LLMachete
LLMachete
Clarity Through Data

From Rice to AirPods

A Five-Part Series on Data, Transformation, and the Skills That Matter

The Reframing

This series exists because we're thinking about AI wrong.

Not wrong in the technical sense. The models work. The capabilities are real. But the frameworks most people use to evaluate AI—feature checklists, vendor comparisons, ROI calculators—miss something fundamental.

We've been here before. Four times, actually. And every industrial revolution followed the same pattern: a general purpose technology emerged, transformed how humans create and exchange value, and reshuffled who wins and who gets left behind.

The question isn't whether AI will transform your industry. It already is. The question is whether you have the analytical frameworks to see the transformation clearly—and position yourself on the right side of it.

The Lens

This is economic history, not a product demo.

I'm drawing on patterns that span 260 years of industrial transformation—from the steam engines of 1760s Britain to the large language models reshaping work today. The same analytical frameworks that explain why some textile manufacturers thrived while others collapsed can explain why some organizations will capture AI's value while others become case studies in missed opportunity.

But here's what most tech narratives get wrong: they focus on the visionaries. The Carnegies. The Fords. The founders on magazine covers.

History tells a different story. Transformations happen through accumulated decisions by ordinary people—workers adapting their skills, small businesses finding new efficiencies, professionals learning new tools. The industrial revolutions weren't won by a handful of Great Men. They were won by the millions who figured out how to adapt.

That's who this series is for.

The Starting Point

We begin with rice. Not metaphorically—literally.

Rice was arguably humanity's first General Purpose Technology (GPT)—a technology so foundational that it transforms not just one sector but an entire economy. GPTs share three defining characteristics: they improve over time, they spawn innovation across multiple industries, and they become essential infrastructure that other technologies build upon.[1]

Rice fits every criterion. Cultivation techniques improved over millennia. Agricultural surpluses enabled urbanization, trade specialization, military expansion, and eventually the craft economies that preceded industrialization. Before steam engines could transform manufacturing, rice (and wheat, and other staple crops) had already transformed human civilization. Rice was infrastructure.

Rice also gives us something else: a way to visualize the invisible.

Data is abstract. Bytes, kilobytes, petabytes—these words mean nothing to most people evaluating AI investments. But a grain of rice? That's tangible. You can hold it. Count it. Scale it up in your mind's eye from a handful to a cup to a warehouse to an ocean.

One grain of rice equals one byte of data. Start there, and suddenly zettabytes become comprehensible. That's where Article 1 begins.

The Framework

Every industrial revolution had its GPT—a General Purpose Technology so foundational it transforms not just one sector but an entire economy. Steam engines didn't just improve mining; they enabled factories, railways, and global trade. Each revolution follows this pattern:

1st
Era1760–1840
GPTSteam Engine
What It EnabledMechanized production, railways, factory system
2nd
Era1870–1914
GPTElectricity
What It EnabledMass production, telecommunications, modern corporation
3rd
Era1950–2000s
GPTComputing
What It EnabledAutomation, digitization, internet economy
4th
Era2022–present
GPTLarge Language Models
What It EnabledHuman-AI collaboration, generative systems, specification-driven work

That last row requires defense.

Klaus Schwab popularized the "4th Industrial Revolution" framing in 2016, pointing to IoT (Internet of Things—sensors and smart devices connected to the internet), cyber-physical systems (machines that integrate computing with physical processes), and ubiquitous connectivity.[2] He wasn't wrong—your Apple Watch and smart thermostat are part of this transformation.

But hindsight clarifies which technology defines an era. Compare the trajectories:

Technology Adoption: Year 0 Comparison

Year 0 launch
Wearables (Fitbit, 2009)~5,000 units shipped
Generative AI (ChatGPT, 2022)1 million users in 5 days
Year 0 + 2 months
Wearables (Fitbit, 2009)~25,000 units
Generative AI (ChatGPT, 2022)100 million users
Year 1 revenue/market
Wearables (Fitbit, 2009)~$5 million (2010)
Generative AI (ChatGPT, 2022)$44–68 billion market (2023)
Years to $1B+ valuation
Wearables (Fitbit, 2009)6 years (Fitbit IPO 2015: $4.1B)
Generative AI (ChatGPT, 2022)<1 year (OpenAI: $10B funding 2023)
Time to mass adoption
Wearables (Fitbit, 2009)6 years to Apple Watch launch
Generative AI (ChatGPT, 2022)Fastest-adopted technology in history

