bay Area–based startup Ventana Micro Systems, designs chips based on RISC-V for data centers. He says design improvements they’ve made—possible only because of the flexibility that an open standard affords—have allowed these chips to perform calculations more quickly with less energy. In 2021, data centers accounted for about 1% of total electricity consumed worldwide, and that figure has been rising over the past several years, according to the International Energy Agency. RISC-V chips could help lower that footprint significantly, according to Baktha.
However, Intel and Arm’s chips remain popular, and it’s not yet clear whether RISC-V designs will supersede them. Companies need to convert existing software to be RISC-V compatible (the Roma supports most versions of Linux, the operating system released in the 1990s that helped drive the open-source revolution). And RISC-V users will need to watch out for developments that “bifurcate the ecosystem,” says Mejdrich—for example, if somebody develops a version of RISC-V that becomes popular but is incompatible with software designed for the original.
RISC-V International must also contend with geopolitical tensions that are at odds with the nonprofit’s open philosophy. Originally based in the US, they faced criticism from lawmakers that RISC-V could cause the US to lose its edge in the semiconductor industry and make Chinese companies more competitive. To dodge these tensions, the nonprofit relocated to Switzerland in 2020.
Looking ahead, Himelstein says the movement will draw inspiration from Linux. The hope is that RISC-V will make it possible for more people to bring their ideas for novel technologies to life. “In the end, you’re going to see much more innovative products,” he says.
Sophia Chen is a science journalist based in Columbus, Ohio, who covers physics and computing. In 2022, she was the science communicator in residence at the Simons Institute for the Theory of Computing at the University of California, Berkeley.
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.
Oh man, it’s brutal out there. One by one, the world’s richest tech companies have announced massive layoffs. Just last week, Alphabet announced it was laying off 12,000 people. There have been bruising rounds of layoffs at Amazon, Meta, Microsoft, and Twitter, too, affecting not only individual AI researchers but entire AI teams.
It was heartbreaking to read over the weekend about how some Googlers in the US found out about the company’s abrupt cull. Dan Russell, a research scientist who has worked on Google Search for over 17 years, wrote how he had gone to the office to finish off some work at 4 a.m., only to find out his entry badge didn’t work.
Economists predict the US economy may enter a recession this year amid a highly uncertain global economic outlook. Big tech companies have started to feel the squeeze.
In the past, economic downturns have shut off the funding taps for AI research. These periods are called “AI winters.” But this time we’re seeing something totally different. AI research is still extremely hot, and it’s making big leaps in progress even as tech companies have started tightening their belts.
In fact, Big Tech is counting on AI to give it an edge.
AI research has swung violently in and out of fashion since the field was established in the late 1950s. There have been two AI winters: one in the 1970s and the other in the late 1980s to early 1990s. AI research has previously fallen victim to hype cycles of exaggerated expectations that it subsequently failed to live up to, says Peter Stone, a computer science professor at the University of Texas at Austin, who used to work on AI at AT&T Bell Labs (now known as Nokia Bell Labs) until 2002.
For decades, Bell Labs was considered the hot spot for innovation, and its researchers won several Nobel Prizes and Turing Awards, including Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. The lab’s resources were cut as management started pushing for more immediate returns based on incremental technological changes, and ultimately it failed to capitalize on the internet revolution of the early 2000s, Jon Gertner writes in his book The Idea Factory: Bell Labs and the Great Age of American Innovation.
The previous downturns happened after the hottest AI techniques of the day failed to show progress and were unreliable and difficult to run, says Stone. Government agencies in the US and the UK that had provided funding for AI research soon realized that this approach was a dead end and cut off funding.
Today, AI research is having its “main character” moment. There may be an economic downturn, but AI research is still exciting. “We are still continuing to see regular rollouts of systems which are pushing back the frontiers of what AI can do,” says Michael Wooldridge, a computer science professor at the University of Oxford and author of the book A Brief History of AI.
This is a far cry from the field’s reputation in the 1990s, when Wooldridge was finishing his PhD. AI was still seen as a weird, fringe pursuit; the wider tech sector viewed it in a similar way to how established medicine views homeopathy, he says.
Today’s AI research boom has been fueled by neural networks, which saw a big breakthrough in the 1980s and work by simulating the patterns of the human brain. Back then, the technology hit a wall because the computers of the day weren’t powerful enough to run the software. Today we have lots of data and extremely powerful computers, which makes the technique viable.
