Technological Revolutions and Financial Capital

A framework for technological paradigms

The main claim is that technology has progressed in succeeding and distinct waves/paradigms since the Industrial Revolution and has done so for financial and social reasons rather than technological ones.

Paradigms progress in phases:

Irruption: A technological revolution - technology with the means to modernize all existing industries and activities - irrupts into the scene. Production capital (entrepreneurs, operators) realizes the opportunity - often after a highly visible attractor event (e.g., ChatGPT) - and starts building to make it increasingly clear to the rest of the world.
Frenzy: Financial capital (venture capital and the rest of finance) aggressively pursues this technology, enabling production capital. A financial bubble forms because financial expectations grow faster than real deployment, partly because of societal inertia slowing adoption and partly because of financiers’ unrealistic expectations and speculation. Social/political tension builds up because of growing inequalities between leaders of the revolution and everyone else.
Bubble/crash/turning point: The financial bubble and political tension lead to a crash. New institutions and regulations are introduced to address tensions.
Synergy / golden age: Production and financial capital converge - with production capital as the leader and financial capital as the enabler - to fully exploit the wave’s potential across all industries and activities, raising everyone’s productivity and life standards.
Maturity: The wave’s potential nears exhaustion with saturated markets. A new generation of production capital wants to leave its mark on the world. Idle financial capital looks for the returns it’s been used to in new places, backing the irruption of the next paradigm.

Why is there a single dominant paradigm at a time?

because the backing of financial capital for the technology that provides the best returns leads to a self-reinforcing loop until the exhaustion of its potential

Why are there crashes mid-paradigm?

financial bubble
- inertia: society adopts technology slower than it is developed
- unrealistic expectations and speculation: financial expectations grow faster than real deployment
social/political tension: inequalities grow between leaders of the revolution and everyone else

Past technological paradigms

From the perspective of 2002, when the book was written:

Where are we today?

My take is that we can complete the table as follows:

Paradigm	Date	Leaders	Cheap input	Crash
Information, computing, software	1970s-2020s	USA, spreading to Asia, Europe	Microprocessor	2000 dotcom bubble, 2008 financial crisis
AI, deep tech	2010s-??	USA & Asia, spreading to Europe, Africa	Digital data	??

Maturity of the age of information, computing, and software

What were its main features?

Internet: archive and instant search of humanity’s knowledge from anywhere
personal computing: personal computer, smartphone
business computing: cloud computing, SaaS

Why are we in the maturity phase?

markets are approaching saturation
more than 5 billion people already have a smartphone and/or personal computer with Internet access
most low-hanging SaaS opportunities have been built already

Irruption of the age of AI and deep tech

AI is the foundation of the current paradigm

it irrupted with deep learning in 2012 enabled by the previous paradigm: Internet data + cloud computing
it has the potential to automate the very foundation of what gives humanity its power over the physical world: our intelligence and ability to create knowledge - this is more powerful of an enabler than any of the technologies driving previous paradigms
we might be entering the frenzy with the amount of VC money being poured into LLMs

If we handle it well, it will make humanity a lot more ambitious in all its endeavors in the synergy phase of the wave

AI-driven personal computing: personal AI life copilot, coach, co-creator, teacher, friend, therapist
AI-driven robotics: robots automating most physical labor, caring for aging populations in homes and hospitals
AI-driven research and knowledge creation
- e.g., in energy, accelerate nuclear fusion development to address global warming
- e.g., in biology, dramatically extend human healthy lifespan

But the crash can be way more painful than any we’ve seen before

labor market disruptions: in the short term, AI will automate large portions of today’s jobs, both intellectual and physical, leading to underemployed, insecure, and angry populations
concentration of power for corporations: the best will enjoy compounding returns on intelligence (use ⇒ data + deployment experience + attract best engineers ⇒ better product ⇒ more use) and challenge nation-states

I have no idea what maturity would look like for this phase, let’s get there and see