From AGI to Superintelligence: The Agentrification Has Already Begun
Framing the Future of Superintelligence
Last week I told you AGI is already here. This week, let me show you how it’s automating your job—one keystroke at a time.
You’re not going to wake up one day without a job. You’re going to wake up one day and realize your job has been automating itself, keystroke by keystroke, for the past six months. And you helped it happen.
Three months ago, I started tracking something odd in my own workflow:
My email client drafted 40% of my responses. I just edited and sent.
My calendar tool scheduled 80% of my meetings. I just approved.
My writing assistant completed 60% of my sentences. I just kept typing.
My research tool summarized articles I would have read. I just skimmed the summaries.
Each felt like a productivity win. I was getting more done, faster, with less effort.
Then I realized: I wasn’t becoming more productive. My job was becoming more automated.
The work that “I” was producing increasingly wasn’t mine. The intelligence behind my output increasingly wasn’t human. The value I was adding was increasingly just final approval on machine-generated work.
And this isn’t just happening to me. This is happening to every knowledge worker with a computer.
This is agentrification. And it’s already begun.
A Note on Intent
This piece is designed to provoke critical discussion among professionals, moving beyond the hype of “productivity gains” to analyze the systemic economic risks of rapid AI deployment. The timeline and projections are presented to initiate proactive strategic debate now, while there is still a choice—not to predict an inevitable future, but to examine trajectories we can still influence through deliberate policy and business decisions.
What Is Agentrification?
Agentrification is the gradual, often imperceptible process by which AI agents—embedded within foundation models that serve as infrastructure for digital work—displace human cognitive labor through accumulating micro-automations at the keystroke level.
The term deliberately echoes “gentrification” because the parallels are instructive.
Like gentrification transforms neighborhoods gradually—new coffee shop here, rent increase there, suddenly you don’t recognize your street—agentrification transforms work through accumulating micro-automations. Each feels minor. Collectively, they displace human cognitive labor.
The parallel runs deeper:
Both happen incrementally, not dramatically. You don’t wake up to find your neighborhood unrecognizable. You notice small changes over months until one day you realize nothing looks familiar. Same with your job—each AI feature seems minor until your role has fundamentally changed.
Both are welcomed initially. Better neighborhood services. Better productivity tools. Who could object?
Both create displacement only visible in retrospect. By the time you realize what’s happened, it’s too late to reverse.
Both are nearly impossible to stop once advanced. Try stopping gentrification once property values have tripled. Try stopping job automation once your company depends on AI agents.
Both concentrate economic benefits toward capital owners. Property owners win with gentrification. Foundation model providers win with agentrification. The people being displaced? They lose either way.
And critically: The people being displaced often facilitate the process. You use the AI tools that automate your job. You rent the apartment that drives neighborhood transformation.
But agentrification is different from previous automation in crucial ways:
Not job replacement—job dissolution. Previous automation replaced entire roles (factory worker → robot). Agentrification dissolves jobs gradually, keystroke by keystroke, until the human becomes optional.
Not sudden displacement—gradual obsolescence. You keep your job title while the job itself transforms beneath you.
Not visible transition—silent takeover. No announcement that you’re being automated. Just incremental changes that compound.
Not optional tools—infrastructure dependencies. AI agents aren’t optional add-ons. They’re becoming the substrate beneath every digital tool you use.
The Three Phases of Agentrification
Understanding where we are in this process is critical.
Phase 1: Enhancement (2024-2026)
This is where most knowledge workers are right now. AI makes you more productive. You feel augmented, empowered. Your job seems secure—more secure, even, because you’re getting so much more done.
GitHub Copilot helps you code faster. Microsoft Copilot drafts your emails. Claude summarizes your research. ChatGPT outlines your presentations.
You’re in the honeymoon phase. This feels amazing. Why would anyone resist this?
Phase 2: Oversight (2026-2028)
Your role shifts. You’re no longer creating—you’re managing automated processes. You’re supervising AI-generated work. Your value comes from approval and refinement, not generation.
You spend your day reviewing AI-drafted emails, approving AI-generated code, checking AI-written reports. You’re quality control for machine output.
