Moneyball for Physical AI - by Animesh Garg
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Moneyball for Physical AI<br>A Scaling Law Perspective for Marginal Utility per Dollar
Animesh Garg<br>Jun 25, 2026
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In 2002, the Oakland Athletics won 103 games despite maintaining the third-lowest payroll in Major League Baseball. This advantage emerged because the market for player assets was mispriced: legacy scouts favored subjective aesthetics, stolen bases, and batting averages, whereas forward-looking management mathematically isolated on-base percentage, the statistic that actually correlated with runs.<br>Finding the signal with the correct statistic in a field full of intuitive pundits: Moneyball !<br>Data for Physical AI is misunderstood, and mis-priced.
Data doesn’t exist for Physical AI. Data has a inherent cost of creation. We need to move beyond from naive scaling data in hours or tokens.
Being scale-pilled often amounts to “believe in data”. However, unlike text, robot data isn't available to be mined. Every useful hour is paid for, so collection scales linearly while costs don't fall. Recently, Ken Goldberg estimated that frontier robotics models might require approximately 100,000 years.<br>AGI revolution will not be supervised with Sweatshop Teleop.
To bypass this bottleneck, the industry has scaled manual teleoperation infrastructure. However, optimizing for cumulative operational hours replicates the “batting average” fallacy of early baseball: it prioritizes a visible, easily fundable metric that correlates weakly with actual downstream model performance. An alternative strategy proposes deploying robots into production to harvest telemetry as a zero-cost byproduct of operational revenue. This model introduces a subtler version of the same statistical error. The niches where deployment is possible today are the ones with least variance and yield low-entropy, correlated data streams with minimal marginal utility.<br>This essay builds a framework for the marginal utility of data, and uses it to discuss value accrual in Physical AI. We take the perspective of the scaling laws that guide how loss behaves with data, and the unit economics that govern what a dollar of data is worth. Together they give an approximate marginal utility per dollar, the on-base percentage of physical AI.<br>Capital efficiency scales not by maximizing data volume, but by accurately computing and pricing data novelty.<br>If you’d rather skip to conclusions, jump to recommendations.
1. Stakeholder Biases in the Data Supply Chain
Varied stakeholders have differing views on data. Conveniently, each worldview happens to make their slice the most valuable.<br>Foundation-model labs sell generalized model scale, as a result overweight the role of large-scale pretraining, operating under the assumption that raw compute scaling will eventually eliminate edge-case errors. Teleoperation vendors are infrastructural utility that prioritize and monetize raw operational hours, since their revenue scales with data volume rather than utility or novelty. Hardware incumbents operate on the assumption of environmental stationarity, since their solution fails out-of-distribution. And large camp of academic roboticists denies it is a data problem at all and expects physics, models, and control to close the gap without the deluge.<br>The key archetype to analyze is the neo-integrator. This model attempts to bypass data-collection bottlenecks by deploying specialized robotic units into commercial production, utilizing human-in-the-loop oversight to manage operational failures. The core thesis relies on an unproven economic flywheel: production telemetry will generate the novelty required to train multi-task capabilities. Evan Beard of Standard Bots makes the case at length. Kyle Vedder pushes back on deployment first, arguing that the environments willing to pay for early-stage deployment are naturally low-variance, creating a "novelty pump" constraint.<br>We analyze this debate through a neutral framework combining empirical scaling laws and the unit economics of data capture , isolating exactly which allocation strategy yields the highest model capability per dollar.
2. Taxonomy of Robot Data
Data operations in physical AI map across three modalities, each defined by trade-offs between cost and information density:<br>Observational Data: Low-cost, high-breadth, action-deficient corpora (e.g., egocentric and exocentric video). This modality expands support of the representation, but lacks direct action supervision.
Interventional Data: High-cost, low-breadth, action-dense demonstrations (e.g., teleoperation). This modality maps explicit state-action trajectories but scales linearly with human labor.
Deployment Data: Endogenous telemetry generated by production systems, often running at a loss. This modality is un-curated and samples an environmental distribution dictated by commercial operations rather than algorithmic design.
Data maximization often introduces...