chriscoey · Oct 28, 2026
@@ -120,10 +120,10 @@ Texas is now the fastest-growing market for AI data center development. Hypersca
 
    ```text
    STAGE 5: OPTIMIZE -- Joint Interconnection + Upgrade
-     Status: OPTIMAL | Objective: 1,579,200,000
+     Status: OPTIMAL
+     Objective: 1,562,600,000.00
 
-     KNEE POINT: $300M -- 5 DCs, 1,500 MW, $264M net value
-     xAI Colossus ($105M/yr) unlocks at $300M -- highest-revenue single request
+     KNEE POINT: $300M -- $300M budget, $252,450,000 net value, 6 DCs, 1,530 MW
 
    PIPELINE COMPLETE: 5 stages executed on shared Energy Grid ontology
    ```
@@ -225,7 +225,7 @@ Three declarative rules consume upstream outputs, then a composite flag combines
 
 An `InvestmentLevel` Scenario Concept holds 5 budget levels ($200M-$600M), each parameterizing one MIP solve into a Pareto frontier. Binary variables `x_approve` (per DC request) and `x_upgrade` (per substation upgrade) are indexed by investment level; substation-capacity constraints use `predicted_load` from Stage 1 so the optimizer sees the same forecasted headroom as the rules engine, and budget constraints are scoped `.per(InvestmentLevel)`. Revenue reflects annual interconnection capacity revenue ($145K-$210K per MW/yr), not energy revenue.
 
-One solve produces the entire frontier. The knee point at $300M (5 DCs, 1,500 MW, $264M net value) shows the highest marginal return, unlocking xAI Colossus ($105M/yr). At $200M, 4 DCs are approved (Microsoft, CoreWeave, Crusoe, Oracle); each additional $100M adds one more DC with diminishing returns ($995K/$M at the knee vs $400K/$M at $600M). Google Metroplex DC (400 MW) and Lambda Labs DFW (200 MW) are never approved at any budget — DFW capacity is fully consumed. Per-level results (approvals, upgrades, MW) are all queried from the ontology via `model.select()` rather than parsed from solver output.
+One solve produces the entire frontier. The knee point at $300M (6 DCs, 1,530 MW, $252M net value) shows the highest marginal return: both DFW upgrades fit the budget, and Google Metroplex DC and Lambda Labs DFW join. At $200M, 4 DCs are approved (Microsoft, Amazon SA, Crusoe, Oracle); beyond the knee each additional $100M adds one more DC with diminishing returns ($1,013K/$M into the knee vs $439K/$M from $400M to $500M). xAI Colossus (500 MW) is never approved at any budget — DFW headroom goes to Google and Lambda. Per-level results (approvals, upgrades, MW) are all queried from the ontology via `model.select()` rather than parsed from solver output.
 
 See `energy_grid_planning.py` for the implementation of all five stages and `runbook.md` for the skill-driven reproduction.
 
@@ -288,11 +288,11 @@ Focus on the first changes most users will make.
 </details>
 
 <details>
-<summary>Google and Lambda Labs are never approved at any budget level</summary>
+<summary>xAI Colossus is never approved at any budget level</summary>
 
-- Both target the Dallas-Fort Worth substation, which is already the only substation predicted to breach capacity (54.6% growth).
-- Even with upgrades, DFW capacity is fully consumed by higher-revenue requests (xAI Colossus at 500 MW, $105M/yr).
-- The optimizer correctly prioritizes revenue-maximizing allocations at the constrained substation.
+- It targets the Dallas-Fort Worth substation, the only substation predicted to breach capacity (54.6% growth). Even with both DFW upgrades, DFW has 699.8 MW of headroom.
+- That fits Google Metroplex DC and Lambda Labs DFW together (600 MW, $108M/yr) or xAI alone (500 MW, $105M/yr); xAI with Lambda would need 700 MW.
+- The optimizer correctly prioritizes the revenue-maximizing allocation at the constrained substation.
 
