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Perspectives

DCN’s must reads: week of June 1, 2023

Here are some of the best media stories our team has read so far this week:

DCN’s must reads: week of May 25, 2023

Perspectives

DCN’s must reads: week of May 25, 2023

Here are some of the best media stories our team has read so far this week:

DCN’s must reads: week of May 18, 2023

Perspectives | Supporter Spotlight

How publishers can make the most of TikTok

For the past few years, TikTok has been the platform at the cutting edge of social media, with a rapidly growing, young cohort of users. It is also the place nearly 1 in 7 people in the 18 to 24 age group turn to for news.

Media companies like ABC News and the Washington Post have developed a strong following on the platform. And BBC News has seen a 2000% increase in followers in the last nine months. 

Reuters Institute reports that 49% of leading news publishers worldwide now regularly post to TikTok and the number of publishers committing to the platform continues to climb. Echobox’s latest Publishing Trends Survey found that 59% of publishers believed TikTok would be more important to them this year compared to 2022 — a jump of 39% in the last year. 

These developments, however, are taking place against the backdrop of growing regulatory pressure on TikTok’s parent company ByteDance. It is conceivable that the world’s fastest growing social media app could soon be banned in the U.S. Why, then, are so many news organizations choosing to invest in a platform whose future is shrouded in uncertainty?

Why invest in a platform with an uncertain future?

1. The short term gain justifies the investment

The simplest answer is that whatever happens to TikTok in the future is less important than the benefit to be gained from using the platform now. When we asked publishers which activities would be more important to them this year, 47% of respondents to our survey answered growing social followers and engagement. Publishers are going where the audiences are, with the hope that readers exposed to their content will either follow them on other social platforms or sign up for newsletters or other subscription products in the event that TikTok becomes unviable.

2. All social media is becoming like TikTok

In the long term, the continued investment in TikTok is an indication of the new realities of social media as a whole. Quite simply, TikTok has changed the game.

In the last couple of years, Facebook, Instagram, and YouTube have all sharpened their focus on TikTok, looking to ape its distinctive allure. The calculus here is that even if the platform was to be banned, the innovations that it pioneered, such as short-form vertical format video, will remain alive and well on other platforms.

Meta’s cross-platform Reels as well as YouTube Shorts have experienced impressive performance stats. Meta announced during its Q1 2023 earnings call that Reels was responsible for a 24% increase in time spent on the platform. 

Investments made into acquiring the skills and technology to produce content on TikTok can therefore be put to productive use on other platforms where short-form vertical videos have been incorporated. 

The results of our publisher survey reinforce this. For 47% of publishers, creating video will be more important this year than last.

The time is right

In the here and now, how can publishers take advantage of TikTok’s popularity and increase the potential virality of their content?

The most fundamental difference between TikTok and Facebook is the nature of its “recommender algorithms” that determine what content is served up to a given user. Instead of relying on who you follow to determine what content you should see, per Facebook’s model, TikTok relies purely on opaque algorithms to serve up content without a user having to follow anyone; the single imperative on TikTok is to watch. 

This removes a barrier to publishers who want to increase their exposure on the platform. Not having the potential visibility of content circumscribed by follower numbers means that, theoretically, it’s easier for any publisher to produce highly-viewed, or even viral content.

Timing is everything

Achieving high performance on TikTok is all a matter of timing.

The process by which TikTok promotes content is simple: it shows content to a small sample of users in its For You feed. Then, if engagement and watch time is high, the video gets promoted to a larger group and so on. Posting video at a time in which a high number of engaged users are active means that the potential for virality is immediately increased. 

Utilizing AI-powered social media management tools to optimize post timings is therefore a simple and cost-effective way to increase visibility. The fact that a publisher’s TikTok follower count has virtually no bearing on which content is promoted allows greater opportunities for AI systems to maximize the potential visibility of a video and help its performance snowball.

The future of TikTok remains uncertain, but the platform is rich in immediate benefits. With over a billion (predominantly young) users, publishers have the opportunity to meet new audiences where they are congregating. With a combination of AI smarts and a forward-looking strategy, publishers can become trusted and indispensable news sources with or without TikTok.

About the author

Ashley Kibler is the Marketing Director at Echobox, the leading solution for publishing automation used by over 2,000 publishers and media groups worldwide to automate and optimize content curation and distribution.

