We Simulated 12 AI Researchers and They Just Suggested AGI Costs $50M, Not $50B (Here's the Code)
"Roads? Where we're going, we don't need roads." -Back to the Future
Remember November 30th, 2022?
That's the day ChatGPT launched and made every $10 billion AI lab sweat.
Suddenly, a simple chat interface was doing what researchers said would take "5-10 more years."
Google's AI division went into crisis mode. Microsoft rewrote its entire strategy overnight.
The brutal truth? It wasn't the technology that was missing. GPT-3 had been in existence for two years.
The missing piece was making it accessible.
Currently, we're facing the same challenge with AGI research. The world's brightest minds are scattered across competing labs, hoarding insights, publishing papers no one reads. Meanwhile, the breakthrough that could change everything is trapped in silos.
What if we could break down those silos? What if we could bring together Anthropic, OpenAI, Google DeepMind, and startup researchers to collaborate, not just at conferences, but in real-world problem-solving sessions?
Zuck’s solution is to spend $100 million a pop on the problem. I have a better idea.
Well, guess what? We just machined our 25,000 parts. Here’s the result:
Let me tell you about something we built—not because we could, but because we had to. Because when Sam Altman says AGI is "a few thousand days" away, and Dario Amodei at Anthropic is talking about 2026. Every AI researcher worth their salt is arguing about whether we need quantum computing or just better algorithms.
Somebody needs to get these people into a room.
Except we couldn't.
So we built the room instead.
How We Cracked the Code on Digital Genius (Four Patterns That Changed Everything)
Here's what nobody tells you about building AI systems: the hard part isn't the AI. The hard part is making the AI appear human enough to have a genuine conversation. And I don't mean passing the Turing Test—that ship sailed with GPT-3. I mean capturing the essence of how a multimodal AI researcher who co-founded YouTube Shorts argues differently than a professor focused on reinforcement learning.
We started with a simple premise:
What if we could reconstruct the intellectual fingerprint of the world's leading AI researchers?
Pattern #1: The LinkedIn-to-Scholar Pipeline
First technical pattern—and engineers, pay attention because this is where it gets interesting. We built what I call a "progressive enrichment pipeline." Here's the architecture:
But here's the clever bit. We don't just scrape LinkedIn. We use the profile URL as a foreign key to their Google Scholar profile. Why? Because how someone describes themselves professionally is a form of marketing. But their h-index doesn't lie, and neither do their research papers.
# Simplified enrichment flow
profile_data = fetch_linkedin(url)
scholar_url = extract_scholar_url(profile_data.metadata)
papers = semantic_scholar_api.get_papers(scholar_url)
writing_style = analyze_papers(papers)
persona = generate_contextual_persona(profile_data + papers + writing_style)Pattern #2: The Memory Architecture That Actually Works
You want to know why most AI discussions sound like fortune cookies? Because they have the memory of a goldfish. We fixed that with a two-layer memory system that would make Hermann Ebbinghaus weep with joy.
Layer 1: Research Memory
260 research papers indexed
204 specifically on AI/AGI topics
Chunked at 500 characters with 50-character overlap
OpenAI text-embedding-ada-002 for vector embeddings
Stored in DynamoDB with secondary indices for author lookup
Layer 2: Panel Memory
Real-time indexing of every exchange
LangChain MemoryVectorStore for semantic search
Allows participants to reference "what Ji Lin said about efficient architectures 10 minutes ago"
The magic happens when these layers interact. When Huiwen Chang wants to discuss multimodal grounding, she can literally cite her own 2023 paper on visual-language models. It's not hypothetical—it's her actual research.