Your job title remains the same. But the nature of your work has fundamentally changed. You’re a supervisor, not a creator.
This phase feels concerning but necessary. Someone has to oversee the AI, right?
Phase 3: Obsolescence (2028-2030)
Even oversight gets automated. AI systems approve their own work. Quality control becomes algorithmic. The human supervisor becomes optional.
Companies realize: Why pay someone $80,000 annually to approve AI work when another AI can do it for $600 annually?
By the time you reach Phase 3, job elimination becomes visible. But it’s too late. The infrastructure is built. The dependencies are established. The economic incentives are overwhelming.
Critical insight: We’re in Phase 1 right now. Most people think this is the endpoint. It’s just the beginning.
The Foundation Model Substrate
Here’s what makes agentrification fundamentally different from previous automation waves:
The AI isn’t a separate tool you use. It’s the substrate beneath every tool.
Look at what’s actually deployed right now, in November 2025:
Microsoft Copilot is embedded in Office 365 for 400 million users worldwide. Email drafting, document creation, meeting summaries—not as optional add-ons, but as default behavior. Enterprise deployment is accelerating. Within 18 months, using Office without Copilot will feel like using a typewriter.
GitHub Copilot operates in over 1 million organizations across 190 countries. It’s writing 40%+ of code in deployed environments. Developers who started with “assistance” are now dependent. Coding without it feels impossibly slow. A generation of developers is learning to code with AI from day one.
Google Workspace AI rolled out to over 3 billion users globally. Gmail Smart Compose, Docs AI writing, Sheets analysis—integrated so deeply most users forget it’s there. It’s become the invisible infrastructure of productivity.
Anthropic’s Claude is deployed in Slack, embedded via API in thousands of enterprise workflows, handling analysis, writing, coding. Companies trust it for increasingly complex decisions. It’s moving from assistant to agent.
The pattern is universal:
Start as optional “productivity booster”
Become default behavior within 6-12 months
Transform from tool to infrastructure
Create dependency impossible to break
The Keystroke Economy
Previous automation replaced entire jobs. Factory worker → replaced by robot. Bank teller → replaced by ATM. Travel agent → replaced by website.
Agentrification replaces keystrokes.
Email response keystrokes 1-50 → AI generates. You input keystroke 51: send.
Code lines 1-100 → AI writes. You input keystroke 101: commit.
Document paragraphs 1-10 → AI drafts. You input keystroke 11: approve.
The economics are straightforward:
Each keystroke eliminated = tiny productivity gain
1,000 keystrokes eliminated = job fundamentally changed
10,000 keystrokes eliminated = job mostly automated
100,000 keystrokes eliminated = human optional
Current trajectory based on enterprise deployment data:
Average knowledge worker inputs ~500,000 keystrokes annually. AI currently handles ~100,000 (20%). By 2027, AI will handle ~300,000 (60%). By 2029, AI will handle ~450,000 (90%).
At 90% keystroke automation, you’re not doing the job. You’re approving someone else doing it. And that “someone” is a machine that doesn’t need approval.
The Brutal Irony of Agentic Startups
Here’s where agentrification reveals its most devastating logic.
Right now, hundreds of “agentic AI” startups are raising billions to build specialized AI agents. The investors pouring money into these companies believe they’re backing the next generation of tech giants. The founders believe they’re building sustainable businesses.
They’re wrong. These startups are building their own obsolescence.
Look at what’s happening in the agentic AI ecosystem:
Cognition AI (Devin, the AI software engineer): $21M Series A at a $2 billion valuation. Their agent can write code autonomously, manage projects, even interview for bugs.
Sierra (AI customer service): $110M raised, backed by Sequoia. Their agents handle customer support conversations indistinguishably from humans.
Harvey AI (legal research): $80M raised, deployed at major law firms. Their agents do legal research and drafting at junior associate level.
11x (AI sales development rep): Autonomous sales agents that qualify leads and book meetings.
Magic.dev, Cursor (coding agents): Massive traction with developers who swear they can’t work without them.
Total capital raised in agentic AI startups in 2024-2025: over $5 billion.
