Why investors are throwing heaps of money at machine learning

Why investors are throwing heaps of money at machine learning

Why investors are throwing heaps of money at machine learning
If you’re a whip-smart investor with big bucks to spend, chances are you’ve got your fingers in the AI pie.

It’s a market where $50 million is chump change, so if you really want to play with the high rollers you’ll need more room on the check.

Sentient’s up to $144 million in an AI platform play, while Vicarious Systems has thrown $67 million at AI algorithms.

So it’s pretty fair to say that if you’re a bot, you’re going to enjoy a top-notch private education.

But why now? What’s made machine learning and AI the hot stuff du jour?

It’s a game changer, that’s what.

AI and machine learning are about process optimization, but taken to the extreme.

The more they learn, the smarter they get and the more they’re going to utterly disrupt the economics of the world as we know it.

But first they’re going to disrupt the economics of software development.

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It won’t be long until a project that currently takes a full year of work and a team of 6-10 devs can be compressed to a couple of months.

And that’s just to start. Because while humans are pretty well optimized, machines are just getting started.

Skeptical? Sure. But we’ve seen it before with the first industrial revolution. And that was when machines weren’t so bright.

Now they’re smart enough to put whole teams of devs – and all the teams supporting them — out to pasture.

Take the stock exchange ticker symbols that epitomize Wall Street. Thomson Reuters and Bloomberg used to spend a small fortune maintaining their databases of AAPLs, MSFTs, and AMZNs – collating their information, tracking prices, and preparing reports.

No longer. Machine learning can automate all of this. Those databases can now run on AI power alone thanks to models primed to identify ticker patterns or news relating to a publicly traded company.

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The fact is, if it’s repetitive, process based, and involves large amounts of data, it’s a prime target for machine learning.

And let’s face it – those are the kinds of problems developers are targeted with solving.

After all, good developers are the ones who are lazy at heart. They want to solve a problem once, not multiple times.

And machines are crafted for efficiency.

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