The coming torrent of AI safety funding, driven by the Anthropic windfall and the sudden liquidation of Silicon Valley’s frontier wealth, is hitting a Trust Wall.
The problem is not a lack of money; it is that we do not have enough ways to spend it without being gamed. Right now, we manage philanthropy through soft trust. We rely on reputation, discretionary grants, and retrospective reports. In economic terms, it is a classic principal-agent problem. Donors (the principals) want to buy existential security, but researchers (the agents) have every incentive to safety-wash their work to capture capital.
When the stakes are billions of dollars, we should not be surprised when the vultures show up. We should not try to fix this by hiring more grant officers to vet vibes. We should fix it by building a fiscal kill-switch.
Trust is the Only Scarce Input
We are currently funding AI safety like a 19th-century charity when we should be treating it like 21st-century critical infrastructure. We need a Physical Oracle, a way to confirm a lab is actually doing the safety work they promised without having to take a CEO’s word for it. In an ecosystem of abundance, trust is the only input that cannot be easily scaled.
The Solution: Deterministic Funding Gates
To handle this windfall, we must move from discretionary to verification-based funding. I propose a system that anchors capital to physical and digital ground truth through three layers of technical enforcement:
1. Eliminating Shadow Compute via Universal IDs
Funding should stay in escrow and only unlock when a lab provides a Zero-Knowledge Proof (ZKP) of their compute telemetry. To ensure labs are not using dark clusters for risky capabilities drift, we require a Universal Compute ID (UCID). By linking physical GPUs to the digital audit via hardware-level sensors and power-draw verification, we can prove the lab’s entire footprint matches their safety claims. If the hardware-linked Proof of Life fails, the money stays in the vault.
2. Auditing via Policy-as-Code
You cannot audit a superhuman system if it knows it is being watched. The audit needs to happen in an Isolated Auditor, which is a hardware-backed Trusted Execution Environment (TEE). This environment does not rely on vibe checks; it runs Policy-as-Code. These are mathematically defined redlines, such as unauthorized API access or self-replication attempts, that monitor the model in real-time. Because this happens in a TEE, the lab’s proprietary intellectual property remains private, removing the primary institutional barrier to transparency.
3. The Millisecond Veto
We need to move the veto power away from slow-moving boards. If the hardware sensors detect a UCID mismatch or the TEE auditor detects a redline violation, the funding should halt in milliseconds via a decentralized network. This creates a fail-secure system where the money locks the moment the safety gate is breached. It turns the race for charisma into a race for technical compliance.
Bridging the Feasibility Gap
While the hardware for TEEs and ZKPs is currently in its early stages of scaling, the bottleneck is not physics but policy. We can bridge this gap by offering Safe Harbor incentives. Labs that opt into these deterministic gates should receive faster access to larger tranches of capital and lower-cost safety insurance. By making hardware-level transparency a competitive advantage, we align the profit motives of frontier labs with planetary safety
Hey! Welcome to the forum and thanks for writing this.
I think this post conflates two different problems. AI safety philanthropic funding (Open Phil, SFF, regranting programs) doesn't generally flow to frontier labs as discretionary capital; it mostly funds specific projects at academic groups, nonprofits, and small research teams, with defined deliverables (when trust is low) or general funding for a charitable purpose (when trust is high). Frontier-lab safety work is funded by the labs themselves out of revenue. So the principal-agent framing doesn't really describe the funding landscape we have, and a philanthropic gate wouldn't touch the actual capital flows we'd care about.
Compute verification (ZKPs of training, hardware-linked attestation, on-chip governance) is extremely promising, but mostly a lever for AI policy, aimed at government regulation of frontier development, not as a grant verification/compliance mechanism. Just in case you're not familiar, I'd recommend looking at FlexHEG and related work on compute verification and AI assurance.