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This is no different from the personal computer, and it is to be expected. The initial years of adopting new tech have no net return because it's investment. T

This is no different from the personal computer, and it is to be expected. The initial years of adopting new tech have no net return because it's investment. The money saved is offset by the cost of setting up the new tech. But then once the processes all get integrated and the cost of buying and building all the tech gets paid off, it turns into profit. Also, some companies adopt new tech better than others. Some do it badly and go out of business. Some do it well and become a new market leader. Some show a net return much earlier than others because they're smarter about it. No "oof" at all. This is how investing in new transformative business processes works.

datavirtue 1y ago
I think it just turns into table stakes.
delusional 1y ago
The document actually debunks this take: > GenAI has been embedded in support, content creation, and analytics use cases, but few industries show the deep structural shifts associated with past general-purpose technologies such as new market leaders, disrupted business models, or measurable changes in customer behavior. They are not seeing the structural "disruptions" that were present for previous technological shifts.
- signatoremo 1y ago
  Changes over which time window? AI projects in enterprises can’t be longer than 2 years, which is practically in testing the water phase, of course there are very few projects of the disruption nature exist yet.
TimTheTinker 1y ago
> transformative business processes Many new ideas came through promising to be "transformative" but never reached anywhere near the impact that people initially expected. Some examples: SOA, low-code/no-code, blockchain for anything other than cryptocurrency, IoT, NoSQL, the Semantic Web. Each of these has had some impact, but they've all plateaued, and there are very good reasons (including the results cited in TA) to think GenAI has also plateaued. My bet: although GenAI has plateaued, new variants will appear that integrate or are inspired by "old AI" ideas[0] paired with modern genAI tech, and these will bring us significantly more intelligent AI systems. [0] a few examples of "old AI": expert systems, genetic algorithms, constraint solving, theorem proving, S-expression manipulation.
- MonkeyClub 1y ago
  > [...] S-expression manipulation. Can't wait for Lisp to be the language of the future again. Some of my friends reckon it'll happen the year after the year of Linux on the desktop. They're on Windows 11, though, so I don't know how to read that.
PhantomHour 1y ago
> This is no different from the personal computer, and it is to be expected. What are you talking about? The return on investment from computers was immediate and extremely identifiable. For crying out loud "computers" are literally named after the people whose work they automated. With Personal Computers the pitch is similarly immediate. It's trivial to point at what labour VisiCalc automated & improved. The gains are easy to measure and for every individual feature you can explain what it's useful for. You can see where this falls apart in the Dotcom Bubble. There are very clear pitches; "Catalogue store but over the internet instead of a phone" has immediately identifiable improvements (Not needing to ship out catalogues, being able to update it quickly, not needing humans to answer the phones) But the hype and failed infrastructure buildout? Sure, Cisco could give you an answer if you asked them what all the internet buildout was good for. Not a concrete one with specific revenue streams attached, and we all know how that ends. The difference between Pets.com and Amazon is almost laughably poignant here. Both ultimately attempts to make the "catalogue store but on the computer" work, but Amazon focussed on broad inventory and UX. They had losses, but managed to contain them and became profitable quickly (Q4 2001). Amazon's losses shrank as revenue grew. Pets.com's selling point was selling you stuff below cost. Good for growth, certainly, but this also means that their losses grew with their growth. The pitch is clearly and inherently flawed. "How are you going to turn profitable?" We'll shift into selling less expensive goods "How are you going to do that?" Uhhh..... ... The observant will note: This is the exact same operating model of the large AI companies. ChatGPT is sold below unit cost. Claude is sold below unit cost. Copilot is sold below unit cost. What's the business pitch here? Even OpenAI struggles to explain what ChatGPT is actually useful for. Code assistants are the big concrete pitch and even those crack at the edges as research after research shows the benefits appear to be psychosomatic. Even if Moore's law hangs on long enough to bring inference cost down (nevermind per-task token usage skyrocketing so even that appears moot), what's the pitch. Who's going to pay for this? Who's going to pay for a Personal Computer? Your accountant.
- simianwords 1y ago
  I highly doubt that the return in investment was seen immediately for personal computers. Do you have any evidence? Can you show me a company that adopted personal computers and immediately increased its profits? I’ll change my mind.

