LinkedIn Post Draft Score: 83/100

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The biggest growth story in the US economy isn't in the GDP report. It's a measurement gap large enough to reprice how you allocate capital. A new Peterson Institute brief by Anton Korinek and Patrick McKelvey put hard numbers on it. Quality-adjusted AI production in the United States grew over 2,000 percent per year in 2024 and 2025. Preliminary estimates put nominal AI GDP at about 250 billion dollars in 2025. In quality-adjusted real terms, the sector grew roughly 2,600 percent per year. None of that lands cleanly in conventional GDP statistics. National accounts were built for an economy where a product's quality changed across years. AI is changing price and capability inside the same quarter. The data infrastructure cannot keep up. The authors trace the gap to three compounding forces. 1) Data-center capacity is expanding faster than it ever has. 2) Hardware efficiency keeps improving on a Moore's Law cadence. 3) Algorithmic progress is the largest of the three, and the one official statistics see the least. The brief proposes an AI GDP framework. Satellite accounts that statistics agencies can stand up before the measurement gap becomes a policy gap. For executives, the implication is operational. If your strategic plan is anchored to the indicators in your monthly board pack, you are reading a gauge calibrated for a different economy. The companies building private dashboards on the inputs (compute spend, capability per dollar, productivity per workflow) will see the curve a year before the headline statistics catch up. That timing advantage compounds. The leaders who gain the most from AI may not be the ones with the best models. They may be the ones with the best measurements. What indicators are you tracking today that tell you more than the headline economic data? #AI, #GDP, #EconomicMeasurement, #Strategy Source: Peterson Institute Policy Brief 26-7 (May 2026).

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main points: 8/10

post length: 7/10

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engagement potential: 8/10