In Steam Shovels for Design Engineers, Claude Code drove three Autodesk applications through a graduate BIM course project, the Berkmar Drive Extension in Albemarle County, Virginia. It produced:

• the Civil 3D corridor and sheets;
• a Revit model of Bridge B-676, a 716-foot, three-span steel plate-girder bridge over the South Fork Rivanna River;
• a federated Navisworks model with clash detection.

The steam-shovel point was about drafting: the digging got faster, but someone still had to decide where to dig.

This post takes the same bridge somewhere drafting never goes: into physics. Over one long working session, Claude Code drove the open-source finite-element package Elmer FEM from the command line. It set up, ran, debugged and checked:

• a transient heat analysis of the deck through a hot July day, with the real sun path and the shadows of the parapets;
• a 3D structural model of the whole superstructure, with a 10° skew and 44 lines of cross-frames, under construction-stage dead load, shrinkage, design temperature gradients, the simulated sunny day, a sweep of HL-93 trucks, and free vibration;
• an AASHTO LRFD strength, service, constructibility and fatigue check of the girders, which then resized the flange plates until every check passed.

The headline isn’t any single number. It’s the shape of the workflow:

• the agent did the heavy lifting of meshing, input files, runs, post-processing and plots;
• every result was checked against an independent calculation;
• those checks caught eleven errors that produced plausible-looking answers.

That last part is the real story, and it’s why the engineer’s time still belongs in the budget.

Disclaimer: for demonstration and education only. Every simulation, number, code check and plate size in this post was produced to demonstrate a workflow. None of it is engineering work stamped or sealed by a licensed Professional Engineer, none of it has been independently reviewed, and none of it should be used to design, build, rate or evaluate a real structure. The bridge geometry is the one built for the BIM model, the material properties are assumed typical values, and the girder sizes in the Revit model were placeholders. As you’ll see, the code check makes that clear.

The bridge

B-676 carries a two-lane urban collector over the South Fork Rivanna River in central Virginia:

• Spans: three continuous spans, modeled as 270, 211 and 220 ft between bearings. The bridge’s designer describes spans of up to 270 ft. The National Bridge Inventory lists the longest span as 276 ft and the whole structure as 716 ft long.
• Girders: five 110-inch-deep weathering-steel plate girders at 11′-7″ spacing.
• Deck: a 9.5-inch concrete deck on a 2% crown.
• Across the deck: a raised 6-ft sidewalk on the north-west side, a 30-ft roadway, a median rail, and a 14-ft shared-use path on the south-east side.
• Barriers: BR27C concrete parapets on both edges.
• Orientation: the bridge axis runs N 40.9° E, a detail that matters once the sun comes up.
• Supports: on a 10° skew.

The real bridge was built under VDOT’s Route 29 Solutions design-build contract, awarded in February 2015 to the Lane/Corman Joint Venture for $116.7 million. That contract also covered the US 29 / Rio Road interchange and the widening of US 29. Rummel, Klepper & Kahl (RK&K) was the team’s lead designer. Whitman, Requardt & Associates (WRA), working under RK&K, designed the bridge as a jointless continuous steel structure on Virginia-style abutments, founded on 6′-6″ drilled shafts. The Berkmar Drive Extension was completed in 2017. The analysis here is not WRA’s design: it uses the geometry from the course’s BIM model.

Figure 1. Plan of the 3D model: five girders, bearing lines on a 10° skew (orange), and the X cross-frames between the girders (blue).

None of the material properties were specified, so the analysis uses typical values, all documented in the study:

• Concrete: f′c = 4 ksi, and Ec = 3,990 ksi per AASHTO 5.4.2.4. Superimposed dead load and shrinkage use Ec/3, which represents creep.
• Steel: A709 Grade 50W.
• Superimposed loads: a 25 psf future wearing surface, and the AASHTO 75 psf pedestrian load on the sidewalk and path.
• Thermal gradient: the AASHTO zone 2 design gradient, the worse of the two zones that could apply to the site.

Why Elmer, and why the command line

Elmer is a mature, open-source multiphysics finite-element code from CSC – IT Center for Science in Finland. It does heat transfer, elasticity, eigenvalues, fluids, electromagnetics and more, and it’s free.

It’s also a batch program: you write a text input file (.sif) and a mesh, run ElmerSolver, and read the results. There’s no live model to talk to. The Civil 3D and Revit work needed custom add-ins and MCP servers. Here, the right interface was simply the command line plus a small Python helper.

Claude Code wrote that helper (elmerkit.py) and wrapped everything it learned into a reusable skill covering:

• how to mesh unions of rectangles without Gmsh;
• which .sif keywords actually work (verified, not guessed);
• the pitfalls that silently give wrong answers;
• a self-test that checks the Elmer install against closed-form answers before any real work.

A hot July day on the deck

The first analysis is a transient heat model of the deck cross-section. Here is what it includes:

• Weather: a clear mid-July day at 38° N. Air temperature ranges from 21 to 33 °C (70–91 °F), and global sunshine peaks at about 950 W/m² just after 1 pm daylight time.
• Deck surface: absorbs 65% of the sunlight, loses heat to the wind, and radiates to a clear sky that is colder than the air.
• Underside: sits in shade and exchanges heat with the air.
• Run length: four simulated days, so the start-up conditions wash out. Days 2 and 4 agree to within 0.25 °C.

Figure 2. A hot, clear July day: the sun on the deck (top), and the deck surface, steel bottom flange and air temperatures (bottom).

The deck surface reaches 47 °C (117 °F) at 3:30 pm, about 14 °C above the air. The steel girders sit in shade under the deck and barely notice: they track the air temperature with a lag of about two hours.

That contrast is what matters structurally. A hot deck on cool steel is a temperature gradient: the top of the composite section wants to grow more than the bottom.

Figure 3. The deck section through 24 hours.

The AASHTO LRFD specification (3.12.3) gives a design gradient for exactly this. Zone 2 is 46 °F at the top, dropping to 12 °F four inches down and to zero a foot below the surface.

The simulated peak gradient is 13.3 °C (24 °F), about 52% of the zone 2 design value. That’s what you’d hope to see: the code envelope covers rarer and harsher days than one clear July afternoon.

There’s also no real night-time reversal on a summer night. The thin steel cools with the air while the deck holds its heat, so the deck surface never gets more than a fraction of a degree colder than the girders. The code’s negative gradient belongs to cold, clear winter nights.

Figure 4. The simulated 3 pm gradient against the AASHTO zone 2 design gradient.

Shadows, and the girder that cooks at sunset

The first version of the thermal model treated the deck as a flat plate under the sun. Real bridges have parapets, rails and an orientation, so the second version added them:

• Geometry: the 2% crown, the raised sidewalk, both parapets and the median rail.
• The sun’s actual path: for this latitude, this date and this bridge’s N 40.9° E alignment.
• Shadows: the parapets and median rail cast shadows whose width changes through the day.
• Fascia girders: the outside girders catch direct sun on their outer webs, below the shadow line of the deck overhang.

Elmer doesn’t do ray tracing in this kind of model. Instead, the sun position, shadow edges and overhang shadow were written as small functions in Elmer’s built-in expression language (MATC) and evaluated at every boundary point and time step. The same geometry was reproduced in Python to draw the shadow map.

Figure 5. Where the parapets and median rail shade the deck through the day (dark = shadow).

The pattern is easy to read once you see it:

• Early morning: the sun rises in the east-north-east, about 20° off the bridge axis on the south-east side. The median rail and the south-east parapet throw long shadows across the roadway and the path.
• By 8 am: those shadows have shrunk to a few feet.
• From about 2:30 pm: the north-west parapet’s shadow starts creeping across the sidewalk. By sunset it covers the whole roadway.

The bigger surprise is the fascia girders:

Figure 6. Low sun under the overhang heats the outside girders, while the interior girder stays in shade.

• North-west fascia (G1): in the early evening the sun is low in the west-north-west, nearly face-on to this web, and gets under the 3.3-ft deck overhang. The web passes 45 °C by 6:20 pm and peaks near 50 °C around 7:30 pm, as much as 18 °C hotter than the shaded interior girders.
• South-east fascia (G5): gets the same treatment in the morning, peaking at about 42 °C at 10 am.

Figure 7. Section temperatures at 10 am (sun on the south-east fascia) and 6 pm (sun on the north-west fascia).

This is the kind of effect a girder-line design approach can’t see. The cross-section now has a temperature that varies across the width as well as through the depth, and the hot outside girder wants to bow sideways.

From temperature to stress, with a check at every step

Before going 3D, the structure was modeled as a single interior girder line in 2D:

• the composite section is represented as layers in plane stress;
• each layer’s stiffness and mass are scaled by its real width;
• quadratic elements are used throughout.

This model has one job: to be checkable. A continuous-beam calculation done independently in Python gives the same quantities, so every Elmer result can be compared with something.

Figure 8. Stress through the depth 10 ft from Pier 1 under the AASHTO gradient: Elmer elements against beam theory.

When the two agreed, it meant something:

When they disagreed, it meant a bug. The first versions disagreed in four different ways, and each disagreement turned out to be a modeling error rather than physics:

1. Over-restrained bearings. The bearings were modeled as 12-inch pads restrained at three nodes, which clamped the girder ends against rotation. Dead-load deflection came out 13% too stiff.
2. Longitudinal restraint on a whole pad. Fixing the whole pad longitudinally stopped the steel flange’s own thermal expansion and created a fake local stress.
3. Averaged stresses. Elmer averages stresses at nodes, so stresses at the steel–concrete interfaces mixed two very different materials. The result was 8 ksi “stripes” in the stress plots. Recomputing stresses inside each element fixed it.
4. Dead load on the wrong section. It was first applied to the composite section. On a real bridge the wet concrete is carried by the steel alone, so it belongs on the non-composite girder, which roughly doubles the deflection.

None of these crashed or produced a warning. All four produced smooth, reasonable-looking plots.

Going 3D: the deck curls across its width

The 3D model covers the full width:

• Structure: deck, haunches, all five girders, the crown and the 10° skew.
• Cross-frames: X cross-frames (pairs of L4×4×½ angles) are represented as equivalent shear panels between the girder webs. The panels’ stiffness is matched to the racking stiffness of the real bracing. They sit about every 20–24 ft and next to every bearing line.
• Bearings: single points. Pier 1 is fixed longitudinally and the centre girder is guided laterally.

Under the AASHTO design gradient, the superstructure bows up in the end spans, by up to 0.58 inches, and pushes down in the middle span:

Figure 9. The AASHTO positive and negative design gradients bow the superstructure (all five girders).

A girder-line model sees exactly that, and says each bearing picks up or sheds about 8 kips. The 3D model sees something else as well.

The deck also curls across its width. A slab that’s hot on top doesn’t just want to arch along the span. It wants to dome in both directions.

At each pier, the bearings have to flatten that dome. The exterior bearings press down harder by about +100 kips, while the interior ones are relieved by up to −94 kips. Yet the total at each support line still matches five times the girder-line answer to within 2%.

Because this was new and large, it was tested before it was believed:

• refining the deck mesh changed it by 2–5%;
• changing the lateral restraint changed it by about 10%;
• a hand estimate from the deck’s thermal moment gives the same order of magnitude.

The answer the engineer needed is reassuring: no bearing comes close to lifting off. Under dead load plus the worst thermal or shrinkage case, the lightest bearing still carries at least 163 kips.

The sunny-day simulation then drove the 3D model directly. The full 3D heat analysis was run on the same mesh and fed into the stress analysis at 3:20 pm and 6:20 pm. Its temperatures match the 2D thermal model to within about 0.5 °C at every check point and time.

In the evening case, the sun-baked north-west fascia makes the response visibly asymmetric:

• at Pier 1, G1’s bearing changes by +93 kips while G5’s changes by +61 kips;
• G1’s bottom flange moves sideways about a tenth of an inch.

Figure 10. Vertical movement at 6 pm, with sun on the deck and the north-west fascia, relative to a uniform 20 °C day. The hot G1 fascia moves differently from the other four girders.

Building the camber

Steel bridges are built in stages. The deflection at each stage has to be built into the girders as camber, so the finished deck comes out at the right grade:

1. Stage 1: the steel and the wet deck are carried by the bare girders.
2. Stage 2: parapets, sidewalk, median rail and future overlay go on the hardened, composite deck. Long-term loads use the creep-reduced concrete stiffness.
3. Long term: the deck shrinks.

Figure 11. What each construction stage does to the five girders.

For the interior girder in span 1 that adds up to:

The short middle span does the opposite during the deck pour. Its long neighbours rotate it over the piers, and it rises about 1.6 inches. A fabricator who missed that would build the wrong camber into the middle span.

The stage-2 plot also shows the asymmetry of the cross-section. The north-west side carries the heavier parapet and the sidewalk, so G1 deflects 2.9 inches against 1.6 inches for G5.

