"Build a visual library" is one of the most valuable design tips I've ever head. Great designers don't try to think up cool ideas out of nowhere, they spend a lot of time browsing works of others, and creating a library of patterns in their head. By using and recombining those patterns/tropes they create new ideas.
I should do the same thing with startups and SaaS tools. Put more effort into browsing and analyzing successful projects to build a library of ideas I can make my own stuff out of.
Taking over a toy market:
- Doesn't take a huge amount of money relative to progress (no need to compete with large incumbents).
- Lower expectations and easier targets to hit.
- Less dilution and more ownership for the founders.
Noone has a formula for reliably identifying promising toy markets, they never look the same twice, and are never obvious from the start. So you need vision, independent thinking, and your own framework for success.
Toy markets become huge in two ways:
- Adjacencies - take over a small but valuable set of customers, use this niche as a base to capture a larger but similar (adjacent) audience. Like Uber expanded from "order a black car" to "order any mode of transportation".
- Behavior Change - start with no market at all, create a company that changes the way humans live their lives. Create demand and own the market you've created. Like Apple did with smartphone and Google did with search.
Bet on "toy markets" - niches that look tiny now, but will be big in the future.
Because of hindsight bias, it's hard to look back at Amazon's decision to sell books online, and understand how small and uncertain the opportunity may have seemed back then. And it's difficult to look at markets that seem tiny now, and realize how fast they will grow.
It's tempting to do what everyone else does, but really huge opportunities look like risky bets on toy markets.
B.S. In Artificial Intelligence – Curriculum
Decided to compile a list of my favorite non-fiction books:
You can expect society to perform suboptimally and find low-hanging unsolved problems in areas where:
- Decision-makes have little to lose or gain personally from making improvements.
- Decision-makers can’t reliably learn the information they need to make decisions, even though someone else has that information.
- Systems that are broken in multiple places so that no one actor can make them better, even though some magically coordinated action could move to a new stable state.
Furthermore, the relationship between a founder’s age and the probability of a successful exit increases monotonically until about age 60. Founders in their early 20s have the lowest likelihood of achieve a successful exit, and a founder at age 50 is almost twice as likely to achieve a successful exit than one at age 30.
Most successful entrepreneurs are middle-aged, not young.
- The mean age across all 2.7 million founders [in a study] is 41.9.
- The mean age of high-tech founders is 43.2, VC-backed founders - 41.9.
- For the top 10%, top 5%, top 1% and top 0.1% (1 in 1000) of upper-tail growth, the mean founder ages are 41.6, 42.1, 43.7, and 45.0 respectively.
- The mean founder age of startups with a successful exit, through IPO or acquisition, is 46.7.
> Unsubscribe from LinkedIn
> Delete email account
> Sell house, live in woods
> Find bottle in river
> Has note inside
> It's from LinkedIn
-what're you doing with that 2KB of RAM?
-sending people to the moon
-what're you doing with that 1.5GB of RAM?
I love Startups, Programming, Science Fiction, and Comedy
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