Tuesday, January 9, 2024

AI Replacement of Human Work: How Soon, How Much?

Nobody yet knows where large language models will ultimately prove to supply the greatest value, but it is fair to say observers believe a few use cases are good candidates for impact. 


High-potential use cases include content creation, such as writing marketing copy. Customer service and communication supporting chatbots, answer queries, and personalized interactions are another use case viewed as having high and nearly-immediate value.


Code generation and software development is a third area of interest. LLMs can assist programmers with tasks like writing code, debugging, and suggesting improvements.


Many also believe LLMs will prove valuable for research, as LLMs can analyze vast datasets, generate hypotheses, and summarize complex information.


Some might argue that up to 60 percent or more of the total value produced by LLMs could come in those four areas. 


But even in areas such as content creation, impact remains unclear. Many doubt LLMs can succeed for creative writing and art. But others might believe there is plenty of room for replacement of human effort.


While some might argue “great art or writing” might not be produced by LLMs, there is quite a lot of passable storytelling LLMs could generate. “Depth and context” might not be LLM's greatest strengths, compared to human creators. 


But an argument can be made that some significant amounts of  “interesting” content can ber created, though we might still debate the degree to which “originality” or “creativity” are demonstrated. 


Still, LLMs ought to be able to develop new arrangements of established patterns in storytelling, for example.  


For example, storytelling often is said to involve only about six or seven key plots. But as with musical chords, though there are only a few basic notes and chords, countless arrangements, melodies and harmonies can be built from a limited number of basic building blocks.


Christopher Booker's Seven Basic Plots include:


  • Overcoming the Monster: The protagonist confronts and defeats a villainous force threatening them or their world (e.g., Star Wars, Harry Potter).

  • Rags to Riches: The protagonist rises from poverty or obscurity to achieve great success (e.g., Cinderella, The Great Gatsby).

  • The Quest: The protagonist undertakes a journey to acquire an object or achieve a goal (e.g., The Lord of the Rings, Moby Dick).

  • Voyage and Return: The protagonist embarks on a physical or metaphorical journey, then returns with newfound knowledge or understanding (e.g., The Odyssey, Alice in Wonderland).

  • Comedy: The protagonist uses humor and wit to overcome obstacles and achieve personal or societal change (e.g., Almost Famous, Shaun of the Dead).

  • Tragedy: The protagonist's noble aspirations lead to suffering and downfall (e.g., Hamlet, Macbeth).

  • Rebirth: The protagonist undergoes a profound transformation, often involving death and renewal (e.g., The Lion King, The Matrix).


A 2016 study by researchers at Northeastern University identified six "core trajectories" based on a statistical analysis of narrative emotional arcs:


  • Rags to Riches: A rise in happiness and well-being.

  • Tragedy: A fall in happiness and well-being.

  • Man in a Hole: Fall followed by a rise.

  • Icarus: Rise followed by a fall.

  • Cinderella: Rise-fall-rise: A cycle of success and setback.

  • Oedipus: Fall-rise-fall: A complex arc with multiple reversals of fortune.


The concern many in the screenwriting, acting and other “creative” areas attests to the fear LLMs and AI in general might be capable of displacing much human effort and output. 


We might be a century away from full AI replacement of human functions. But the concern seems well placed enough.

Echoes of "Dotcom" for Large Language Models

Given the amount of hype around large language models, which seemingly has lots of firms deploying it at some level just to be seen as doing it, it won’t be long now before we start seeing lots of articles lamenting the fact that large language models are not producing results for firms that are using them. 


No truly-important general purpose technology is going to produce clear results right away. And even technologies most observers would consider useful, but not GPTs, similarly take some time to reach as much as 10 percent use by the relevant potential-user base (people, homes, businesses). 


Technology

Year of Introduction

Time to 10% Adoption (Years)

Notes

Electricity

1873

46

Homes in US

Telephone

1876

39

Households in US

Automobile

1886

51

Individuals in US

Radio

1920

14

Households in US

Television

1948

12

Households in US

Computer

1975

23

Households in US

Internet

1995

10

Individuals in US

Smartphone

2007

6

Individuals globally

Social Media

2004

7

Individuals globally


As was true during an earlier time for the internet, when many firms engaged in a mania to rename themselves X.com, there is lots of almost-blind posturing going on. 

Large language models will be deployed where it does not make much sense, and where measurable results will be nil, if clear benefits exist at all. 

Saturday, January 6, 2024

Will There be an AI "Killer App" or Use Case?

It should come as no surprise that Nvidia is the clearest beneficiary yet of the emergence of large language models. If AI in general is destined to become a general purpose technology, one would expect infrastructure to be the starting point for commercial deployment. 