datavirtue 1y ago

I think it just turns into table stakes.

delusional 1y ago

The document actually debunks this take: > GenAI has been embedded in support, content creation, and analytics use cases, but few industries show the deep structural shifts associated with past general-purpose technologies such as new market leaders, disrupted business models, or measurable changes in customer behavior. They are not seeing the structural "disruptions" that were present for previous technological shifts.

signatoremo 1y ago
Changes over which time window? AI projects in enterprises can’t be longer than 2 years, which is practically in testing the water phase, of course there are very few projects of the disruption nature exist yet.

TimTheTinker 1y ago

> transformative business processes Many new ideas came through promising to be "transformative" but never reached anywhere near the impact that people initially expected. Some examples: SOA, low-code/no-code, blockchain for anything other than cryptocurrency, IoT, NoSQL, the Semantic Web. Each of these has had some impact, but they've all plateaued, and there are very good reasons (including the results cited in TA) to think GenAI has also plateaued. My bet: although GenAI has plateaued, new variants will appear that integrate or are inspired by "old AI" ideas[0] paired with modern genAI tech, and these will bring us significantly more intelligent AI systems. [0] a few examples of "old AI": expert systems, genetic algorithms, constraint solving, theorem proving, S-expression manipulation.

MonkeyClub 1y ago
> [...] S-expression manipulation. Can't wait for Lisp to be the language of the future again. Some of my friends reckon it'll happen the year after the year of Linux on the desktop. They're on Windows 11, though, so I don't know how to read that.

PhantomHour 1y ago

> This is no different from the personal computer, and it is to be expected. What are you talking about? The return on investment from computers was immediate and extremely identifiable. For crying out loud "computers" are literally named after the people whose work they automated. With Personal Computers the pitch is similarly immediate. It's trivial to point at what labour VisiCalc automated & improved. The gains are easy to measure and for every individual feature you can explain what it's useful for. You can see where this falls apart in the Dotcom Bubble. There are very clear pitches; "Catalogue store but over the internet instead of a phone" has immediately identifiable improvements (Not needing to ship out catalogues, being able to update it quickly, not needing humans to answer the phones) But the hype and failed infrastructure buildout? Sure, Cisco could give you an answer if you asked them what all the internet buildout was good for. Not a concrete one with specific revenue streams attached, and we all know how that ends. The difference between Pets.com and Amazon is almost laughably poignant here. Both ultimately attempts to make the "catalogue store but on the computer" work, but Amazon focussed on broad inventory and UX. They had losses, but managed to contain them and became profitable quickly (Q4 2001). Amazon's losses shrank as revenue grew. Pets.com's selling point was selling you stuff below cost. Good for growth, certainly, but this also means that their losses grew with their growth. The pitch is clearly and inherently flawed. "How are you going to turn profitable?" We'll shift into selling less expensive goods "How are you going to do that?" Uhhh..... ... The observant will note: This is the exact same operating model of the large AI companies. ChatGPT is sold below unit cost. Claude is sold below unit cost. Copilot is sold below unit cost. What's the business pitch here? Even OpenAI struggles to explain what ChatGPT is actually useful for. Code assistants are the big concrete pitch and even those crack at the edges as research after research shows the benefits appear to be psychosomatic. Even if Moore's law hangs on long enough to bring inference cost down (nevermind per-task token usage skyrocketing so even that appears moot), what's the pitch. Who's going to pay for this? Who's going to pay for a Personal Computer? Your accountant.

simianwords 1y ago
I highly doubt that the return in investment was seen immediately for personal computers. Do you have any evidence? Can you show me a company that adopted personal computers and immediately increased its profits? I’ll change my mind.