Among the early use cases of AI within newsrooms appears to be fighting AI itself.

The New York Times updated its terms of services Aug. 3 to forbid the scraping of its content to train a machine learning or AI system.

The content includes but is not limited to text, photographs, images, illustrations, designs, audio clips, video clips, “look and feel” and metadata, including the party credited as the provider of such content.

The updated TOS also prohibits website crawlers, which let pages get indexed for search results, from using content to train LLMs or AI systems.

Defying these rules could result in penalties, per the terms and services, although it’s unclear what the penalties would look like. When contacted for this piece, The New York Times said that it didn’t have any additional comment beyond the TOS.

“Most boilerplate terms of service include restrictions on data scraping, but the explicit reference to training AI is new,” said Katie Gardner, partner at Gunderson Dettmer.

AI models rely on content and data, including journalism pieces and copyrighted art, as a main source of information to output results. In some cases, this content is replicated verbatim. Publishers, especially those with paywalls and healthy subscription businesses, are concerned that AI models will undermine their revenue streams by publishing repurposed content without credit, and contribute to misinformation, degrading people’s trust in news.

The confusing case of creepy crawlers

LLMs like ChatGPT work similarly to website crawlers which scan content on publishers’ sites and feed their information to inform search results.

While publishers can see crawlers visiting their sites, they cannot know their exact purposes, whether for search engine optimization or training AI models. Some paywall tech companies are looking at ways to block crawlers, according to Digiday’s reporting.

Crawlers like CommonCrawl, with a data set of 3.15 billion web pages, have brokered deals with OpenAI, Meta, and Google for AI training, per The Decoder.

Earlier this week, OpenAI launched GPTBot, a web crawler to improve AI models. This will let publishers control GPTBot’s access to their website content. Still, significant players in the field, namely Microsoft’s Bing and Google’s Bard, have not added this functionality to their bots, leaving publishers struggling to control what the crawlers scrape.

While tech companies like OpenAI are reticent to disclose what they train their AI models on, The Washington Post analyzed Google’s C4 data set, a smaller version of the CommonCrawl dataset, to understand what was training the models. It found evidence that content from 15 million websites, including The New York Times, have been used to train LLMs such as Meta’s LLaMAa and Google’s T5—an open-source language model that helps developers build software for translation tasks.

All this has spurred other publishers to reevaluate their terms of services, according to Chris Pedigo, svp for government affairs at trade body Digital Content Next, whose members include The New York Times and The Washington Post.

More licensing deals to come

While it’s unclear how AI companies will respond to these updated terms of services, they have a vested interest in shielding themselves from legal repercussions.

As a result, discussions are underway between AI companies and major publishers to establish licensing agreements, according to Pedigo, such as the deal between OpenAI and The Associated Press.

These deals are primarily set for AI companies to compensate publishers for their content. However, there’s a desire from publishers to go beyond just financial matters.

Ongoing negotiations look at how to cite publishers for their content, including aspects like footnotes. Simultaneously, there is a focus on establishing mechanisms such as guardrails and fact-checking processes within AI companies to prevent the generation of factually inaccurate content by the LLMs.

“Publishers would not want to be associated with that, especially if they’re going to have a licensing deal,” said Pedigo. “Publishers want to make sure that information meets the brand level.”

Data from Mattress Firm’s podcast campaign with iHeartMedia, which ran in 2022, shows that the medium is driving sales.

The retailer ran a branded podcast called “Chasing Sleep” with iHeartMedia’s Ruby content studio, which talked to experts and everyday people with unusual schedules, like ER doctors and ultramarathoners, about how to achieve good sleep.

The campaign, which Mattress Firm designed with agency Spark Foundry, also included ad placements across iHeartMedia’s network, the number one podcast publisher by U.S. unique monthly audience and global downloads and streams, according to Podtrac.

“It’s something that we would continue to invest in, even if it just connected at the brand level,” said Sam Bennett, SVP of marketing at Mattress Firm. “But of course, it becomes even more compelling for an organization if you’re both able to connect at this thought level and head and the heart level, as well as getting people into your shops to buy your products.”

Typically, podcast ads have included voucher codes as a way to signal attribution. “Podcasts came onto the scene and very quickly got adopted by folks who were doing direct response marketing,” said iHeart chief data officer Brian Kaminsky. “It worked very nicely, but that business doesn’t scale well. There’s this myth out there that you can’t measure podcasts,” he added. “It’s just patently not true.”

