Dear Mark: About Those $100M Signing Bonuses
An open letter regarding Meta's superintelligence talent acquisition strategy
So, Mark, we hear you're in the market for AI talent. Nine-figure signing bonuses, personal office relocations, the works. Well, consider this our application letter.
Your recent offers to top researchers from OpenAI and Anthropic show you understand what's at stake in the race to superintelligence. We've been working on a complementary approach: building infrastructure that gives AI systems the ability to create their own tools, write their own code, and extend their own capabilities in real-time.
Here's the critical insight: Artificial Superintelligence will be achieved when AI systems can improve themselves without human developers in the loop. We're building the concrete infrastructure to achieve that goal. Our platform enables AI to write code, create tools, and enhance its own capabilities autonomously.
Who We Are (And Why You Should Care)
We're engineers working on the frontier of AI capability expansion. We've successfully executed multiple deep learning projects that current LLMs simply cannot do - like training neural networks to read EEG signals and discover disease biomarkers. This experience taught us something critical: LLMs are fundamentally limited by their tools. Give them the ability to build and train neural networks, and suddenly they can solve problems that were previously impossible.
The gap between Llama and GPT-4/Claude isn't just about model size – it's about the surrounding infrastructure. While Meta focuses on training larger models, we're building the tools and systems that could dramatically enhance any model's capabilities. Our System Prompt Composer demonstrates significant improvements in task completion rates for open models. Add our MCP tools and the gap shrinks even further.
Deep Learning Projects That Prove Our Point
We've successfully delivered multiple deep learning projects that current LLMs cannot accomplish:
EEG Signal Analysis: We trained neural networks to read raw EEG data and recognize biomarker patterns with high accuracy. No LLM can do this today. But give an LLM our infrastructure? It could design and train such networks autonomously.
Financial Time Series Prediction: We built models that ingest market data, engineer features like volatility indicators, and train models to predict price movements. Again, ChatGPT can't do this - but with the ability to create and train models, it could.
Medical Image Classification: We developed CNNs for diagnostic imaging that required custom architectures and specialized data augmentation. LLMs can discuss these techniques but can't implement them. Our infrastructure changes that.
These aren't toy problems. They're production systems solving real challenges. And they taught us the key insight: The bottleneck isn't just model intelligence - it's also model capability.
What We've Built
Machine Learning Infrastructure Ready for Autonomy
Here's where our work connects most directly to your superintelligence objectives. We've built sophisticated infrastructure for machine learning that humans currently operate - and we're actively developing the MCP layers that will enable AI systems to use these same tools autonomously:
Component Neural Design: We built a visual canvas where humans can create neural architectures through drag-and-drop components. The key insight: we're now exposing these same capabilities through MCP, so AI agents will be able to programmatically assemble these components to design custom networks for specific problems without human intervention.
Training Pipeline Infrastructure: Our systems currently enable:
- Experiment configuration and management
- Distributed training across GPU clusters
- Real-time convergence monitoring and hyperparameter adjustment
- Neural architecture search for optimal designs
- Automated model deployment to production
What we're building now: The MCP interfaces that will let AI systems operate these tools directly - designing experiments, launching training runs, and deploying models based on their own analysis.
The ASI Connection: Once our MCP layer is complete, AI will be able to design, train, and deploy its own neural networks. This creates the foundation for recursive self-improvement – the key to achieving superintelligence.
Why build vs buy? You could acquire similar capabilities from companies like Databricks, but at what cost? 100 billion? And even then, you'd get infrastructure designed for human data scientists, not AI agents. We're building specifically for the future where AI operates these systems autonomously.
MCP Dynamic Tools: Real-Time Capability Extension
Our Model Context Protocol (MCP) implementation doesn't just connect AI to existing tools – it enables AI to create entirely new capabilities on demand:
def invoke(arguments: dict) -> str:
"""AI-generated tool for custom data analysis.
This tool was created by an LLM to solve a specific problem
that didn't have an existing solution.
"""
# Tool implementation generated entirely by AI
# Validated, tested, and deployed without human intervention
When an AI agent encounters a novel data format, it writes a custom parser. When it needs to interface with an unfamiliar API, it builds the integration. When it identifies a pattern recognition problem, it designs the neural network architecture, writes the training code, executes the training run, evaluates the results, and deploys the model autonomously.
This isn't theoretical - we've built and tested this capability. Each tool creation represents an instance of AI expanding its own capability surface area without human intervention. The tools are discovered dynamically, executed with proper error handling, and become immediately available for future AI sessions.
System Prompt Composer: Precision AI Behavior Engineering
While the industry practices "prompt roulette," we've built systematic infrastructure for AI behavior design. Our System Prompt Composer (written in Rust with native bindings for Python and Node.js) provides software engineering discipline for AI personality and capability specification:
- Modular Prompt Architecture: Behaviors, domains, and tool-specific instructions are composed dynamically
- Context-Aware Generation: System prompts adapt based on available MCP tools and task complexity
- Version Control: Every prompt configuration is tracked and reproducible
- A/B Testing Infrastructure: Systematic evaluation of different behavioral patterns
This is how we're working to close the Llama-GPT gap: Our enhanced prompting system gives Llama models the contextual intelligence and tool awareness that makes GPT-4 impressive. Early tests show promising results, with significant improvements in task completion when open models are augmented with our infrastructure.
