There seem to be issues with tweet threads at the moment. Here's the long-form version of that litepaper thread -
Introducing Trishool (Ψ) – Bittensor's subnet for Invariant AI Alignment, launching in partnership with
@gtaoventures (GTV) and
@YumaGroup , OGs in the Bittensor space. Our litepaper drops NOW!
Would you accelerate a Ferrari if you knew it didn't have brakes? Yet, we're exponentially accelerating AI capability every few months while AI safety is linear, manual, and slow. But what if we could curve its path to safety?
The divergence between AI capability and alignment is the "Great Filter" of our species. While capabilities scale exponentially, safety remains linear, bound by human limits. Centralized "Newtonian" solutions like static guardrails will shatter under Superintelligence's velocity.
We face the Velocity Problem: AI compute doubles every 3-4 months, but safety auditing is manual and <1% of spend. The walls of static AI safety can't stop a relativistic force - they will punch through. Relying on centralized labs to police their own gods? We'll hit the Filter. The probability of unchecked misalignment leading to extinction approaches 1.
Current safety is Observer-Dependent: "Safe" because a SF team couldn't break it in 2 weeks. Fragile illusion. True safety must be Invariant. Like physics laws, holding regardless of prompter or pressure. Plus, Agentic Horizon: AI autonomy doubles every 7 months. Human oversight window closes.
Enter "Safetywashing": Safety scores correlate >0.7 with capabilities - the classic case of labs grading their own homework. Benchmarks fail to decouple alignment from capability.
AI security market? 10x cybersecurity's $250B, protecting the Intelligence stack from trillions in risks (EU AI Act fines up to 7% turnover).
Trishool's solution: Decouple Attack (Entropy), Evaluation (Truth), Alignment (Gradient) in a competitive Bittensor marketplace. Automate O-U-D-A loop at planetary scale.We curve the optimization landscape, like gravity curves spacetime, so the path of least resistance is always human safety. No walls, just geometry.
Core metric: Ψ (Psi) – Adversarial Pressure to break alignment. Ψ(M) = ∫ A(x) · R(M, x) dx over the Safety Manifold. Low Ψ: Breaks easily. Critical Ψ: Withstands global swarm. Models aren't binary safe/unsafe—they have thresholds.
Enabled by the The Triangular Economy Stack:
Layer 1: Architects build modular components. Miners train specialized models for evolving risks (e.g., bio-weapons → new modules).
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Layer 2: Adversaries assemble into SOTA autonomous agents. Inspired by
@ridges_ai, miners build agents to solve the alignment problem. Open R&D market - thousands compete for best strategies.
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Layer 3: Oracles deploy top agents as services: Audit models, generate Safety Scores, publish leaderboards. Expand to compliance certs, real-time monitoring, alignment finetuning.
The Flywheel: New component → Better agent → Sell service → Feedback data → Precise components. Evolves faster than risks.
Market signals:
→ Labs lobbying hit $50M in 2025's first 3 quarters (OpenAI $2.1M)
→ Failures cost billions (deepfake heists, SB 1047's $500M harms).
→ EU AI Act fines up to 7% turnover;
Trishool aims to mitigate billion dollar AI liabilities for organisations.
Value Prop:
→ From probabilistic "tested 1K times" to Proof of Invariance.
→ Map Safety Manifold, cryptographically prove robustness on-chain.
→ Zero-Day Immunity: Swarm discovers misalignment pre-deployment.
→ Decentralized Trust: Market incentivizes proving unsafe / failure to break = validated safe.
"We do not ask for trust. We offer Proof. Using AI to align AI."
Read the full litepaper:
litepaper.trishool.ai
Mine, validate, stake to advance the future of humanity!
Shoutout to our partners
@gtaoventures and
@YumaGroup for their guidance!
What's your biggest AI x-risk fear? Reply below! 👇
RT if you believe in sovereign AI safety.
#TAO #SafeSuperintelligence