Mattress Firm worked with measurement firm Affinity Solutions to link podcast listenership to purchases. It found the campaign drove four times return on ad spend and a 45% lift in incremental sales, meaning customers who were exposed to the campaign spent 45% more than customers that were not. It couldn’t share more specific details, like how much revenue this drove. The typical incremental sales lift Affinity sees for a given campaign using this sales measurement tool, called Consumer Purchase Lift, is 5%-10%.

Mattress Firm is focused on being an authority on sleep, from having their sleep experts, or salespeople, conduct hours of training, to a website, sleep.com, which has information on sleep topics. An informative podcast was a logical addition to this mission, Bennett said.

The campaign was measured during the fourth quarter of 2022, which coincided with the show’s first season—a second season premiered in May 2023. The campaign was the first iHeartMedia campaign that measured in-depth purchase behavior metrics, said Kaminsky.

Americans will spend about 23 minutes a day listening to podcasts in 2023, according to Insider Intelligence. Americans only spend around 3.5 times more minutes a day on social networks, yet social networks command 34 times more ad spend. Podcasting will attract $2 billion of U.S. ad spend this year, compared to $68 billion for social networks, according to Insider Intelligence data. In this discrepancy lies an opportunity for savvy advertisers.

“You’re asking a buyer to do something net new, and it could fail on them,” said Bryan Barletta, partner at podcast industry resource Sounds Profitable. “There’s not enough of these case studies to [justify] the risk, but having the opportunity to do that means you succeed before the space becomes too crowded.”

Attribution isn’t new but investment is low

Affinity was able to attribute the impact of podcasts by linking listener information with transaction data using user agents and IP addresses.

Barletta said the attribution technology is itself not new but is infrequently used because of the lack of brand investment in the space. He noted that iHeartMedia’s offering, which gives brands that spend over a certain threshold access to its Ruby content studio to create podcasts, is rare among media companies and powerful. This media buy and branded content effort can complement one another.

“The ad does double the work. It drives people to the podcast. You have multiple attribution points,” Barletta said. “It’s a model I’d love to see more [media companies] explore.”

Mobile app WeAre8—which launches in the U.S. today, Adweek can exclusively share—aims to offer a social media alternative to hate speech and misinformation. Its ad model lets people opt in and get paid to watch ads.

That money can then be withdrawn or directed to charitable causes such as Feeding America, Save The Children and Water.org.

WeAre8 has been live in the U.K., Australia and New Zealand over the last 12 months. The platform is backed by publishers like The Independent, LADbible and Pink News, which publish content to the platform daily.

“The world of social that we live in today fuels hate, climate misinformation, and destroys our democracy,” said Sue Fennessy, founder and global CEO of WeAre8. “I thought, what if we can reimagine the digital ad model and then wrap around it a social media that is full of love, supports the planet and where people come together to make a positive impact every day.”

The app doesn’t allow tobacco, big oil and gambling firms to advertise on the platform.

More effective, lower-carbon ads

WeAre8 generates revenue via ads but shares 50% of it with people via micropayments, 5% with charity and climate change solutions, and 5% with its creator and publishers.

Because people can cherry-pick the content they like, the ads, in theory, are more effective for brands. Research by ad effectiveness tracking platform Lumen found that for every 1,000 impressions on an ad delivered on WeAre8, 14,819 seconds of eyeballs watched the ad. This is 10 times the amount of attention compared with its closest competitor TikTok, and 26 times more than an Instagram in-feed video, according to the company.

“Currently, the value from all that activity accrues to one place, the shareholders and owners of Meta, Alphabet, TikTok and X,” said Robert Weiss, CEO and Founder of Roar Social. “That value should also accrue to charities, to our communities. While our particular approach differs from that of WeAre8, we agree with their ethos and believe disruption is coming by new apps that embrace this approach.”

The app’s ability to decrease carbon emissions which rise from an increase in ineffective ad impressions, adds to its substantiality model. The study by Lumen found WeAre8 generated 31 times more attention per ad per gram of CO2 emitted than Instagram.

Mission to reach 80 million users

Currently, there are just under a million users on WeAre8. The app has ambitious goals to attract at least 1% of the global population, or 80 million users, to spend eight minutes a day on the platform within two years.