 </details>
 

@@ -1010,17 +1010,18 @@ def _query_flag(relationship, flag_name):
 x_u_c = Float.ref("xu_c")
 effective_load = Substation.predicted_load | Substation.current_load_mw
 
+# Each upgrade's capacity and each request's MW is weighted by its own variable.
 problem.satisfy(model.where(
-    DataCenterRequest.x_approve(InvestmentLevel, x_a_c),
-    SubstationUpgrade.x_upgrade(InvestmentLevel, x_u_c),
+    InvestmentLevel,
     DataCenterRequest.substation(Substation),
-    SubstationUpgrade.substation(Substation),
 ).require(
     Substation.max_capacity_mw - effective_load
-    + sum(x_u_c * UpgRef.capacity_increase_mw).where(
-        UpgRef.substation == Substation).per(Substation, InvestmentLevel)
-    >= sum(x_a_c * DCRef.requested_mw).where(
-        DCRef.substation == Substation).per(Substation, InvestmentLevel)
+    + sum(UpgRef, x_u_c * UpgRef.capacity_increase_mw).where(
+        UpgRef.substation == Substation, UpgRef.x_upgrade(InvestmentLevel, x_u_c),
+    ).per(Substation, InvestmentLevel)
+    >= sum(DCRef, x_a_c * DCRef.requested_mw).where(
+        DCRef.substation == Substation, DCRef.x_approve(InvestmentLevel, x_a_c),
+    ).per(Substation, InvestmentLevel)
 ))
 
 # C2: Budget per investment level
@@ -1032,7 +1033,7 @@ def _query_flag(relationship, flag_name):
 
 # Objective: maximize total DC revenue across all levels
 problem.maximize(
-    sum(x_a * DataCenterRequest.annual_revenue_per_mw * DataCenterRequest.requested_mw).where(
+    sum(InvestmentLevel, x_a * DataCenterRequest.annual_revenue_per_mw * DataCenterRequest.requested_mw).where(
         DataCenterRequest.x_approve(InvestmentLevel, x_a)
     )
 )

@@ -8,7 +8,7 @@ ERCOT processes 10 hyperscaler interconnection requests (2,930 MW) against a 12-
 ERCOT has 10 hyperscaler interconnection requests totalling 2,930 MW
 on a 12-substation grid where DFW is the binding capacity bottleneck.
 The chain produces a Pareto frontier across 5 budget levels — the knee at
-$300M unlocks 5 DCs (1,500 MW, $264M net value) including xAI Colossus.
+$300M unlocks 6 DCs (1,530 MW, $252M net value) including Google and Lambda at DFW.
 
   ─────────────────────────────────────────────────────────────────
   STAGE 1  Predictive   ──►  Substation.predicted_load        (12)
@@ -37,8 +37,8 @@ $300M unlocks 5 DCs (1,500 MW, $264M net value) including xAI Colossus.
   STAGE 5  Prescriptive ──►  DataCenterRequest.x_approve  (per InvestmentLevel)
                               SubstationUpgrade.x_upgrade  (per InvestmentLevel)
                               OPTIMAL across 5 budget levels in one solve.
-                              Knee $300M · 5 DCs · 1,500 MW · $264M net.
-                              Google + Lambda never approved — DFW full.
+                              Knee $300M · 6 DCs · 1,530 MW · $252M net.
+                              xAI never approved — DFW headroom fits Google + Lambda.
   ─────────────────────────────────────────────────────────────────
 ```
 
@@ -152,7 +152,7 @@ OPTIMAL MIP across 5 `InvestmentLevel` values in one solve; `x_approve` and `x_u
 
 **Response**
 
-Pareto frontier with knee at $300M (5 DCs, 1,500 MW, $264M net); marginal $995K/$M at knee, declining to $400K/$M by $600M; Google + Lambda never approved (DFW full).
+Pareto frontier with knee at $300M (6 DCs, 1,530 MW, $252M net); marginal $1,013K/$M into the knee, declining to $439K/$M by $500M; xAI Colossus never approved (DFW headroom goes to Google + Lambda).
 
 ### 10. Persist solution concepts into the ontology
 
@@ -164,7 +164,7 @@ Pareto frontier with knee at $300M (5 DCs, 1,500 MW, $264M net); marginal $995K/
 
 **Response**
 
-Ontology gains an `InvestmentPortfolio(InvestmentLevel)` Concept (5 rows, one per budget) with `dc_count`, `total_mw`, `annual_revenue`, `upgrade_cost`, `net_value`, `marginal_per_m_to_next_level`, `is_knee_point`. All five frontier rows — $200M ($165M net) → $300M ($264M net, knee) → $600M ($395M net) — are queryable as ontology rather than stdout.
+Ontology gains an `InvestmentPortfolio(InvestmentLevel)` Concept (5 rows, one per budget) with `dc_count`, `total_mw`, `annual_revenue`, `upgrade_cost`, `net_value`, `marginal_per_m_to_next_level`, `is_knee_point`. All five frontier rows — $200M ($151M net) → $300M ($252M net, knee) → $600M ($398M net) — are queryable as ontology rather than stdout.
 
 ## Data