RESEARCH — June 2026
The Scarcity Premium
Constrained supply boosts earnings expectations on lower volumes, U.S. & LatAm lead
By Daniel J. Sandberg and Drew Bowers
Since Jan. 1, sell-side analysts have lowered observable energy production expectations by 1.9% (1.5 million boe/d1) while raising energy earnings forecasts by 57% ($227 billion). This divergence signals a market revaluation consistent with a scarcity premium: higher profits despite lower expected output. AI adds a related transmission channel. As AI-linked growth increases demand for energy and other constrained inputs, scarcity in one part of the system can manifest as cost pressure elsewhere. At the same time, broader markets have been comparatively stable over the first five months of 2026.
Key findings:
- Observable supply is tightening. Since Jan. 1, sell-side analysts have revised observable energy production expectations for FY26 down from 77 to 75.5 million boe/d.
- Scarcity producers collect a premium. Despite lower expected production volumes, energy producer margin expectations rose 247 bps (from 9.8% to 12.3%) on earnings expectations increases of $227 billion (57%). The largest margin revisions are concentrated in the U.S., LatAm, and Nordic regions.
- Sell side prices a fast resolution. Margin expansion expectations are heavily front-loaded relative to 2022, suggesting expectations for current geopolitical disruptions to fade faster than the Russia-Ukraine shock.
- Broader markets sanguine. Outside of energy and AI, revisions to revenue, earnings and margins since Jan. 1 remain modest relative to the magnitude of the geopolitical backdrop.
Explore the data used to conduct this research:
Visible Alpha Estimates and S&P Capital IQ Estimates
The S&P Capital IQ and Visible Estimates datasets combine the forecasts, assumptions, and logic from full working sell-side models with an extensive global contributor network and deep history to provide the most comprehensive view of market expectations. Through the breadth of S&P Capital IQ Estimates and the granularity of Visible Alpha Estimates, the data provides both scale and precision, enabling better forecasting, benchmarking, and decision-making.
The ProntoNLP dataset processes earnings call transcripts to extract vital insights and generate key performance indicators for corporate performance. It utilizes natural language processing (NLP) and an optimized Large Language Model to analyze textual data, identifying and scoring important phrases while filtering out irrelevant information. The dataset includes tagged paragraphs from earnings calls worldwide