Advertising | Featured Post | Innovation | Perspectives

Take a breath. Ok, now let’s figure out AI

Generative AI has got folks in a frenzy. From excitement about everything it can do to concerns about biased algorithms, plagiarism, and potential job displacement, there’s a raft of optimism and concern around GAI.  

Take a breath. Are you good? Ok, now let’s figure out AI together. 

Sorry, with all the talk and emotion behind generative AI, I thought it may be a good time for everyone to catch a breather. While I understand the concerns, I happen to be a constant optimist. And as a digital media revenue leader, I’m always looking for optimal strategies at the highest level to secure the brightest future.  

In that vein, even though AI in general has been around for some time, generative AI rings my “opportunity” bell – unlike many other developments in recent years. And I don’t think I’m alone. It’s not only because generative AI is the latest trending phenomenon but because it’s a resource that delivers applicable convenience spanning virtually all industries.  

If you’re a curious optimist like me, you’ve noted GAI’s potential cost and time-saving implications by now. If you’re a futurist, you may have already concluded that AI will become mainstream, embedded within corporate culture, and likely governed in one way or another. It will also continue to evolve.  

Yes, I’m fully aware and respectfully conscious of GAI’s prospective danger in infringing upon the authenticity, stylistics, and nature of writing, journalism, and creative arts as we know it. However, glass half full, I’m also confidently optimistic about our ability to address, structure, regulate, and develop solutions for varying segmented conventional concerns.  

That said, AI’s current and developmental beta states still hold colossal potential for new applications, especially for those in leading digital media sales or revenue roles. To unfold the magnitude of what GAI could represent for the digital media industry, I took a step back to understand a broader scope of capability to illustrate how strategies may be developed and actualize its role in digital media organizations. 

What it can do 

If we assume that GAI is here to stay, it would be irresponsible not to consider the ways in which it will impact the revenue side of digital media. What if I told you I’ve seen GAI applications create custom sales scripts in seconds? What If I said GAI could develop cold email communication flows or custom marketing suggestions? Or that it can craft an elevator pitch or presentation about why a prospect should advertise with a specific organization?  

If you can feel your chest tighten, believe me when I say I’m only scratching the surface of the impact GAI will have on the business of media. Take a breath. Are you good? Okay, let’s identify how GAI could help digital media revenue strategies by starting at the highest level and eliminating the noise. 

What could it represent 

A calculator doesn’t diminish the need to understand math; it enhances our ability to output more complex algorithms conveniently. Similarly, as a resource, GAI can improve the output of nearly all matters and tasks related to revenue growth for digital media. Therefore, the most straightforward digital media growth strategy in leveraging it may be to aid digital executives in making more sales.  

GAI can help representatives articulate the benefits and features of products or services custom-tailored to specific businesses and industries. It can also create and send emails or assign process tasks that are traditional time and sales focus deterrents.  

Once you take a step back and begin to digest the immensity and capabilities of GAI, the reactionary credulous grim outlook begins to wane. It is soon replaced by optimism around the opportunity it offers to enhance varying aspects of an organization, which clears a path for future strategies to formulate and blossom. 

What AI’s role could be 

Furthermore, I believe the key to leveraging AI, and generative AI specifically, for growth in digital media is custom automation in higher volumes, especially when it comes to sales. AI epitomizes the evolution of automating the proverbial “sales processes” or even replicating the techniques, communication tactics, and follow-up habits of top sales executives.  

However, it is important to be conscious, balanced, and diligent so that you don’t fall into a silo of convenience alone. Keep an elevated perspective to implement AI tactics that affect and flow seamlessly throughout the entire revenue department.  

The guiding north star in any AI strategy should be to deliver robust, sustainable solutions that enhance all processes and growth opportunities within an organization’s revenue sector and with advertising partners. Add value to products and services by leveraging AI to address advertisers’ specific needs at higher volumes. Don’t send recycled AI-generated communication about the organization’s history to new prospects.  

Actualizing through prompting 

The balance of knowing how to leverage GAI for growth or even enhancing sales communication and output is going to be important to brand value. As prevailing use cases for AI become more established and widespread, so will the need for understanding how to leverage the best prompts or the phrasing used to initiate a conversation or command with various AI technology.  

