OpenAI's 'Useful Intelligence Per Dollar' Is a Metric That Belongs onchain

Let’s be clear: OpenAI’s CFO Sarah Friar just dropped a bomb that should echo through every DeFi protocol and L1 burning gas. The 'useful intelligence per dollar' scorecard is not just a corporate KPI. It is a direct challenge to how we measure value in an ecosystem built on trustless execution. The data suggests that OpenAI is trying to do for AI what Ethereum did for computation: quantify output against input in a way that forces honest accounting. But I’ve been around long enough to know that when a single entity standardizes a metric, the incentives get corrupted faster than a flash loan attack on a naive AMM. Context is everything. On July 10, 2025, Sarah Friar, during a fireside chat at a private investor summit, outlined a framework to evaluate AI investments using a cost-efficiency ratio. The numerator: 'useful intelligence' — a vague term for model capability in real-world tasks. The denominator: dollars spent on training, inference, and infrastructure. The goal is to move adoption metrics (user count, API calls) toward economic value. For crypto natives, this sounds eerily familiar to 'gas per transaction' or 'cost per block'. But where DeFi has the transparency of onchain data, OpenAI’s scorecard remains a black box. That’s where the deep analysis must start. Core technical insight: the 'useful intelligence per dollar' metric is fundamentally a protocol design problem. In my years auditing Solidity contracts, I learned that any efficiency metric can be gamed if the numerator and denominator are not tightly defined. For OpenAI, ‘useful intelligence’ is undefined. Is it a composite benchmark score? User satisfaction? Task completion rate? Based on my experience reverse-engineering the Terra/Luna oracle manipulation vectors, I can tell you that fuzzy numerators are the first sign of systemic risk. The denominator is equally suspect: does it include the cost of human alignment, red-teaming, or downtime? If not, the metric becomes like a DEX that only counts swap fees while ignoring impermanent loss. Let’s dissect this from a blockchain engineering perspective. In protocol development, we optimize for 'transactions per dollar' or 'proof generation per dollar'. The closest analogue is the concept of 'gas efficiency' in EVM. I once spent 40 hours auditing a Crowdfund.sol template and found a stack underflow that could drain funds if the balance exceeded 2^256-1 wei. That bug existed because the developers focused on raw feature output (raising funds) without considering the cost of edge cases. Similarly, OpenAI’s metric could incentivize squeezing out safety checks to boost the ratio. Imagine a smart contract that skips boundary validation to save gas — that’s the same mentality. The code does not lie, but it often forgets to breathe. An efficiency metric that ignores failure modes is not a robustness signal; it’s a fragility mask. During DeFi Summer 2020, I audited a liquidity mining contract and found a reentrancy vulnerability in the reward distribution. The team had optimized for high APY to attract liquidity, but the hidden cost was infinite token minting. The parallel is stark: OpenAI wants to show high 'useful intelligence per dollar' to attract enterprise clients, but the hidden cost could be catastrophic model failures or safety bypasses. The NFT minting gas wars of 2021 taught me that naive metrics (like 'cheapest mint') lead to congestion and user exploitation. 'Useful intelligence per dollar' without a formal specification of 'useful' will produce similar market failures. Gas wars are just ego masquerading as utility — and this scorecard could become the same for AI. Contrarian angle: the blind spot of this metric is its assumption that intelligence is a linear, homogeneous good. In crypto, we know that liquidity pools have different risk profiles. A stablecoin pool and a meme coin pool cannot be compared by APR alone. Similarly, an AI used for nuclear fusion research and an AI used for customer service chatbots have fundamentally different 'usefulness'. Equating them under one metric is like comparing Bitcoin’s security budget to a shitcoin’s marketing budget. Worse, the metric ignores the cost of decentralization — or the lack thereof. OpenAI’s inference runs on centralized servers. In DeFi, we accept higher gas costs for the security of decentralization. The 'useful intelligence per dollar' metric would rate a centralized AI higher than a decentralized one, even if the latter offers censorship resistance. That’s the same fallacy as Chainlink claiming decentralization while running only 21 nodes. Oracle feed latency is DeFi’s Achilles’ heel; this AI metric is OpenAI’s version of the same flaw. Moreover, the metric threatens to kill long-tail innovation. If every AI startup must prove its 'useful intelligence per dollar' to get funding, they will optimize for safe, common tasks rather than risky breakthroughs. This mirrors the DAO grant problem: most committees fund safe projects, while Optimism’s RetroPGF actually rewards real public goods. Without a retroactive mechanism or an onchain verification layer, OpenAI’s scorecard will become a tool for narrative control, not value discovery. After the fourth halving, Bitcoin miner revenue collapsed; hash power now concentrates in three pools. The same centralization pressure will hit AI when only a few can afford to show positive scores. The takeaway is sobering. OpenAI is trying to define the unit of intelligence the way we define the unit of gas. But gas has a fixed cost on Ethereum because of EIP-1559 and transparent block data. Intelligence is subjective and context-dependent. The only way this metric could gain real trust is if it were implemented as an onchain oracle, with verifiable inputs and public audits. Until then, it’s a marketing scorecard, not a technical one. Code does not lie, but it often forgets to breathe — and this metric might forget to account for the cost of trust, safety, and decentralization. The question every crypto developer should ask: can we build a better, onchain version of 'useful intelligence per dollar' that captures the true cost of autonomous systems? If not, we will watch the same centralization patterns repeat, this time in the AI stack.