Back in 2016, economist Robert Gordon wrote in his book The Rise and Fall of American Growth that the post-Civil War century brought technological innovations that led to leaps in productivity—for both companies and individuals—that never existed will be matched.
In fact, he argued that the technological marvels of the post-1970 era, measured in terms of computing power and the Internet, take a backseat, at least in terms of productivity gains, to advances in plumbing, television, and electricity.
Generative AI is poised to change all of that as it accelerates the way information is used to solve real-world problems—right here, right now.
Maybe not.
As Amias Geretypartners at QED investorsKaren Webster said Gordon would probably be impressed by generative AI — but the technology wouldn’t affect his views on technology and productivity.
He remarked to Webster, “Time is the scarcest resource. And electricity creates more “productive time” in a day — and more mechanically and predictably — than AI will, in the sense of more time making certain tasks easier.” To give another example: increasing indoor plumbing life expectancy (by making daily life more hygienic), which also increases productivity and living standards in ways that AI can’t match.
“We’re not ready to put AI in that pantheon,” Gerety said, referring to the economic revolution sparked, for example, by the advent of the automobile.
Generative AI has actually made some strides in saving time traditionally spent on specific work functions in business. And the immediate impact of AI can be measured by reducing the cost of knowledge-based work—creating content, video, and images.
The cost of generating new code would go down, he said. The costs of researching new ideas and researching promising breakthroughs in technology, science and health are also falling.
“True innovation comes from a combination of insight and hard work,” he reflected, “and as the ‘weld side’ becomes easier, so does the ability to see if different ideas result in worthwhile innovations,” through faster time-to-market .
The three platform shifts
To sue Technology’Earth’s evolution and the promise and reality of seismic shifts can be viewed through three platform shifts that have occurred and are still occurring over the past few decades.
Gerety listed them: The first shift at the end of the last millennium was the move from desktop to mobile, then came the move from server centers to cloud-based systems. And in those decades, we saw some “fake” changes – voice computing didn’t quite make it. That is certainly not the case with crypto.
Now comes generative AI, which Gerety said has all the makings of a platform shift driven by large language models. (By the way, he mused, there’s a lot AI can do to make interacting with voice technology a lot easier, making voice notes as easy to consume as they are to create.)
Time to drink the “magic potion”?
“It’s an exciting moment for VC and private equity investors,” noted Gerety, who likened the process of investing in the new technology to the gift of a magic potion.
There’s no way of knowing if the potion will turn you into a superhero – or prove deadly.
It’s the job of venture capital “to embark on those scary avenues,” he said.
Easier said than done. The level of interest and enthusiasm for AI can be seen in the sheer volume of proposals that roll across Gerety’s desk every day — and which have come in quickly and furiously over the past few years — he said, where AI is mentioned in every investor deck and is appended as a prefix to each new product and service.
As he told Webster, there will be companies that get funding and get high ratings because they have AI in their pitch. But that’s not the same as finding startups that can do it successful because they have their stars coupled to the AI.
“When I look at a business plan,” he said, “I’m always looking for startups that can credibly say, ‘Because of my insights, experience, or expertise, what is difficult for others will be easy.'” for me.’” With that in mind, he said, whichever startup is thriving will do better (and stand out as an investment opportunity) relative to its peers.
Currently, the most interesting areas of application for AI are, he said Fraud and risk management and underwriting – because these are industries where practitioners are highly sophisticated and can use AI as a tool to get quick answers. There’s a layered approach, he said, as GPT is used for speed but proprietary analytics and machine learning are “layered” on top to ensure results are correct.
In an experiment he personally conducted, Gerety said he recently worked with an anti-money laundering attorney to create an anti-money laundering policy using GPT — and then translated it into Chinese. What GPT returned was useful, if far from perfect.
“GPT,” he said, “is a ‘First Draft’ machine.” But there’s no real substitute for conscious thinking, putting words and ideas in the right place, and making sure things make sense (the last time he used ChatGPT, he said, was to help brainstorm art project ideas for schoolwork with his kids). ).
The regulation discussion
Any discussion of AI seems to contain the counter-argument to innovation, namely that we will experience an apocalyptic scenario similar to a “Terminator” movie, in which the machines turn against us in ways no one expected.
And that, of course, leads to a discussion about regulation.
“My number one piece of advice here is to resist the temptation to go down the technological rabbit hole,” he said.
When technologists go up the Hill to debate policy, the mindset seems to be that lawmakers need to get a lot smarter and understand the technology. That’s a mistake, he said. Congressmen must contend with a variety of voter needs and are often understaffed, so delving into all the nuances of AI would be fruitless.
The best approach is to identify simple questions related to the interface between technology and everyday life that get to the heart of the matter. The Turing test – named after the computer scientist Alan Turing – is a measure of whether a computer can “trick” a person into thinking that the computer is a human – and the Turing test should be used to policy creation.
“This is a place where I hope technologists will focus less on ‘Terminator’ scenarios and more on what’s happening today and tomorrow.”
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