New breakthroughs, such as the chatbot ChatGPT and the text-to-image model Stable Diffusion, seem to come every few months. Technologies like ChatGPT are not fully explored yet, and both industry and academia are still working out how they can be useful, says Stone.
Instead of a full-blown AI winter, we are likely to see a drop in funding for longer-term AI research and more pressure to make money using the technology, says Wooldridge. Researchers in corporate labs will be under pressure to show that their research can be integrated into products and thus make money, he adds.
That’s already happening. In light of the success of OpenAI’s ChatGPT, Google has declared a “code red” threat situation for its core product, Search, and is looking to aggressively revamp Search with its own AI research.
Stone sees parallels to what happened at Bell Labs. If Big Tech’s AI labs, which dominate the sector, turn away from deep, longer-term research and focus too much on shorter-term product development, exasperated AI researchers may leave for academia, and these big labs could lose their grip on innovation, he says.
That’s not necessarily a bad thing. There are a lot of smart people looking for jobs at the moment. Venture capitalists are looking for new startups to invest in as crypto fizzles out, and generative AI has shown how the technology can be made into products.
This moment presents the AI sector with a once-in-a-generation opportunity to play around with the potential of new technology. Despite all the gloom around the layoffs, it’s an exciting prospect.
Before you go… We’ve put together a brand new series of reports inspired by MIT Technology Review’s marquee 10 Breakthrough Technologies. The first one, which will be out later this week is about how industrial design and engineering firms are using generative AI is set to come out soon. Sign up to get notified when it’s available.
Deeper Learning
AI is bringing the internet to submerged Roman ruins
Over 2,000 years ago, Baiae was the most magnificent resort town on the Italian peninsula. Wealthy statesmen were drawn to its natural springs, building luxurious villas with heated spas and mosaic-tiled thermal pools. But over the centuries, volcanic activity submerged this playground for the Roman nobility—leaving half of it beneath the Mediterranean. Today it is a protected marine area and needs to be monitored for damage caused by divers and environmental factors. But communication underwater is extremely difficult.
Under the sea: Italian researchers think they’ve figured out a new way to bring the internet underwater: AI and algorithms, which adjust network protocols according to sea conditions and allow the signal to travel up to two kilometers. This could help researchers better study the effects of climate change on marine environments and monitor underwater volcanoes. AI research can be pretty abstract, but this is a nice, practical example of how the technology can be useful. Read more from Manuela Callari.
Bits and Bytes
How OpenAI used low-paid Kenyan workers to make ChatGPT less toxic OpenAI used a Kenyan company called Sama to train its popular AI system, ChatGPT, to generate safer content. Low-paid workers sifted through endless amounts of graphic and violent content on topics such as child sexual abuse, bestiality, murder, suicide, torture, self-harm, and incest. This story is a good reminder of all the deeply unpleasant work humans have to do behind the scenes to make AI systems safe. (Time)
Inside CNET’s AI-powered SEO money machine Tech news site CNET has started using ChatGPT to write news articles. To absolutely nobody’s surprise, the site has already had to issue corrections for factual errors in those articles. The Verge looked at why CNET decided to use AI to write stories, and it’s a sad tale of what happens when private equity collides with journalism. (The Verge)
China could offer a model for deepfake regulation Governments have been reluctant to regulate deepfakes over fears that such efforts may curtail free speech. The Chinese government, which isn’t so troubled by that risk, thinks it has a solution. The country has adopted rules that require deepfakes to have the subject’s consent and bear watermarks, for example. Other countries will be watching and taking notes. (The New York Times)
Nick Cave thinks a song written by ChatGPT in his style sucks Perfection. No comments. Chef’s kiss. (The Guardian)
RISC-V is one of MIT Technology Review’s 10 Breakthrough Technologies of 2023. Explore the rest of the list here.
Python, Java, C++, R. In the seven decades or so since the computer was invented, humans have devised many programming languages—largely mishmashes of English words and mathematical symbols—to command transistors to do our bidding.
But the silicon switches in your laptop’s central processor don’t inherently understand the word “for” or the symbol “=.” For a chip to execute your Python code, software must translate these words and symbols into instructions a chip can use.