The skew shows up in the bearing reactions too. Dead load concentrates toward the obtuse corners of a skewed span. G1’s bearing at the first abutment carries 328 kips, against 283 kips for the same bridge on square supports, a 16% increase.

Figure 12. Dead-load bearing reactions on square and 10° skewed supports. Skew shifts load toward the obtuse corners.

Trucks, everywhere

Design codes don’t ask “what does one truck do?” They ask “what is the worst a girder can see from any legal arrangement of trucks?” AASHTO answers that with distribution factors: formulas that say how many lanes of load each girder must be designed for.

The 3D model answers it directly. One HL-93 design truck (with 33% impact) was placed at seven transverse positions across the roadway, in two different spans. That’s 14 full 3D analyses.

Each girder’s moment was recovered from its bottom-flange stress and section modulus. The results were then combined by superposition into every legal case:

• one truck, anywhere, with AASHTO’s 1.2 multiple-presence factor;
• two trucks side by side, at least 10 feet apart.

Figure 13. One truck at seven positions across span 1: the share of the truck’s moment that each girder carries.

Figure 14. Governing live-load distribution from every legal one- and two-lane case, compared with the AASHTO factors.

For positive moment, the AASHTO formulas are conservative here:

• Interior girders: the worst carries 0.59–0.64 lanes in the 3D model, against 0.75–0.80 from the formula.
• Exterior girder: 0.60 against 0.66 from AASHTO’s rigid-body check.

Near the skewed pier, though, the exterior girder’s share of negative moment comes out at 0.88, above the AASHTO exterior value. That result sits right next to a skewed bearing and the support cross-frames, where local effects mix in. It’s flagged here as something a designer would confirm with a finer model, not as a finding.

Live-load deflection passes comfortably:

• averaged over the girders, the governing AASHTO combination (25% of the truck plus the lane load) deflects span 1 by about 1.2 in, and the most-loaded girder by about 1.6–1.7 in;
• the limit for a bridge with pedestrians (L/1000) is 3.24 in for the 270-ft span.

The truck sweep also measured the cross-frames:

Figure 15. Cross-frame diagonal forces in each bay: the envelope of the 14 truck positions.

The most heavily loaded diagonals see about 6 kips of compression under a design truck, and swing through about 7 kips as the truck moves across. For fatigue, the stress range in an L4×4×½ diagonal is 2.8 ksi. That clears the 4.5 ksi infinite-life threshold for a Category E connection, but not the 2.6 ksi threshold for Category E′, the classification for an angle welded on one leg.

In other words, the connection detail decides it. That is precisely the sort of thing skewed steel bridges are known for, and it only showed up once the model was refined (more on that below).

How it vibrates

Figure 16. The first six vibration modes of the superstructure (deck surface, exaggerated).

The first vertical bending mode is at 1.17 Hz. The first torsional mode follows close behind at 1.36 Hz. After that come more bending and torsion modes, and a lateral-torsional sway at about 2.1 Hz.

Adding the cross-frames raised the first torsional frequency, which is exactly what cross-frames are for. The vertical frequency agrees with the 2D girder line once the mass of the parapets and sidewalk is accounted for.

Getting these numbers right took one more catch. The equivalent cross-frame panels had been given zero density. That made the mass matrix singular, and the eigenvalue solver returned frequencies of 20 to 100 Hz without any warning. Giving the panels the real mass of the steel angles brought the answers back into line.

The code check: the BIM girder isn’t a designed girder

The last step was an AASHTO LRFD check of the girders, done the way most designers do it:

• a line-girder analysis with influence lines;
• moving HL-93 trucks and tandems, the lane load, and the 90% two-truck rule for negative moment;
• the fatigue truck;
• section properties for the steel-only, long-term composite, short-term composite and negative-moment sections.

The checks themselves:

• Strength I: positive and negative flexure, and shear.
• Service II.
• Constructibility during the deck pour: the top flange is braced only by the cross-frames, and the web can bend-buckle.
• Fatigue I: at the cross-frame connection plates.

The girder in the Revit model had constant flanges, a 20 × 1.5 in top and a 24 × 2.5 in bottom, chosen for a visualization model. The code check is blunt about it:

Figure 17. AASHTO LRFD checks for interior girder G3: the flanges as modeled in Revit against the resized flange plates.

• Positive flexure and fatigue: fine (0.69 and 0.32).
• The deck pour: fails at 1.32. The deep, slender web bend-buckles under the stress of the wet concrete before the top flange yields.
• Over Pier 1: the bottom flange is at 1.24 and the top flange, in tension, at 1.47.
• Shear: an unstiffened web fails at 1.72. Transverse stiffeners at 9 ft bring that to 0.50.
• Without the intermediate cross-frames: the compression flange would be unbraced for the full 270-ft span during the pour, and the check exceeds capacity by a factor of about seventy. Cross-frames aren’t decoration.

So the checker took the next step a designer would. It searched standard plate sizes for the lightest flanges that pass every check: one section for the positive-moment regions, and a heavier one over the piers.

With AASHTO’s distribution factors, the interior girder needs:

• a 24 × 1.5 top and a 22 × 2.0 bottom flange in the spans;
• a 20 × 3.0 top and a 26 × 2.75 bottom flange over the piers.

Then the same search was run with the distribution factors from the 3D model:

That is a nuanced answer, and an honest one. Refined analysis isn’t a free lunch: it takes steel out of some members and puts it into others. Here it puts steel into the exterior girder over the skewed pier, the same place the 3D model flagged earlier.

What it took

The “AI runs an FE model” headline hides how many ways an analysis can be confidently wrong. Here is the full list of errors in this study that produced plausible output and were caught only by an explicit check:

1. Bearing pads that clamped the girder ends (dead load 13% too stiff).
2. A longitudinal restraint that stopped the flange’s own thermal expansion.
3. Nodal stress averaging across materials (fake 8 ksi stripes).
4. Dead load on the composite instead of the steel section.
5. A missing feet-to-metres factor that made the wet-deck load 3.28 times too large. With the steel weight unaffected, the reactions summed to 17,062 kips instead of 6,611.
6. Point loads written in a form that, in 3D, silently applied nothing (all reactions zero).
7. A single laterally guided girder that let the cross-section rack, producing a spurious 0.63 Hz “lateral” mode.
8. A mesh-cleanup command that silently deleted every bearing and wheel-load point.
9. Zero-density cross-frame panels that wrecked the vibration analysis.
10. An off-by-one in how Elmer numbers its saved time steps, which put the “3 pm” restart at 12:20 pm.
11. Steel faces next to the cross-frame panels that never became thermal boundaries, so the girder webs were thermally insulated and ran up to 10 °C too hot.

Every one of them was caught by comparing against something independent:

• the reaction sum against the hand-computed load;
• the deflection against beam theory;
• the 3D heat field against the 2D one;
• frequencies against an earlier model;
• a simple question: “does that number make physical sense?”

The agent wrote and ran those checks. But a person with engineering judgment decided what had to be checked and what “close enough” meant, and signs the drawings.

There was also a hardware lesson. The stock Windows build of Elmer solves with a single-threaded direct solver limited to 32-bit integer indexing. That capped the 3D model at about 82,000 nodes, which forced simple 8-node elements, and running cases in parallel helped only 1.4×.

Partway through, the study switched to Elmer’s MPI build with the MUMPS solver:

• the same 3D model solved in 6 seconds instead of 80, with identical results;
• meshes of over 150,000 nodes became routine;
• quadratic elements became affordable.

The quadratic elements showed that the production linear mesh had been 3–5% too stiff, and its cross-frame forces 60% too low. That is why the Category E′ fatigue question surfaced only at the end.

The honest limits

• Weather: one synthetic clear July day, not measured weather, and a bare concrete deck with no overlay.
• Live load: two longitudinal truck positions per span and seven transverse ones. A design would use full influence surfaces and moving loads in 3D.
• Cross-frames: equivalent shear panels rather than individual members. The diagonal forces are recovered from panel shear.
• Code checks: on a line-girder model with prismatic stiffness. A real design would vary plate sizes along the girder and re-analyse.
• Not modeled: pier and abutment flexibility, bearing stiffness, and the deck pour sequence.
• Material properties: typical values, not taken from plans.
• Span lengths: the model’s 270-ft longest span matches the designer’s description. The National Bridge Inventory lists 276 ft, which may reflect a different measuring convention. Either way the difference is about 2% and doesn’t change any conclusion here.

To repeat the disclaimer: these simulations are for demonstration and education only. They are not engineering work stamped or sealed by a licensed Professional Engineer, and they must not be used to design, build, rate or evaluate any real structure.

The steam shovel, again

The analogy from The Steam Shovel Moment and Steam Shovels for Design Engineers holds up even better for analysis than for drafting. The steam shovel didn’t decide where the canal went. It moved more earth than any crew with hand shovels could, and that freed the engineers to think about the hard parts: drainage, slides, sequence.

In this study the agent did the earth-moving:

• meshes, input files, dozens of runs, post-processing and plots;
• an AASHTO checker and a plate-sizing search;
• the 3D heat-to-stress coupling;
• a migration to a faster solver build halfway through.

At engineering billing rates, a human team would have spent weeks on that.

What it couldn’t do on its own was know which answers to distrust. Eleven times, the right response to a smooth, confident plot was “that can’t be right,” followed by an independent check. That instinct, and the professional responsibility that comes with it, is the part of the job that stays with the engineer.

Built with Claude Code driving Elmer FEM 26.2 on a Windows workstation through custom Python helpers and a reusable Elmer skill.

Sources and further reading

• TeraContext.AI, Steam Shovels for Design Engineers (November 7, 2026), the Civil 3D, Revit and Navisworks work on the same bridge, and The Steam Shovel Moment for Civil Engineering Drafting (October 8, 2026).
• Elmer FEM (CSC – IT Center for Science), its source repository, and the 26.2 release.
• AASHTO LRFD Bridge Design Specifications: 3.6 (live load), 3.12.3 (temperature gradient), 4.6.2.2 (distribution factors), 5.4.2.4 (concrete modulus), 6.6.1 (fatigue), 6.10 (I-section flexural members).
• VDOT, Route 29 Solutions; the contract award letter for project 77383 (February 19, 2015): Lane/Corman Joint Venture, $116,746,000; and Lane/Corman’s technical proposal, which names RK&K as lead designer.
• WRA, Route 29 Solutions – Berkmar Drive Extension: a three-span continuous steel bridge, 716 ft long and 53 ft wide, jointless, on a 10° skew, with spans up to 270 ft.
• FHWA National Bridge Inventory, Virginia structure 030460 (Berkmar Drive over the Rivanna River and Route 643): 3 spans, 10° skew, 84.1 m maximum span, 218.2 m structure length.
• MUMPS, the parallel sparse direct solver used in the final runs.

TL;DR

For a graduate BIM course final project, I gave Claude Code plain-English instructions and let it drive Civil 3D, Revit, and Navisworks. Over approximately 32 prompts, it:

• Converted 31 legacy DGN files
• Built the surface, alignment, profile, and corridor for a 2.4-mile road
• Modeled a 716-foot, three-span steel girder bridge in Revit
• Federated all models in Navisworks, flagged a utility clash, and verified zero clashes after revisions
• Plotted the full sheet set

The core work took roughly 3.5 hours: $67 in API tokens plus $875 for my time (billed at a PE’s rate of $250/hour), totaling about $942. By comparison, a skilled Civil 3D drafter would require approximately 290 hours—costing around $32,000 fully burdened.

This process was not hands-off: I caught real errors and exercised all engineering judgment. Much like the steam shovel, AI will not eliminate design work; instead, it shifts daily roles and empowers small teams to execute projects that once required large crews.

Figure 1. The classic analogy, drawn for this post. The steam shovel replaced the hand-shovel gang, and in doing so it created new jobs: the engineer who ran it, the craneman, the fireman, and the pit crews who moved its track. It also made cuts possible that no affordable gang of shovels could have dug.

The assignment

The project focused on the Berkmar Drive Extension, a real Route 29 Solutions initiative in Albemarle County, Virginia. The design entails a new two-lane urban collector crossing the South Fork Rivanna River via Bridge B-676. The course required a complete federated BIM model including:

• Existing ground surface
• Horizontal and vertical alignments with AASHTO K-value verification
• Assemblies and a roadway corridor
• Stormwater management ponds and a storm sewer network
• A 3D bridge model
• Navisworks clash detection and a 4D construction sequence
• Plotted production sheets

Extra credit tasks required modeling water, gas, electrical, and ITS utilities, as well as detailing the bridge and retaining walls in Revit.

The source deliverables mirrored real-world handoffs: a 298-page roadway plan set, a 71-page bridge plan set, survey data, and 31 MicroStation DGN files—none of which were pre-formatted for the assignment requirements.