If adequately equipped and informed, the impact of critical initial engaging touchpoints specific to an advertiser’s needs generated from AI may result in higher close ratios. Prompting may even become a future language or quintessential skill set to propel revenue growth within digital media. This requires the art and creativity in understanding how to position a command or question with AI versus using it to do something just because you can.  

For example, the result generated by prompting an AI chatbot to create an advertising sales script for a local restaurant may seem compelling but needs to be more developed to position an organization as a solution provider. Begin with a prompt asking, “what a restaurant’s most important success metrics are” and then follow it up with a request to create a sales script based on that result. The difference should better convey the organization’s commitment to delivering a solution and not simply selling an audience.  

Opportunity abounds 

Generative AI is still in its early stages and is continuously adapting and evolving because it needs to. Now is the time to begin setting the stage for how digital media can configure and fine tune various applications of generative AI, particularly within the revenue vertical, to support advertising partners, organizations, and communities.  

Certainly, AI is not without its flaws. And, much like the evolution of the internet and search engines, mass adoption will take time. The very existence of AI represents and embodies human creativity and ingenuity, and the optimist in me tends to applaud its boldness and innovation. If you’re concerned, you’re not alone, as I firmly believe in thorough risk assessment and evaluations. However, if fear of the unknown is causing hesitancy about AI, then take a breath, and know that we’re all going to figure this out together.  

About the author 

Richard E. Brown is a News/Media Alliance Rising Star recipient, the former director of renewals and digital sales strategy at LPi, and the former director of digital operations and sales of the Milwaukee Journal Sentinel. He recently served as the head of digital subscriber churn for Gannett | USA TODAY NETWORK and is now the senior director of retention for The Daily Beast. He is also a member of the board of directors for the Wisconsin Newspaper Association Foundation.   

Perspectives

DCN’s must reads: week of May 18, 2023

Here are some of the best media stories our team has read so far this week:

DCN’s must reads: week of May 11, 2023

Advertising | Featured Post | Perspectives

Will tech’s “signal loss” be our opportunity?

In media, “signals” have long driven the business, whether for must-see entertainment or brand-defining journalism. Unfortunately, these signals, which convey value, meaning and relevance, are becoming harder to distinguish amidst the increasing noise of modern digital media. Yet the ability to distinguish between signal and noise is just as important, if not more so, in the age of digital media given the abundance—and velocity—of information.

Today, there are fewer and fewer high-value signals that can cut through the noise without interference. Brands invest upwards of seven million dollars for a 30-second commercial during the Super Bowl or six figures to run a page in the September issue of Vogue so that they can send a signal to a large and focused audience, not only about their product, but also make a statement about the significance of their market position. In other words, “we have so much confidence in our offering that we can spend this kind of money to tell you about it.” There is very little noise surrounding these types of advertisements. The message is clear. And everyone makes bank.

However, there are also high-value signals that must compete with endless noise. To see this in action, just take a stroll through Times Square in New York City, one of the busiest and noisiest places in the world, with a constant barrage of sights and sounds competing for attention. Amidst all this noise and the tens of thousands of people rushing about their days, one might think it would be challenging for any brand to resonate. Yet countless companies do just that by spending millions to share their message in this hub of entertainment and commerce. With their brand messages broadcast on LED screens hundreds of feet wide, or plastered on buildings that stretch to the sky, they send an unambiguous signal of value and importance.

The Super Bowl offers one of the year’s must-watch experiences—both on the field and in the ad breaks. The pages of September Vogue overflow with lush photos and unparalleled fashion coverage with ads deftly woven into the fabric. Times Square is one of the most exciting, chaotic, and sense-stimulating places on earth. In every case, the context provides a signal boost: What you see here has value.

In the world of digital media, the rise of the tech giants has allowed them to suppress signals and amplify noise. Amidst the endless content scroll are interchangeable commodities and it’s hard to differentiate between much of anything on these platforms. In many ways, they’ve turned the world upside down by turning traditional signals into something else altogether through their black-box algorithms.

Often these secret signals happen below the surface shrouded in mystery, as seen in the case of the Google search engine. This tool was built in a revolutionary way, using “signals” to sort all the world’s information. This grand innovation offered a way to create structure by ranking a site’s ability to collate high value links around the web. Fortunes have been won and lost in how Google values those signals. Over time, Google matured and evolved into using authority and other proxies to value sites across search and YouTube. The signals favored by this search superpower frequently recalibrate, but we know they are there under the surface, controlling what people see.  

