We know from history that when new technologies are found to be broadly useful, engineers drive down costs and make them easier to use. The latter is the “user interface,” in the jargon of tech. Again, witness the capabilities of ChatGPT versus, say, Alexa. With Natural Language Processing (NLP), the human-machine interface makes it easier for non-experts to engage casually in computational feats previously reserved for supercomputers and the expert class. The overall effect of NLP, in addition to taking up the burden of routine tasks, will also be to reduce routine burdens for employees in non-routine types of work. It will also enable the upskilling of more people to become “knowledge workers,” including even coding. It’s no coincidence that AI tools are bringing greater productivity to writing computer code. One company touts that its AI-based tool can help a coder write software ten to 100 times faster.
The good news, at least from a macroeconomic perspective, is that there’s been a land-rush of activity to develop mission-specific machine-learning algorithms. One measure of the scale of that activity is in the amount of private capital chasing AI deals and companies. We’re in the early stages of billions of dollars directed at another tech hype cycle.
[Color me skeptical. The next headline explains why. — Ed]
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