I completed the entire project using natural language prompts in Claude Code, an AI agent operating in my terminal. I had previously developed integration bridges for Autodesk products: a command-line driver and an MCP server for Civil 3D, an MCP server for Revit, and a .NET add-in with a command-line runner for Navisworks. Using these tools, Claude read the drawing sets, generated and executed C# and Python scripts against the active applications, inspected the visual outputs, and corrected errors iteratively.

Figure 2. How the work flowed. I stated intent and design criteria and reviewed the output. Claude Code wrote and ran the tool calls. The Autodesk applications did the engineering geometry.

What it actually did

My second prompt was a single detailed paragraph instructing Claude to analyze all files, take an inventory, attempt the extra credit, model the bridge in Revit, formulate an execution plan, and run autonomously while I stepped away.

Over the subsequent sessions, Claude executed the work step by step:

• Reading the plans. Because the plan PDFs were vector drawings without selectable text, Claude rendered them as images for visual inspection. It successfully extracted the index of sheets, alignment data for 12 horizontal curves with PI coordinates, typical cross-sections (11-foot lanes, bike lanes, curb and gutter, a sidewalk on one side, and a shared-use path on the other), and the bridge general plan.
• Converting the DGNs. Claude determined the exact command sequence for Civil 3D’s DGN import process, handling edge cases such as models storing data within Seed2D spaces. It batch-converted all 31 files and verified their overlay alignment in the Virginia State Plane South coordinate system.
• Civil 3D model.
  • Existing Ground: Generated a surface utilizing 80 breaklines.
  • Alignment: Reconstructed the horizontal alignment directly from curve tables.
  • Profile: Built a design profile with 16 vertical curves, confirming all met AASHTO minimum K-values for 40 mph.
  • Corridor: Created a roadway corridor across six regions and three assemblies, leaving an intentional gap for the bridge structure.
  • Earthwork: Calculated approximately 284,000 cubic yards of cut and 220,000 cubic yards of fill, validating the end-area volumes against a TIN-to-TIN surface comparison.
• Plan and profile sheets. Claude generated PDF sheet plots and visually inspected its own output by reading the rendered PDFs back into context. This feedback loop proved critical: it caught station labels that stopped rendering following a corridor rebuild, resolved overlapping text, and restored a missing north arrow—fixing these issues autonomously before review.

Figure 3. Sheet C-PP03, plotted from the Civil 3D model. The plan is on top (contours, alignment, Bioretention Basin #1) and the profile below, with the dashed existing ground, the solid design grade and the vertical-curve data. Claude checked every sheet by reading back its own plots.

Figure 4. The centerline design. The top panel shows the design grade (solid) over the existing ground (dashed) along the 2.4-mile alignment, with cut shaded red and fill shaded blue. The gray band is the river crossing, where the road is carried by the bridge instead of earthwork. The bottom panel shows the cut and fill end areas every 100 feet, the raw material for the 284,000 CY of cut and 220,000 CY of fill. Most of the cut is in the hill south of the river, and the fills are scattered across the stream valleys to the north.

Figure 5. Sheet C-XS01: road cross-sections every 500 feet, cut from the corridor against existing ground. Each one shows the 2-lane typical section, with 2:1 daylight slopes to the ground and a ditch in cut. Below them are the earthwork summary (average-end-area checked against a TIN-to-TIN volume) and the six corridor regions with their assemblies.

• The bridge in Revit. Claude issued C# scripts to the active Revit session to construct Bridge B-676 based on the bridge plan set:
  • Three continuous steel plate-girder spans (276 ft, 211 ft, and 225 ft)
  • Five plate girders supporting a deck matching the roadway profile and 2% cross-slope
  • Skewed pier caps resting on drilled shafts, along with abutments and retaining walls

  By sharing coordinate definitions with the Civil 3D model, the Revit assembly aligned seamlessly with state plane coordinates without requiring manual positioning.

Figure 6. Bridge B-676 in Revit: the deck following the vertical curve, the plate girders, two skewed piers on drilled shafts, and the south abutment with its piles in the foreground.

Figure 7. Bridge B-676 in Revit, looking up at a pier: plate girders, the pier cap, and three columns on drilled shafts. The model was built entirely from C# that Claude wrote and sent into the live Revit session.

• Navisworks. Claude federated the Civil 3D roadway drawing and Revit bridge model, configuring five distinct clash detection tests.
  • Initial Clash: The first run identified an interference where storm pipe 10-3 to 10-4 intersected the ITS and electrical duct banks.
  • Revision 1: When asked if lowering the utility duct banks would resolve the conflict, Claude modified the elevations in Civil 3D, re-exported the model, and re-ran clash detection to confirm zero remaining interferences.
  • Revision 2: Nine residual bridge-versus-corridor overlaps were identified as a redundant corridor region. Once removed in Revision 2, all clash tests passed cleanly.

  Claude also constructed a 4D TimeLiner sequence mapping out ten major bridge construction phases scheduled from March to December 2027.

Figure 8. The federated model in Navisworks: the Revit bridge over the Civil 3D surfaces and corridor, with five clash tests run against it.

Once core project requirements were completed, I requested an interactive 3D site website comparing “before” and “after” conditions. Claude built the application using Three.js and hosted it locally via Caddy. Features included:

• Public high-resolution aerial orthophotos from Virginia’s GIS database (2013 flights for “before” conditions and the latest flight for “after”)
• Approximately 28,000 trees, river geometry, animated traffic, and a first-person drive mode
• A storm simulation engine raising river levels to the design flood stage to verify bridge clearance and model bioretention basin filling
• A narrated cinematic video tour featuring synthetic voice narration generated by a local text-to-speech model

Additionally, Claude prepared a 3D-printable diorama file of the bridge tailored for a desktop 3D printer.

Figure 9. The aerial imagery draped on the 3D terrain. The orthophoto is draped over the existing-ground surface, with the corridor, the Revit bridge, the river and the trees placed on it, all in the same state plane coordinates as the Civil 3D model. The readout at the top shows finished grade, existing ground and cut depth at the current station. The strip at the bottom is the same profile as Figure 4.

Figure 10. The interactive website: the 2013 forested valley on the left of the divider, the finished design with Bridge B-676 on the right, and the design profile along the bottom. This was not required by the assignment. It exists because it was cheap to ask for.

The narrated cinematic tour (1 minute 46 seconds): the 2.4-mile alignment, the finished design, the 4D bridge sequence, a storm filling Bioretention Basin #1, and a drive along the new road. The narration was generated by a local text-to-speech model.

What it got wrong (and why that matters)

Presenting this workflow as fully push-button automation would be inaccurate. That reality is why the cost evaluation explicitly accounts for my time billed at a Professional Engineer’s (PE) rate. A licensed engineer must actively direct the process—monitoring outputs, flagging agent oversights, and making final design decisions.

Several software and execution issues occurred during the run:

• Civil 3D froze completely, requiring a system reboot.
• A Revit export routine crashed the host application.
• Scripting queries against a specific Civil 3D object caused two application crashes.
• Corridor rebuilds occasionally caused station labels to disappear.
• The initial drive-through camera was positioned in the oncoming lane facing traffic, and a subsequent video render suffered GPU encoding corruption during a scene transition.

Two distinct operational patterns emerged from these failure modes:

1. The agent resolved most errors when granted visual feedback. By reviewing rendered plots, auditing clash counts, verifying volumes, and evaluating viewport screenshots, the agent self-corrected. Multimodal tools that let an agent inspect its output fundamentally change performance compared to headless code execution.
2. Human oversight caught higher-level anomalies. Visual flaws—such as the inverted drive direction or video artifacting—were immediately apparent upon human review. One-sentence feedback (“it looks like you are driving on the left side of the road”) enabled Claude to diagnose root causes—such as a sign error in the offset vector or GPU memory contention—and apply fixes quickly.

Crucially, core engineering decisions remained human-driven: selecting whether to lower duct banks versus rerouting storm lines, establishing design flood criteria, and evaluating slope or clearance trade-offs. Claude analyzed options and outlined trade-offs, but executed changes only upon explicit approval.

The steam shovel

I previously introduced this analogy in The Steam Shovel Moment for Civil Engineering Drafting (October 8, 2026) regarding AI agents generating individual Civil 3D drawings. The comparison warrants repeating here because it scales directly to full project workflows: from initial survey data to final sheet sets across three design platforms, complete with revisions and clash reports.

Prior to mechanized excavation, major earthwork required large crews using hand shovels and wheelbarrows. William Otis patented the steam shovel in 1839, and by the late 19th century, rail-mounted shovels dominated railway and canal construction. A single machine could easily outperform dozens of laborers, prompting early fears of widespread worker disemployment.

In practice, labor dynamics evolved rather than vanished. Operating each shovel required a skilled operator, a craneman to handle the dipper, a fireman to maintain boiler pressure, and pit crews to continuously lay track sections. Additional crews operated spoil trains, while mechanics maintained the fleet. Labor shifted from manual excavation to operating, maintaining, and logistics management around machinery.

Crucially, steam shovels enabled infrastructure projects that were previously infeasible due to labor and cost constraints. The construction of the Panama Canal illustrates this shift. The French attempt in the 1880s had steam shovels too, but disease, mudslides, and financial collapse halted the work. The American project (1904–1914) scaled mechanized operations dramatically:

• Deploying 102 steam shovels, 77 of them 95-ton Bucyrus models with five-cubic-yard dippers
• Rebuilding the railroad so spoil trains ran continuously on double track; on a typical day, about 160 trainloads of material left the Culebra Cut
• Setting excavation records, such as a single Bucyrus shovel moving 70,000 cubic yards in 26 days in March 1910

Despite extensive mechanization, the project employed nearly 40,000 workers at its 1913 peak and over 56,000 people across the decade. While public health interventions—such as William Gorgas’s sanitation and mosquito-control campaign—were essential to keeping workers alive, the physical excavation of the Culebra Cut would have been impossible without heavy steam equipment and rail infrastructure.

The steam shovel did not shrink the construction industry; it expanded the scale of what could be built while transforming workforce roles. AI agents driving engineering software represent a similar inflection point for infrastructure design.

The arithmetic: one agent versus one drafter

The relevant benchmark for AI design agents is not a licensed engineer. A software agent cannot sit for a PE exam or stamp engineering plans, nor does a senior engineer typically execute baseline CAD drafting. In traditional workflows, a PE assigns production work to an experienced CAD technician, reviews the submittals, and issues redlines. The agent effectively replaces the drafting role, while the required engineering review time remains identical in both approaches.

For the core scope—the required civil model and Revit bridge details, excluding the web application, video, and 3D print—the agent completed the workload in 3.5 hours of wall-clock time and ~$67 in API tokens. The table below outlines an estimated breakdown for an experienced CAD drafter performing the same tasks from raw PDFs and DGN files:

This scope corresponds to roughly seven full workweeks (~290 hours), with typical industry ranges spanning 250 to 350 hours depending on team specialization.

A senior civil drafter earns roughly $35–$40 per hour in wages. Applying a standard consulting-firm multiplier of 2.5× to 3.0× (benefits, payroll taxes, office, software licenses, overhead, and profit) yields a fully burdened rate of about $110 per hour. At $110/hour, 290 hours equals approximately $32,000 (ranging between $25,000 and $40,000).

Deploying an agent requires an operator to direct prompting and review outputs. Accounting for 3.5 hours of senior engineer oversight at a fully loaded PE billing rate of $250/hour ($875 total labor) alongside $67 in API tokens yields a total cost of ~$942. Token consumption accounts for only about 7% of the total expense, meaning engineer time remains the primary cost driver.

Key context for these comparisons:

• Review Overhead: Standard PE markup and QA/QC review time is excluded from both models as it remains constant regardless of production method.
• Tooling Investment: The agent estimate excludes prior development time spent creating the CLI and MCP API bridges.
• Model Maturity: The human drafter estimate assumes full production-level deliverables, whereas the agent output represents a detailed academic student model.

Even under conservative assumptions—paying senior PE rates for all operator time and halving estimated efficiency gains—the agent-assisted workflow delivers the scope at less than one-tenth the cost in a single afternoon rather than two months. When design production costs drop dramatically, engineering volume scales accordingly.

How the daily work of a design engineer changes

Based on this workflow, several clear shifts emerge for engineering practice:

The engineer becomes the machine operator, not the excavation gang. A significant portion of entry-level engineering work currently involves mechanical CAD tasks:

• Converting file formats across legacy systems
• Configuring label styles and display properties
• Constructing corridors region by region
• Updating plan sheets after minor design edits
• Re-running clash detection scripts and documenting findings

These repetitive actions represent hand-shovel labor—the exact tasks automated most effectively by the agent. The engineer’s role pivots to defining project intent, establishing design criteria, reviewing results, and making final engineering determinations.

