On Perplexity and the challenge it poses for Digital Media

Perplexity is a fascinating AI application that I sometimes use to read the news, but it has me worried for a while now.

It takes a Wikipedia-like approach to compilation of news and information: it takes verified news sources and compiles the information in a format that is simple and easy to understand, with all the key facts. It also allows you to ask questions, answers to which it creates on the fly, based once again, on sources. 

A recent Forbes article calls Perplexity’s aggregation and generation a “cynical theft,” pointing out that:

Perplexity had taken our work, without our permission, and republished it across multiple platforms — web, video, mobile — as though it were itself a media outlet.

Google has also rolled out its AI summaries feature, where it seeks to address queries we ask while searching by aggregating information from sources. AI summaries pose some key challenges for society and news publishers:

Firstly, both Google AI summaries or Perplexity have issues with accuracy. Unlike Wikipedia they don’t have human oversight or verification, and can pick up unreliable or unverified information from articles as facts. A user who doesn’t know better, or can’t easily spot issues, may accept that as news.

For news publishers, there is an issue of their work being used as raw material to generate these summaries, without any compensation. Facts are not protected by copyright because their dissemination is an essential public service, and there’s greater benefit for society if facts can be re-reported. There is a concept in copyright called “Fair Usage”, which enables this, and also depends on whether the information copied is a part of a broader composition, rather than just a copy. Basically, you need to add your own value if you’re copying. Perplexity and Google’s AI summaries add zero original value: they add no reporting, no original context, and literally nothing that makes it theirs. It is not copyright violation, but as the Forbes article pointed out, it is plagiarism. 

A reaction from news publishers is warranted: such tools are extractive and cannibalistic, rather than value additive for publishers. They end up reducing the need for someone to visit the source of that information, effectively stealing their audience and means of monetization via advertising and subscription. This illustrates a gap in copyright law that needs to be addressed to ensure incentives for news organisations. If sources of news die, what will Perplexity or Google AI summaries copy news from? Without news reporting, all you’ll be left with is unverified tweets and Twitter’s fairly disastrous crowdsourced reader reviews.

One might argue that Wikipedia does the same thing as these AI services, in terms of aggregating information from sources, and linking out to them. Wikipedia is an encyclopedia, and not a source of news: it doesn’t replace a news article or cannibalise a publications audience. Instead it enables discovery of news sources for more context, while Perplexity hides them being an additional tab.  and actually enables discovery of news sources. Wikipedia doesn’t include the complete context for a development while Perplexity aggregates context from its news sources. 

Pranesh Prakash, former Research Director at the Centre for Internet and Society, likened such AI tools to a human being reading something and sharing that information in their own words. A couple of issues with this line of thinking. Firstly, just because something is public to read doesn’t mean that it’s open to copying, including for training Large Language (AI) Models. There needs to be a permission check. Much of GenAI is built on taking information from third parties without permission for training AI models. Secondly, the mass accumulation of content for training without permission or compensation cannot be treated the same way as human learning: there is a power law applicable here, and with ability to ingest, learn and replicate (even if not verbatim) at scale: the impact is disproportionate and exponential.

Perplexity also positions itself like an “answer engine”, similar to a search engine. This is a convenient positioning of the platform. Publishers choose to allow search engines to scrape their work because they index the content like a library does, and directs users to the appropriate website: it’s a symbiotic relationship, because it doesn’t replace the source of the information. In fact, publishers use tools to optimise their articles so that it’s easier for users to discover their content. They try to aid search engines in enhancing the ability of a user to make a decision to go and read something on a website or an app.

Perplexity and AI summaries do exactly the opposite: address the users’ needs directly, thus alleviating the desire to go to the source. The user and the “answer” engines win, but the publisher loses. This means that publishers cannot monetize the users presence via subscription or advertising, or encourage repeat usage via newsletters and apps. It can be argued that publishers can exclude themselves from indexing by AI bots which are scraping their content, but quite often AI bots refuse to honor codes that are meant to prevent such scraping. Huggingface, a repository of AI models, has added over 1500 new models per over the past two months: even if 10 per day are for language models, it’s a significant task for publishers to exclude such bots daily.

