Sourced issue — exhibit data from the June 10, 2026 client workbook (Morgan Stanley Research, Byrd); each exhibit cites its tab
WAGGS TOKEN RATE OBSERVER
Interest rates priced money. Token rates price minds.
Vol. I · No. 003Tuesday, June 23, 2026Byline: Waggs
Token rate (frontier, per M): $1.10
US power gap thru ’28: 37.7 GW
Value of a watt, 3 yrs early: $13.33
Shortfall TAM, ’26–’30: $1.62T

Can the grid deliver the watts the chips have already ordered?

No — not by itself, and not in time. The chips NVIDIA has already scheduled imply 67.6 GW of new US data-center demand by 2028 against ~30 GW of construction and grid headroom. Every fix on the table — gas turbines, fuel cells, nuclear siting, bitcoin-mine conversions — still leaves America roughly 11 GW short. The shortage is the investment thesis.
9 min read · 4 exhibits · 3 signals

The most useful spreadsheet I have read this year does not forecast a single token of demand. It counts boxes. Take the semiconductor team's shipment schedule — how many B100s, Rubins, TPUs, and Trainiums leave the loading dock each year — divide by eight to get servers, and multiply by the nameplate draw of the actual Dell chassis they ship in: 8,419 watts for an 8×H100 box, 11,787 for Blackwell, 14,733 for Rubin, 23,021 for Feynman. Add 10% for storage, gross up for cooling. No narrative anywhere in the chain — just physics times a purchase order. The output: the global AI fleet needs ~109 cumulative gigawatts by 2028, consuming more than 800 TWh a year, with roughly 60% of it trying to plug in inside the United States.

Here is the issue's framework, and it fits on a napkin: the Watts Ledger. Demand is a purchase order; supply is a construction schedule; the difference is a price. Chips arrive in 12 months. Transmission arrives in 5 to 7 years. When one side of a ledger moves six times faster than the other, the gap doesn't close — it gets priced.

Exhibit 1
The purchase order, converted to gigawatts
Cumulative AI data-center power demand quintuples in four years, and the US line assumes it captures 60% of everything. Every bar is already on a shipment schedule.
Workbook tabs: "AI Power_Global" (cumulative GW, server-level build), "AI Power_US" (60% US allocation). Server wattage from Dell PowerEdge nameplate specs.

Now the American ledger. Demand 2026–28: 67.6 GW. Subtract what's actually being built — 19.8 GW under construction, haircut to 14.85 at a 75% completion rate — and 15 GW of available grid interconnection. The hole: 37.7 GW before heroics. Stretch the window to 2030 and the hole grows to 121.7 GW. The analyst then prices the heroics, each with a probability attached — and this is where the issue gets interesting, because the largest single rescue on the list is not a turbine. It is the bitcoin-mining industry's real estate.

Exhibit 2 · click through
The shortfall ledger — flick between the rescue scenarios
Even with every fix at its midpoint, the US barely reaches zero — and the analyst's own note says the outcome "skews towards the low end," i.e. ~11 GW still missing. Bitcoin conversions are the biggest single solution, larger than gas turbines.
Workbook tab: "US Shortfall Analysis" — solutions at low/mid/high GW with probabilities (gas 90%, fuel cells 90%, nuclear siting 75%, crypto conversions 90%).

Why does a missing gigawatt command such a price? Because the thing waiting for power is not a warehouse — it is a depreciating bar of silicon. A Blackwell server burns about $10,000 of electricity a year and $40,000 of depreciation. Idle time costs four times more than energy. The workbook runs the trade explicitly: save 30% on power forever, but wait 24 extra months to energize, and you destroy three dollars of GPU depreciation for every dollar of power saved. A rational developer will therefore pay nearly double the price of electricity to get it years sooner — and the value of jumping a three-year queue computes to $13.33 per watt, against roughly $3.50 per watt for what a bitcoin mine is worth as a bitcoin mine.