Initial infrastructure is always the starting point because without it, no technology can become a GPT. 


For electricity, power plants, transmission lines, and distribution networks were the starting point. For the internet: communication networks and internet service providers were the beginning. 


For artificial Intelligence of any sort, it is specialized hardware (GPUs, TPUs), software development tools and data centers equipped to provide LLM training and inference operations. 


Typically, there are lead industries or use cases. Electricity saw early deployment in manufacturing, transportation and lighting.


The internet lead to disintermediation (replacement of roles in the distribution function) in retailing communication and media. 


We can expect similar progressions for AI as well. Infra is the initial focus, but then lead use cases will emerge. Steam power, for example, found early application for pumping water out of mines, before other use cases opened up. 


GPT

Initial Infrastructure Focus

Industries Gradually Affected

Examples

Electricity (1870s-1910s)

Power generation and distribution networks

Manufacturing, transportation, communication, homes

Factories shifting to electric motors, streetcars replacing horse-drawn carriages, telephones powered by electricity, electric appliances.

Internal Combustion Engine (1900s-1940s)

Fuel production and refining, car manufacturing, road networks

Transportation, agriculture, construction, leisure

Rise of automobiles, tractors replacing horses, trucks revolutionizing freight transport, car tourism and road trips.

Internet (1990s-2000s)

Telecommunication infrastructure, web browsers, servers

Retail, finance, media, education, communication

E-commerce, online banking, digital media streaming, online learning, email replacing traditional mail.

Mobile Internet (2000s-2010s)

Cellular networks, smartphones, app development

Communication, navigation, entertainment, social media, healthcare

Texting and mobile calls replacing landlines, GPS navigation apps, on-demand services, social media platforms, remote patient monitoring.


Between 1712 and 1775, for example, steam engines were used to pump water out of mines. 


From 1775 to 1800, steam power was harnessed for iron and steel production. James Watt's improved steam engine, with its higher efficiency and reliability, powered bellows in ironworks, replacing inefficient waterwheels and enabling larger furnaces and increased steel output. The industrial revolution was built on it. 


From 1785 to 1830 steam power was applied in the textile industry. Steam engines replaced water wheels in textile mills, for example, leading to mass production.


From 1800 to 1850 steam power was applied in transportation, powering steamboats and locomotives.


After 1850 steam power was progressively applied in agriculture, printing, papermaking, food processing and other industries where mechanization became possible. 


The clear point is that AI investment and operations will begin with infrastructure. Only later will we develop and create commercial applications, and then likely in stages. Not every industry, function or use case will be equally compelling at first. 


As always, we will be looking for the AI equivalent of “killer use cases or apps.”


Is "Telco to Techco" Ultimately Only the Lastest in a Long Line of "Rebranding" Efforts?

Many connectivity service providers now talk about becoming a techco. That assertion can have several plausible meanings. 


“What we do” could change. Among the most-sweeping possible interpretations is that “telcos” provide “connectivity” but “techcos” supply platforms or applications. In an advanced version, techcos are themselves owners of apps and services running on their networks. 


“How we do it” is an alternative. An arguably less-challenging understanding is that “techcos” are firms that are innovative and agile or customer-focused; embracing risk-taking and experimentation. 


A related shift might have telcos becoming “ecosystem orchestrators” that assemble partners and functions to create a “one stop shop” for customers. In this vein, perhaps techcos are noted for an embrace of open systems. 


What all such understandings arguably include, though, is a shift from basic connectivity to “additional services, apps or value” beyond the core connectivity function. 


Some will remain skeptical about whether telcos really can become techcos in that sense. And some might argue that, though laudable, the “techco” emphasis is really only the latest in a long series of marketing-related efforts to change perceptions about “telcos.”


If you have been in the “telecom” industry long enough, you have seen many efforts to shift language from a “traditional” to a “new” term. 


Traditional term

Preferred new term

Telecommunications company

Digital services company

Phone company

Communications company

Carrier

Platform

Incumbent

Challenger

Old economy

New economy

Brick-and-mortar

Digital-first

Slow-moving

Agile

Traditional

Disruptive

Outdated

Innovative

Boring

Exciting

Telecommunications company

Digital services company

Phone company

Communications company

Carrier

Platform

Incumbent

Disruptor

Old guard

New guard

Brick-and-mortar

Digital-first

Slow-moving

Agile

Traditional

Innovative

Telephone company

Media company

Network operator

Digital enabler

Service provider

Platform provider

Infrastructure company

Solutions company

Utility

Ecosystem player

Telco

Techco

      

“Telco to techco” is merely the latest iteration.


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