Sanctity.AI

If you build AI infrastructure, the bottleneck moved.
It is not chips. It is not data. It is a grey steel box with copper windings: the high-voltage transformer.
Half of planned US AI data centers for 2026 are blocked or delayed. Only 5 GW of 16 GW planned capacity is under construction. The grid queue in ERCOT is 226 GW (quadrupled in a year). Wait times for a transformer have tripled since 2019, from 24 months to 60. China XD and TBEA have order books filled through 2027.
So hyperscalers stopped waiting.
Meta is building Hyperion (7.46 GW, gas, behind-the-meter with Entergy) and Socrates. Microsoft has a 1.4 GW West Virginia LOI plus 2.5 GW in talks with Chevron in West Texas. AWS already takes 1.9 GW behind-the-meter from Susquehanna nuclear. NGI forecasts 35 GW of behind-the-meter AI capacity by 2030.
This is not a workaround. It is a model inversion.
When the grid is a queue, power is a line item. When every hyperscaler owns its own generation, power becomes the moat. Compliance follows the meter. The regulated utility relationship turns into a private industrial deal. A company that owns its silicon, its models, its fibre, and now its electrons is not subject to the same regulatory geometry as a tenant of the public grid.
Three questions worth holding before the next build.
Price power like real estate, not a utility bill.
Ask who owns the microgrid, because compliance follows the meter.
Follow the transformer, because supply chain is sovereignty.
Sources in the first comment.
If AI owns its own power, who owns AI?

The startup is Doctronic. New York-based. Founded by Dr. Adam Oskowitz and Matt Pavelle. They just closed a $40M Series B led by Abstract and Lightspeed. Total funding above $65M. Over 300,000 unique weekly visitors.
The pilot launched quietly in December 2025 under Utah’s AI regulatory sandbox, a legal framework the state legislature created in 2024 that allows specific laws to be temporarily waived while regulators monitor for harm. The Department of Commerce oversees it, not the Department of Health. That distinction matters.
The 190 medications include: blood pressure, diabetes, cholesterol, birth control, SSRIs, asthma, COPD, anxiety, migraine, muscle relaxants, and erectile dysfunction. Full formulary is public on Doctronic’s site.
What is excluded: all painkillers, all ADHD medication, all controlled substances, all injectables, and anything requiring regular lab monitoring. The AI cannot start new prescriptions or modify existing ones. Refills only.

The 250-patient threshold. Before the AI operates independently in any drug class, human physicians must review its first 250 decisions in that class. After that, it prescribes autonomously. Four months in, that threshold has likely been crossed for several drug categories.
States watching: Doctronic is in active talks with Texas, Arizona, and Missouri. Their co-founder has publicly said he expects a dozen states to approve something similar in 2026.
The federal angle: H.R.238, the Healthy Technology Act of 2025, introduced by Rep. David Schweikert (R-AZ), would classify AI as a “practitioner licensed by law” eligible to prescribe any FDA-approved drug, pending state authorization. It is sitting in the House Committee on Energy and Commerce. Utah did not wait for it to pass. They moved first.
The cost of inaction: 125,000 deaths per year in the US from medication noncompliance (PMC, Duke Health, multiple peer-reviewed sources). Nearly 200,000 in the EU (European Commission, OECD). Globally, the WHO estimates half of all chronic patients do not take their medication as prescribed.

It costs roughly $9,000 to build a humanoid robot.

They sell it for $25,000. The newest model starts at $4,900.

And production just doubled in three months.

At what price does the math change for every factory, warehouse, and logistics company in the world?