Engineers designate specific binary sequences to prompt the hardware to perform certain actions. The code “100000,” for example, could order a chip to add two numbers, while the code “100100” could ask it to copy a piece of data. These binary sequences form the chip’s fundamental vocabulary, known as the computer’s instruction set.
For years, the chip industry has relied on a variety of proprietary instruction sets. Two major types dominate the market today: x86, which is used by Intel and AMD, and Arm, made by the company of the same name. Companies must license these instruction sets—which can cost millions of dollars for a single design. And because x86 and Arm chips speak different languages, software developers must make a version of the same app to suit each instruction set.
Lately, though, many hardware and software companies worldwide have begun to converge around a publicly available instruction set known as RISC-V. It’s a shift that could radically change the chip industry. RISC-V proponents say that this instruction set makes computer chip design more accessible to smaller companies and budding entrepreneurs by liberating them from costly licensing fees.
“There are already billions of RISC-V-based cores out there, in everything from earbuds all the way up to cloud servers,” says Mark Himelstein, the CTO of RISC-V International, a nonprofit supporting the technology.
In February 2022, Intel itself pledged $1 billion to develop the RISC-V ecosystem, along with other priorities. While Himelstein predicts it will take a few years before RISC-V chips are widespread among personal computers, the first laptop with a RISC-V chip, the Roma by Xcalibyte and DeepComputing, became available in June for pre-order.
What is RISC-V?
You can think of RISC-V (pronounced “risk five”) as a set of design norms, like Bluetooth, for computer chips. It’s known as an “open standard.” That means anyone—you, me, Intel—can participate in the development of those standards. In addition, anyone can design a computer chip based on RISC-V’s instruction set. Those chips would then be able to execute any software designed for RISC-V. (Note that technology based on an “open standard” differs from “open-source” technology. An open standard typically designates technology specifications, whereas “open source” generally refers to software whose source code is freely available for reference and use.)
A group of computer scientists at UC Berkeley developed the basis for RISC-V in 2010 as a teaching tool for chip design. Proprietary central processing units (CPUs) were too complicated and opaque for students to learn from. RISC-V’s creators made the instruction set public and soon found themselves fielding questions about it. By 2015, a group of academic institutions and companies, including Google and IBM, founded RISC-V International to standardize the instruction set.
The most basic version of RISC-V consists of just 47 instructions, such as commands to load a number from memory and to add numbers together. However, RISC-V also offers more instructions, known as extensions, making it possible to add features such as vector math for running AI algorithms.
With RISC-V, you can design a chip’s instruction set to fit your needs, which “gives the freedom to do custom, application-driven hardware,” says Eric Mejdrich of Imec, a research institute in Belgium that focuses on nanoelectronics.
Previously, companies seeking CPUs generally bought off-the-shelf chips because it was too expensive and time-consuming to design them from scratch. Particularly for simpler devices such as alarms or kitchen appliances, these chips often had extra features, which could slow the appliance’s function or waste power.
Himelstein touts Bluetrum, an earbud company based in China, as a RISC-V success story. Earbuds don’t require much computing capability, and the company found it could design simple chips that use RISC-V instructions. “If they had not used RISC-V, either they would have had to buy a commercial chip with a lot more [capability] than they wanted, or they would have had to design their own chip or instruction set,” says Himelstein. “They didn’t want either of those.”
RISC-V helps to “lower the barrier of entry” to chip design, says Mejdrich. RISC-V proponents offer public workshops on how to build a CPU based on RISC-V. And people who design their own RISC-V chips can now submit those designs to be manufactured free of cost via a partnership between Google, semiconductor manufacturer SkyWater, and chip design platform Efabless.
What’s next for RISC-V
Balaji Baktha, the CEO of Bay Area–based startup Ventana Micro Systems, designs chips based on RISC-V for data centers. He says design improvements they’ve made—possible only because of the flexibility that an open standard affords—have allowed these chips to perform calculations more quickly with less energy. In 2021, data centers accounted for about 1% of total electricity consumed worldwide, and that figure has been rising over the past several years, according to the International Energy Agency. RISC-V chips could help lower that footprint significantly, according to Baktha.