Sources: Fitbit data[3][5]; ChatGPT data[4]; Market data[6][7]

Market Growth Trajectories

Wearables
2024 Market Size~$80–98 billion
2030 Projection~$175–190 billion
CAGR (Annual Growth Rate)12–17%
Generative AI
2024 Market Size~$67 billion
2030 Projection$100–325 billion+
CAGR (Annual Growth Rate)30–43%
LLMs (specifically)
2024 Market Size~$6 billion
2030 Projection~$35 billion
CAGR (Annual Growth Rate)37%

Sources: Wearables[8]; Generative AI[9]

CAGR (Compound Annual Growth Rate) measures how fast an industry's value grows year-over-year—a standard metric for comparing market trajectories.

The growth differential tells the story. IoT extended the 3rd revolution's logic: more computing, more connectivity, more data collection. LLMs—Large Language Models, the AI systems powering tools like ChatGPT—represent something different. They don't just process information; they generate it, reason about it, and collaborate with humans in ways that create a cleaner break from what came before.

The 4th Industrial Revolution started when AI stopped being a tool you query and became a partner you work with.

The Arc

From Rice to AirPods traces this transformation across five articles:

1

Data Scale

One grain of rice. One byte. Build intuitive data literacy using physical objects, then scale to the zettabytes flowing through your pocket.

START HERE
2

Volume, Velocity, Variety

Doug Laney's "3 Vs of Big Data" wasn't just describing databases. It's the universal language of industrial transformation—a framework that explains all four revolutions.

3

Steam to Electricity (1760–1914)

How the first two industrial revolutions followed the 3 Vs pattern. The macro-economic history, and what it reveals about transformation dynamics.

4

Computing to AI (1950–Present)

The same framework applied to the revolutions shaping your career. Why the 4th IR demands different skills than the 3rd.

5

The Specification Imperative

Understanding transformation frameworks tells you what's changing. It doesn't tell you how to direct the change. The AI era's most valuable skill isn't coding—it's specification clarity. What you need to know, and how to develop it.

The Approach

A note on method, in the spirit of full disclosure.

I used AI tools to help create this series—for ideation, drafting, research synthesis, and building the website you're reading. I make this explicit because such disclosures are becoming standard practice, and because it's the right thing to do.

The Dead Internet Theory—the idea that AI-generated content is displacing human creativity online—is real and accelerating. I won't pretend otherwise. But I also won't pretend I built this alone.

What I will claim: the perspective is mine. The analytical frameworks, the historical connections, the arguments about where this is all heading—those come from years of working at the intersection of technology and business in regulated industries. The AI helped me express and build. The thinking is human.

This series is for people who understand that adapting to new tools isn't weakness—it's how you stay in the game. The industrial revolutions rewarded those who learned new skills. This one will be no different.

Begin

Ready to start?

Article 1 uses a single grain of rice to make data scale intuitive. Two ways to experience it:

References

  1. [1] Bresnahan, Timothy F. and Trajtenberg, M. "General Purpose Technologies 'Engines of Growth?'" Journal of Econometrics, Vol. 65, No. 1, 1995, pp. 83-108. DOI: 10.1016/0304-4076(94)01598-T
  2. [2] Schwab, Klaus. "The Fourth Industrial Revolution: what it means, how to respond."World Economic Forum, January 14, 2016. weforum.org
  3. [3] Perry, Tekla S. "The First Fitbit: How the Fitness Tracker Was Engineered."IEEE Spectrum, November 2020. spectrum.ieee.org
  4. [4] "ChatGPT Statistics 2025-2026: Key Insights and Growth Trends."SEOProfy, 2025. seoprofy.com
  5. [5] "Fitbit - statistics & facts." Statista, 2024. statista.com
  6. [6] "Generative AI Market Size, Share & Growth Report, 2032."Fortune Business Insights, 2024. fortunebusinessinsights.com
  7. [7] "51 Generative AI Statistics 2025 (Market Size & Reports)."DemandSage, January 2025. demandsage.com
  8. [8] "Wearable Technology Market Size, Share & Trends Report 2030."Mordor Intelligence, 2024. mordorintelligence.com
  9. [9] "Generative AI Market Size And Share | Industry Report, 2033."Grand View Research, 2024. grandviewresearch.com
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