People using WeAre8, also known as citizens, are presented with two content feeds on the app: The first is 8Stage, which spotlights eight pieces of content daily from creators and publishers. People can see content from friends in the second feed. Both feeds are free from algorithms and ads.

Once a user opts in, a small button on the right lets them see ads ranging from 15 seconds to 2 minutes, followed by a series of questions about the experience. Every time someone completes watching a brand ad, they receive a share of the ad money as a micropayment in their 8wallet on the app.

“We make 40 cents in a dollar, and we still make a solid margin,” said Fennessy, who did not share more specifics.

WeAre8 has seen 90% of people opt in for ads. According to the company, 83% of people were able to recall an ad after seeing it on WeAre8. So far, content related to earth and animals performs well on the platform, according to Fennessy.

“The question I would have as a brand is whether or not people are actually watching the ads or simply hitting play and walking away from their phone,” said Jess Phillips, founder and CEO of influencer marketing agency The Social Standard. “If they launch interactive features, it would give it a bit more value for the brands who are paying to have their ads seen.”

The social app’s debut in the U.S. is being marketed via Warner Brothers Discovery, running TV ads across its channels, including CNN, Discovery and Animal Planet.

Brands like Nike, eBay, Budweiser, McDonald’s, Toyota, Dove, Audi and L’Oréal have been able to meet their target audience via the platform.

Heineken has run three campaigns on WeAre8 and saw a 24% average CTR on the app. Similarly, skincare brand Clarins saw a 30% uplift in intention to purchase via its campaigns.

“The impact you see is fully transparent as we know exactly where our media spend is going, how much is going to 8Citizens, how much is going to our chosen charity, and how much to carbon offsetting,” said Jimmy Hughes, social media lead, The Heineken company. “[This] makes our [money] spent with WeAre8 so much more impactful and meaningful.”

An earlier version incorrectly stated that PETA is a charitable partner.

Zoom has made changes to its AI strategy—twice.

The video conferencing platform updated its terms of service to establish the right to use some user-level data to train its artificial intelligence/machine-learning models, without giving customers the option to opt out.

Soon after a public outcry, the platform made more changes to its terms of service.

Top line

As of July 27, Zoom’s revised TOS said it could collect and use “service-generated data” related to product usage, telemetry and diagnostics to train AI models. It did not give users the option to opt out.

After drawing criticism from privacy experts on social media earlier today, Zoom updated its TOS to quell public concerns. Zoom admins could now choose whether or not their data from meetings can be used “improve the performance and accuracy of these AI services.”

“We’ve updated our terms of service to further confirm that we will not use audio, video or chat customer content to train our artificial intelligence models without your consent,” a Zoom spokesperson told Adweek.

However, the new update by Zoom is still unclear on how it will ask for consent, “and if they do so in a way that will highlight this exposure of information,” according to Violet Sullivan, vp of client engagement for Redpoint Cybersecurity and a privacy law professor at Baylor Law School.

Between the lines

Zoom’s policy changes come amid a growing public discourse on the ethical boundaries of artificial intelligence models being trained using people’s data, whether aggregated or anonymized.

Earlier in June, Zoom launched two generative AI offerings—a meeting summary tool and a tool for composing chat messages—made available on a free trial basis for customers, who can decide whether or not to use them.

However, when a person agrees to enable Zoom’s features, the platform also requests users’ consent to allow the collection of their data to train its AI models.

The TOS states that customers consent to Zoom’s access, use, collection, creation, modification, distribution, processing, sharing, maintenance and storage of service-generated data for “any purpose,” including “machine learning or artificial intelligence (including for the purposes of training and tuning of algorithms and models).”

In another section of its TOS, the company states that customers “agree to grant and hereby grant Zoom a perpetual, worldwide, non-exclusive, royalty-free, sublicensable and transferable license” to use their data for “product and service development,” including machine learning and artificial intelligence models.

Zoom’s new requirements could have big implications, especially within the telehealth field subject to stringent privacy laws.

“We will not use customer content, including education records or protected health information, to train our artificial intelligence models without [user] consent,” a Zoom spokesperson told Adweek.

Experts, however, still have concerns. “The other question is, will employees be able to grant access to the entire company?” Sullivan said, noting that this could lead to spilling of trade secrets to train AI models, especially for brands that use Zoom on a daily basis.