Amazon has built the world’s largest retail store where its Prime signal, in a classic flex of monopoly power, has arguably become the most important indicator of whether the customer will buy a product or not. Sure, there may be dozens of vendors offering nearly identical products, but a single signal undeniably tilts the scale.

However, we have also seen that signals can be “lost.” Facebook built what is arguably the most powerful targeted advertising business in history by mining and exploiting data across the web and mobile app ecosystem. Targeted ads have consistently comprised more than 97% of the company’s revenues for more than a decade. This was never clearer than in 2021: When Apple stepped up its privacy efforts, Facebook lost nearly half of its market value, which the company’s CFO attributed to “signal loss.” That loss continues to haunt the company’s fortunes as the company scrambles for a new moat to protect its empire. 

The value of signals amidst the noise is also key to the rapid-fire (and controversial) development of large language models, such as those used by Google’s Bard and Microsoft’s Bing to train generative AI. The value of an answer engine will undoubtedly lie in the quality of its results. Therefore, such models must differentiate between things learned through Financial Times versus Reddit, Washington Post versus Twitter, and so on. Although they may claim to treat all domains generically, there is little doubt the source matters. All content is not equal.

As digital technology continues to advance, distinguishing between signals and noise becomes ever more challenging. Like Times Square, the noisiness of the digital world can be stimulating and provocative. However, with so much noise, signals that can convey meaning and relevance to a broad audience have more value than ever.

Media companies must focus on developing strategies that allow them to differentiate themselves and their messaging, so they can send signals that cut through the noise and resonate with their target audience.  And they must seek out platforms, partners, and technology companies that fully recognize and fairly compensate these valuable signals as they launch, thrive, and grow into maturity.

Amidst the nearly infinite access to content created by anyone and everyone—from influencer-ally or trusted brand to bot armies and monetizers of disinformation, signal has never been more valuable. Ultimately, the ability to differentiate between signals and noise is essential to success in the digital age, and those who can do so effectively will have a significant advantage, and that advantage will be one shared by the audiences they serve.

Perspectives

DCN’s must reads: week of May 11, 2023

Here are some of the best media stories our team has read so far this week:

DCN’s must reads: week of May 4, 2023

Perspectives | Revenue | Supporter Spotlight

Friend or foe: is AI the latest big tech assault on media revenues?

Every once in a while, a trend rippling through the digital media industry that springs up from the trade publications and into general-interest media. The launch of ChatGPT has been one of those times. AI content generation feels like more than ones and zeroes to media consumers at large. It feels tangible, even visceral, because it has direct ramifications on the production of content itself. Clearly media leaders need to make their voices heard in order to help guide the conversation for users as well as AI developers.

It would appear every Big Tech business wants in on AI chat to power search. But like identity and privacy, media companies are not about to sit back and watch which of the tech giants “wins” generative AI. They need to understand how AI will change content consumption, how they can use it to their advantage, and how to keep walled gardens from absorbing more of their revenue.

A new era for the search business

We’re seeing real and atypical disruption today in AI chat’s applications for search. Bing’s ChatGPT search function removes the onus of poring over multiple search results, and simply generates what feels like a coherent response upfront – be it right or wrong, strong or flimsy, fair, or biased. And history shows that human nature tends to prefer convenience over perfection. Search providers are already investigating how AI search may be monetized, to avoid losing out on the serious revenue search advertising traditionally has delivered (58% of Alphabet’s total revenue last year, for those keeping score).

That brings us to media companies’ reliance on search traffic to monetize their own sites and content. The prospect of users being disincentivized from visiting publisher sites to verify and provide context for information looks ominous to publishers. Today, 26% of all publisher traffic comes from search, in an environment where open web publishers are already competing against walled gardens for traffic and advertisers are reducing their budgets. And as it stands, not all AI search tools link back to their sources, and marketers are still trying to understand how the AI search ad experience should appear. And while search bots already crawl publisher pages, AI chatbots present a less balanced exchange between publisher and search engine.

