Desert episode.
Remember when we thought the internet spread fast? Anthropic just dropped its Economic Index Report, and Claude's adoption makes the dot‑com boom look like a horse‑drawn carriage racing a Formula 1 car.
Last time we dissected America's AI Action Plan and its infrastructure plays.
Today we zoom out to see the actual adoption battlefield who's using AI, how they're using it, and why the difference between automation and augmentation might be the most overlooked economic split since white‑collar versus blue‑collar.
What you'll hear in the show
The fastest technology adoption in human history and why everyone missed the real story
Claude reached millions faster than electricity, automobiles, the internet, and smartphones combined.
But here's what's overlooked: adoption velocity creates winner‑take‑all geography
The Singapore surprise nobody talks about
While everyone watches Silicon Valley, Singapore quietly leads global AI intensity per capita
The overlooked pattern: city‑states move faster than nations (think Venice during the Renaissance)
Coding ate the world (before software could)
62% of Claude.ai usage? Pure code generation
API enterprise? Also coding‑dominated
What everyone misses: we're watching the birth of a new economic class the AI‑augmented developer who codes at 10× speed
The automation‑augmentation split that rewrites labor economics
Enterprise API usage = pure automation (delegate and forget)
Consumer Claude.ai = deep augmentation (collaborative ping‑pong)
The overlooked insight: automation creates unemployment, augmentation creates super‑employment
The geographic concentration everyone ignores
Think of AI adoption like a medieval map here be dragons everywhere except a few golden cities. The report shows crushing concentration:
United States commands the fortress
Singapore, Israel, and select European capitals hold the outposts
Everyone else? Digital serfs in the new technofeudalism
What's overlooked: This isn't about wealth it's about talent density. Saudi Arabia has capital but low adoption.
Singapore has less GDP than Texas but leads in intensity. The motivation? Talent clusters compound exponentially, capital doesn't.
Task‑specific usage reveals the real disruption
Everyone debates "will AI replace jobs?" Wrong question. The report shows AI replacing tasks within jobs:
Consumer patterns (Claude.ai):
Coding/debugging: 30.9%
Content creation: 20.1%
Data analysis: 10.7%
Education: 10.5%
Enterprise patterns (API):
Customer service automation: 35%
Code generation: 28%
Content pipelines: 22%
The overlooked angle: Jobs aren't disappearing they're becoming Frankenstein monsters of human judgment plus AI execution. Think centaurs, not replacements.
The automation versus augmentation divide
This is where the report gets spicy. Two different economic futures are emerging simultaneously:
Automation path (enterprises via API):
Zero human in the loop
Example: Customer service bots handling 1000s of tickets
Economic impact: Cost reduction, margin expansion
Overlooked risk: Creates brittle systems with no human redundancy
Augmentation path (consumers via Claude.ai):
Human remains the orchestrator
Example: Developer using Claude to write tests while designing architecture
Economic impact: Productivity multiplication, capability expansion
Overlooked opportunity: Creates anti‑fragile workers who level up with each AI advance
The motivation pattern nobody discusses: Automation buyers want to eliminate costs. Augmentation users want to multiply capabilities.
Same tool, opposite dreams.
Seven plays for builders based on the data
Geographic arbitrage accelerator: Build tools that let Singapore‑level AI users collaborate with talent in adoption‑lagging regions. The wage differential plus AI leverage creates 50× productivity arbitrage.
Task‑specific micro‑models. Everyone's building general assistants. The data screams for vertical depth. A coding‑only model that's 10× better at debugging beats GPT‑5 at everything else.
Augmentation coaching marketplace. The report shows augmentation users extract 10× more value than automation deployers. Create the Peloton for AI augmentation live coaching for professionals learning the collaborative dance.
Anti‑automation insurance. New product category: insurance for companies against automation brittleness. When their zero‑human customer service fails, you provide instant human backup. Price it at 10% of their "savings."
AI intensity scoring API. Rate companies/countries/cities on their actual AI usage intensity (like Anthropic's index). Sell to VCs for due diligence, to governments for policy, to enterprises for competitive intelligence.
Cross‑border AI bridges. Geographic concentration creates opportunity. Build secure, compliant pipelines for low‑adoption regions to access high‑adoption AI capabilities without data leaving borders.
Human‑AI interface optimizer. The augmentation users need better tools. Design interfaces that make the human‑AI collaboration feel like thinking, not prompting. Think Notion but for human‑AI work.
What everyone overlooks about speed
The report emphasizes unprecedented adoption velocity. But here's what they don't say: Speed creates fragility. The faster a technology spreads, the less time for cultural adaptation, regulatory frameworks, and safety nets.
Think about it through this analogy: We're building a Formula 1 race car while driving it at 200mph. No pit stops. No safety inspections. No practice laps.
The motivation to watch: The first major AI failure won't come from the technology. It'll come from the speed mismatch between adoption and adaptation.
Personal take
Reading this report, I heard the same pattern we saw with Belkins scaling from zero to eight figures. The winners weren't the first movers or the best funded. They were the ones who understood the difference between automating away problems and augmenting toward opportunities.
The geographic concentration also mirrors what we discussed in Technofeudalism—digital resources concentrating in fewer hands, creating new power dynamics. Except now it's not just compute ownership, it's adoption capability itself becoming scarce.
After you listen
Which side of the automation‑augmentation divide does your work fall on? Reply and tell me. I'm collecting patterns for the next episode on defensive AI strategies for augmentation workers.
Want the raw data? Anthropic published the full methodology. I've annotated a copy with founder‑relevant insights DM for access.
Worth reading while the episode downloads
AI Generalist - The augmentation playbook in practice
The Great Restructuration - Why task unbundling creates opportunity
Ideation - Finding gaps in the adoption curve
Main Character - Positioning yourself in concentrated markets
Post‑credit scene
Still here? Good. Here's what the report didn't say but the data screams: We're watching the birth of economic inequality that makes current wealth gaps look quaint.
The overlooked pattern: It's not about who has AI access (everyone will). It's about who develops AI intuition the ability to dance with the model rather than command it. That's unteachable at scale. That's the new moat.
See you in the next edition, where we decode the European counter‑strategy to this concentration.
Thanks for reading and listening.
Vlad
