What are the characteristics and how to avoid 'tar pit ideas' that seem attractive even though many people have failed?

'Ideas that look attractive even though many predecessors have failed' are called ' tar pit ideas ' because their characteristics resemble a tar pond. Y Combinator , an accelerator that invests in startups and teaches know-how, explains the characteristics of tar pit ideas and how to avoid them.

Tarpit ideas - what are tarpit ideas & how to avoid them : YC Startup Library | Y Combinator
https://www.ycombinator.com/library/Ij-tarpit-ideas-what-are-tarpit-ideas-how-to-avoid-them

Avoid These Tempting Startup Ideas - YouTube


Dalton Caldwell and Michael Seibel , founders of Y Combinator, have consulted with many entrepreneurs in the past. However, a significant proportion of them bring in ideas that many other entrepreneurs have tried but failed to come up with.

These ideas are called 'tar pit ideas' after tar ponds. Tar ponds have a shiny surface that makes them easy for animals to mistake for normal, clean ponds, and large tar ponds can contain fossils of animals that died long ago after accidentally falling into them. In the same way, tar pit ideas may seem like 'simple, wonderful ideas that you can't believe no one has tried before,' but in reality, many entrepreneurs have tried similar ideas and failed.

As an example of a tar pit idea, the pair cited 'a service that lets you find things you've never found before.' Y Combinator has received many product ideas, such as 'a service that lets you find restaurants that match your tastes that you didn't know existed' and 'a service that lets you find music that matches your tastes that you didn't know existed.' The main feature of these ideas is that they use machine learning to find things that are difficult to find with existing apps.

There are certainly many people who want to find something that they can't find in existing food review apps or music apps, so the demand for the product seems high. However, most entrepreneurs have been unable to monetize their ideas. Regarding the reason why these apps failed, Seibel pointed out, 'The world seems infinite, but it's not. If you can't find a restaurant that suits you on Yelp (a review service for restaurants, beauty salons, etc.), it's not because of Yelp, but because the restaurant that suits you doesn't exist in the first place.' In other words, even if the idea of 'using machine learning to present recommendations that are better than existing products' itself seems good, the reason for the failure of the idea was that there was no such thing as a 'recommendation that is better than existing products.'

The two authors suggest that ways to avoid tar pit ideas include: 'If your idea has been tried before, analyze and understand why the previous examples didn't work,' 'Instead of thinking of entrepreneurs who failed in the past as stupid, analyze them as 'they were very smart and strong-willed, but still failed,'' and 'Analyze supply and demand to see if there is an oversupply, or if there are areas where there is a lot of demand but too little supply.'