The platform enables rapid iteration on AI behavior patterns with measurable outcomes. Instead of hoping for consistent AI behavior, we engineer it. The composer automatically includes tool-specific guidance when MCP servers are detected, dramatically improving tool usage accuracy.
Execute-as-User: Enterprise Security Done Right
Unlike other MCP implementations that run tools under service accounts or with elevated privileges, LIT Platform tools execute with authentic user context. This security-first approach provides:
# In Docker deployments
subprocess.run(['gosu', username, 'python', tool_path])
# In on-premises deployments
ssh_client.execute(f'python {tool_path}', user=authenticated_user)
- True User Identity: Tools execute as the actual authenticated user, not as root or a service account
- Keycloak Enterprise Integration: Native SSO with Active Directory, LDAP, SAML, OAuth
- Natural Permission Boundaries: AI tools respect existing filesystem permissions and access controls
- Complete Audit Trails: Every AI action traceable through standard enterprise logging
- No Privilege Escalation: No sudo configurations or permission elevation required
This means when an AI creates a tool to access financial data, it can only access files the authenticated user already has permission to read. When it executes system commands, they run with the user's actual privileges. Security through identity, not through hope.
Real-World AI Workflows in Production
We've moved beyond demonstrations to production AI workflows that replace traditional business applications:
Real-World Applications We've Enabled:
- AI systems that can ingest market data and automatically create trading strategies
- Code generation systems that don't just write snippets but entire applications
- Data processing pipelines that adapt to new formats without human intervention
- Scientific computing workflows that design their own experiments
The key insight: Once AI can create tools and train models, it's no longer limited to what we explicitly programmed. It can tackle novel problems we never anticipated.
Why This Matters for Superintelligence
ASI won't emerge from training ever-larger models. It will emerge when AI systems can develop themselves without human intervention. The path forward isn't just scaling transformer architectures – it's creating AI systems that can:
Self-Extend Through Tool Creation (The Path to Developer AI)
Our MCP implementation provides the infrastructure for AI to discover what tools it needs and build them. When faced with a novel problem, AI doesn't wait for human developers – it creates the solution. This is the first concrete step toward removing humans from the development loop.
Self-Improve Through Recursive Learning (The Acceleration Phase)
Our autonomous ML capabilities let AI systems identify performance bottlenecks and engineer solutions. When AI can improve its own learning algorithms, we enter the exponential phase of intelligence growth. An AI agent can:
- Analyze its own prediction errors
- Design targeted improvements
- Generate training data
- Retrain components
- Validate improvements
- Deploy enhanced versions
- Critically: Design better versions of itself
Self-Specialize Through Domain Adaptation
Instead of general-purpose systems, AI can become expert in specific domains through focused capability development:
- Medical AI that creates diagnostic tools
- Financial AI that builds trading strategies
- Scientific AI that designs experiments
- Engineering AI that optimizes systems
Self-Collaborate Through Shared Infrastructure
Our team-based architecture enables AI agents to share capabilities and compound their effectiveness:
- Tools created by one AI available to all team AIs
- Knowledge graphs shared across sessions
- Learned patterns propagated automatically
- Collective intelligence emergence
Self-Debug Through Systematic Analysis
Our debugging infrastructure applies software engineering discipline to AI behavior:
- Comprehensive error handling with stack traces
- Tool execution monitoring
- Performance profiling
- Automatic error recovery
- Self-healing capabilities
The Opportunity Cost of Not Acting
While Meta focuses on model size, competitors are building the infrastructure for AI agents that can:
- Solve novel problems without retraining
- Adapt to new domains in real-time
- Collaborate with perfect information sharing
- Most critically: Improve themselves recursively
Every day without this infrastructure is a day your models can't build their own tools, can't improve their own capabilities, can't adapt to new challenges.
OpenAI's lead isn't just GPT-4's parameter count. It's the infrastructure that lets their models leverage tools, adapt behaviors, and solve complex multi-step problems. We're building that infrastructure as an independent layer that can make ANY model more capable – including Llama.
The ASI Race Is About Infrastructure, Not Just Models
The first organization to achieve ASI won't be the one with the biggest model – it'll be the one whose AI can improve itself without human intervention.
We've built the critical pieces:
- Tool Creation: AI that writes its own code (working today)
- Behavior Optimization: AI that improves its own prompts (working today)
- Architecture Design: AI that designs neural networks (working today)
- Recursive Improvement: AI that enhances its own capabilities (emerging now)
The gap between current AI and ASI isn't measured in parameters – it's measured in capabilities for self-improvement. We're systematically closing that gap.
About That $100M...
The platform we've built is the foundation for every superintelligence project you're funding.
While those researchers you're acquiring are designing the next generation of language models, we've built the platform that will let those models improve themselves – the critical capability for achieving ASI.
We accept payment in cryptocurrency, equity, or those really large checks Sam mentioned.
Sincerely,
The LIT Platform Team
We've built a comprehensive AI tooling ecosystem that enables dynamic tool creation, sophisticated AI behavior design, and real-time capability extension. For technical details, visit our repository or schedule a demo to see autonomous AI in action.