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.
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?
With AI in a full-fledged mania, 2017 will be the year of reckoning. Pure hype trends will reveal themselves to have no fundamentals behind them. Paradoxically, 2017 will also be the year of breakout successes from a handful of vertically-oriented AI startups solving full-stack industry problems that require subject matter expertise, unique data, and a product that uses AI to deliver its core value proposition.
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.