Author: Tom Lynch

Problem space

People want to know about:

• Local crime
• Restaurants
• Weather
• Traffic
• Local events
• Planning and housing
• Local services (council, schools, handymen)
• Local interest

The most expensive to produce are:

• Investigations: These are expensive because of the higher time investment required and often higher count of staff involved due to breadth of skills required

Journalists need to:

• Accumulate information from different sources
• Verify information in these sources, establish what is important and irrelevant
• Document all their actions taken throughout the process
• Evaluate their findings and construct a story
• Expensive and potentially extensive legal cross-checks
• Create content to publish

The expensive part of investigations is the time spent by a journalist to understand information from different sources and verify the information

• It isn’t going to be viable to just ‘apply AI’ here, but are there recurring aspects of investigations where it could help?

The highest revenue generating are:

• Assuming an ad-based model, the highest revenue generating are likely to be clickbait
• Or could be broad and general - i.e. exactly what local news can’t be

This is a problem because:

• The revenue for each article is too low to sustain the journalistic effort to produce it
• This reduces the incentives for media outlets to create quality content, reducing access to local news

Solution space

Key opportunities:

• Automatically generate articles/video.
• Planning data
• AI could potentially improve the quality, breadth and relevance of local news stories
• Suggest articles based on trends in/new data
• Can AI comb through financial records to find wrongdoing?
• Can journalists gather information from sources in a lower-cost way?
• Can journalists gather expert opinions in an automated way?
• Identify leading voices or subject matter experts in a particular subject, gather their contact details together, and submit requests for comment