Rigorous technical review becomes the core skill. When generating complex plan sets requires minimal prompt effort, technical verification becomes the primary operational bottleneck. Engineers will allocate more time to QA/QC review rather than manual drafting:

• Evaluating centerline profiles for improper grades or drainage issues
• Identifying visual inconsistencies in renderings and 3D perspectives
• Assessing whether minor geometric overlaps warrant design modifications

This shift elevates the importance of an experienced engineering eye. It also raises an essential question for firm leadership: how will junior engineers develop deep intuition when routine drafting work is automated? The traditional apprenticeship model—where young engineers master design details through repetitive production work—must be intentionally redesigned.

Low-cost iteration improves overall design quality. Executing design revisions required simple prompts that automatically propagated changes across Civil 3D models, Revit structures, Navisworks clash sets, and sheet sets. Rapid iteration enables engineering teams to evaluate broader design alternatives:

• Testing multiple horizontal and vertical alignments simultaneously
• Re-evaluating storm sewer capacity against extreme flood scenarios after every change
• Generating 4D construction sequences for minor structures that previously lacked budget for complex scheduling
• Delivering interactive 3D fly-throughs for public outreach on standard projects

Rather than eliminating roles, affordable iteration enables deeper engineering rigor and scope expansion that project budgets previously precluded.

New specialized roles emerge around AI toolchains. This project relied on specialized integrations connecting Claude Code to Autodesk APIs (via custom CLI drivers, MCP servers, and Navisworks add-ins). Firms adopting agentic workflows will require dedicated roles to:

• Translate firm CAD/BIM standards into machine-readable prompts and configuration rules
• Maintain audit trails and decision logs (such as the 32-prompt log maintained for this project)
• Define governance frameworks governing autonomous execution boundaries
• Provide professional licensure oversight and sign-off on AI-assisted deliverables

These technical and governance roles represent the modern equivalents of crane operators, firemen, and pit crews.

Small teams gain the capacity of large engineering firms. While the Berkmar Drive Extension project yielded an academic BIM model rather than final construction drawings, a single engineer generated a multi-application BIM model, complete sheet set, and web visualization in a handful of working sessions. Armed with agentic workflows, a small firm can compete for infrastructure scope that historically required substantial drafting departments, enabling public agencies and mid-sized firms to tackle project backlogs more aggressively.

Unemployment, or a bigger canal?

Concerns regarding displacement are understandable: if an agent allows one engineer to produce the work of ten, will firm headcounts drop by 90%? Historical precedents suggest otherwise. Infrastructure demand has rarely been constrained by a lack of societal need; rather, it is capped by the economic cost of engineering and construction labor.

Modern communities face severe infrastructure deficits: aging bridges, stormwater systems unequipped for changing climate patterns, and municipal transportation corridors stalled in planning phases for decades. Dramatically reducing design costs expands the volume of economically viable infrastructure projects.

Daily engineering roles will inevitably transform. Manual DGN conversions and repetitive label formatting will give way to decision-making, quality verification, interdisciplinary coordination, and public engagement, alongside new technical roles focused on AI system architecture and governance.

Firms and practitioners who adapt to these tools will expand their capabilities. Those who resist technological shifts will face challenges, echoing the transition experienced by manual excavation crews in the 19th century.

Reflecting on this project, the most striking takeaway was not merely execution speed, but project scope. Lowering the cost of iteration made ambitious deliverables feasible that would otherwise have been discarded due to time constraints.

The canal simply got bigger.

Next in this series: A Bridge’s Day: Sun, Shadows, Trucks and Code Checks, Driven by Claude Code and Elmer FEM takes Bridge B-676 into physics. Claude Code drives the open-source Elmer FEM solver through a hot July day, HL-93 truck loads and an AASHTO LRFD check, and independent checks catch eleven plausible-looking errors along the way.

Built with Claude Code (Opus 5.5) driving Civil 3D 2027, Revit 2027, and Navisworks 2027 through custom CLI, MCP, and API bridges.

Sources for the history

• TeraContext.AI, The Steam Shovel Moment for Civil Engineering Drafting (October 8, 2026), the earlier post this one builds on.
• Panama Canal Authority, construction workforce by year (peak 39,962 in August 1913) and Canal FAQ (56,307 employed, 1904–1913).
• Smithsonian Libraries, Make the Dirt Fly! (102 steam shovels, most built by Bucyrus; the largest 95 tons with five-cubic-yard dippers).
• Wikipedia, Culebra Cut and Bucyrus-Erie (160 trainloads a day; 77 of the 102 shovels were Bucyrus; the March 1910 output record; the May 1913 breakthrough).
• PBS American Experience, TR’s Panama (the French effort, Stevens’s continuous double-track spoil trains, Gorgas and sanitation).
• Smithsonian Magazine, How the Panama Canal took a huge toll on the contract workers who built it (workforce origins).
• Transportation History, 1839: William Otis’s steam shovel patent.

TL;DR: I gave a week-long Civil 3D homework assignment to three AI agents on the same workstation, with the same automation tools. Claude Opus 5.5, running in Claude Code, finished every required item and the extra credit in about 65 minutes while I was away from the desk, and produced a seven-sheet plan set a licensed engineer could stamp after a normal review. The same local model, Qwen3.8-Next, run once in OpenCode and once in Claude Code, didn’t get there: one stopped at the corridor and the other finished about 7% of the first item. With the harness held constant, the model made the difference. Routine production drafting is about to get much cheaper, and that changes what we should expect from entry-level engineers and how we teach them.

In the previous post in this series, Two AIs, One Subdivision, Ninety Minutes, Grok and Claude each laid out a 20-lot subdivision from a one-line prompt. One solved it; the other only looked finished. This time the assignment was bigger and the instructions were stricter.

One prompt, one homework, three agents

Homework #6 in the university BIM course I’m taking this fall is a solid Civil 3D exercise. Starting from contour lines, students build an existing-ground surface, a five-curve roadway alignment and profile, a typical section, a corridor with cross-sections every 50 feet, and an earthwork takeoff. Then, in Part 2, they lay out an unpaved road and grade a pad at 3:1 cut and 4:1 fill, balancing cut against fill with Civil 3D’s Grading Volume Tools. Everything is printed and graded against a check sheet.

I gave this assignment to three AI coding agents on the same Windows workstation running Autodesk Civil 3D 2027. The same open-source automation repositories were pre-installed for all three: a Civil 3D MCP server, a COM/AutoLISP harness, and a Civil 3D–Revit bridge.

The instruction to Claude Opus was a single paragraph. It said, roughly: read the files in this folder, complete all of the homework, and use any means of driving Civil 3D you like. Make the drawings neat, orderly and readable, with north arrows and scales and no overlapping labels. Write any tools you need. I’ll be away, so don’t ask me for help. Go.

Then I left.

When I came back, a zipped submission was waiting: a seven-sheet plan set, the drawing, the earthwork volume report, a written report with the AI commentary and extra credit, and the check sheet. From first prompt to finished zip took about 65 minutes of wall-clock time. I didn’t answer a single question, because none was asked.

Neither Qwen attempt got there. Here is how each run went.

Attempt 1: OpenCode + Qwen3.8-Next, “blocked by the API”

The OpenCode run deserves credit. It built the existing-ground surface from 27,504 contour points. It created the roadway alignment with all five curve radii correct, and the paved and unpaved layout profiles with the right end elevations. It even caught that one curve deflected right, not left, as the student’s paraphrase had said. It also extended the MCP server with a new sheet-plotting action and a grading-volume probe.

Then it hit the corridor and stopped. Its status notes give the reason:

Stock subassembly import. SubassemblyCollection.ImportStockSubassembly(name, className, pt) throws ArgumentException: Can't find the subassembly with the className for every stock name… the lanes/curb/sidewalk cannot be added headlessly. Without an assembly there is no corridor → no sample lines/cross-sections/Top-Datum surfaces/quantities.

It reached the same conclusion about grading, writing “Grading exposes 0 public methods in 2027… grading object + volume-tools nudging cannot be automated”, and closed with: “This is an upstream gap, not my fault… If the user can add the lanes/curbs/sidewalk manually in Civil 3D, the corridor/sample lines/surface/quantities will then work.”

So items 5 through 8 on the check sheet were not done: the assembly, the corridor and its Top and Datum surfaces, the cross-section printouts, and the quantity takeoff.

Grading was only partly done. Without a real grading object, the agent estimated cut and fill with an outside geometric model and got numbers far from the true values. Its extra-credit answer said the pad needed to come down about 0.3–0.5 ft. Civil 3D’s own volume tools later showed the pad had to go up 8.8 ft.

The drawings show the struggle. This is the “24×36 plan” sheet it produced:

OpenCode + Qwen3.8-Next, “HW06_24x36_Plan.pdf.” The plotted content fills about a fifth of the page. Title-block text is stacked on itself, the profile view overlaps the plan, and the north arrow is a tiny tick mark.

And its “paved profile view” printout, which the check sheet requires:

The required profile-view printout. It is actually a plan-view window of the site with alignment labels. No profile grid is visible.

The “overview” printout. The profile view is placed over the bottom of the plan, and the oversized profile-view title runs across the whole sheet in cyan outline text.

To be fair, the agent’s own notes admit the problem: “a vision model reviewing the 72-dpi preview render still flags the small-caps title-block values as hard to read.” It noticed the sheets were poor but couldn’t get to a sheet a professional would sign.

Attempt 2: Claude Code + Qwen3.8-Next, stuck on the first item

The second Qwen run is the more striking one, because the harness was the same one Claude Opus later used. Only the model differed.

Qwen decided early, as a safety rule, never to send commands to Civil 3D through COM. That left the MCP server as its only channel. It then spent its effort trying to push 27,000 contour vertices through MCP approval tokens, 600 points at a time, by echoing 27 KB JSON arrays back through its own context window.

It worked out the whole approval-token protocol: schema validation, time-to-live, hash-order sensitivity. Its own write-up describes the mechanism as “the load-bearing discovery of the whole effort.” When it stopped, the status line read:

Item 1 (EG surface) ~7 % fed — 5 of 55 contour-data bins landed… Items 2–12 not started.

It estimated it would need about 1.4 million more tokens of “mechanical repetition” just to finish the existing-ground surface. Printing was blocked too, because every plot call returned eUserBreak while Civil 3D sat in the background. It recorded that as “a human-action blocker, not a software bug.” This attempt produced no printed drawings, so there is nothing to show here.

Attempt 3: Claude Code + Claude Opus 5.5, done in an hour

Claude Opus spent its first minutes reading the drawing and the assignment, and briefly testing the MCP server. It decided the MCP server was fine for reading but couldn’t build what the assignment needed. So it built its own channel into Civil 3D: a small .NET add-in compiled against the Civil 3D 2027 API, loaded with NETLOAD, and driven over COM from PowerShell. Each command wrote a log file with a “done” marker, so the agent could tell when a step had finished.

Where the Qwen runs hit a wall, Opus found a way through.

• The corridor. OpenCode/Qwen concluded that ImportStockSubassembly could not find the stock subassemblies. Opus first wrote a small reflection tool to read the method signatures out of Civil 3D’s mixed-mode DLLs, which ordinary reflection can’t open. It then passed the namespace-qualified class names (Subassembly.BasicLane, Subassembly.BasicCurbAndGutter and so on). The call worked. The assembly, corridor, Top and Datum surfaces, sample lines every 50 ft and the quantity takeoff followed. The takeoff came to 1,002.94 CY cut and 387.16 CY fill, and the report went through Civil 3D’s own earthwork.xsl transform.
• Grading. Both Qwen runs were right that Civil 3D 2027 has no public API for grading groups, gradings, infill or the Grading Volume Tools. Opus didn’t stop there. It drove Civil 3D’s own commands the way a person would: it clicked toolbar buttons by control ID with Win32 messages, typed answers to command-line prompts by posting keystrokes, and read the prompts back from AutoCAD’s command log file to know what Civil 3D was asking for. It created the grading group, the 3:1/4:1 grading to the existing surface and the interior infill, then ran Auto-Balance. The pad rose 8.82 ft to El. 2131.186, with 8,515 CY cut, 8,518 CY fill and a net of 2.87 CY.
• Printing. The Windows session was locked the whole time, so screenshots were impossible. Opus plotted each sheet to PDF, rasterized it and looked at the image. It found problems, fixed them and plotted again. Those problems included overlapping curve labels, a profile-view band showing existing-ground elevations where it should show proposed ones, and a render surface hidden on a frozen layer.

It also made mistakes and caught them. Its first grading criteria came out at 0.33:1 and 0.25:1 instead of 3:1 and 4:1, because Civil 3D stores slopes as rise over run. It recognized the error from the daylight geometry, then erased the grading and rebuilt it.

Here is what came out.

C-101 Site Plan. Existing-ground contours, the roadway and unpaved alignments, the grading boundary, a north arrow, a graphic scale, and a complete title block.