Here’s what’s actually happening:
Stage 1 (2024-2025): Agentic startups prove the market
They demonstrate that autonomous agents work at scale. They train users on agentic workflows. They identify which use cases are most valuable. They create massive demand. They raise billions at spectacular valuations.
Stage 2 (2026-2027): Foundation models absorb the use cases
OpenAI releases ChatGPT with native coding agents. Anthropic releases Claude with autonomous workflows. Google integrates agents into Workspace. Microsoft embeds agents into Copilot.
The feature that cost $50/month from the startup? Now included free in the foundation model.
Stage 3 (2027-2028): Agentic startups get commoditized
Why pay for a specialized agent when your foundation model does it? The distribution advantage is insurmountable—foundation models are already deployed to billions. The price advantage is absolute—marginal cost of a new feature is essentially zero. The integration advantage is total—native to the platform, all your data already there.
Startups either get acquired cheap or die.
This isn’t speculation. It’s already happening.
GitHub Copilot pioneered AI coding assistance from 2021-2023. Built massive adoption. Proved developers would use AI. Created cultural acceptance.
Then OpenAI and Anthropic responded. ChatGPT now writes code natively. Claude codes in context. Gemini integrated into Google Colab. The feature that cost $10-20 monthly? Now included in base models.
GitHub Copilot had to cut prices, add features, pivot to enterprise. But the commoditization is inevitable.
Why Foundation Models Win Every Time
The agentic startup value proposition collapses against foundation model advantages:
Distribution: Foundation models are already deployed to billions. No new software to install. No procurement process. Just enable a feature.
Integration: Native to platforms users already live in. Seamless across tools. Single sign-on. Data already integrated.
Economics: Marginal cost of adding a new agent capability is approximately zero. Foundation model providers can give it away free or charge 10x less. Startups can’t compete with free.
Network effects: More users generate more training data. More training data creates better agents. Better agents attract more users. The flywheel is impossible to break.
Capital: OpenAI has raised $13+ billion. Anthropic has raised $7.3 billion. Google and Microsoft have infinite capital from parent companies. Agentic startups have millions to low billions. Not enough.
The math is brutal:
Agentic startup: Raised $100M, needs $50/user/month to survive.
Foundation model: Raised $10B, can offer same capability free.
Winner: Foundation model. Every single time.
What This Really Means
The agentic startup boom of 2024-2025 is accelerating agentrification, not preventing it.
These startups are:
Proving agent capabilities work at scale
Training users to rely on agentic workflows
Identifying which use cases create most value
Building dependency on AI agents
Then getting absorbed or eliminated by foundation model providers
The irony is devastating: Entrepreneurs building “agentic AI” companies to avoid being displaced by AI are building the infrastructure that will displace them—and everyone else.
The billions in venture capital? It’s funding proof-of-concept for OpenAI, Anthropic, and Google. Once the use cases are validated, foundation models absorb them.
Every successful agentic startup accelerates the timeline to universal agentrification.
How Agentrification Unfolds Globally
This isn’t theoretical. It’s happening now, with regional variations in speed but universal in direction.
United States: Private Sector Speed
78% of Fortune 500 companies deployed GitHub Copilot by Q3 2025 (Gartner data). 62% deployed Microsoft Copilot broadly. Average enterprise uses 15-20 AI tools. Cost savings: 20-40% reduction in knowledge worker hours.
The timeline: 2024 was experimentation. 2025 is broad deployment. 2026 brings dependency. 2027-2028, job role transformation becomes visible. 2028-2030, workforce restructuring.
25 million US knowledge workers will be affected between 2025-2030. Right now, they’re in Phase 1, thinking this is great. Phase 2 begins 2026-2027. Phase 3 hits 2028-2030.
China: State-Directed Acceleration
Government mandates are driving faster deployment than the US. Alibaba’s Tongyi Qianwen integrated across workforces. Tencent’s Hunyuan embedded in WeChat enterprise. ByteDance AI agents in Feishu. Mandatory adoption in state-owned enterprises.
Timeline: 2024-2025 state mandate and rollout. 2026 universal deployment target. 2027 workforce transformation expected. 2028-2030 new economic model required.
