Mortgage banking is a huge market, but it’s been a notoriously bad market for startups. Late-funnel-high-intent keywords for real estate are among the most lucrative and efficient in online ads. So the only real way to build a great business is to find a more cost effective way to scale acquisition. One approach is to go earlier in the funnel, where there may be more inefficiency in indirect intent, and try to build products that solve problems earlier in the home buying process.
Startup CEOs and Product leaders often fancy themselves as Steve Jobs types, launching the iPhone on stage to a wave of shocked and adoring applause. When startups conflate the destination (the marketing launch) with the journey (the product build), we call it the Big Bang Launch.
PM fit, or zero to one, is an important but murky concept. I think that ‘one’ is supposed to refer to ‘repeatability of sales’ in the case where there is a clear product that meets a market need and scales well enough to create a venture-backable startup.
Low level task-based AI gets commoditized quickly and more general AI is decades off. In the meanwhile, will new AI startups succeed or will the value accrue to Google, Facebook, and Amazon?
This guide aims to present you with an easy way to understand and apply empathy better. It will hopefully be useful to everyone, and is written especially for leaders.
As quantitative finance has matured and the importance of computation has exploded, it’s time to use machine learning to harvest the new low hanging fruit. Traditionally, quants might work alongside engineers and computer scientists — the quants provide the statistical expertise, and the computer scientists and engineers provide the computational expertise. Machine learning folks combine statistics and computation in one brain to build models that leverage new levels of scale and richness to generalize better to unseen data and tackle new problems.