Continuing my deep dive on chatbots and news publishers, today I want to dig into several considerations for companies considering building their own chat product.

How do we present content?

1. Interrogate your own journalism first. Show the sources and encourage people to go deeper to look at the original content (note this can be words, audio, or other formats).

2. If the chatbot doesn’t know the answer, programme it to say so. Planet Money was very conscious that they wanted the chatbot to admit when it didn’t know something rather than make something up (aka hallucinate). They created an alert that was shown if they didn’t have anything and only then offered to search the open Web. 

3. Where you don’t have content available, you may want to search the open Web using an LLM. There is an inherent risk that you can’t control, but on the flipside it can be clearly marked and doesn’t run the risk of frustrating a user. One AI company I have spoken to is looking to build a central repository of shared content from reliable news sources, which, if it contains a vast amount of data, could be a great compromise.

4. Consider the timeliness of content. Clearly you want to surface the most recent results first. You also need to be clear on whether it is information to be relied upon, e.g. a stock price or a breaking news story, and, as above, consider if you want to link to an external resources. 

5. Consider including possible follow-up prompts.

Whatever you decide to do, label it. With Bild, you see clear labeling that this is experimental. Others, Forbes, and Plant Money seem to only pull from their own journalism.

It’s a two-way street

The queries you get can also inform your newsroom or other departments. Is there an angle to the story that is missing? Is something unclear? Or is there a subject that people want to hear about that has not yet been covered? Chatbots could be an excellent listening device as well as offer users two-way conversations. 

An internal chatbot

The other example that we haven’t talked about here is that a chat doesn’t need to be for users. It can be for your own journalists who want to dig into the archives to see what has already been written. This could be step one of testing before anything is released externally.

How do you build a chat product? 

We’re not yet in a place where there are many tried-and-tested solutions to build chat. Here are the main options:

1. You can build your own, basing it off larger LLM such as ChatGPT. This is what Planet Money did.

2. You can use a third-party out-of-box solution. We’ve seen very few, but you can check out On Platform (for larger orgs), Miso AI, and Turing (a Brazilian startup used by Rede Gazeta).

3. You can use a hybrid with components from big providers such as AWS, Google, Chat GPT, etc.

What is the cost and ROI?

As my colleague Sonali wrote recently, the ROI of chat isn’t yet clear.

• Cost: It’s expensive right now. As Bernd Volf, managing director for Ringier Media Tech, pointed out at the Webinar: “If you’re successful, it could bankrupt you.” Steve Henn at Stanford, who built the PlatMoney chatbot, said by using CHatGPT 3.5, he got costs down to US$6 per 1,000 queries. Either way, this is something everyone is struggling with. For example, a Google GenAI search costs 25X that of a regular search. Just know that costs are likely to come down so even if you don’t want to launch now, you can prepare for when you do. 

• Revenue: It’s unclear but let’s look at the options:

• In extreme cases you could charge, although doing this specifically for news seems unlikely to get a lot of traction at this point. 

• This could be seen as a cost to engage and retain subscribers.  

• As per the Skift example above, this could be seen as a lead generator for subscribers. 

• And, of course, there is advertising/sponsorship. I have not yet seen use cases, but I am fairly sure they will come once we’re out of the experimental phase and showing real traction.

• So when will companies get to break even/profitability? This is the question that’s on everyone’s mind. If you have an answer, please let me know.

Last but not least

It doesn’t end here. Bernd Volf told us this is the precursor to audio. And I am with him 100%. 

If we are able to converse with our computers using questions and responses, and as audio technologies improve, it is only natural that this moves to audio. The devices and habits are already there as audio is built into many things: phones, smartwatches, earphones (AirPods have changed the game), and hundreds of millions of smart speakers in homes, which are currently underused playing music and setting timers. 

As consumers get used to chat, we’ll see audio get a new life.

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