However, Intel and Arm’s chips remain popular, and it’s not yet clear whether RISC-V designs will supersede them. Companies need to convert existing software to be RISC-V compatible (the Roma supports most versions of Linux, the operating system released in the 1990s that helped drive the open-source revolution). And RISC-V users will need to watch out for developments that “bifurcate the ecosystem,” says Mejdrich—for example, if somebody develops a version of RISC-V that becomes popular but is incompatible with software designed for the original.
RISC-V International must also contend with geopolitical tensions that are at odds with the nonprofit’s open philosophy. Originally based in the US, they faced criticism from lawmakers that RISC-V could cause the US to lose its edge in the semiconductor industry and make Chinese companies more competitive. To dodge these tensions, the nonprofit relocated to Switzerland in 2020.
Looking ahead, Himelstein says the movement will draw inspiration from Linux. The hope is that RISC-V will make it possible for more people to bring their ideas for novel technologies to life. “In the end, you’re going to see much more innovative products,” he says.
Sophia Chen is a science journalist based in Columbus, Ohio, who covers physics and computing. In 2022, she was the science communicator in residence at the Simons Institute for the Theory of Computing at the University of California, Berkeley.
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.
Oh man, it’s brutal out there. One by one, the world’s richest tech companies have announced massive layoffs. Just last week, Alphabet announced it was laying off 12,000 people. There have been bruising rounds of layoffs at Amazon, Meta, Microsoft, and Twitter, too, affecting not only individual AI researchers but entire AI teams.
It was heartbreaking to read over the weekend about how some Googlers in the US found out about the company’s abrupt cull. Dan Russell, a research scientist who has worked on Google Search for over 17 years, wrote how he had gone to the office to finish off some work at 4 a.m., only to find out his entry badge didn’t work.
Economists predict the US economy may enter a recession this year amid a highly uncertain global economic outlook. Big tech companies have started to feel the squeeze.
In the past, economic downturns have shut off the funding taps for AI research. These periods are called “AI winters.” But this time we’re seeing something totally different. AI research is still extremely hot, and it’s making big leaps in progress even as tech companies have started tightening their belts.
In fact, Big Tech is counting on AI to give it an edge.
AI research has swung violently in and out of fashion since the field was established in the late 1950s. There have been two AI winters: one in the 1970s and the other in the late 1980s to early 1990s. AI research has previously fallen victim to hype cycles of exaggerated expectations that it subsequently failed to live up to, says Peter Stone, a computer science professor at the University of Texas at Austin, who used to work on AI at AT&T Bell Labs (now known as Nokia Bell Labs) until 2002.
For decades, Bell Labs was considered the hot spot for innovation, and its researchers won several Nobel Prizes and Turing Awards, including Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. The lab’s resources were cut as management started pushing for more immediate returns based on incremental technological changes, and ultimately it failed to capitalize on the internet revolution of the early 2000s, Jon Gertner writes in his book The Idea Factory: Bell Labs and the Great Age of American Innovation.
The previous downturns happened after the hottest AI techniques of the day failed to show progress and were unreliable and difficult to run, says Stone. Government agencies in the US and the UK that had provided funding for AI research soon realized that this approach was a dead end and cut off funding.
Today, AI research is having its “main character” moment. There may be an economic downturn, but AI research is still exciting. “We are still continuing to see regular rollouts of systems which are pushing back the frontiers of what AI can do,” says Michael Wooldridge, a computer science professor at the University of Oxford and author of the book A Brief History of AI.
This is a far cry from the field’s reputation in the 1990s, when Wooldridge was finishing his PhD. AI was still seen as a weird, fringe pursuit; the wider tech sector viewed it in a similar way to how established medicine views homeopathy, he says.
Today’s AI research boom has been fueled by neural networks, which saw a big breakthrough in the 1980s and work by simulating the patterns of the human brain. Back then, the technology hit a wall because the computers of the day weren’t powerful enough to run the software. Today we have lots of data and extremely powerful computers, which makes the technique viable.
New breakthroughs, such as the chatbot ChatGPT and the text-to-image model Stable Diffusion, seem to come every few months. Technologies like ChatGPT are not fully explored yet, and both industry and academia are still working out how they can be useful, says Stone.
Instead of a full-blown AI winter, we are likely to see a drop in funding for longer-term AI research and more pressure to make money using the technology, says Wooldridge. Researchers in corporate labs will be under pressure to show that their research can be integrated into products and thus make money, he adds.