There is a risk that this might be seen as a big-tech vs publishers issue, and treat it the same way as the “link tax” imposed on Google and Meta in Canada and Australia, forcing them to pay publishers for links aggregated or added by users, but it isn’t. That idea is antithetical to how the Internet functions as an interconnection of links, and the pay-it-back approach of linking out.

Such an approach adds value to publishers. Instead of targeting platforms for a service that adds value, perhaps publishers are better served focusing on AI summaries that end up cannibalising their work.

Signing content licensing deals with AI companies is an exercise in feeding the beast that is devouring them.

Bring on the unpredictable

The world is rapidly getting inundated with automated content: We’re seeing faceless YouTube videos grow, TV channels are deploying AI anchors. Some services that can ingest hours of someones audio, in order to generate new speech with their voice and intonation. Others allow you to get your entire body mapped to create a lifelike digital replica. The world of deepfakes is here.

The problem here is predictability and lack of personality. What often makes us interested in other humans is not the predictable part of their behavior, but what surprises us about them: what will they say, ask, or do. Some amount of predictability is important for comfort, but really what hooks us is the unpredictable.

The voices may no longer be robotic, the facial movements might now be in sync with the audio, but I’d like to believe that there are some things in a human being that are human, and inspire intrigue and trust at the same time.

So in a world of people playing it safe with AI generated content, with mass generation of how to videos made from scraping Reddit and Wikipedia, I think there’s going to be comfort in personalities, because they are both predictable and unpredictable, in predictable and unpredictable ways.

There is, of course, talk of Artificial General Intelligence that can replicate this human behavior. I’d like to believe that it can’t, for example, replace me. I suppose you’d like to believe that too.

TV Shows: Addressing job loss fears around AI

Are AI tools a boon or a bane. On TV shows and on conference panels, I’ve spoken about how AI tools actually make our work easier, and more efficient. Whether an organisation wants to do more with the greater efficiency or make people redundant is their decision, and no fault of the AI tools. It’s important for people to learn these tools, and add these skill sets.

Two TV shows:

My show notes from the Jan 2023 discussion:

  1. Here’s where AI is being used:
  • Drafting legal contracts
  • Designing rooms according to a theme and size
  • Generating written content, such as articles, blog posts, and scripts
  • Creating dialogue for characters in video games, movies, and TV shows
  • Generating song lyrics and poetry
  • Assisting with brainstorming and idea generation for advertising and marketing campaigns
  • Creating chatbot responses for customer service and virtual assistants
  • Finding the right image for a mood
  • Writing emails
  • Creating proposals
  • Writing marketing content
  • Improving emails you’ve written to be more respectful
  1. ChatGPT AI has a clear bias: the dataset that it references. The final output should always be reviewed and edited by a human to ensure it is of high quality and appropriate for the intended use.
  2. AI is going to get better with time. It uses a feedback loop to understand what humans want and what they dont.
  3. AI can improve efficiency and productivity of individuals but an also lead to job losses for some.  The market will change.
  4. Impact industries like data entry, customer service, and other tasks that rely on language. ChatGPT can be used to quickly and accurately understand and respond to customer queries, allowing companies to improve their customer service and support. This can lead to improved efficiency and cost savings for businesses.
  5. The development and deployment of ChatGPT and other language models can also create new job opportunities in fields such as data science, machine learning, and software engineering. People will need skilling.

On AI and its impact on News Media

I spoke at the Media Foundation Dialogues, organised by the Foundation for Media Professionals, held at the India International centre.

I really don’t think that journalists have anything to fear: AI tools can help them in their work, and will never entirely replace reporting. At MediaNama, we’re already using AI tools for transcription, and have started experimenting with prompts to clean copy, correct grammar, generate tweets from article, suggest article ideas, headlines, questions for interviews, speakers for conferences. We’re also creating tweets, threads, generating text to video, scripts for video. There’s so much more. The list will expand, as we become better at prompt engineering. AI will make things easier for journalists, not more difficult.

Creating and optimising videos for different platforms will be easier and faster. Processing videos will become so much more efficient.

The part that excites me most is how much translation and voice to text and text to voice will become over the next few years. That gives us the opportunity to cater to language audiences in India.

The business model challenges are of course going to increase, especially with increased advertising inventory, and fewer people required to do the same job. At the same time, this helps smaller publications punch far above their weight. How they navigate AI over the next few years is going to be critical.

A repeated some of the points I had made during the discussion, in a separate video for MediaNama: