Part I The Concept Problem - Rethinking AGI's True Purpose

Introduction: Beyond Execution

The prevailing narrative around Artificial General Intelligence (AGI) frames it as an execution problem: build better algorithms, deploy more compute, automate more tasks. But this framing misses something fundamental. AGI is not an execution problem—it's a concept problem. The question isn't just "can we build it?" but "what should it actually do?"

Consider the structure of modern work. Big leaders—CEOs, founders, decision-makers—have assistants, executive teams, frameworks they operate within. If AGI's goal is simply to replace these support functions, to automate the work of EAs and middle managers, then the project, while impressive in scope, amounts to little more than vanity. Sophisticated automation, yes, but not civilization advancement.

The real question becomes: If AGI is meant to take civilization forward, shouldn't we revisit the entire concept through a different lens? Not JEPA (Joint Embedding Predictive Architecture) or raw compute scaling, but through the lens of what actually blocks human progress.

The Inversion: When Intelligence Becomes Abundant

Here's where conventional thinking breaks down. We assume that democratizing analytical capability—giving everyone access to AI-powered analysis, coding, and creative work—will level the playing field. The logic seems sound: today's inequality stems partly from capability gaps, so democratizing capability should reduce inequality.

But consider what happens when AI reaches mid-level to above-mid-level creative and analytical capability. Suddenly, the "good but not exceptional" engineer, analyst, or designer finds their advantage evaporating. Everyone becomes AI-augmented to similar levels. The capability that distinguished them—that justified their premium wages—becomes commoditized.

Meanwhile, two groups remain unaffected: the truly outstanding (top-tier meta-thinkers who operate at levels AI cannot reach) and those doing physical, context-dependent work (electricians, nurses, skilled tradespeople whose work requires bodily presence and situational judgment).

This creates an unexpected inversion: the knowledge worker middle class, which spent forty years extracting value from both above and below, suddenly finds itself squeezed. Their information advantage disappears. Their coordination function gets automated. Their gatekeeping becomes obsolete.

The Political Economy of the Middle Class

To understand why this matters, we need to examine what the middle class—specifically the professional/managerial class—actually does. For the past 40-50 years, particularly since the computer revolution of the 1970s, this class has positioned itself as essential intermediaries.

They claimed to defend workers against capital: "You need us—union representatives, HR professionals, compliance officers, diversity consultants—to protect you from exploitation."

They claimed expertise warranted their position: "You need us—financial advisors, consultants, analysts—because we have specialized knowledge you lack."

But look closer at the actual function. The middle class created complexity to justify their existence. They established credential requirements that gatekept access. They manufactured dependency through obfuscation. They extracted rents from information asymmetry.

Consider financial services: a wealth manager charging 1.5% AUM to put clients in index funds isn't providing sophisticated analysis—they're extracting fees for basic allocation. The insurance agent selling high-commission products isn't optimizing for the client—they're optimizing for their payout. The financial advisor making the markets seem impossibly complex isn't educating—they're creating dependency.

This isn't unique to finance. Legal services, healthcare administration, real estate, education—across domains, a professional class emerged that claimed to help while actually extracting. They took their cut from both directions: fees from clients below, captured regulations that required their services, and blocked direct exchange between those who had capital and those who provided actual value.

The Computer Era Anomaly

This extraction was enabled by a specific historical moment: the computer era of roughly 1970-2020. Computers created an explosion of information work. Data needed processing, systems needed coordinating, knowledge needed gatekeeping. A massive professional class emerged to fill these functions.

But here's the crucial insight: this was an anomaly. Before computers, the professional middle class was small. After AGI, it will shrink again. The 50-year explosion of knowledge work was a unique moment when information processing was valuable but not yet automatable.

The middle class used this window to entrench themselves—through credentials, through regulatory capture, through cultural messaging that "knowledge work" was inherently more valuable than "physical work." They convinced society that clean hands were superior to dirty hands, that air-conditioned offices beat weather exposure, that college degrees predicted capability better than demonstrated skill.

But AGI removes the foundation. If AI can process information, coordinate systems, and provide analysis better than mid-level humans, what exactly is the middle class for?

The Reconceptualization

So here's the reconceptualization: AGI's purpose isn't to "help everyone" in some vague, egalitarian sense. Its purpose—whether by design or emergence—is to eliminate the extractive intermediary class that blocks efficient resource allocation and direct value exchange.

Think of it as removing a systemic bottleneck. Capital at the top wants to deploy efficiently. Capability at the bottom wants fair compensation. The middle captures the spread—through fees, through artificial complexity, through manufactured gatekeeping.

AGI eliminates this by:

• Democratizing information (no more information asymmetry)
• Automating coordination (no more middle managers justifying their existence through process)
• Removing gatekeeping (credentials become less predictive when capability is verifiable through AI-augmented demonstration)

This isn't about making everyone equal. It's about removing the parasitic layer that was extracting from both productivity and capital without creating proportional value.

The question then becomes: if this is AGI's actual function—middle-class elimination—how should we think about building it? Not as a general intelligence project trying to replicate human cognition, but as a strategic tool for restructuring political economy.

This reframing changes everything: the metrics we care about, the capabilities we prioritize, the deployment strategies we pursue. It's no longer "can we build human-level intelligence?" but "can we build sufficient intelligence to eliminate parasitic intermediaries while enabling direct value exchange?"

That's a more bounded problem. More achievable. More measurable. And perhaps more honest about what we're actually building.