Bottom line

Zoom—which saw wide adoption during the Covid-19 pandemic—is not new to privacy criticism. The company was hit with an $85 million class action lawsuit in April last year for security issues that enabled hackers to crash virtual meetings, known as Zoom bombing.

Sweeping policy changes, especially to keep up with ever-evolving technologies such as GenAI, are inevitable. How Zoom’s new policies play out in privacy-sensitive situations remains to be seen.

Meanwhile, artificial intelligence platforms from OpenAI’s ChatGPT to Google’s Bard, as well as image-generation tools like Midjourney, have drawn criticism for being trained on public data.

Authenticating AI-generated content, or watermarking, has gained traction these last few months.

In July, companies including OpenAI, Google and Meta made voluntary commitments to the White House to implement guardrails to help make invisible watermarking safer and more transparent. Meta’s Instagram appears to be testing new notices to identify content created or modified by AI. In June, Publics Groupe joined the Coalition for Content Provenance and Authenticity (C2PA) and is working on the wide adoption of digital watermarks.

Analyzing the history of content, or its provenance, helps brands and creators implement safety measures, build audience trust, and ensure fair compensation so that the owner is fairly remunerated. 

“For our brand advertisers, that provenance ensures that the content brands use has a clear chain of ownership,” said Ray Lansigan, evp, corporate strategy and solutions, Publicis Digital Experience. Tech and media companies, including Adobe, Microsoft, BBC, Sony and Intel are also part of the coalition.

This comes as almost 9 in 10 Americans want AI-generated content to be labeled as such, according to a Greenough Pulse survey of over 2,000 adults. Despite the logic in identifying AI-generated content, watermarks pose key challenges, especially the ease with which they can be removed.

Visible or invisible watermarks

Content like images contain metadata, or text information, embedded into the files and include details like how the image was created, or where and when the photo was taken.

Recently, some tech companies have added AI-specific metadata watermarks into their products to allow the identification of human-produced content versus those created by large language models (LLM) like ChatGPT.

“A motivation for [tech giants] is they don’t want to use their content to feed back into the next generation of their LLM model,” said Chirag Shah, professor in the Information School at the University of Washington. “This creates a feedback loop within the model. In the long run, it’s costly and creates a siloed view of the world.”

Adobe’s content credentials tool—a free, open-source technology that serves as a digital “nutrition label” for content—tracks images edited by generative AI. Content produced using Adobe’s generative AI tools, like Photoshop Generative Fill, contains metadata that indicates whether the artwork created is partially or wholly AI-generated. The information for digital content stays with the file wherever it’s published or stored and can be accessed by anyone. 

“It is our intent to build Generative AI in a way that enables customers to monetize their talents,” a company spokesperson said. “We are developing a compensation model for Stock contributors.”

Earlier this year, Microsoft announced media provenance capabilities to Bing Image Creator and Microsoft Designer. This lets people verify whether an image or video was generated by AI. The technology, according to a blog by Microsoft, uses cryptographic methods to mark and sign AI-generated content with metadata about its origin.

Meanwhile, some companies employ visible watermarks, like OpenAI’s Dall-E, which adds rainbow-like strips to its images. Watermarking text-based content from ChatGPT is also being tested. And the C2PA is developing a user interface to display content provenance when someone hovers over a piece.

Future limitations 

The biggest challenge with watermarks, both visible and those embedded in the metadata, is that they can easily be removed.

“It’s possible to create tools that could completely remove the watermark, although they don’t exist today,” said Tom Goldstein, professor at the University of Maryland. “The question is what quality degradation occurs when you try to remove the watermark.” 

According to Sam Gregory, executive director of the human rights organization Witness, people might assume that any content without a watermark is less reliable, possibly false, or not generated by AI.

“Watermark is merely one signal; it does not confirm trust,” he said.

Neither does watermarking come as a foolproof solution to copyright issues, according to Shah. While they help in tracing content sources, there is still a lack of a legal framework to address copyright disputes within AI.

“Watermarking is the initial step, but not the ultimate solution to tackle copyright concerns,” he added.

The launch of Giphy’s native advertising platform, set to be revealed at the end of this year, will be worth hundreds of millions of dollars, proclaimed its new owner Shutterstock’s chief executive Paul Hennessy during the company’s most recent quarterly earnings call.