Publishers that control their data have an advantage

Let’s avoid tunnel vision here, though. Publishers’ imperative to soften any blow to revenue from search traffic overlaps with their existing efforts to reduce reliance on revenue from the programmatic open market. Those efforts involve building loyalty among users and advertisers alike. For users, publishers are offering subscription packages, exclusive newsletter content, and the like. For advertisers, they’re offering private marketplaces and direct deals. Publishers can better navigate AI disruption by continuing and evolving those business strategies. They need to position themselves as sources of trustworthy, high-quality content worthy of the lifetime audience loyalty that can grow CPMs and incentivize deeper advertiser relationships. And AI has real benefits for those strategies.

If and when search becomes a less reliable source of traffic for media companies, they’ll need to ramp up efforts in driving traffic from channels such as social and video. In finding the best channels and business partners, they can turn to AI to analyze large volumes of first- and second-party data, and to do lookalike modelling. Leading publishers have already been doing this as part of their data strategy for withstanding third-party cookie deprecation. Advertisers are taking bold data strategy steps, too, and have the resources to be very advanced here. Publishers should compare notes with their brand partners to learn how to best leverage AI and machine learning for predictive audience creation, modelling, data cleaning and processing, and probabilistic matching.

We’re also seeing publishers take interest in generative AI’s capacity to accelerate and personalize content production, with human staff overseeing and completing the job. AI can do some of the lifting on lower-level SEO writing, bolster production of sponsored content, and source background from the publisher’s content archives. With ad revenues dipping on account of economic pressure, publishers are actively seeking new workflow efficiencies and content initiatives simultaneously without cutting headcount.

IP law will establish new limits for AI crawling

While we’ve all heard chatter about AI displacing human creative talent, media companies have more leverage than they might appear from a slight distance. That human-created input that drives AI output is guarded by intellectual property law. Rights holders are pushing back, and we can expect to see more robust IP regulation, and even technology that protects content from being scraped by AI tools, coming into play in the near future.

Publisher trade body The News/Media Alliance intends to pursue Big Tech companies for IP infringement, and its members want generative AI providers to agree to pay to licence their content (there’s a precedent for such licensing terms, from the use of text snippets in traditional search results). It’s questionable whether scraping publisher sites to train AI would ever fall under “fair use.”

But publishers shouldn’t simply wait for regulators to clear up these issues. That’s a path with unpredictable length and outcomes. At the top of publishers’ to-do lists should be bracing for shortfalls in search traffic, powering their data resources, and finding applications for AI to embolden content production. And Big Tech businesses should be forewarned: AI can’t be a higher priority than the sustainability of the digital publishing business that generative AI models depend on. An input of quality content makes the difference between useful output and unappealing nonsense.

Perspectives

DCN’s must reads: week of May 4, 2023

Here are some of the best media stories our team has read so far this week:

DCN’s must reads: week of April 27, 2023

Perspectives

DCN’s must reads: week of April 27, 2023

Here are some of the best media stories our team has read so far this week:

DCN’s must reads: week of April 20, 2023

Featured Post | Perspectives

What your audience thinks of you

The latest trust-in-media research is not pretty. But it’s not hopeless, either.

It was one of those random-but-when-you-think-of-it-not-entirely-coincidental accidents of timing, ideal for an article lede. The same day that Fox News settled its defamation lawsuit with Dominion Voting Systems, the Reuters Institute for the Study of Journalism unveiled the latest installment of research from its Trust in News Project. The Fox case showed just how little Fox journalists deserve their audience’s trust. And the Reuters survey showed just how little our audiences trust the rest of us, whether we deserve it or not.

That Americans have lost trust in news media is not exactly a stop-the-presses revelation. But the new Reuters report adds an emotional overlay to this sorry but well-reported fact by elevating the attitudes of marginalized populations ignored by previous surveys. Suffice it to say that adding emotion doesn’t make the data any less grim. We are losing hearts and minds at a dizzying pace.

Still, there are hints of hope. The research makes clear that even those who mistrust us value the work we do, when we do it well. People still need journalism, and they know it. Out of that fact arise some suggestions for how we might dig our way out. And we must dig our way out.

In case you haven’t been tracking surveys about trust in media—and I don’t blame you if you have been avoiding them—the data has been steadily getting worse for decades. According to a Knight Gallup poll last year, just 16% of Americans have a lot of faith in newspapers and 11% in TV news. Both figures are down about two thirds as compared to 30 years ago.

