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