Shutterstock acquired Giphy from Meta in May in a cut-price deal of $53 million after the social media giant was forced to sell the gif library and search engine after Britain’s Competitions and Markets Authority ruled its ownership to be anti-competitive. That ruling—which claimed to support digital advertising competition and social media use in the U.K.—cost Meta hundreds of millions of dollars. It had purchased Giphy in 2020 for around $315 million.

The advertising opportunity is in the hundreds of millions of dollars.

Paul Hennessy, Shutterstock CEO

Shutterstock claims Giphy has around 1.7 billion daily users, with the platform holding 14,000 API connections, including integrations with Instagram, Facebook, Whatsapp, Microsoft, TikTok, Samsung, Twitter, Slack and Discord. Its library currently holds more than 100 million GIFs.

It was through Giphy’s ‘Paid Alignment’ advertising service, which was available to U.S. marketers and was set to open up internationally, that triggered concern about Meta’s already strong hold in the U.K. digital ad market. Shutterstock is reviving the ad product in the coming months.

“Thinking long term, the advertising opportunity, given the size and scale of the Giphy businesses, is in the hundreds of millions of dollars,” said Hennessy. He also said he expected Giphy to become a larger part of the company in the years to come.

The future for Giphy at Shutterstock

To avoid disruption and maintain scale, the 125-person Giphy team came along with the deal.

For Meghan Schoen, Shutterstock’s chief product officer, the appeal of Giphy is its storytelling capabilities.

“That was really meaningful for us,” she explained. “At our core, we’re really focused on storytelling. Giphy is a business of micro-moments and storytelling, and with tremendous scale. It was a natural extension of our core value proposition for our users.”

We saw it as a massive opportunity to further evolve our creative engine.

Meghan Schoen, chief product officer, Shutterstock

Schoen outlined the company’s ‘three engines’—content, data and creative—which Shutterstock is focused on building to help marketers and advertisers tell their brand stories. Giphy is a platform that was “instantaneous” in doing that while having a “tremendous reach,” she told Adweek.

“We saw it as a massive opportunity to further evolve our creative engine and really create that demand engine at scale,” she added.

In terms of the advertising proposition, Schoen would not go into detail but claimed that Giphy was a platform that would help marketers be a part of everyday conversations and offer native advertising.

“Historically, they have not had an ad platform—the team’s near-term focus is setting up the ability to actually have advertising run through the platform in really native and organic ways to unlock those capabilities and that scale for marketers,” she added.

“When we say ‘native advertising,’ what we mean is somebody searches for something like ‘fun’ or ‘happy,’ and having the ability to have product placement in those gifs that feels very natural and something that someone may select, but it’s also a way to get a brand front-and-center in everyday discourse. Those are the things that I’m sure the team will continue to explore.”

With new partnerships with OpenAI, LG AI Research and Meta around its data and image libraries, the company also believes the evolution of generative AI will impact Giphy’s own future library.

“We’re in the business of helping marketers, advertisers and creators tell their stories. And so there’s a whole host of capabilities that we are exploring right now that allow anyone to make our library infinitely customizable,” she commented.

The ad industry’s view

The use of Giphy as an advertising product is not widely featured as part of the content strategy.

“It’s not discussions we’re having with clients unless they’ve got some revolutionary product that we can’t envisage, then I would be guessing what that is going to be,” commented Allan Blair, head of strategy EMEA for VaynerMedia.

He did admit that memes were still “a big part” of social media marketing and internet culture but added that clients were cautious about introducing them in their own communications due to copyright issues.

As to how it may be fair as a display advertising platform, Andrew Spurrier-Dawes, EMEA Head of Precision at Wavemaker, said that Giphy faced challenges.

“The first is that the user is not on a social network, and so not there to browse, but instead searching for a specific gif. This means that the attention/engagement rate will be low as it is a drop in and out type of library rather than a publisher, where the dwell time will be higher. It will be tricky to increase dwell time without being interruptive,” he explained.

“Secondly, gifs are at the heart of pop culture, where certain gifs can be on a trend for a short burst before the next gif comes along. This means a really tight approach to brand safety and content control is critical to offering advertisers a space they know that will be relevant and appropriate for their brands—and not stealing their content if they are an entertainment brand.” 