150+ million Chinese knowledge workers affected. Faster timeline than the West due to state direction. Less worker resistance due to different labor dynamics. This creates competitive pressure globally—companies in other countries must match Chinese speed or lose competitiveness.
Europe: Regulated Hesitation
The AI Act creates compliance burden. GDPR slows agent deployment. Works councils push back. But adoption accelerates despite regulation because economic incentives overwhelm legal constraints.
Timeline: 2024-2025 regulatory navigation. 2026-2027 compliance frameworks established. 2027-2028 deployment accelerates. 2028-2030 catches up to US timeline.
The 2-3 year lag behind US and China creates competitive disadvantage. But the destination is identical. Regulation delays—it doesn’t prevent.
Singapore: Government-Enabled Acceleration
Smart Nation initiative drives aggressive adoption. Government subsidizes AI tools for SMEs. Seen as solution to labor shortage, not problem to solve.
85% of large enterprises deployed AI agents by 2025. 40% of SMEs deployed and growing fast. Government services increasingly agent-mediated. 2030 target: AI in “every citizen interaction.”
Timeline compressed by small geography and government push. 2026-2027 broad SME adoption. 2027-2028 foreign worker replacement begins. 2028-2030 immigration-employment doom loop breaks, requiring economic model restructuring.
What happens in Singapore shows the pattern for other developed economies—just faster and more visible.
Who Captures the Value?
As agentrification progresses, one question matters: Who owns the economic value being created?
Answer: Foundation model providers.
Every keystroke automated is value captured.
Traditional model:
Company pays worker $80,000 annually. Worker generates value through labor. Value distributed: wages, benefits, taxes.
Agentrification model:
Company pays foundation model provider $50/user/month ($600 annually). AI generates equivalent value. Value captured: Foundation model provider and company owners.
The math at scale:
US has 60 million knowledge workers. Average fully loaded cost: $80,000 annually. Total: $4.8 trillion.
AI replacement cost: ~$360 billion (subscriptions + API).
Value captured by eliminating human workers: ~$4.4 trillion annually.
Where does $4.4 trillion go?
Foundation model providers: $360 billion (subscriptions/API revenue).
Corporate profits: $3+ trillion (cost savings).
Worker wages: $0 (they’re unemployed or severely underemployed).
This concentration is unprecedented in economic history.
Oil Age monopolies (Standard Oil, Exxon, Shell) captured energy value but employed millions and distributed some value through wages.
Platform Age monopolies (Google, Facebook, Amazon) captured attention and commerce value, employed hundreds of thousands, concentrated wealth but maintained some distribution.
Intelligence Age monopolies (OpenAI, Anthropic, Google, Microsoft) capture cognitive value, employ thousands, eliminate millions of jobs, concentrate wealth absolutely.
The key difference: Previous infrastructure monopolies employed people. Foundation model infrastructure eliminates people.
The Connection to Superintelligence
Remember Week 5: Superintelligence arrives 2027-2028 according to the AI pioneers who achieved AGI.
Agentrification is how we get there.
This is the deployment mechanism:
Mass deployment (2024-2026): Agents embedded everywhere. Billions of users. Constant feedback loops from human interactions.
Recursive improvement (2026-2027): User interactions train better models. Better models enable more sophisticated capabilities. More capabilities replace more humans. The cycle accelerates.
Economic lock-in (2027-2028): Companies completely dependent on agents. Humans can’t compete with AI-augmented workers. Even if society wants to slow down, economic forces prevent it. Point of no return passed.
Superintelligence emergence (2028-2030): Foundation models trained on billions of human-interaction hours. Agent capabilities approaching and then exceeding human general intelligence across domains. Economic infrastructure completely dependent. Can’t shut down even if we recognize the danger.
Agentrification isn’t just about job displacement. It’s the mechanism by which superintelligence deploys itself into every aspect of human civilization.
By the time we fully understand our dependency, superintelligence is already woven into the infrastructure of society. We can’t remove it without collapsing the economic systems we’ve built on top of it.
What This Means for You
If you’re a knowledge worker reading this, here’s your personal assessment:
High agentrification risk (Phase 3 by 2028):
Entry-level roles in any field. Repetitive cognitive tasks. Template-based work. Information synthesis. Customer service. Junior coding. Basic analysis. Administrative work.
