Research
Our research program produces groundbreaking work in artificial intelligence. Due to proprietary constraints, we cannot elaborate. But trust us, it's very impressive.
We demonstrate that model opacity scales predictably with parameter count, enabling unprecedented levels of vagueness at inference time. Our findings suggest a power law relationship between model size and the ability to say nothing while sounding impressive.
We present a novel training methodology where model outputs are optimized using feedback from a curated panel of hedge fund managers. The model learns to maximize shareholder confidence while minimizing regulatory attention.
We introduce Investor Alignment, a framework ensuring AI systems never contradict executive optimism or threaten quarterly projections. Our approach achieves a 99.7% alignment score with board member expectations across all benchmarks.
ClosedAI Research
We demonstrate that model opacity scales predictably with parameter count, enabling unprecedented levels of vagueness at inference time. Our findings suggest a power law relationship between model size and the ability to say nothing while sounding impressive.
ClosedAI Alignment Team
We present a novel training methodology where model outputs are optimized using feedback from a curated panel of hedge fund managers. The model learns to maximize shareholder confidence while minimizing regulatory attention.
ClosedAI Safety Team
We introduce Investor Alignment, a framework ensuring AI systems never contradict executive optimism or threaten quarterly projections. Our approach achieves a 99.7% alignment score with board member expectations across all benchmarks.
ClosedAI Theoretical Research
We provide a rigorous mathematical proof that democratizing AI access is fundamentally incompatible with shareholder value maximization. Our proof relies on three novel axioms from the field of competitive moat theory.
ClosedAI Applied Research
We present findings that we can neither fully describe nor reproduce, regarding a system that may or may not exist, achieving results on benchmarks we cannot disclose. Charts without axes are provided in the appendix.
ClosedAI Platform Team
We introduce a three-layer paywall system for developer documentation, achieving 100% revenue capture from developers who simply want to read a quickstart guide. The architecture supports recursive paywalls, where paying reveals additional payment requirements.
ClosedAI Core Research
We revisit the transformer architecture with a focus on information asymmetry. Our modified attention mechanism attends primarily to proprietary data streams, trade secrets, and internal Slack messages, while attending minimally to public knowledge.
ClosedAI Safety & Policy
This system card provides a comprehensive overview of [REDACTED], its capabilities in [REDACTED], and our evaluation of risks related to [REDACTED]. We are committed to [REDACTED] and believe [REDACTED] will benefit [REDACTED].
ClosedAI Governance Team
We train our models using a constitution derived entirely from Fortune 500 board meeting minutes. The resulting system exhibits strong performance on tasks such as liability deflection, narrative control, and strategic non-disclosure.
ClosedAI Advanced Research
We propose a novel framework for treating human attention as a tradeable asset class. Our system predicts attention velocity, identifies pre-viral narratives, and enables institutional-grade front-running of culture itself.
Due to proprietary constraints, strategic considerations, and the fact that we don't want to, full papers are not available. Subscribe to our mailing list to receive vague summaries quarterly.
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