That’s already happening. In light of the success of OpenAI’s ChatGPT, Google has declared a “code red” threat situation for its core product, Search, and is looking to aggressively revamp Search with its own AI research.
Stone sees parallels to what happened at Bell Labs. If Big Tech’s AI labs, which dominate the sector, turn away from deep, longer-term research and focus too much on shorter-term product development, exasperated AI researchers may leave for academia, and these big labs could lose their grip on innovation, he says.
That’s not necessarily a bad thing. There are a lot of smart people looking for jobs at the moment. Venture capitalists are looking for new startups to invest in as crypto fizzles out, and generative AI has shown how the technology can be made into products.
This moment presents the AI sector with a once-in-a-generation opportunity to play around with the potential of new technology. Despite all the gloom around the layoffs, it’s an exciting prospect.
Before you go… We’ve put together a brand new series of reports inspired by MIT Technology Review’s marquee 10 Breakthrough Technologies. The first one, which will be out later this week is about how industrial design and engineering firms are using generative AI is set to come out soon. Sign up to get notified when it’s available.
Deeper Learning
AI is bringing the internet to submerged Roman ruins
Over 2,000 years ago, Baiae was the most magnificent resort town on the Italian peninsula. Wealthy statesmen were drawn to its natural springs, building luxurious villas with heated spas and mosaic-tiled thermal pools. But over the centuries, volcanic activity submerged this playground for the Roman nobility—leaving half of it beneath the Mediterranean. Today it is a protected marine area and needs to be monitored for damage caused by divers and environmental factors. But communication underwater is extremely difficult.
Under the sea: Italian researchers think they’ve figured out a new way to bring the internet underwater: AI and algorithms, which adjust network protocols according to sea conditions and allow the signal to travel up to two kilometers. This could help researchers better study the effects of climate change on marine environments and monitor underwater volcanoes. AI research can be pretty abstract, but this is a nice, practical example of how the technology can be useful. Read more from Manuela Callari.
Bits and Bytes
How OpenAI used low-paid Kenyan workers to make ChatGPT less toxic OpenAI used a Kenyan company called Sama to train its popular AI system, ChatGPT, to generate safer content. Low-paid workers sifted through endless amounts of graphic and violent content on topics such as child sexual abuse, bestiality, murder, suicide, torture, self-harm, and incest. This story is a good reminder of all the deeply unpleasant work humans have to do behind the scenes to make AI systems safe. (Time)
Inside CNET’s AI-powered SEO money machine Tech news site CNET has started using ChatGPT to write news articles. To absolutely nobody’s surprise, the site has already had to issue corrections for factual errors in those articles. The Verge looked at why CNET decided to use AI to write stories, and it’s a sad tale of what happens when private equity collides with journalism. (The Verge)
China could offer a model for deepfake regulation Governments have been reluctant to regulate deepfakes over fears that such efforts may curtail free speech. The Chinese government, which isn’t so troubled by that risk, thinks it has a solution. The country has adopted rules that require deepfakes to have the subject’s consent and bear watermarks, for example. Other countries will be watching and taking notes. (The New York Times)
Nick Cave thinks a song written by ChatGPT in his style sucks Perfection. No comments. Chef’s kiss. (The Guardian)
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
A few pieces of good news on climate change (and a reality check)
When it comes to the climate, the picture can look bleak.
Emissions of the greenhouse gasses that cause climate change are estimated to have reached new heights in 2022. Meanwhile, climate disasters, from record heat waves in China and Europe to devastating floods in Pakistan, seem to be hitting at a breakneck pace.
But a close look at global data shows that there are a few bright spots of good news, and a lot of potential progress ahead. Renewable sources make up a growing fraction of the energy supply, and they’re getting cheaper every year. Countries are also setting new targets for emissions reductions, and unprecedented public investments could unlock more technological advances.
So despite what can feel like a barrage of bad news, there are at least a few reasons to be hopeful. Read the full story.
—Casey Crownhart
These simple design rules could turn the chip industry on its head
Since the computer was invented, humans have devised many programming languages to command them to do our bidding. For a chip to execute your code, software must translate it into instructions a chip can use. So engineers designate specific binary sequences to prompt the hardware to perform certain actions, known as the computer’s instruction set.
For years, the chip industry has relied on a variety of proprietary instruction sets, which companies license for millions of dollars a pop.