Exhibit 3
What a year of queue-jumping is worth, per watt
The curve is linear and brutal: ~$4.44 per watt per year saved. "Time to power" is not a slogan; it is a yield curve for electrons.
Workbook tabs: "Indicative Crypto Math" (value of time saved, $/W vs years), "Time Value" (depreciation 4× power; 93% power-price premium willingly paid; DC Example Math B100).

Multiply the 2026–30 hole by the value of filling it fast and you get the number that should be on the cover of the report: 121.7 GW × $13.33/W ≈ $1.62 trillion of value waiting for whoever can deliver energized capacity early. That is the prize pool that has gas-turbine order books sold out, fuel-cell makers re-rating, nuclear owners hosting hyperscalers behind the fence, and — the subject of No. 005 — bitcoin miners trading like infrastructure REITs. America has quietly decided the marginal electron belongs to the machine that talks; the only argument left is who collects the toll.

Exhibit 4
The ledger, totalled
2026–20282026–2030
Power needed67.6 GW151.5 GW
Under construction−14.85 GW (19.8 GW × 75% completion)−14.85 GW
Grid headroom−15 GW−15 GW
Raw shortfall37.7 GW121.7 GW
Rescues, prob.-weighted27–46 GW (mid 36.5)not yet visible at scale
Net position−10.7 GW low case; −1.2 mid−121.7 GW × $13.33/W = $1.62T
The 2026–28 window is a knife fight over ~38 GW. The 2026–30 window is a $1.6 trillion repricing of who owns time.
Workbook tabs: "Power Summary" (US ledger, value-creation block), "US Shortfall Analysis."
The other side — steelmanned

The shortfall is a forecast of a forecast: it inherits every assumption in the chip schedule. If NVIDIA's 2027–28 volumes slip even 20%, the gap halves. If chip designers pivot hard to efficiency — the report's own Risk #2 — watts per token fall faster than tokens grow. The 75% utilization assumption flatters demand; real fleets idle. The 15 GW grid-headroom figure is deliberately conservative, and ERCOT alone has surprised to the upside before. And the rescue list is not exhaustive: behind-the-meter gas, imports from Canada, and demand response don't appear at all.

If several of those break friendly, the "shortage" resolves into a glut of contracted capacity coming online into decelerating demand — at which point $15/W conversions and $13/W queue-jumping premiums mean-revert violently toward construction cost. Scarcity pricing is the most perishable pricing there is. That is precisely what a holder of miner equities at +400% implied upside needs to keep in the front pocket.

Signals to watch
Construction vs. the curve

JLL counts 5.55 GW under construction and 12.25 GW planned. Demand needs ~16–29 GW/yr of increments. The pipeline must roughly double, every year.

Trigger: UC pipeline growth < 6 GW/yr for two quarters
Chip-order deferrals

Risk #1 in the workbook: data centers can't absorb the chips. The tell is upstream — optical and hardware suppliers (COHR, LITE, DELL, STX) flagging push-outs.

Trigger: any major GPU order deferral confirmed
Turbine lead times

Gas turbines are rescue #1 (15–20 GW at 90%). If aeroderivative lead times stretch past three years, the low case becomes the base case.

Trigger: quoted lead times > 36 months
The coda

Nobody in this story wants electricity. The hyperscaler wants to never be the one who ran out of compute while a rival trained the next model; the miner wants, after a decade of being despised for burning power on nothing, to be told his megawatts were the point all along; the utility wants to matter again. The desire under the data is the oldest one — to not be left behind — and it is mimetic all the way down: each buyer's appetite is set by watching the other buyers, not by watching their own customers.

Which is why the ledger will overshoot. Gaps priced by rivalry always do. The grid is the one counterparty in the trade that does not feel envy, and it will deliver its watts on the schedule of concrete and copper, indifferent to what the chips cost whoever is waiting.

Waggs