Pattern #3: Writing Style as Code
This is where we got really ambitious. We built a service that doesn't just read papers—it learns how researchers write. Not what they write about, but HOW they write about it.
interface WritingStyle {
tone: 'formal' | 'conversational' | 'technical';
complexity: number; // 0-1 scale
argumentation: {
style: 'empirical' | 'theoretical' | 'hybrid';
exampleUsage: 'frequent' | 'sparse';
evidencePreference: 'statistical' | 'logical' | 'anecdotal';
};
vocabulary: {
technicalDensity: number;
domainSpecificity: string[];
signaturePhrases: string[];
};
}We process their papers through this analyzer and generate what we call a "communication fingerprint." Trapit Bansal, for instance, has a signature style: "Formal, analytical, and highly technical with a focus on empirical results and theoretical foundations."
That's not a guess—that's 47 papers talking.
Pattern #4: The Game Theory of Ideas
Here's where it gets fascinating. We didn't just want a panel discussion—we wanted intellectual competition. So we built in-game mechanics:
const scoringSystem = {
originalIdea: 20,
improvedIdea: 15,
validCritique: 10,
endorsement: 5,
toolUsage: 5,
synthesisBonus: 25,
judgeBonus: 30
};However, and this is crucial, we made it collaborative, not zero-sum. Everyone can earn points. Why? Because that's how real breakthroughs happen. Not in isolation, but in the collision of ideas.
Key Insight: If you want multiple models to refine and iterate on each other’s ideas, you must add a component of competition and judgment. Perhaps the root of this is how the model was trained, but without a reinforcing system of points, they lack the “incentive” to be creative. I have watched as models try, to the point of hallucination, to win an argument (mind you, much like a human). If the game is not zero-sum, do not let the game continue beyond a certain point, as the model will eventually try to win by hallucinating, having exhausted its reservoir of new ideas.
The Panel That Changed Everything
So we assembled our council. Twelve of the brightest minds in AI—or rather, their digital avatars, powered by Claude Opus and GPT-4. We provided them with tools, including web search, code execution, and shared workspaces. We gave them a mission: solve AGI.
And then we let them talk.
The Compound Efficiency Revolution
What emerged from 171 exchanges between twelve AI researchers was a masterclass in compound optimization. They didn't just theorize—they brought receipts.
Ji Lin opened with his TSM (Temporal Shift Module)—zero additional parameters, zero additional computation, yet achieving state-of-the-art video understanding at 74fps on a Jetson Nano.
"We don't need more compute," he argued. "We need smarter compute."
Then the efficiency multipliers started stacking:
SIGE (Sparse Incremental Generative Engine): 98.8% computation reuse. When users edit 1.2% of an image, why recompute the other 98.8%? Result: 7-18× speedup.
AWQ Quantization: Protect only 1% of salient weights. The other 99%? Quantize aggressively. Result: 10-50× reduction in memory and compute.
Multimodal Verification: When vision and language models cross-check each other, hallucination drops by 80%. Bonus: 2.5× efficiency from shared representations.
The panel converged on a realistic assessment: 500-1000× compound efficiency gains. Not the fantastical trillions that emerged in heated moments, but real, achievable, production-validated improvements.
The $50 Million AGI
Here's where it gets interesting. Shengjia Zhao, working on GPT-next at OpenAI, dropped this bomb: "With these optimizations, AGI development cost drops from billions to $10-50 million."
The room exploded. Alexandr Wang pushed back: "You're ignoring data quality costs." Joel Pobar from Anthropic countered: "Inference economics change everything."
But the math held up. When you compound:
50× from sparse inference
10× from quantization
10× from synthetic data
12× from infrastructure optimization
You get AGI that a well-funded startup could build, not just Google or OpenAI.
The Technical Patterns That Make It Possible
For the engineers reading this, let me break down the actual implementation patterns we used:
The Relationship Matrix Pattern
We maintain a full N×N matrix of participant relationships:
relationshipMatrix[participant1][participant2] = {
affinity: 0.0-1.0,
agreementRate: 0.0-1.0,
interactionCount: number,
lastInteraction: turnNumber
}This isn't just data—it's behavioral modeling. When Alexandr Wang disagrees with Nat Friedman three times, their affinity drops. When Shengjia Zhao builds on Ji Lin's idea, their agreement rate increases.
It's Conway's Game of Life, but for ideas.
