The election result may speed up, not slow down, Big Tech’s AI spending, according to Rich Privorotsky, Goldman Sachs’s European head of equity trading, in a note published on Wednesday, as summarized by BigGo Finance. After the vote, he argued, corporate decision-makers “may instead pull capital deployment forward into 2027, rushing to take advantage of current approval conditions and complete strategic deals before planning policies change.”
The view lands on a map Goldman drew the same day: states likely to tighten data center rules hold about 45% of planned US capacity, and Texas alone about a quarter, investingLive reported. By our arithmetic, Texas is about 56% of the capacity at risk.
The key insight: A Goldman Sachs trading-desk note says the midterm result may lead tech giants to pull AI capital spending forward into 2027, before planning rules change. Goldman puts about 45% of planned US data center capacity in states likely to tighten rules; by our arithmetic Texas is about 56% of that.
The @pequityresearch account posted the view at 18:00 UTC on 5 November, tagging Alphabet, Amazon, Oracle, Meta and Microsoft, with a screenshot of a text summarizing the note.
Timing: companies may pull capital deployment into 2027 to use current approval conditions before planning policies change, per BigGo’s summary of the note.
The 2029 horizon: he noted that by 2029 the incoming president may push tax changes while tightening administrative oversight and merger reviews, “a backdrop under which hyperscalers may choose to further accelerate the pace of buildout,” BigGo reported.
Existing compute: if future expansion is restricted, the value of compute already in operation rises, which in theory benefits Amazon, Microsoft and Alphabet; for Nebius and CoreWeave the case “will depend heavily on whether their compute resources are locked into long-term contracts,” per the summary.
Positioning: AI names have been “sold to oversold levels,” he wrote, adding that “fundamentals remain strong, election uncertainty has cleared, and positioning is much cleaner,” as quoted by BigGo.
Risks he named: some projects may be delayed or shelved, which could weigh on the chip, cooling, power-equipment and energy-services trade, though he said those sectors had already corrected.
Planned US data center capacity in states Goldman Sachs expects to tighten rules after the 3 November 2026 elections, as reported by investingLive: about 45% in all, with Texas about a quarter. The split is approximate; the 20% and 56% figures are our arithmetic.
Goldman’s economists expect Democrats in Texas, Georgia and Michigan to impose short-term pauses on new data center projects while lawmakers weigh rules on electricity use, water and tax incentives, according to investingLive’s account of the note. Goldman does not expect the curbs to last, reasoning that data centers are not a top voter priority, the outlet said. In that framing the risk is one of timing, and Privorotsky’s point is that timing can cut both ways.
A short explainer on grid cost allocation: who pays when a large new load such as a data center joins the grid. It teaches the general idea only; the figures in this piece come from the sources named in the text.
As we read the note, the policy risk becomes a deadline: the clearer the rules ahead, the stronger the incentive to secure approvals under today’s.
As we read the compute point, capacity already running gains value when new capacity is harder to approve, which is the note’s case for the largest clouds.
As we read the neocloud caveat, the note ties their outcome to contract terms, not to demand.
Rich Privorotsky, Goldman Sachs European head of equity trading, note of 4 November 2026 as quoted by BigGo Finance
“(AI-related names’) fundamentals remain strong, election uncertainty has cleared, and positioning is much cleaner.”
Three Implications
FOR HYPERSCALERS Privorotsky says decision-makers may move capital deployment into 2027 to use current approval conditions.
FOR NEOCLOUDS The note ties the case for Nebius and CoreWeave to whether their compute is locked into long-term contracts.
FOR SUPPLIERS He says some projects may be delayed or shelved, which could weigh on chips, cooling and power equipment, sectors he says have already corrected.
The framework says: “The structural bottleneck isnโt computing, itโs electrons.”
As we read it, the election added a permit layer on top of the power one: Goldman’s map of at-risk capacity is a map of where approvals may slow. The framework itself does not discuss this election.
What Is Not Established
We have not read the Goldman note itself; its contents reach us through BigGo Finance’s and investingLive’s summaries and a screenshot shared on X. The note is a trading-desk view and does not report any company’s plans. None of the five companies tagged in the X post has said it will move spending into 2027. The 45% and 25% shares are approximate, as reported.
This Week In The AI Supercycle
Anthropic’s Reality Check, The Capital Squeeze, & The New World Order
This week’s issue: for two years the AI build-out was priced as a growthstory. This week it started being priced as a credit story.
Goldman’s Rich Privorotsky argues the midterm result may push tech giants to pull AI capex forward into 2027 to use current approval conditions, while Goldman’s economists see about 45% of planned US capacity in states likely to tighten data center rules, with Texas alone about a quarter of planned US capacity.
A note on sourcing. The Goldman Sachs note was published on 4 November 2026; we have not read it directly. Its contents are as summarized by BigGo Finance (Rich Privorotsky’s view) and investingLive (the 45% capacity estimate), and as shown in a screenshot shared by @pequityresearch on X. The 20% and 56% figures are our arithmetic.
Gennaro Cuofano is a CRO and tech executive who has worked in AI since late 2015, starting with bringing NLP, natural-language generation, voice and chatbot products to the marketing industry. His background is in law and finance: he holds a Master's degree in Law and an International MBA with an emphasis on corporate finance (LUISS Business School and the University of San Diego, 2012), and worked as a financial analyst at a real-estate investment firm in San Diego and as an assistant controller. His work focuses on business model strategy, business engineering and, more broadly, structural analysis: how companies make money, read from their own filings. He created FourWeekMBA and leads research there and at The Business Engineer, his newsletter on AI and business strategy, with over 95,000 subscribers and more than 1,000 published analyses. His writing has also appeared on Entrepreneur (for example, How to Digitize a Family Business in Europe) and HackerNoon. Find him on LinkedIn and Substack. See how we source.