Lately, though, many hardware and software companies worldwide have begun to converge around a publicly available instruction set known as RISC-V. It’s a shift that could radically change the chip industry, and empower smaller companies and budding entrepreneurs along the way. Read the full story.
—Sophia Chen
RISC-V is one of MIT Technology Review’s 10 Breakthrough Technologies of 2023. Explore the rest of the list, and tell us what you think the 11th technology should be by voting in our poll.
The economy is down, but AI is hot. Where do we go from here?
Over the past few weeks, the world’s richest tech companies have announced massive layoffs. From Alphabet, Amazon and Meta, to Microsoft and Twitter, the job losses are affecting not only individual AI researchers, but entire AI teams.
Economists predict the US economy may enter a recession this year amid a highly uncertain global economic outlook and big tech companies have started to feel the squeeze.
In the past, economic downturns have shut off the funding taps for AI research. These periods are called “AI winters.” But this time we’re seeing something totally different. AI research is still extremely hot, and it’s continuing to make big leaps in progress—even as tech companies have started tightening their belts. Read the full story.
—Melissa Heikkilä
Melissa’s story is from The Algorithm, her weekly newsletter giving you the inside track on all things AI. Sign up to receive it in your inbox every Monday.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 Elon Musk has defended his controversial tweet in court He insists his proposal to take Tesla private at $420 a share wasn’t a weed joke. (The Verge) + Tesla investors claim they lost billions because of the tweet. (WP $) + Musk says his SpaceX stake could have funded a buyout. (Reuters) + Meanwhile, Twitter is being sued over its UK HQ’s unpaid rent. (Bloomberg $)
2 Microsoft plans to invest billions into OpenAI Just days after it confirmed plans to lay off 10,000 workers. (CNN) + It’s undeniably a major coup for Microsoft’s AI credentials. (Vox) + CEO Satya Nadella first invested in OpenAI back in 2019. (The Information $) + Here’s how Microsoft could use ChatGPT. (MIT Technology Review)
3 Silicon Valley has run out of cheap money It’s tough for even the biggest players at the moment. (NYT $) + All these layoffs are especially bad news for the metaverse. (Insider $) + Spotify is the latest company to announce it’s cutting jobs. (Engadget)
4 Crypto investors are going it alone They’re withdrawing their holdings from exchanges to their own wallets. (Reuters) + What it’s like to investigate super rich fraudsters. (The Guardian)
5 US banks’ green credentials are being assessed The Federal Reserve wants to know how they’ll handle climate emergencies. (Vox)
6 The US Government is poised to sue Google Over the company’s digital ad dominance. (Bloomberg $)
7 What will it take to make electric vehicles truly mainstream? Customers need to be convinced the rewards outweigh the potential risks. (IEEE Spectrum) + In theory, EV owners could help to prop up the power grid. (Wired $) + Why EVs won’t replace hybrid cars anytime soon. (MIT Technology Review)
8 What it’s like to be the only person with a medical condition It’s not much fun to be in a situation where no one else is known to have the exact same genetic mutation as you. (New Yorker $)
9 Spare a thought for the sneaker resellers Bot crackdowns and a potential recession spell tough times ahead. (Insider $)
10 Corecore is taking over TikTok It’s an oddly beautiful expression of existential angst. Vice) + What’s up with TikTok, exactly? (Wired $)
Quote of the day
“I didn’t give my wife enough time. Now that World of Warcraft is gone, I want to make amends.”
—Wu, a longtime fan of video game World of Warcraft, tries to find an upside to the game being taken offline in China, the Guardian reports.
The big story
How mobile money supercharged Kenya’s sports betting addiction
April 2022
Mobile money has mostly been hugely beneficial for Kenyans. But it has also turbo-charged the country’s sports betting sector.
Experts and public figures across the African continent are sounding the alarm over the growth of the sector increasingly loudly. It’s produced tales of riches, but it has also broken families, consumed college tuitions, and even driven some to suicide. Read the full story.
+ Jane Fonda just sounds like the best friend ever (thanks Charlotte!) + I enjoyed Gritty, the Philadelphia Flyers’ mascot, recreating the iconic DVD screensaver live at a game. + Communicating with cats is not for the faint of heart. + This shipwrecked teddy bear’s restoration journey is truly heartwarming. + You’ll never separate me from my useless little treats!
