Except it's not really a disruptive technology. It's (primarily) a sustaining technology. In Clayton Christensen's original work, disruptive innovations are worse than existing alternatives in the dimensions that existing markets care about, but they're either cheaper (think of microcomputers vs. minicomputers) or have same other attribute that makes them appeal to a new market segment (think of portability for laptops & cell phones, or ease of publishing for social media). This makes them create new market segments among new customers doing new things - think of how crypto's use cases have been DeFi protocols and NFTs, while it's utterly failed as a currency. Sustaining innovations, by contrast, are ones that deliver better performance for an existing customer's existing use cases. Think of faster CPU speed, bigger hard drive capacity, more pixels on a camera. If a technology is better at any use case that people are already making money off of, it's not a disruptive technology, because by definition those are worse at existing use cases but let people do things they haven't been doing before.
There are probably some AI-related disruptive startups out there that will apply it to use-cases that are not currently computerized. But by-and-large, it targets existing use-cases, for the simple reason that you need training data to build the AI in the first place. That gives an advantage to incumbents who have the training data and massive amounts of capital to train the AIs in the first place.
>But by-and-large, it targets existing use-cases, for the simple reason that you need training data to build the AI in the first place.
This doesn't strike me as a true statement. People are embracing the term "AI" to represent recent ML advancements because modern Large Models often generalize to unseen tasks without retraining. The main gold rush so far is in consumer and productivity applications for high cost knowledge work. However for a tech that's only been around for a year... that's to be expected. There will be a next shoe.
Consider the disruption that could emerge in the near future if things like the following occur.
- AI Agents which actually work and generalize across many domains.
- AI Agents which can communicate to solve complex challenges spanning multiple companies/problem domains.
- Large multi-modal models which can be used on stock consumer hardware thanks to hardware/software improvements
- Large Multi-modal models which can effectively operate robotics to complete tasks specified via demonstration/natural language.
- Large Models/Agents which can complete extremely large design tasks in CAD/code spanning multiple days of inference with equivalent performance to an average expert in those fields e.g. 10-100s of millions of tokens in context.
I could go on... In my opinion, we are at best in the 1992 internet phase - or the 1960s of the computer revolution. Those markets both saw large early firms emerge to solve existing problems, then saw those firms displaced as market leaders by subsequent innovations.
The types of processes that AI is shaping up to disrupt are processes that are currently performed by people.
Take, for example, accounting. A $15,000/year subscription to an accounting AI might be slightly worse than a full time $150,000/year human accountant. But as long as it's not $100,000 worse, or breaking laws, then a small company is better off with the AI.
As another example, take plumbing. An experienced plumber could charge $200 for a job. An AI by itself can't replace that plumber, but an inexperienced 21 year old kid assisted by an expert plumbing AI might be able to accomplish the same job at almost the same level of quality and only charge $50. As long as the pipes don't leak, you might be happy to save $150 and hire the kid instead of the guy with 20 years of experience and a family to support.
Neither of those scenarios are certain, but they're within the possibility of what we could see in the next decade. And you could extend these examples to so many other professions.
Both of those examples require licenses in many states. Not to mention an apprenticeship for plumbers, so you’d never get a dumbass kid working from his phone if you expect a permit lol
AI plumbing sounds like an idea that burns money and quickly runs out. Have you ever seen what plumbing looks like across different properties? Or ever talked to a plumber as to what their job entails? It's not nuclear physics, but it's also not legos or software.
The plumber reference is probably a scope for small home jobs such as snaking a clogged toilet, replacing a garbage disposal, replacing a toilet, installing a bidet, etc. Small plumbing jobs may look simple but require attention to detail and I'm sure an AI tool can help with that as opposed to reading fine print on the toilet instructions.
While I understand what you're saying and agree that people processes are a place where the AI value will be captured, the particular cases you've cited are in fields where there are very real legal and/or liability issues related to "mistakes".
yep, the main problem with "automated" bookkeeping or accounting software so far has been that it's so prone to putting things in the wrong category or miscalculating small taxes because it can't read the document Etc that it requires a human to overlook it. a small company ends up paying twice for bookkeeping services then, so it makes more sense to just do it in house.ai and lms will make this work so trivial that it will put a lot of people out of work.
> think of how crypto's use cases have been DeFi protocols and NFTs, while it's utterly failed as a currency
That’s a first world centric view. Many, if not most people in my social circle use crypto for transfers of significant sums and as store of value pretty much all the time.
I'm in Argentina right now, and most of my social circle have immigrated out of Russia to a variety of different countries across the world in the last couple of years.
Also a disruptive innovation. Those are new customers who turn to it because existing incumbents (whether governments or financial institutions) don't serve a specific population.
That's not what "disruptive technology" means though. There's a very specific book [1] that described a specific dynamic to new technology innovations, and you lose a lot of precision by generifying the term.
In the colloquial sense, most workers jobs' get disrupted by garden variety financial innovation anyway. It doesn't really matter whether your job can be done by machine, the PE firm or corporate finance types will just lay you off, leave your job undone, and boost margins so they can unload the stock before customers realize they're not getting anything for their money.
I don't think that we should dogmatically stick to one specific definition for such a generic term like "disruptive technology," especially considering that's not even the first usage of the term -- https://en.wikipedia.org/wiki/Disruptive_innovation
The original paper linked to there describes the same dynamic - innovations originating outside the industry which incumbents can't compete with because they have a different set of customers.
There are probably some AI-related disruptive startups out there that will apply it to use-cases that are not currently computerized. But by-and-large, it targets existing use-cases, for the simple reason that you need training data to build the AI in the first place. That gives an advantage to incumbents who have the training data and massive amounts of capital to train the AIs in the first place.