Today I’ll expand the scanner series to include business and economics. The idea here is to better understanding how AI intersects with the world, by looking into different domains. That’s a solid way to prepare for forecasting AI futures, and for situating AI in higher education. Obviously economics is a crucial field.
I would be surprised if we did not start seeing more radical job market changes across the rest of this decade. Software development, law, customer services seem some areas ripe for disruption. This could speed-up rapidly if there is a GPT-5 class model released in the next few months.
"In 2023, it was announced that The Beatles were releasing their final song, “Now & Then”. This was an interesting piece of news given that two of the group’s members had been dead for decades. The song was made possible by advances in stem separation powered by AI.
In short, John Lennon recorded a demo at his home in 1977 that the remaining members of The Beatles tried to complete in the mid-1990s. The problem was that the demo was very noisy and Lennon’s piano playing sometimes drowned out his voice. Jump forward to today and a neural network was able to extract Lennon’s voice from the demo with incredible clarity, making it possible for Paul McCartney and Ringo Starr – the two living Beatles – to finish the song.
While this technology has already become accessible through companies like LaLaL, Deezer, and Serato, it will likely continue to improve and enable a whole slew of new things. For example, we might be able to remix and remaster degraded recordings from decades ago. Furthermore, a producer who is looking to sample a piano from an older recording will be able to extract the sound without hearing the bleed from the other instruments."
I think the real opportunity is to see Generative LLMs as platform for tool construction. The growth opportunities lie in creating new tools that weren't possible or prohibitively expensive before LLMs. Most of the tools I've seen so far are concentrated in the "doing something we already do better" category. What everyone seems to be missing is "how can we do something we hadn't thought of doing before?"
For instance, consider how VisiCalc changed computing and business forever. It was an application that no one had imaged before the PC (spreadsheets were manual, time-consuming, arcane, paper-based constructions restricted to business schools). VisiCalc allowed us to shape the future and the PC democratized that access. However, it was not the first thing people thought to do with personal computers.
So what will those tools look like? One I have been suggesting we develop (anyone want to take me up on that?) is a visualization tool (https://ideaspaces.net/generative-augmented-perspective/). I've seen a few concept-mapping tools out there including one I know you've been playing with (https://www.mymap.ai/). However, these are just the first step.
I draw your attention to this quote from "As We May Think" - "For mature thought there is no mechanical substitute. But creative thought and essentially repetitive thought are very different things. For the latter there are, and may be, powerful mechanical aids."
What kinds of "powerful mechanical aids" are now possible using the affordances that generative AI has given us? For that, we need to perceive what Generative AI is and what it isn't. What it is is a connection machine. What it isn't is a replacement for thinking. Now's the time to realize that and build tools that connect, not "think."
I would be surprised if we did not start seeing more radical job market changes across the rest of this decade. Software development, law, customer services seem some areas ripe for disruption. This could speed-up rapidly if there is a GPT-5 class model released in the next few months.
Re: Economics of the Music Industry
"In 2023, it was announced that The Beatles were releasing their final song, “Now & Then”. This was an interesting piece of news given that two of the group’s members had been dead for decades. The song was made possible by advances in stem separation powered by AI.
In short, John Lennon recorded a demo at his home in 1977 that the remaining members of The Beatles tried to complete in the mid-1990s. The problem was that the demo was very noisy and Lennon’s piano playing sometimes drowned out his voice. Jump forward to today and a neural network was able to extract Lennon’s voice from the demo with incredible clarity, making it possible for Paul McCartney and Ringo Starr – the two living Beatles – to finish the song.
While this technology has already become accessible through companies like LaLaL, Deezer, and Serato, it will likely continue to improve and enable a whole slew of new things. For example, we might be able to remix and remaster degraded recordings from decades ago. Furthermore, a producer who is looking to sample a piano from an older recording will be able to extract the sound without hearing the bleed from the other instruments."
https://aisupremacy.substack.com/p/ai-and-the-future-of-music-production
I think the real opportunity is to see Generative LLMs as platform for tool construction. The growth opportunities lie in creating new tools that weren't possible or prohibitively expensive before LLMs. Most of the tools I've seen so far are concentrated in the "doing something we already do better" category. What everyone seems to be missing is "how can we do something we hadn't thought of doing before?"
For instance, consider how VisiCalc changed computing and business forever. It was an application that no one had imaged before the PC (spreadsheets were manual, time-consuming, arcane, paper-based constructions restricted to business schools). VisiCalc allowed us to shape the future and the PC democratized that access. However, it was not the first thing people thought to do with personal computers.
So what will those tools look like? One I have been suggesting we develop (anyone want to take me up on that?) is a visualization tool (https://ideaspaces.net/generative-augmented-perspective/). I've seen a few concept-mapping tools out there including one I know you've been playing with (https://www.mymap.ai/). However, these are just the first step.
I draw your attention to this quote from "As We May Think" - "For mature thought there is no mechanical substitute. But creative thought and essentially repetitive thought are very different things. For the latter there are, and may be, powerful mechanical aids."
What kinds of "powerful mechanical aids" are now possible using the affordances that generative AI has given us? For that, we need to perceive what Generative AI is and what it isn't. What it is is a connection machine. What it isn't is a replacement for thinking. Now's the time to realize that and build tools that connect, not "think."