THE FUSE

# **The AGI Question: Intelligence Democratized, Purpose Uncertain**

## The Race and The Question

We're racing toward AGI. But do we need it? Can current LLMs even give us AGI? Can the LLM architecture itself lead there?

Two questions to unpack.

## What Counts as AGI?

LLMs have already surpassed benchmarks that represent expert-level human performance. Claude Opus 4.5 scores 87.0% on GPQA Diamond (graduate-level science questions), where PhD-level experts score 65-80%. Gemini 3 Pro scores even higher at 91.9% on the same benchmark. On MMLU-Pro testing advanced knowledge, Claude Opus 4.5 reaches 90% while Gemini 3 Pro achieves similar levels. In mathematics, Gemini 3 Pro scores 95% on AIME 2025 without computational tools—a test that challenges even top high school math competitors.

This is professional-level, expert-grade intelligence—the kind that top-tier specialists possess—now available at everyone's fingertips.

## Intelligence Democratized

If AI has reached this mark, consider what that means. Let's say 20% of the population, or even 10%, wants to do something requiring that level of intelligence. It's now available to them.

Success requires perhaps 10 parameters, and intelligence is just one of them. We've essentially negated that single factor.

Look at the world today: probably 3-4% of the population runs businesses, runs companies, drives abundance, creates something. If 20% or 10% wanted to do it, but only 4% could because of X, Y, Z factors—and intelligence was one of those factors—this changes everything.

Intelligence being one of those barriers has now been removed. This opens up whole new possibilities for the remaining percentage of people. Access to knowledge, interference factors—these are no longer blocking the path.

## So Why Chase AGI?

When this is already happening, what is the purpose of AGI? Why are we chasing it?

Unless something completely different comes in—something other than an LLM—that can maybe solve intergalactic travel or... what? What do we want AGI to solve?

AI at this level has already accelerated the rate at which we'll probably cure diseases, find alternatives to combustion engines, solve the current set of world problems. We'll be able to solve them decades ahead of whatever we would have done without this.

## The Solution Equation: Beyond Intelligence

Here's the fundamental truth that philosophers and researchers have long understood: a solution to a problem doesn't come from intelligence alone.

Psychologist Angela Duckworth, in her groundbreaking research on success, found that talent and intelligence are merely starting points. Her formula is revealing: **Talent × Effort = Skill, and Skill × Effort = Achievement**. Notice that effort counts twice, while talent counts once. As she puts it, "Our potential is one thing. What we do with it is quite another."

There are multiple factors that lead to solutions: knowledge, interest, perseverance, resilience, execution capability, resources, timing. IQ contributes perhaps 10%, 20%, 30%, but the remaining 70-90% is everything else—your knowledge, your interest, your perseverance.

Einstein himself recognized this primacy of imagination over mere knowledge: "Imagination is more important than knowledge. For knowledge is limited, whereas imagination embraces the entire world."

Take that into consideration. You have a high-IQ brain available now. You feed it the knowledge. You build a simulator that can pursue the idea continuously with different parameters—providing the perseverance and sustained effort that Duckworth identifies as critical. You can build the same solutions that probably AGI will build.

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Maybe AGI will do it faster—okay, let's concede to that—but that makes the chase for AGI pointless when the democratization of intelligence has already removed the primary barrier.

## Can LLMs Even Get There?

Second question: can LLMs reach AGI?

Look deeper at what an LLM does. It's looking at previous tokens and then predicting the next. The improvement that happened with transformers is that it gives attention to all the tokens while doing it—using self-attention mechanisms. This is different from what earlier recurrent neural networks were doing, which processed sequences step-by-step without this global attention.

But here's the fundamental limitation: if your next tokens are the results of previous tokens, based on the data it's fed, this cannot result in AGI. It will only do the next thing based on what's already there.

AGI is not putting together what already exists. Creating new intelligence—genuinely new conceptual frameworks not derivable from existing patterns—doesn't look possible with this architecture.

If we've missed something in human knowledge, the LLM can't discover it. It can only recombine, synthesize, pattern-match from what it knows. Chain-of-thought reasoning and emergent capabilities show impressive results, but these remain sophisticated pattern synthesis, not the generation of fundamentally novel knowledge.

## The Uncertain Chase

We've democratized expert-level intelligence. We've removed the intelligence barrier for the majority who want to create, solve, build. As Duckworth reminds us, "Enthusiasm is common. Endurance is rare." And now we have both: the intelligence (enthusiasm) freely available, and the capacity to build systems with endurance through continuous operation.

We're accelerating solutions to humanity's problems decades ahead of schedule. The combination of human creativity, perseverance, and AI capability may be sufficient for the challenges we face.

The chase for AGI continues, but its purpose remains unclear. Unless we're pursuing something categorically different from current AI—something that can generate truly novel conceptual frameworks beyond recombination—we may be chasing a solution without a clearly defined problem.

As Einstein observed, "The measure of intelligence is the ability to change." We've already changed the game by democratizing intelligence. Perhaps that's enough.

# ** References**

## Benchmarks

**Claude Opus 4.5**
- GPQA Diamond: 87.0% | Source: Vellum AI, "Claude Opus 4.5 Benchmarks" (https://www.vellum.ai/blog/claude-opus-4-5-benchmarks)
- MMLU-Pro: 90% | Source: Artificial Analysis (https://artificialanalysis.ai/articles/claude-opus-4-5-benchmarks-and-analysis)

**Gemini 3 Pro**
- GPQA Diamond: 91.9% | Source: Vellum AI, "Google Gemini 3 Benchmarks" (https://www.vellum.ai/blog/google-gemini-3-benchmarks)
- AIME 2025: 95.0% without tools, 100% with code execution | Source: VentureBeat (https://venturebeat.com/ai/google-unveils-gemini-3-claiming-the-lead-in-math-science-multimodal-and)

**PhD Expert Performance on GPQA**
- 65-80% range | Source: Context from GPQA benchmark descriptions

## Quotes

**Angela Duckworth**
- "Talent × Effort = Skill, and Skill × Effort = Achievement" | Source: *Grit: The Power of Passion and Perseverance* (2016), verified via Goodreads
- "Our potential is one thing. What we do with it is quite another." | Source: *Grit: The Power of Passion and Perseverance* (2016), verified via Course Hero
- "Enthusiasm is common. Endurance is rare." | Source: *Grit: The Power of Passion and Perseverance* (2016), verified via Goodreads

**Albert Einstein**
- "Imagination is more important than knowledge. For knowledge is limited, whereas imagination embraces the entire world." | Source: Verified via BrainyQuote, Indeed.com, ManageMagazine
- "The measure of intelligence is the ability to change." | Source: Verified via BrainyQuote, Indeed.com

## Research Sources

- Duckworth, Angela. *Grit: The Power of Passion and Perseverance*. Scribner, 2016.
- Duckworth, Angela. "Grit: The power of passion and perseverance." TED Talk, 2013.