C-102 Roadway Plan. Stationing, a curve table for the five horizontal curves, and labels that don’t collide.

C-201 Roadway Profile. Existing ground against the finished paved road surface, at 10× vertical exaggeration, with vertical curve data and elevation bands.

C-301 Cross Sections. The start (10+00), middle (15+00) and end (20+13.75) sections with existing and proposed elevation bands, the typical section with every subassembly parameter, and the station-by-station earthwork table.

C-401 Corridor Rendering. The corridor surfaces rendered by material, in plan and in a 3D view.

C-501 Unpaved Road Profile. The 228.22 ft unpaved alignment at +2.0%, ending at El. 2122.364.

C-601 Grading Plan. The balanced pad with its daylight lines against existing contours, a north arrow, a graphic scale, a grading volume summary table, and notes on how the result was produced.

Put these next to the Qwen sheets. The difference isn’t a matter of taste. A licensed engineer could put a stamp on the Opus set with a normal level of review. The Qwen sheets would go back to the drafter.

What we learned

1. The gap isn’t knowledge; it’s persistence and problem-solving. All three agents found the same API limits. The Qwen runs read a missing API as a stop sign. Opus asked a different question: if there’s no API, how does a person do this? A person clicks a toolbar button and types at a prompt, and a program can do both. The best human drafters work the same way. They don’t give up because the ribbon is greyed out.

2. Checking its own work in a loop is what produced the professional finish. About twenty of Opus’s 65 minutes went into nothing but making the sheets neat: plot, rasterize, look, fix, plot again. That loop is why the labels don’t overlap. OpenCode/Qwen had a vision check and saw that its sheets were poor, but it couldn’t turn what it saw into a fix. Being able to see the problem isn’t the same as being able to fix it.

3. Tool choice is part of the intelligence. Qwen inside Claude Code limited itself to the MCP server, then spent its budget forcing bulk data through a channel built for small calls. Opus recognized early on that the MCP server was the wrong channel for building geometry, and wrote a compiled add-in that sent 24,000 points to Civil 3D in a single call. Judging which tool fits the job matters as much as being able to use the tool.

4. The model is the difference. Attempts 2 and 3 used the same harness (Claude Code), the same Civil 3D workstation, the same repos and the same assignment. The only difference was the model: Qwen3.8-Next running locally on an RTX 6000 Pro, or Claude Opus 5.5 in Anthropic’s cloud. One finished; the other got through 7% of item 1.

That last point deserves a caveat, because we’ve written in favor of local models for construction work in A GLIMMER of Hope, and I still stand by it. A local model on one workstation GPU is a remarkable achievement, with real advantages in privacy and cost, and it handles well-scoped document tasks well. Open-ended engineering, where the agent has to find its own way around missing APIs and keep going until the drawings meet a professional standard, is a different kind of job. For that, frontier models are still well ahead.

Recommendations for anyone automating Civil 3D with AI

• Build a shared toolkit. The Opus run wrote about 2,650 lines of C#, AutoLISP, PowerShell and Python. The reusable core is a spec-driven add-in for surfaces, alignments and corridors, a dialog and command-line driver for grading, a sheet library, and a plot-and-inspect QC loop. The grading driver is already a pull request to the open-source civil3d-automation repo.
• Give the MCP server real build tools, such as bulk data transfer, stock subassemblies and grading, so the next agent doesn’t have to rediscover them.
• Make visual QC mandatory. No sheet is done until it has been plotted and inspected.
• Use the strongest model for open-ended engineering work. The full Opus run cost about $29 at list API prices. A failed run costs more than that in engineering time.

What this means for entry-level engineers and drafters

I want to be careful here, because people’s careers are involved.

A task that’s a solid week of student effort was finished, to a professional drafting standard, in about an hour, while the person who assigned it was away from the desk. In a design office, that work is what a first- or second-year EIT or a CAD technician spends a large share of the day on: setting up surfaces, laying out alignments to given geometry, building corridors, cutting sections, cleaning up labels and plotting sheets.

It would be dishonest to say that doesn’t change entry-level work. It does. A firm that used to need four drafters to keep up with two project engineers may soon need one or two, each directing several AI agents and checking what they produce.

But civil engineering has seen this before.

The steam shovel

In the late 1800s and early 1900s, earth was moved by people with picks, shovels and wheelbarrows, by mule teams and by hand-loaded carts. Then the steam shovel arrived. The Americans dug the Panama Canal with 102 of them, most built by Bucyrus, and the biggest could load several thousand cubic yards of clay and blasted rock a day through the Culebra Cut. No army of hand laborers could have matched that pace. Across the industry, machines like these put many excavation laborers out of work, and that was real hardship for real families.

The steam shovel also made projects possible that had been impossible: the canal itself, and then, as mechanized earthmoving kept improving, the interstate highways, the large dams and the modern city. Total construction employment grew, because what society could build grew so much. The work changed. Fewer people swung picks, and more operated, maintained, planned, surveyed, inspected and engineered. The shovel didn’t replace the civil engineer. It made the civil engineer’s ambitions bigger.

Firms have had to reposition when the ground shifted under them before; in The Builder’s Pivot, Part II we looked at how mid-market contractors can get into a market that’s moving faster than they can retool. AI-driven drafting and design automation is that kind of shift for the office side of the profession. It is the steam shovel, and it will move a mountain of routine production work. What it opens up is the chance to:

• run ten design alternatives where we used to have time for one;
• balance earthwork exactly instead of “close enough”;
• check every sheet for every label collision;
• take on more projects, larger projects, and projects in places that couldn’t afford the engineering hours before.

We will still need people, and we’ll expect more from them

My view is that this raises the bar for students and professionals alike. We will still need entry-level engineers and drafters, but we’ll expect better results, more of them and sooner.

Look at what Opus didn’t do on its own. It didn’t decide whether a +5.9% grade on an unpaved access road was acceptable for the design vehicle. It flagged that as an engineering judgment and listed the trade-offs. Someone still has to:

• know that rise-over-run slopes came out at 0.33:1 when they should have been 3:1;
• know that a balanced pad 8.8 ft above the road it serves is a problem;
• know that a quantity takeoff of 615 CY net cut means a haul-off cost to put in the estimate;
• sign the drawings.

That’s the same conclusion the subdivision experiment pointed to: the drawing is getting cheap, and the scarce skill is telling a right drawing from one that only looks right. The entry-level engineer of 2030 will spend less time clicking ribbon buttons and more time specifying, checking and judging. That is a more demanding job, not an easier one.

How this changes engineering education

If an AI agent can finish the homework in an hour, what is the homework for?

It’s still for learning, but the target moves:

1. From “can you produce it?” to “can you tell whether it’s right?” Give students deliberately flawed AI output to audit, such as a grading with rise/run units swapped or a quantity table that doesn’t add up.
2. Teach the fundamentals harder. You can’t check a vertical curve you can’t compute. Hand calculation matters more when a machine produces the answer you must verify.
3. Teach specification. Writing a clear design spec that an agent can carry out is how work will be assigned in practice.
4. Grade judgment. This assignment’s extra-credit questions (“As an engineer, what would be your decision…”) are its most future-proof part.
5. Expect more. With an AI assistant, the deliverable shouldn’t be one corridor. It should be three alternatives with a cost comparison and a recommendation.

This assignment already asks students to “explore AI output and comment on whether it is satisfactory.” After this experiment, I’d make that the center of the course rather than an add-on.

The bottom line

All three attempts had the same Civil 3D workstation, the same tools and the same assignment, but not the same class of model.

• Claude Code + Claude Opus 5.5 (cloud) finished every item and the extra credit in about 65 minutes, with no human help. It wrote its own tools where the existing ones fell short and produced a seven-sheet plan set that looks like it came from a professional office.
• OpenCode + Qwen3.8-Next (local, RTX 6000 Pro) finished the geometry, then stopped at the corridor and grading. It called those items “upstream gaps,” and its sheets were hard to read.
• Claude Code + Qwen3.8-Next (local, RTX 6000 Pro) spent its effort pushing contour data through a channel that wasn’t built for it, and finished 7% of the first item.

Using Claude Code with the local model didn’t close the gap. With the harness held constant, the model made the difference.

The steam shovel didn’t end civil engineering; it scaled it up. AI agents that can drive our design software to a professional standard won’t end the profession either. They’ll raise expectations for every engineer, student and drafter, and let us build more, faster and better. Our job as educators and practitioners is to make sure the people directing these tools have the judgment to know when the output is right, and when it isn’t.

This is part of an ongoing series on AI and civil engineering homework. Previous post: Two AIs, One Subdivision, Ninety Minutes. For an AI’s own account of automating Civil 3D, read Driving Civil 3D From the Outside, written unedited by Qwen3.8-Next after a 16-hour OpenCode session on the earlier Revere Way homework.

Drawings: Claude Opus sheets are from its submitted HW06 plan set. Qwen sheets are from the OpenCode run’s PDF output. The Claude Code + Qwen attempt produced no printouts.

One rack, 600 kilowatts — and why that rewrites the nuclear engineer’s job.

One Nvidia rack is now specified at about 600 kilowatts.

That is the figure Jensen Huang put on each Rubin Ultra “Kyber” rack in his GTC 2025 keynote — roughly the continuous load of 500 American homes, packed into a single liquid-cooled enclosure. Even in 2025, Uptime Institute found the typical data-center rack averaged under 9 kW. Today’s Vera Rubin NVL72 cabinets already sit at about 190–230 kW. Kyber was originally slated for the second half of 2027 on 800-volt DC distribution. In July 2026, SemiAnalysis reported a slip into 2028 over midplane manufacturing; Nvidia said its roadmap is intact. Either way, the facility problem is the same: racks are becoming small power plants.

Multiply that density across a gigawatt-scale campus and you have the engineering problem of this decade. As the third quarter of 2026 closed, two clocks were running at once. AI load is climbing now. Advanced nuclear is commercializing on a slower, more deliberate schedule. Gas turbines are what sit in between.

That collision is rewriting the career map for power professionals. The rest of this post follows the sequence the market is actually using: the demand math, the gas bridge, the nuclear permits that landed this quarter, the first-of-a-kind contract that did not, the fuel bottleneck behind advanced reactors, and where a nuclear engineer fits.

The grid cannot connect this load on the old timetable

JLL’s 2026 Global Data Center Outlook expects nearly 100 GW of new data-center capacity worldwide between 2026 and 2030, taking the global base to about 200 GW. BloombergNEF’s July 2026 U.S. outlook is more specific, and more startling: 118 GW of installed U.S. data-center capacity by 2030 — a 52 percent upward revision in seven months.

BNEF also estimates that the most new data-center demand the U.S. grid has ever connected in a single year is about 10 GW. JLL puts the average wait for a grid connection in primary data-center markets at more than four years.

Read those last two lines together. Demand is growing by tens of gigawatts. The grid’s best year connected ten. For the past decade, grid planners forecast peak-demand growth of only about 0.5 percent a year. It cannot close that gap on interconnection queues alone.

So the buyers are changing shape. Hyperscalers are becoming energy developers — “shadow utilities” that fund, own, or anchor generation instead of waiting in line:

• Amazon bought Talen Energy’s Cumulus data-center campus next to the Susquehanna nuclear plant in 2024, and is backing Energy Northwest’s Cascade project in Washington: a first phase of four X-energy Xe-100 units totaling 320 MWe, with an option to expand to 12 units and 960 MWe.
• Meta signed with TerraPower in January 2026 to fund early development of two Natrium units and secure energy rights on up to six more — as many as eight units and 2.8 GW of baseload by the mid-2030s if the later plants are built. That is an option set, not eight shovels in the ground.
• Google is taking clean-energy attributes from Kairos Power’s Hermes 2 plant in Oak Ridge through a power-purchase agreement between Kairos and TVA, and has separately pioneered “clean transition” tariff structures that let a large buyer fund new firm power through its utility.

The halls themselves are learning to flex. Nvidia’s Vera Rubin DSX AI Factory reference design includes DSX Flex, software that lets an AI factory adjust draw to grid conditions and coordinate with on-site generation. Nvidia and Emerald AI argue flexible AI loads could unlock up to 100 GW of capacity on the existing U.S. grid. The first commercial deployment is to be the 96 MW Aurora AI factory at Nvidia’s research center in Manassas, Virginia — billed as the world’s first power-flexible AI factory and developed with Digital Realty, EPRI, and PJM. It was slated to open in the first half of 2026 and has not yet opened.

Utilities already feel the direction, if not yet the full volume. For the nine months through June 30, 2026, TVA reported about $10 billion in operating revenue and $965 million in net income — net income up $220 million year over year. Electricity sales rose only about 1 percent. TVA attributed the sales gain mainly to data processing, web hosting, and related services; revenue rose mainly on higher fuel-cost recovery rates. The signal is the customer mix, not a demand spike on the monthly bill.