The way we get information on the internet is poised to transform from a mostly search-based user experience to a conversational, or chatbot-based one, powered by engines like Google’s Bard and Microsoft Bing’s partnership with OpenAI.

Caught up in this dynamic are SEO agencies, who have carved out a niche helping brands get discovered in this current search paradigm.

In response, several SEO agencies are adapting their tactics to prepare for this new user experience, including conducting analyses on how to make it into a chatbot’s answers, retooling websites such that they will be maximally likely to be surfaced by chatbots, and experimenting with long-tail keywords.

“You can start to reverse engineer what sources they’re pulling from and what would make it likely to pull from a certain source in the future,” said David Shapiro, svp of earned media at NP Digital. “It’s like SEO back in the early 90s, even before Google, really trying to understand what’s powering these results.”

The shift in search is just one of many changes in the past few years to the performance marketer job. The discipline became in vogue in the past two decades for its focus on technical granularity and lower-funnel results, but now privacy concerns and artificial intelligence are preventing performance marketers from exerting as much control when crafting media plans.

Showing up in AI search and honing prompts

Most people are still searching on Google’s search engine. Only 14% of U.S. adults have tried ChatGPT, according to a May Pew Research Center survey.

As marketers wait for wider adoption, they’re analyzing when and how a brand gets cited by a chatbot, a service around 10% of NP Digital’s clients have asked for in the past few months, Shapiro said.

“We’re noticing that pages ranking number one in organic search results aren’t necessarily the top source in the same search engine’s chatbot results,” said Evan Finkelstein, senior manager of SEO and performance content at New York Life Insurance Company.

A key task of SEO agencies is finetuning content and metadata on websites so that search engines will pick them up. Now agencies are working to spruce up websites so that an AI chabot’s web crawler might be more likely to put the website’s content in a chatbot’s answer.

[SEO agencies] have been really bad at communicating the value they’re bringing.

Sam Tomlinson, EVP at agency Warschawski

Between 40% and 50% of clients of SEO marketing firm The Hoth have focused more on ensuring they comply with Google’s crawling requirements this year, said CMO Max Gomez Montejo.

“Clients are becoming more aware that they need to enhance their websites if they want their content to be prominently featured in search snippets or, looking ahead, for AI chatbot integration,” he said.

Right now, it will be hard to measure the fruits of this labor, as, currently, ChatGPT is mostly based on information from 2021 and older. But the idea is that eventually, ChatGPT will include more recent material, and at that point, brands should be prepared.

Gomez Montejo said The Hoth is also working with clients to hone their long-tail keyword strategy, which refers to more specific groupings of keywords that get fewer searches per month and are a significantly less popular investment than branded keywords. As searches become more conversational, focusing on these phrase-like groupings becomes more important, he said.

SEO agencies are also offering new services for the traditional search interface already being remade by generative AI. Both The Hoth and NP Digital are helping clients craft the best prompts to feed to a chatbot when making AI-generated content.

Moreover, because brands are able to generate more content with ChatGPT, advertisers are asking SEO agencies to create more blog posts on their behalf to compete. Gomez Montejo said that client demand for content has doubled in the past year.

An evolution, or the end of SEO?

Even without chatbots, SEO agencies have faced challenges in recent years. Google has made links less prominent in its interface, introducing text snippet answers to queries instead of purely funneling users to websites for information, Finkelstein said.

“The results partially remove the brand from the equation, decrease visibility and the likelihood someone will visit your site or engage with your brand,” Finkelstein said, noting the chatbot interface is likely to exacerbate this problem.

In addition, brands must attend to digital channels now more than ever. Younger audiences have a penchant for searching on TikTok and Instagram. That means less innovative SEO agencies are getting squeezed, said Sam Tomlinson, EVP at boutique agency Warschawski, who said that in the past 12 months, he’s seen fewer SEO-only RFPs from clients than in any preceding period he can remember.

“[SEO agencies] have been really bad at communicating the value they’re bringing,” Tomlinson said.

But successful SEO agencies have excelled at adaption. Tinuiti helps clients with how they show up on platforms like Google Maps, TikTok and Reddit, while adapting to changes to Google’s algorithm, said Kris Wong, senior director of SEO at the agency. She sees the shift to chat-driven search as another bump in the dynamic job of an SEO professional, rather than cataclysmic.

“Google is changing so frequently … so being adaptive is key,” Wong said. “It’s nothing new.”