This time, it’s personal

What does the new research say that we didn’t already know? For one thing, because the Reuters researchers conducted focus groups, rather than taking surveys, the report gathers some arresting personal expression from audiences. In addition, Reuters sought out subjects from groups we journalists don’t often hear from, so the perspectives are sometimes revealingly different (or depressingly the same). In the UK, researchers interviewed working class people in Yorkshire and greater London. In the US, they drew on two separate audiences, Black and rural Americans, both from Iowa. (The research also targeted comparable marginalized groups in Brazil and India.)

Across all these groups, panelists repeatedly said that they felt unseen and unheard by media. In a conversation we had at conversation at the Virginia Local News Summit in April (separate from the survey), Tracie Powell, founder of the Pivot Fund, which invests in BIPOC owned media, offered a poignant example of how that inattention feels. Describing what happened when the big-city press parachuted in to cover a three-fatality traffic accident in rural Georgia, she noted that local Black residents felt that the metro reporters lavished attention on the two white victims and gave short shrift to the Black one. “The Journal-Constitution’s reporting was accurate,” she said. “Accurate—but not true. And everyone in the Black community knew it.”

Professional judgement

When professional media do pay attention to marginalized populations, the focus group participants complained, we tend to highlight the negative—deprivation, crime and racism. “[W]eek after week, month after month, it’s mentally and emotionally exhausting,” said one Black participant. Others believe that we too easily resort to stereotypes. In the other Iowa focus group, for example, a participant told researchers that she felt that coverage of rural America quickly resorted to tropes of “corn fields and pig farms,” and depicting people like her as “a bunch of hicks.”

During a phone call, Benjamin Toff, a senior research fellow at the Reuters Institute and leader of the three-year-old Trust in News Project, said that this highly personal reaction to coverage was a key difference from previous Reuters trust studies. “When you see reporting on some remote issue as sensationalistic, it sparks one kind of judgment,” he told me in an interview. “When the hype involves someone you know and reinforces stereotypes that harm you and your children, the mistrust is a lot more visceral.”

What are we in the profession supposed to do about this? To some extent, we already know the solutions, even if we have not been universally successful at putting them into practice: bring more diverse representation into our newsrooms, develop more awareness of the biases that we unconsciously import when we write about groups less white or educated or liberal then the typical newsroom.

Make the connection

The report also hinted at some more hopeful paths back to trust. Audiences in marginalized groups told the Reuters researchers they want unbiased, fair and accurate coverage of matters relevant to their lives—the same desire other audiences have. Most trust surveys, including this one, show that audiences tend to be suspicious of our motives and shockingly unaware of how we operate. That suggests that we don’t need to abandon our most cherished values; we just need to get them across better.

Joy Mayer, founder of the Trusting News Project, responded to this aspect of the Reuters research in a Medium post, writing (among many other useful suggestions): “If you work hard to be fair, consider articulating what fairness looks like for your newsroom. Get on the record about it. (Take inspiration from the San Diego Union-Tribune’s fairness checklist.) Then draw attention to that commitment day to day.”

Personal connections between individual journalists and their audience can build trust in ways that aren’t necessarily available to media brands. “[The research suggested] that people are much more willing to give individual journalists leeway,” said Toff. “Many are suspicious of the commercial and political agendas they see media institutions pursuing. It’s easier for individual journalists to build trust personally.”

It’s impossible to read the Reuters’ report and not conclude that it will take anything less than a generation for media to rebuild trust, if we can rebuild it at all. But it would also be wrong to conclude that there’s nothing we can do. Even the audiences addressed by the Reuters study—who are not, generally speaking, active seekers of accountability stories—recognize the importance of our role.

To reach them, though, we need to be aware of how they see us and to be humble in our approach. Margaret Talev[1], the long-time White House correspondent and Axios political editor who recently joined Syracuse University’s Democracy, Journalism & Citizenship Institute as director.  In a panel two days after the Reuters report Talev said “It’s not enough to tell people they should trust us because we’re good for them.” She said. “We need to meet people where they are and give them what they want.”

About the author

Eric Schurenberg is the founder of the Newsroom Trust Project, a research project seeking solutions to the erosion of trust in media, and the host of the podcast In Reality. Prior to that, Eric was the CEO of Mansueto Ventures, the owner of Inc. and Fast Company.