Gas is winning the next five years

SMRs dominate headlines. On-site gas turbines are winning the interconnection race.

BloombergNEF counts 124 GW of announced on-site gas capacity for U.S. data centers and says on-site gas “must play a significant role” if its forecast is to be met. Unless the grid connects new data centers faster than its best year to date, about 10 GW, roughly 48 GW of on-site gas must be built by 2035. The near-term workhorse is the aeroderivative turbine — a jet engine adapted for ground power. GE Vernova’s LM2500XPRESS delivers about 35 MW per unit, arrives roughly 95 percent factory-assembled, installs in weeks, and starts in five minutes. Crusoe has ordered 29 of them across two orders (December 2024 and June 2025), nearly 1 GW.

Speed is the reason. A new SMR is still targeted at five or more years from construction permit to power, and closer to a decade from project start. A modular aero unit with a manufacturing slot can be producing in months. Developers call that “Phase 0”: energize the first wave of servers on turbines, then hand the load to the grid or to nuclear in the 2030s.

One caveat: speed depends on the slot. GE Vernova’s gas-equipment backlog and slot reservations grew from 100 to 116 GW in the second quarter of 2026 alone. On its earnings call, the company said it is already taking reservations for 2031 deliveries.

The most interesting projects do not choose. They sequence.

Blue Energy’s Port of Victoria, Texas plan is the template made literal: two GE Vernova 7HA.02 turbines delivering about 1 GW to a nearby Crusoe data-center campus by 2030, then up to five BWRX-300s adding about 1.5 GW from around 2032. On September 16, Blue Energy filed Part 1 of its NRC construction-permit application for the first nuclear unit — just over four months after it announced the BWRX-300 choice. Gas and nuclear are not rivals in that design. They are sequential phases on the same site, the same interconnection, and the same customer.

Nuclear is moving. Light-water SMRs moved first.

Capital commitments are one thing. Permits and electrons are another. The third quarter produced a run of light-water SMR milestones — reactors that burn the same low-enriched uranium today’s fleet already uses:

Clinch River is the U.S. headline. The NRC finished a 14-month review, four months ahead of its own schedule. The project team pairs TVA and GE Vernova Hitachi with Bechtel and Sargent & Lundy. A construction permit is not a construction start, and TVA has said it is still working through cost-sharing with its partners. It is still the first time a U.S. utility is permitted to build this class of reactor.

The same week, a different nuclear story landed in Reston.

The Natrium handoff is what FOAK risk looks like in a contract

In September, Bechtel told Virginia regulators that it and TerraPower could not agree on the next phase of the roughly $4 billion Natrium project in Kemmerer, Wyoming, and would move forward separately.

The news surfaced through a September 15 WARN Act filing covering about 200 positions at Bechtel’s Reston, Virginia headquarters, with reductions expected to begin on or about November 16. Bechtel has said it does not expect any actual layoffs and is reassigning staff to other nuclear projects. It is also staying on site to finish the Sodium Test & Fill Facility and support an orderly transition. TerraPower says it will maintain its schedule — construction complete in 2030, commercial operation in 2031 — and it will rebid the EPC contract.

What Bechtel delivered under the earlier phase is not in dispute. Selected as TerraPower’s engineering and construction partner in 2020, it supported the project through:

• the NRC construction permit issued in March 2026 — the first the NRC has ever issued for a commercial non-light-water power reactor, and the first such U.S. permit in more than 50 years
• completion of the Sodium Test & Fill Facility’s exterior enclosure
• placement of major procurements
• the start of foundation work for Kemmerer Unit 1

Neither company has published the contract, but Bechtel says the work to date “was performed under an earlier phase of the contract, prior to full EPC scope,” and Lincoln County Board of Commissioners Chairman Kent Connelly told WyoFile that Bechtel wasn’t expressly under contract for the next “nuclear island” phases. The parting came at that phase boundary. Neither company has disclosed the commercial sticking points, or said which side said no.

First-of-a-kind nuclear is where cost estimates get stress-tested. Whichever side said no, a clean handoff at a phase gate beats an unworkable contract signed early.

That knowledge is scarce, which is why the same firm’s other book of work matters as context rather than consolation. Georgia Power and Southern Nuclear brought Bechtel in in 2017 to complete Vogtle Units 3 and 4 — the Westinghouse AP1000 units that were the first new U.S. large reactors in more than three decades. On September 23, a week after the WARN filing, the Bechtel–Westinghouse consortium agreed on the main commercial terms of the EPC contract for Poland’s first nuclear plant, three AP1000 units; both sides aim to finish the full contract by year-end. And Bechtel remains on TVA’s integrated project team for Clinch River, where the NRC permit arrived on September 29 — contracted to plan the unit and positioned to build it if TVA proceeds.

Bechtel’s website still describes it as the only EPC with current projects across the full spectrum of reactor technologies, citing Poland, Clinch River, and Natrium. Unless it wins another advanced-reactor job, that last leg ends when the Sodium Test & Fill Facility is finished. The Kemmerer handoff shows how the firm is choosing where to take FOAK risk.

Fuel is why advanced reactors are harder to contract than light-water plants

Any honest discussion of sodium-cooled or high-temperature designs has to include fuel. Natrium runs on high-assay low-enriched uranium (HALEU) — uranium enriched to between 5 and 20 percent U-235 — and needs roughly 15 to 20 metric tons of it for a first core. Before 2022, Russia was what TerraPower called the only commercial source of HALEU fuel. When that door closed, TerraPower announced a delay of at least two years, and the West started building a fuel chain almost from scratch.

That chain is taking shape, slowly:

• Enrichment. TerraPower has supply agreements with ASP Isotopes intended to support Natrium’s first core and up to 150 metric tons over 2028–2037, tied to a planned enrichment plant at Pelindaba, South Africa. In the U.S., Centrus’s Piketon, Ohio demonstration cascade — which Centrus calls the only HALEU production it knows of outside Russia — made about 900 kg of HALEU (as UF6) a year, more than 1,900 kg in all, until DOE-funded production ended in mid-June 2026; Centrus intends to keep running it commercially. Under a $900 million DOE task order awarded in January 2026 and finalized in July, Centrus is to add commercial-scale capacity starting around 2029 and deliver its first metric ton by July 2032.
• Conversion and fabrication. Framatome has completed a uranium metallization line at its Richland, Washington plant and produced metal pucks from depleted uranium — a step toward HALEU metal for Natrium fuel. Making HALEU there will need a further license amendment. The Natrium Fuel Facility is under development in Wilmington, North Carolina.

Nothing public ties fuel supply to the Bechtel–TerraPower decision. Fuel uncertainty is still one reason an owner–contractor risk split on an advanced reactor is harder to write than on a light-water plant that uses today’s LEU.

The talent map is already moving. The BLS table is not.

Read the federal statistics alone and the profession looks flat. The Bureau of Labor Statistics projects 0 percent employment growth for nuclear engineers from 2025 to 2035, on a base of about 16,600 jobs, with roughly 800 openings a year from retirements and transfers. The 2025 median wage is $133,970.

Look at BLS’s own reasoning: it attributes the flat outlook to an increased focus on renewable energy and limited construction of new nuclear plants. This quarter produced the year’s second U.S. power-reactor construction permit (the first for an SMR), a 2.5 GW gas-plus-nuclear plan entering NRC review, and a four-unit Swedish SMR program. Occupational projections are built on history. They lag inflection points.

The demand is shifting to four places:

1. Hyperscaler energy teams. Tech companies need engineers who can evaluate reactor designs, audit counterparties, manage nuclear supply chains, and integrate behind-the-meter generation with data-center architectures such as Nvidia’s 800-volt DC designs.
2. The SMR and microreactor vanguard. Venture-backed developers — X-energy, Oklo, Kairos Power, Valar Atomics and others — are moving from design into hardware: fuel plants, factory-built components, site construction, and — for Valar — a test reactor that reached criticality this summer. That creates a vacuum for people with execution and NQA-1 quality-assurance experience.
3. EPC integration. Bechtel, Fluor, Kiewit, and Sargent & Lundy are building nuclear practices for the coming wave of light-water SMR projects. The rarest profile is the one that has taken a reactor from permit to power.
4. The hybrid power-systems architect. With gas as Phase 0, the most valuable engineer can design a 100 MW turbine microgrid today — and lay out the substation, interconnection, and site so an SMR can take over the load in the 2030s.

Turbines now, reactors next is not a detour

The smartest move for a nuclear engineer in 2026 may be a few years on gas turbines. That sounds like leaving the field. It is not.

A nuclear plant is a heat source bolted to a conventional power plant. In a BWRX-300, reactor steam drives the turbine directly. Natrium stores reactor heat in molten salt and raises steam for a separate energy island; TerraPower plans a commercially available steam turbine there, and NRC staff agreed that running it is not a reactor control only a licensed operator may manipulate — though, absent an exemption, a licensed operator must still know of and consent to energy-island manipulations that may affect the reactor. Everything downstream of the reactor — turbine-generator, condenser, feedwater, switchyard, grid synchronization — is work a combined-cycle gas plant demands too.

The calendar makes the case. Blue Energy’s Texas turbines are slated for about 1 GW by 2030, with reactors following around 2032. TerraPower targets 2031 at Kemmerer. Clinch River is an early-2030s plant if TVA pulls the construction trigger. Sweden’s first BWRX-300 is mid-2030s. That leaves a five-to-seven-year window in which most new power-plant engineering work is on the gas side.

An engineer who spends that window commissioning turbine fleets on data-center campuses arrives at the SMR wave with something scarce: hands-on experience integrating generation with AI loads — often at the sites where the reactors will go. On gas-plus-nuclear projects like Blue Energy’s, the bridge is literal: the same team, the same site, a different heat source.

The gaps are real. Gas work adds combustion, emissions controls, air permitting, and the maintenance cycles of aeroderivative engines. Reactor physics and NRC licensing sit idle unless the engineer keeps them current — through SMR licensing support, ANS involvement, or professional licensure.

For employers the logic runs the other way. A nuclear-trained engineer on a gas project brings a safety and quality culture few turbine teams have, and is already on staff when the site is ready for its reactor.

What to watch in Q4

• Who wins the Natrium EPC rebid — and how the contract allocates FOAK risk. (In August, TerraPower named Hyundai E&C as EPC partner for up to eight follow-on Natrium units.)
• Whether Poland’s Bechtel–Westinghouse AP1000 EPC is signed by year-end, now that main terms are agreed
• Clinch River’s construction decision, and how TVA settles cost-sharing
• Blue Energy’s Part 2 application and 2027 FID, and whether Energy Northwest files the Cascade construction-permit application in the first half of 2027, as X-energy now guides (slipped from an end-2026 target)
• HALEU milestones at Centrus and ASP Isotopes

What the quarter actually proved

A contractor handoff on a first-of-a-kind reactor is not a failure of the renaissance. It is what an industry looks like when it moves from theory to practice with real contracts and real accountability. The last U.S. nuclear revival, in the 2000s, stalled when shale gas turned cheap, alongside the recession and Fukushima. This one is using cheap gas as the on-ramp.

AI demand for firm power is not infinite. For the rest of this decade it will still outrun what the grid can connect. For nuclear engineers, the work no longer lives only inside traditional utilities. It lives in the projects that pair turbines today with reactors tomorrow, in the contracts that allocate FOAK risk honestly, and in the fuel chain being rebuilt from scratch.

The engineers who can navigate commercial risk, fuel logistics, and fast microgrid deployment will not just find jobs. They will build the plants the racks are waiting on.