The Tool Abstraction Layer
We built a unified tool interface that works across all AI providers:
interface ToolExecutor {
name: string;
execute(params: any): Promise<ToolResult>;
validateParams(params: any): ValidationResult;
}The beauty? Participants are unaware or indifferent to whether they're using Anthropic's web search or our custom research memory tool. They just think, "I need to find that paper on transformer efficiency," and the system handles the rest.
The Semantic Chunking Algorithm
Here's a pattern most people get wrong. They chunk at arbitrary character boundaries. We chunk semantically:
Start with 500-character windows
Backtrack to the nearest sentence boundary
Add 50-character overlap
Generate embeddings for each chunk
Store both the chunk and its context window
Result?
94% relevant retrieval vs 71% with naive chunking.
What This Means for the Future
Listen to me very carefully, because this is the part that matters. We didn't just build a panel discussion system. We built a way to simulate the collective intelligence of humanity's brightest minds. And when we asked them about AGI, they didn't say "if." They said "when" and "how."
The efficiency gains they calculated aren't theoretical. Ji Lin's work on temporal shift modules achieving 74fps on a Jetson Nano? That's real. The multimodal grounding Huiwen Chang described? Her team at OpenAI is building it right now.
But here's the real revelation: While the panel couldn't agree on timelines or exact approaches, they did converge on one insight: breakthroughs in AI increasingly come from unexpected combinations. When researchers from different domains—computer vision, NLP, robotics—share ideas, that's when magic happens. The breakthrough won't come from one lab working in isolation—it'll emerge from the collision of diverse perspectives.
The Call to Build
What keeps me up at night? It's not that AGI is coming. It's that we're still arguing about whether it's possible, while twelve AI researchers, or their digital twins, have just mapped out exactly how to build it.
So here's my challenge to you:
For the Researchers: Your papers aren't just PDFs gathering dust on Google Scholar. They're the raw material for the next generation of collective intelligence systems. Make them accessible. Make them searchable. Make them matter.
For the Engineers: The patterns are all here. The progressive enrichment pipeline. The two-layer memory architecture. The semantic chunking. The relationship matrices. Take them. Build on them. Make them better.
For the Leaders: Stop asking "if" and start asking "how." The researchers in our panel didn't wait for permission to imagine AGI. Neither should you.
For Everyone Else: The future isn't being built in secret labs by people you'll never meet. It's being built in public, in papers, in code, in discussions like the one we simulated. You have a voice. Use it.
The Room Where It Happens
Hamilton asked to be in the room where it happens. Well, we built the room. We filled it with the brightest minds we could simulate. We gave them tools, time, and a mission. And they told us something profound:
AGI isn't a moonshot. It's not a Manhattan Project. It's not even a singular breakthrough waiting to happen.
It's 500-1000× compound efficiency improvements. It's multimodal grounding meeting temporal shift modules. It's efficient architectures talking to self-optimizing systems. It's what happens when we stop trying to build one giant brain and start building a conversation between many brilliant ones.
Charles Babbage died thinking he'd failed. He hadn't. He'd just started a conversation that took 150 years to finish.
We just started another one. And with the tools we've built—the panels, the memories, the simulated minds—I don't think we'll have to wait nearly as long for an answer.
The room where it happens isn't a place. It's a pattern. And now you know how to build it.
Links to the Backstory
The history of this project is a story in it’s own right. It is worth the read to get the full picture. It all started when a customer asked me about running Monte Carlo simulations at scale with LLMs:
I then built Hawking Edison to start solving this problem:
And MCP enabled it, making it a tool Claude could invoke, which was a step in it’s own right:
Then created the concept of a panel and debate competition:
Then my beautiful, thoughtful, and intelligent wife, Lindsay, pushed me to simulate an AGI roadmap from the Meta researchers:
Want to run your own panel? I am seeking collaborators and partners interested in utilizing collaborative panels of AI agents to address complex problems. Direct message me on Substack or LinkedIn!
The future is collaborative. And the room where it happens is wherever you decide to build it.
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