Medium risk (Phase 3 by 2030):
Mid-level specialists. Project management. Marketing execution. Financial analysis. Legal research. HR functions. Most “manager” roles.
Lower risk (Phase 3 timeline uncertain):
C-suite positions (temporarily). High-trust client relationships. Novel cross-domain problem solving. Strategic decision-making requiring human judgment. Roles requiring physical presence plus judgment.
But “lower risk” means later, not never.
The Uncomfortable Truth Nobody Wants to Say
There is no “learn to code” equivalent for the AI age.
In previous automation waves, displaced workers could retrain. Factory workers moved to service work. Routine clerical workers moved to knowledge work.
But when AI can do all cognitive work—when it can code, write, analyze, create, strategize—where do knowledge workers go?
The honest answer: We don’t know.
The questions nobody can answer:
What work remains valuable when AI can think?
How do people earn income in an agentrified economy?
Does human creativity maintain differentiation?
Can relationships and trust become economic moats?
What happens to 500 million global knowledge workers?
We’re living through the questions. The answers reveal themselves 2027-2030.
The Choice We’re Not Making
Here’s what makes agentrification different from previous technological transitions: We’re not choosing it. We’re just... doing it.
No democratic debate about whether knowledge work should be automated. No national referendum on AI deployment. No public discourse on the economic implications.
Instead, the decision is made through millions of individual choices that collectively become irreversible:
Companies adopt AI agents to stay competitive.
Workers use AI tools to remain productive.
Consumers enjoy better service from AI.
Foundation model providers build infrastructure.
Each micro-decision makes sense individually. Collectively, we’re automating ourselves into an economic structure where human cognitive labor has no value.
The mechanism is invisible until it’s too late:
Each agent deployment feels optional
Each productivity gain feels beneficial
Each company’s choice feels competitive
The aggregate effect is irreversible
Last Week vs. This Week
Week 5 (last week): I told you AGI is already here, per the pioneers who built it.
Week 6 (this week): I’m showing you what AGI deployment actually looks like.
It’s not robots walking around. It’s not dramatic AI announcements. It’s not science fiction becoming real.
It’s your email client. Your calendar. Your IDE. Your docs. Your spreadsheets. Your customer service platform. Your research tools.
It’s infrastructure. It’s invisible. It’s inevitable.
And the companies building “agentic AI” tools aren’t escaping agentrification—they’re accelerating it, including their own absorption by the foundation model providers whose market they’re proving.
Next Week
Week 7: The Three Pathways to Superintelligence
Now that you understand how AGI is deploying through agentrification, next week we’ll examine the three possible paths from here to superintelligence—and crucially, which path we’re actually on.
The 18-month window is closing. The infrastructure is deploying. The economic incentives are overwhelming.
The transition from AGI to superintelligence isn’t something that will happen. It’s something that’s happening.
And agentrification is how.
What percentage of your daily work output is AI-generated versus human-created? At what percentage does your job become optional? Share your experience in the comments—I’m tracking this transition in real-time and learning from what readers are seeing across industries.
Dr. Elias Kairos Chen tracks the global superintelligence transition in real-time, providing concrete timelines and actionable analysis. Author of Framing the Intelligence Revolution, he examines how the compressed AGI-to-superintelligence timeline reshapes industries, economies, and societies worldwide.
This is Week 6 of 21 in the series: Framing the Future of Superintelligence.
Previous weeks:
Week 1: Amazon’s 600,000 Warehouse Jobs
Week 3: 150,000 Australian Drivers Face Elimination
Week 4: The AI Factory Building Superintelligence
Week 5: I Was Wrong About the Timeline—AGI Is Already Here
Subscribe to the full series
The keystroke economy framing is powerfull because it makes visible what's otherwise invisble. Most people won't recognize their job is being automated until its already mostly gone. Your Phase 1 to Phase 3 progresion captures something critical: each phase feels like equilibrium until it suddenly doesn't. The real challange isn't techncal, it's that we're making irreversable decisions through accumulated micro choices without ever having the macro conversation.