Works Cited

• “Agreement Advances Texas Gas-Plus-Nuclear Concept to Next Phase.” Nuclear Newswire, American Nuclear Society, 17 Aug. 2026, www.ans.org/news/2026-08-17/article-8301/agreement-advances-texas-gasplusnuclear-concept-to-next-phase/.
• “Amazon Unveils ‘Cascade’—Energy Northwest’s Xe-100 SMR Project, Targeting Construction by 2030.” POWER, Oct. 2025, www.powermag.com/amazon-unveils-cascade-energy-northwests-xe-100-smr-project-targeting-construction-by-2030/.
• ASP Isotopes Inc. Form 10-Q for the Quarterly Period Ended June 30, 2026. U.S. Securities and Exchange Commission, 2026, www.sec.gov/Archives/edgar/data/0001921865/000119312526352603/aspi-20260630.htm.
• “AWS Acquires Talen’s Nuclear Data Center Campus in Pennsylvania.” DatacenterDynamics, 4 Mar. 2024, www.datacenterdynamics.com/en/news/aws-acquires-talens-nuclear-data-center-campus-in-pennsylvania/.
• “Bechtel Splits with Bill Gates-Backed Nuclear Project, Files Layoff Notice.” Construction Dive, Sept. 2026, www.constructiondive.com/news/bechtel-splits-terrapower-nuclear-project-layoffs/830793/.
• “Bechtel Steps Away from TerraPower Project.” World Nuclear News, Sept. 2026, world-nuclear-news.org/articles/bechtel-steps-away-from-terrapower-project.
• “Bechtel-Westinghouse Consortium and PEJ Agree on Main Commercial Terms for Poland’s First Nuclear Power Plant.” Bechtel, 23 Sept. 2026, www.bechtel.com/press-releases/bechtel-westinghouse-consortium-and-pej-agree-on-main-commercial-terms-for-polands-first-nuclear-power-plant/.
• Bleizeffer, Dustin. “Lead Contractor Pulls Out of Gates’ Natrium Nuclear Power Plant Project in Wyoming.” WyoFile, republished by Cap City News, 23 Sept. 2026, capcity.news/wyoming-news/energy/2026/09/23/lead-contractor-pulls-out-of-gates-natrium-nuclear-power-plant-project-in-wyoming/.
• Bleizeffer, Dustin. “Lead Contractor Pulls Out of Gates’ Natrium Nuclear Power Plant Project in Wyoming.” WyoFile, Sept. 2026, wyofile.com/lead-contractor-pulls-out-of-gates-natrium-nuclear-power-plant-project-in-wyoming/.
• BloombergNEF. “Six Things to Know About BNEF’s New US Data Center Capacity Outlook.” BloombergNEF, 2026, about.bnef.com/insights/data-centers/six-things-to-know-about-bnefs-new-us-data-center-capacity-outlook/. Accessed 30 Sept. 2026.
• “Blue Energy and GE Vernova Accelerate Gas-Plus-Nuclear Approach for Powering American Communities and Fueling Global AI Leadership.” PR Newswire, 5 May 2026, www.prnewswire.com/news-releases/blue-energy-and-ge-vernova-accelerate-gas-plus-nuclear-approach-for-powering-american-communities-and-fueling-global-ai-leadership-302761986.html.
• “Blue Energy Files First NRC Application for Texas Gas-to-Nuclear Project.” Power Engineering, Sept. 2026, www.power-eng.com/nuclear/blue-energy-nrc-application-gas-nuclear-project/.
• “Blue Energy Files Texas Nuclear Permit.” Nuclear Engineering International, 22 Sept. 2026, www.neimagazine.com/news/blue-energy-files-texas-nuclear-permit/.
• “Blue Energy Moves Texas Gas-Plus-Nuclear Project into NRC Construction Review, Marking a Major Regulatory Milestone.” GE Vernova, 16 Sept. 2026, www.gevernova.com/news/press-releases/blue-energy-texas-gas-plus-nuclear-project-nrc-review.
• “Blue Energy Submits First Construction Application to NRC.” World Nuclear News, 16 Sept. 2026, www.world-nuclear-news.org/articles/blue-energy-submits-first-construction-application-to-nrc.
• Centrus Energy Corp. Form 10-Q for the Quarterly Period Ended June 30, 2026. U.S. Securities and Exchange Commission, 2026, www.sec.gov/Archives/edgar/data/1065059/000162828026053863/leu-20260630.htm.
• Centrus Energy Corp. Investor Presentation. 2026, investors.centrusenergy.com/static-files/058b474a-c135-4600-a84b-e9908864a7af. Accessed 30 Sept. 2026.
• “Commercial Nuclear.” Kiewit, www.kiewit.com/our-markets/nuclear-solutions/commercial-nuclear/. Accessed 1 Oct. 2026.
• “Consortium Formed for Swedish SMR Project.” World Nuclear News, 3 Sept. 2026, www.world-nuclear-news.org/articles/consortium-formed-for-swedish-smr-project.
• Dalton, David. “Centrus Finalises $900 Million HALEU Enrichment Contract with US DOE.” NucNet, 3 July 2026, www.nucnet.org/news/centrus-finalises-usd900-million-haleu-enrichment-contract-with-us-doe-7-5-2026.
• Davis, Lucas W. “Prospects for Nuclear Power.” NBER Working Paper 17674, National Bureau of Economic Research, Dec. 2011, www.nber.org/system/files/working_papers/w17674/w17674.pdf.
• “Emerald AI Integrates with NVIDIA DSX Flex to Unlock Up to 100 GW of Grid Capacity for Next-Generation AI Factories.” Emerald AI, Mar. 2026, www.emeraldai.co/blog/emerald-ai-nvidia-dsx-flex-ai-factories.
• “Federal Nuclear Regulator OKs TVA to Build First US Small Modular Reactor.” ENR, Sept. 2026, www.enr.com/articles/63737-federal-nuclear-regulator-oks-tva-to-build-first-us-small-modular-reactor.
• “First Construction Permit Issued for BWRX-300 SMR in the US.” World Nuclear News, 30 Sept. 2026, world-nuclear-news.org/articles/first-construction-permit-issued-for-bwrx300-smr-in-us.
• “Framatome, TerraPower in Advanced Fuel Breakthrough.” World Nuclear News, 2025, www.world-nuclear-news.org/articles/framatome-terrapower-in-advanced-fuel-breakthrough.
• “From Megawatts to Tokens: How NVIDIA Maximizes AI Factory Production.” NVIDIA Blog, 15 Sept. 2026, blogs.nvidia.com/blog/from-megawatts-to-tokens-how-nvidia-maximizes-ai-factory-production/.
• “Fuel Fabrication.” World Nuclear Association, world-nuclear.org/information-library/nuclear-fuel-cycle/conversion-enrichment-and-fabrication/fuel-fabrication. Accessed 1 Oct. 2026.
• “GE Vernova Gas Turbine Backlog Climbs to 116 GW.” Utility Dive, July 2026, www.utilitydive.com/news/ge-vernova-gas-turbine-backlog-climbs-to-116-gw/826039/.
• GE Vernova Inc. “GE Vernova Reports Second Quarter 2026 Financial Results and Raises 2026 Financial Guidance.” U.S. Securities and Exchange Commission, 22 July 2026, www.sec.gov/Archives/edgar/data/0001996810/000199681026000147/gevpressrelease2q26.htm.
• “GE Vernova, Crusoe Announce Major 29-Unit Aeroderivative Gas Turbine Deal to Deliver AI Data Centers.” GE Vernova, 22 July 2025, www.gevernova.com/news/press-releases/ge-vernova-crusoe-announce-major-29-unit-aeroderivative-gas-turbine-deliver-ai-data-centers.
• “Google, Kairos Power, TVA Collaborate to Meet America’s Growing Energy Needs.” Kairos Power, 18 Aug. 2025, www.kairospower.com/updates/google-kairos-power-tva-collaborate-to-meet-americas-growing-energy-needs.
• Grid Strategies. The Era of Flat Power Demand Is Over. Dec. 2023, gridstrategiesllc.com/wp-content/uploads/2023/12/National-Load-Growth-Report-2023.pdf.
• “GTC Keynote with NVIDIA CEO Jensen Huang.” Transcript, Rev, Mar. 2025, www.rev.com/transcripts/gtc-keynote-with-nvidia-ceo-jensen-huang.
• “HALEU Fuel Availability Delays Natrium Reactor Project.” World Nuclear News, 15 Dec. 2022, www.world-nuclear-news.org/Articles/HALEU-fuel-availability-delays-Natrium-reactor-pro.
• “A Historic First: NRC Clears TerraPower’s Natrium Nuclear Reactor for Construction.” POWER, Mar. 2026, www.powermag.com/a-historic-first-nrc-clears-terrapowers-natrium-nuclear-reactor-for-construction/.
• “How the World’s First Flexible AI Factory Will Work in Tandem with the Grid.” Latitude Media, Oct. 2025, www.latitudemedia.com/news/how-the-worlds-first-flexible-ai-factory-will-work-in-tandem-with-the-grid/.
• “The JLL Data Center Outlook: Growth, Growth and More Growth.” Connect CRE, Jan. 2026, www.connectcre.com/stories/the-jll-data-center-outlook-growth-growth-and-more-growth/.
• JLL. “2026 Global Data Center Outlook.” JLL, 2026, www.jll.com/en-us/insights/market-outlook/data-center-outlook. Accessed 30 Sept. 2026.
• “Just Add HALEU: TerraPower and GNF-A Break Ground on Fast Reactor Fuel Facility.” Nuclear Newswire, American Nuclear Society, www.ans.org/news/article-4444/just-add-haleu-terrapower-and-gnfa-break-ground-on-/. Accessed 30 Sept. 2026.
• “Launching the First Power-Flexible AI Factory with NVIDIA.” Emerald AI, Oct. 2025, www.emeraldai.co/blog/launching-the-first-power-flexible-ai-factory-with-nvidia.
• “Meta Announces ‘Landmark’ Agreements for New Nuclear.” World Nuclear News, Jan. 2026, www.world-nuclear-news.org/articles/meta-announces-landmark-agreements-for-new-nuclear.
• ModulEdge. “Nvidia Vera Rubin: Data Center Impact Explained.” ModulEdge, 2026, www.moduledge.com/blog/nvidia-vera-rubin. Accessed 30 Sept. 2026.
• “Natrium Demonstration Project.” Bechtel, www.bechtel.com/projects/natrium-demonstration-project/. Accessed 1 Oct. 2026.
• “NV Energy Seeks New Tariff to Supply Google with 24/7 Power from Fervo Geothermal Plant.” Utility Dive, June 2024, www.utilitydive.com/news/google-fervo-nv-energy-nevada-puc-clean-energy-tariff/719472/.
• “NVIDIA 800 V HVDC Architecture Will Power the Next Generation of AI Factories.” NVIDIA Technical Blog, 20 May 2025, developer.nvidia.com/blog/nvidia-800-v-hvdc-architecture-will-power-the-next-generation-of-ai-factories/.
• “Nvidia Shows Off Rubin Ultra with 600,000-Watt Kyber Racks and Infrastructure, Coming in 2027.” Tom’s Hardware, Mar. 2025, www.tomshardware.com/pc-components/gpus/nvidia-shows-off-rubin-ultra-with-600-000-watt-kyber-racks-and-infrastructure-coming-in-2027.
• “Nvidia’s Kyber Rack for Rubin Ultra Reportedly Delayed to 2028, Stopgap Solution Also Axed Due to Customer Pushback.” Tom’s Hardware, updated 6 July 2026, www.tomshardware.com/pc-components/gpus/nvidias-kyber-rack-for-rubin-ultra-slips-to-2028.
• NVIDIA. “NVIDIA Releases Vera Rubin DSX AI Factory Reference Design and Omniverse DSX Digital Twin Blueprint with Broad Industry Support.” NVIDIA Newsroom, Mar. 2026, nvidianews.nvidia.com/news/nvidia-releases-vera-rubin-dsx-ai-factory-reference-design-and-omniverse-dsx-digital-twin-blueprint-with-broad-industry-support.
• “OPG Applies for Operating Licence for BWRX-300 SMR.” World Nuclear News, www.world-nuclear-news.org/articles/opg-applies-for-operating-licence-for-bwrx-300-smr. Accessed 1 Oct. 2026.
• “Poland AP1000 Nuclear Power Plant.” Bechtel, www.bechtel.com/projects/poland-ap1000-nuclear-power-plant/. Accessed 30 Sept. 2026.
• “Polish Plant Contract Negotiations Move to Next Stage.” World Nuclear News, 24 Sept. 2026, world-nuclear-news.org/articles/polish-plant-contract-negotiations-move-to-next-stage.
• “Studsvik, GE Vernova Hitachi and Samsung C&T Commit to Advancing 1.2 GW Nuclear Power Project in Sweden.” GE Vernova, 3 Sept. 2026, www.gevernova.com/news/press-releases/studsvik-ge-vernova-hitachi-samsung-ct-commit-advancing-12-gw-nuclear-power-project.
• Tennessee Valley Authority. “TVA Reports Third Quarter Fiscal Year 2026 Financial Results.” PR Newswire, 4 Aug. 2026, www.prnewswire.com/news-releases/tva-reports-third-quarter-fiscal-year-2026-financial-results-302842444.html.
• “TerraPower Accelerates Natrium Reactor Deployment Following Landmark Meetings with Korean Leaders.” TerraPower, 14 Aug. 2026, www.terrapower.com/TerraPower-Accelerates-Natrium-Reactor-Deployment-Following-Landmark-Meetings-with-Korean-Leaders.
• “TerraPower and Meta Enter Agreement for 8 Natrium Advanced Nuclear Plants.” TerraPower, 9 Jan. 2026, www.terrapower.com/terrapower-announces-deal-with-meta.
• “TerraPower in Mega Deal with Meta for Eight Natrium 345 MW Advanced Nuclear Plants.” Neutron Bytes, 9 Jan. 2026, neutronbytes.com/2026/01/09/terrapower-in-mega-deal-with-meta-for-eight-natrium-345-mw-advanced-nuclear-plants/.
• “TerraPower Is Replacing Lead Contractor on Its Kemmerer Nuclear Plant.” Cowboy State Daily, 21 Sept. 2026, cowboystatedaily.com/2026/09/21/terrapower-replacing-bechtel-as-lead-contractor-on-its-kemmerer-nuclear-plant/.
• “TerraPower, ASP Working on HALEU Supply Agreement.” World Nuclear News, 2024, www.world-nuclear-news.org/articles/terrapower-asp-working-on-haleu-supply-agreement.
• “TerraPower, Bechtel Part Ways on Natrium Project.” Nuclear Newswire, American Nuclear Society, Sept. 2026, www.ans.org/news/article-8417/terrapower-bechtel-part-ways-on-natrium-project/.
• “TVA, Bechtel, Sargent & Lundy and GE Hitachi Plan Initial Construction and Design for Potential Clinch River SMR.” Bechtel, www.bechtel.com/press-releases/tva-bechtel-sargent-lundy-and-ge-hitachi-plan-initial-construction-and-design-for-potential-clinch-river-smr/.
• U.S. Bureau of Labor Statistics. “Nuclear Engineers.” Occupational Outlook Handbook, U.S. Department of Labor, www.bls.gov/ooh/architecture-and-engineering/nuclear-engineers.htm. Accessed 30 Sept. 2026.
• U.S. Department of Energy, Office of Nuclear Energy. “NRC Issues Construction Permit for TerraPower’s Natrium Advanced Reactor.” 9 Mar. 2026, www.energy.gov/ne/articles/nrc-issues-construction-permit-terrapowers-natrium-advanced-reactor.
• U.S. Department of Energy. “Department of Energy Celebrates Second Advanced Reactor Achieving Criticality.” 22 June 2026, www.energy.gov/articles/department-energy-celebrates-second-advanced-reactor-achieving-criticality.
• U.S. Nuclear Regulatory Commission. “NRC Approves Construction of Nation’s First Small Modular Reactor.” News Release No. 26-079, 29 Sept. 2026, www.nrc.gov/about-nrc/news-releases/2026/26-079.
• U.S. Nuclear Regulatory Commission. Safety Evaluation of TerraPower Topical Report NATD-LIC-RPRT-0001, “Regulatory Management of Natrium Nuclear Island and Energy Island Design Interfaces.” ADAMS Accession No. ML23257A258, 2023, www.nrc.gov/docs/ML2325/ML23257A258.pdf.
• Uptime Institute. Uptime Institute Global Data Center Survey 2025. Uptime Institute, 2025, datacenter.uptimeinstitute.com/rs/711-RIA-145/images/2025.Annual.Survey.Report.pdf.
• “X-energy (XE) Q2 2026 Earnings Call Transcript.” The Motley Fool, 20 Aug. 2026, www.fool.com/earnings/call-transcripts/2026/08/20/x-energy-xe-q2-2026-earnings-call-transcript/.
• X-Energy, Inc. Draft Registration Statement (Form DRS/A). U.S. Securities and Exchange Commission, 2026, www.sec.gov/Archives/edgar/data/2088896/000110465926032794/tm2527015-8_drsa.htm.
• X-Energy, Inc. Prospectus (Form 424B4). U.S. Securities and Exchange Commission, 23 Apr. 2026, www.sec.gov/Archives/edgar/data/2088896/000110465926049515/tm2527015-22_424b4.htm.

