How much time do you spend reading the news?

In a day? A month? A year? A decade?

…and what if you could read the same news, just faster?

Welcome to NRKort!

About NRKort

NRKort is a service summarizing the news from the main Norwegian news provider, NRK. Every ten minutes, the front page of NRK is scanned for new articles, which are consecutively fed into a large language model instructed to extract only the most important factual points. The result is a three-point synopsis of each article along with its heading.

This process increases the information density by reducing chatter, allowing the user to first read a synopsis of the subject at hand before deciding whether or not to invest the time to read the full article.

This puts the cost of reading up front: The user does not actually have to read or scan large parts of the full article in order to decide whether or not it would be interesting to read, saving both time and cognitive bandwidth.

The name 'NRKort' is a wordplay which only works out in Norwegian: Its first constituent 'NRK' is the name of the originating news provider; the second part, 'Kort', means both card and short in Norwegian, signifying the essence of having NRK news presented briefly through synoptic cards.

NRKort is to the best of the author's knowledge the first and only service of its kind in Norway.

Statistics

Monthly Visitors

NRKort was launched in late August 2023 and averages around 500 unique visitors every month, depending on which data is taken into account.

To be specific, here is the number of unique visitors per month since its launch in August 2023:

August: 502

September: 890

October: 461

November: 496

December: 333 (as of December 24th)

Summaries Posted

Counting from September, a total of 4204 summary cards have been posted on NRKort, with an average of 37 new cards being added every day. This averages out to approximately 1115 unique summaries being posted every month.

Here is a breakdown of the number of summaries posted per month:

September: 1138

October: 1100

November: 1124

December: 842 (as of December 24th)

The Question of Copyright

The obvious question to ask is whether AI summaries, such as the ones provided by NRKort, are copyright infringements or fair use. Evidence for the latter is a citation of the original source (URL), displayed on every card.

The author has contacted NRK on several occasions, but has as of this writing not succeeded in getting an official statement from NRK on these matters.

A New Take on AI Journalism

Instead of humans writing about AI, we might have AI writing about humans.

At this point, one would not have had to expend much effort to cross paths with AI journalism – rather the opposite. AI journalism entails human authors writing about the latest innovations and trends within AI.

It is not far-fetched to imagine that the tables may turn: AI writing about the latest trends and fashions concerning humans.

Picture this: The events of the world are serialized into subscribable data streams past the threshold that it only remains to parse out what has actually happened since, e.g., yesterday or the past hour.

With access to such data streams, there would no longer be a need for humans to digest and present the news from the source material: Large language models could do that.

There of course remains the problem of gathering said data to begin with, but as far as the writing process goes, the benefits from the point of view of productivity are obvious:

Humans are out of the loop, thus reducing costs and time spent writing.

News can be generated on demand and in a voice or persona appropriate for the given context.

News consumption may be tuned more factual with less chatter, making it possible to stay updated in less time.

Perhaps only the human reporter will remain, in its literal sense, feeding our AI systems with the data it needs to satiate our news delivery demands.

Behind NRKort: Using AI to Summarize the News

The cross-functional product team is the most effective and efficient unit for delivering digital products. How does the new world of generative AI impact the product team? Read on to find out.

AI as a Team Member

Cross-functional product teams bring together professionals with diverse skill sets such as engineering, design, product management, and data science. This amalgamation of skills fosters a holistic approach to product development, encouraging innovative solutions and faster problem-solving.


Generative AI, like Large Language Models (LLMs), has exploded in both use and usefulness this past year. Tools such as GitHub Copilot and ChatGPT, with the ability to create content, solve complex problems, and automate tasks, are set to revolutionize how our teams operate. From designing user interfaces to predicting market trends, generative AI tools are not just assistants but active contributors in the creative process—a shift allowing for less busywork, as routine tasks can be completed more efficiently with AI help.


Generative AI can significantly enhance creativity within product teams. For example, AI-driven design tools can generate multiple design options, enabling designers to explore creative avenues they might not have considered. Similarly, AI-powered data analysis can unveil insights that drive more informed decision-making.


Despite its benefits, integrating generative AI into cross-functional teams is not without challenges. Teams must navigate issues related to data privacy, ethical AI use, and maintaining a balance between human creativity and AI efficiency.


As generative AI continues to evolve, its impact on cross-functional product teams will only grow. Teams that effectively integrate these technologies will find themselves at the forefront of innovation in digital product development.


AI can do the thunking; humans can do the thinking. Increase your productivity, automate more advanced menial tasks, and use the freed-up time to improve yourself, your team, and your product.

AI as a Product Feature

Users' experiences with AI-enhanced products like Lightroom, Notion, or even Snapchat put new pressure on teams wanting to deliver top-notch UX experiences. But for most teams, simply putting an LLM in the front end and calling it a day is not enough. Even though access to AI is only an API call away, they excel at giving plausible examples, not necessarily the correct answer to user requests.

So don't just embed GPT and pine for the fjords. Understand your user's jobs to be done and use AI to actually improve their experiences with your product. Today's tools are good at summarizing qualitative information, understanding user input, and generating funny images and avatars. But what is unique about the experience of your product in the context of your user base?

Find an answer to that question, either as a golden use case enabling new features that confer a long-term advantage, or improve your table stakes features to enhance your users' efficiency within your product. Iteration, as always, is key, so move fast, run experiments, and understand the effects these have on your users.

If your company has an AI strategy (as it should), use that to guide you. If not, this simple hierarchy of steps might be useful:

Experiment with LLM in the surface of your product. Where can it easily provide user value? These are your low-hanging fruits.

Automate workflows. Make experiences faster, summarize, and simplify.

Replace workflows. Redesign using the new tools, enabling new workflows while removing others.

Just do the customer's work outright.

Learn as you go along, and don't forget about the fundamentals of digital product development. Focus on your users' pain points and your golden use cases. And, if you're good, leverage first-party data as your moat.

Summary

To summarize, in the hands of a skilled team, AI tools can be huge productivity boosters, and the time for experimenting with them is now! Generative AI is to products as salt is to food. Every dish needs some, but additional spice is most of the time needed (except for in traditional Norwegian cuisine).

Artificial Intelligence

Behind NRKort: Using AI to Summarize the News

How to Deal with AI as a Product Team