Featured Post | Perspectives | Policy

Generative AI: threats and promise for a new world

At DCN, we’ve been diving deep into Generative AI – trying to understand what it is and what it isn’t and sort through the implications for professional content creators. We have met with hundreds of executives, tech specialists, journalists, artists, and creators who work within our member companies to create and monetize premium original content. We’ve heard first-hand what the content community is excited about and what gives them pause. Based on these conversations and our own research, we see several topics around Generative AI (GAI) that merit a closer look.

Protection for creators

Generative AI systems need to be designed in a way that respects professional content creators and the value of their IP. Perhaps the biggest concern is the loss of control over the content, in which they have heavily invested. GAI systems train by using digital content such as journalism and photos. However, they also scrape that content for reuse in a variety of ways and for a variety of purposes – some of which may undermine the business model of the content creator.

While the scraping of original content for training may seem relatively benign, Generative AI companies will clearly reap huge benefits. The use of premium trusted content helps these systems understand cultural nuances and avoid problems of misinformation and factual inaccuracies. If the old axiom of “garbage in, garbage out” applies, companies profiting from GAI should appropriately value premium trusted content.

That said, scraping of content for reuse seems highly problematic as the creator might lose the ability to distribute and monetize the content as they see fit. We’ve heard stories of Generative AI systems providing subscription content to non-subscribers and amalgamations of general news content without any attribution to the organizations which funded its creation. Stripping content creators of control over their work effectively deprives them of the ability and incentive to invest in new content. It seems like there should be a win-win here where GAI systems fairly compensate and credit professional content creators for their work.

Transparency

Companies that develop GAI systems should be transparent with the public so that we can fully assess the impacts on our society and address the consequences. If Generative AI fulfills its promise, then whole industries and sectors will be radically changed. In particular, consumer experiences with news and entertainment are likely to be upended.

With all that change, intentional and unintentional consequences are likely to occur. Generative AI systems have been shown to be flat out wrong, making up convincing sounding sources or generating “quotes” and articles to back up their responses which may be misattributed to individuals, institutions, or media brands.

There is an open question about the extent that these systems could be manipulated by bad actors looking to flood the public square with misinformation. As anyone can see, the world isn’t a perfect place. And, sadly, the internet is loaded with hateful, harmful speech. More subtly, however, there are conscious and unconscious biases baked into all kinds of reporting and opinion pieces. To the extent that Generative AI systems are training on content they scrape from the web, they may be recycling hate speech or perpetuating biases.

Therefore, allowing these systems to be developed and operate in a black box would invite all kinds of problems without an opportunity to correct mistakes before they run wild. Most of the early legislation around AI calls for transparency so that regulators and researchers can assess the potential impacts and offer solutions before actual harms are committed.

Competition

The tech industry currently has a competition problem – a lack of competition to be exact. While those dominant companies are currently under intense antitrust scrutiny, there is a concern that they may use the hype cycle of Generative AI to wrongly argue that competition exists and that regulators need to back off.

But the reality is that policymakers should be watchful to ensure that major tech companies don’t extend their dominance to the AI market. Generative AI systems are expensive to create and maintain. With such a high barrier to enter this marketplace, there is a danger that the biggest, most powerful tech companies will leverage their position and unrivaled resources to dominate the nascent GAI field.

This same playbook has been used before – Facebook leveraged its dominant position to buy out the competition. Google allocated its search dominance to acquire a monopoly position in the digital advertising infrastructure. And Amazon continues to leverage its position to drive competing ecommerce platforms into irrelevance. We should be careful to ensure that GAI systems aren’t used to maintain or increase dominance in markets and that these systems aren’t self-preferencing services from a handful of major companies to the exclusion of competitors. 

TL:DR

Undoubtedly, Generative AI is an exciting new tool that offers efficiencies for artists, journalists, and other creators along with the promise of a bright new future for industries and consumers alike. And these changes are coming fast. Undoubtedly, the speed and scale of the development and deployment of GAI systems will only accelerate.

In the right hands, these new tools could revolutionize the way we interact, create, problem solve and so much more. However, bad actors also have access to these powerful tools. Therefore, problems with competition, misinformation, election meddling, and sabotage are likely to worsen.

As an industry and society, we should encourage the development of ethical guardrails to minimize the harms and maximize the potential associated with Generative AI technologies. In doing so, we can strengthen our commitment to protect consumers and incentivize new and established voices.

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