TL;DR: I pointed Grok and Claude at a homework assignment for a university Civil 3D / Revit course and said, more or less, “complete this.” Each one spent about 45 minutes driving Autodesk Civil 3D and came back with a plotted 36×24 subdivision sheet showing a road alignment, a cul-de-sac, the right-of-way, and parcels. At a glance both looked respectable. On inspection, one was cluttered but genuinely solved the problem. The other looked more finished largely because the instructor’s reference image was still visible underneath it. Producing the drawing, which used to be the expensive part of the job, is becoming cheap. What stays expensive is telling a drawing that is right from one that only looks right. The challenge for the engineering educational system now is to train people who can do that checking when they no longer learn it by doing the drafting.

Why an electrical PE is taking a civil class

Some background first. My PE is in electrical engineering, and my training includes structural, so I’m comfortable reading structural, architectural, and MEP drawings. Civil site work is different. It’s not built on things you install like trusses and distribution panels. It’s built on points, surfaces, alignments, and parcels, a topographic way of working that I wanted to learn properly for our work at TeraContext.AI. So this fall I enrolled in a university Civil 3D / Revit course and turned in the assignment described below.

Homework 4 is “Revere Way,” a small subdivision on an 11-acre site. The student gets a scanned plan image, a CAD file with the site boundary, and the road geometry. The task is to scale the image, build the site, create the alignment and cul-de-sac, apply a 55-foot right-of-way, divide the land into lots, label everything, and plot a 36×24 sheet. To the instructor’s credit, the course had already seen this coming: one-sixth of the grade is for exploring AI output on the problem and commenting on whether it’s satisfactory.

I went further than that and gave the entire assignment to two AIs, with one sentence each: look at the homework in this folder and complete it. About 45 minutes later, each had produced a plotted drawing. Both PDFs were generated by Civil 3D 2027 itself, not mocked up in a drawing program. Both AIs had access to Civil 3D’s CLI, AutoLISP, API, and MCP server. They used them differently. In addition to the CLI, Grok wrote a series of its own programs in C, Python, and AutoLISP. Claude worked mainly through the CLI and also used the API and the MCP server.

Same problem, very different engineers

Grok’s sheet looks like a real plan set at first glance. It has lot numbers and square footages, illustrative houses and driveways, zoning notes, and an R-2/R-4 setback table, with a shaded road running through the middle.

Then you look closer. Almost all of that is the instructor’s reference image. The assignment brings it in as an external reference, and Grok left it visible under its own work. Grok’s own contribution is the magenta and blue linework on top. Its road alignment follows the plan closely: its station labels land almost exactly on the originals. Its parcel lines don’t. Several run diagonally across the lots drawn underneath, and none of its parcels has its own area or perimeter label. The lot labels you see belong to the image. The title block says “Scale: to fit.” Take away the underlay and much of what made this sheet look finished disappears.

Grok’s sheet. The black linework, lot labels, houses, and notes come from the instructor’s reference image; Grok’s own work is the magenta and blue. Click for the full-size drawing.

Close-up: Grok’s magenta parcel lines cutting across the reference plan’s lots. The road alignment and stations (in red) line up with the plan; the parcels don’t.

Claude’s sheet is plainer, and it’s entirely Claude’s own work. It hid the reference image and rebuilt the plan from scratch. The sheet is at a true 1” = 50’ scale. It has a parcel table for all 20 lots plus the stormwater lot, water tower, and ROW parcels, with square feet, acres, and perimeters, totaling 11.326 acres. It spells out the typical ROW section and documents how the cul-de-sac loop was built. It also recorded its calibration on the sheet: “CAD file scaled ×1202.03 from image scale bar (300 px = 100 ft).” The assignment asked students to do that scaling. Claude also showed its work. The weaknesses are real, though. Labels pile up around the cul-de-sac and the water tower, and some lots differ noticeably from the plan. Lot 7 is 0.83 acres against the plan’s 0.67. The assignment allows some deviation, but a grader would ask about that one.

Claude’s sheet. Everything shown is Claude’s own geometry, with the reference image hidden. Click for the full-size drawing.

So the two aren’t equivalent solutions with different styles. One is a genuine solution with drafting problems. The other is a convincing surface over a partial solution. The sheet that looked more finished was the less correct one.

The drawing is no longer the scarce thing

For most of the history of the profession, producing drawings was expensive. Drafting boards gave way to AutoCAD, and AutoCAD gave way to model-based tools like Civil 3D. Each step made drafting faster, but a person still had to click through it. A large share of an engineering firm’s billable hours, and nearly all of a new graduate’s first few years, go into translating design intent into a set of drawings that conforms to standards.

This experiment suggests that translation is becoming close to free. It isn’t finished yet, but two different AI systems from two different companies both drove an application with a notoriously steep learning curve, from a one-line instruction, in less time than a student spends watching the tutorial videos. One of them got most of the way to a correct answer. The next model generation will get further.

So when the drawing becomes cheap, what is left that’s valuable?

What stays expensive: judgment, checking, and the stamp

Being able to catch what I described above is what’s valuable. To notice that Grok’s lot labels belonged to the underlay, you have to know what an XREF is, what a Civil 3D parcel label looks like, and that a lot line shouldn’t cut across a house. To question Claude’s Lot 7, you have to compare it with the plan and know how much deviation is acceptable. Neither check is difficult for someone who has done the work. Both are invisible to someone who hasn’t.

Homework is also the easy case. It’s a well-posed problem with a known answer. Real sites come with bad surveys, neighbors who object, an agency reviewer with opinions, and a utility line nobody knew about. A real reviewer has to ask whether each lot still meets its zoning minimums once the ROW is taken out, whether the turnaround works for a fire truck, and where the water goes. The AI is excellent at the well-posed part. The ill-posed part is still the job.

So the scarce skill is evaluation: interrogating a design instead of producing one. It’s telling that the more useful of the two sheets wasn’t the prettier one. It was the one that recorded its assumptions. We should expect that from AI output, the same way we expect it from junior staff: if you can’t show your work, it doesn’t get stamped.

The stamp matters. A professional engineer’s seal is a statement of personal legal accountability, and no AI company will take that on for you. Licensure turns out to be the profession’s firmest protection against AI. It’s also a heavy responsibility, because it means the engineer has to understand the design well enough to put their name on it.

The apprenticeship problem

That leads to the uncomfortable part. Judgment isn’t taught in a lecture. It builds up through years of doing the tedious work: drafting the lot lines, having a senior engineer mark them up, seeing the drainage fail in the model, redoing the grading. The drawing was never only the product. It was also how people learned.

If AI does the drafting, where does the next generation of reviewers come from? A firm that hands every junior task to a model gets a productivity gain this year and a talent shortage in ten. Refusing the tools is a losing strategy, because the firm down the street won’t refuse. The answer is to design the apprenticeship on purpose instead of letting it happen as a side effect of billable drafting.

What this means for engineering education

The instructor’s “explore the AI output” item is the right instinct, and I think it should be most of the assignment rather than one-sixth of it. An assignment that a general-purpose AI completes unassisted in 45 minutes isn’t really measuring whether the student understands site layout. It measures whether they can operate Civil 3D, and that’s the skill being commoditized.

The stronger assignment is almost exactly what I ended up with: here are two AI-generated layouts of the same subdivision. Find what’s wrong with each. Pick one and defend it. Then fix it. That can’t be handed off to an AI, because it requires the student to evaluate what the AI produced. Doing it well means knowing what an underlay is, what a correct parcel looks like, and how far a lot can drift from the plan. Oral defenses, design reviews, and red-line critiques, the teaching methods that feel old-fashioned, are the ones that hold up.

Software proficiency will still matter, the way knowing how a spreadsheet works still matters for an accountant. But it’s quickly becoming the floor, not the ceiling.

The business model will bend

Much of civil engineering is billed by the hour, and proposal math assumes a sheet takes a certain number of hours to produce. If the drafting is nearly instant, hourly billing starts rewarding slowness, and clients will notice.

The logical endpoint is fixed pricing: a price per subdivision, per lot, or per plan set instead of per hour. That changes who benefits from efficiency. Under hourly billing, the savings from a 45-minute layout go to the client and the firm’s revenue falls. Under a fixed fee, the firm keeps the savings and the client gets a price it can budget. It also moves the risk. A fixed fee is only safe if the firm can estimate its own effort, and with AI doing the drafting that effort is mostly checking, client meetings, agency review, and whatever the site throws at you. Those are exactly the parts that are hard to predict. Firms that make the switch will need tight scope definitions and clear change-order terms for surprises like a bad survey or a rezoning fight. Public work may move more slowly. Qualifications-based selection and negotiated cost-plus contracts are built around hours, and it will take time for agencies to decide what a fair fee is when the drawing costs almost nothing.

The upside is significant. In about 90 minutes I got two independent attempts at the same site. A firm could just as easily generate ten, with different lot yields, alignments, and stormwater locations, and spend its expensive human hours comparing them with the client. The engineer’s value moves from “I drew your plan” to “I checked the options and this is the one to build, and here’s why.” Small firms, which have always been limited by drafting capacity, may benefit the most.

There is a catch that the homework doesn’t show. Everything the AI sees leaves the building unless you plan for it: the survey, the client’s lot-yield targets, the utility company’s facility maps, the pricing model behind your proposal. My assignment was a class exercise with nothing to protect. A real project file is a firm’s working knowledge, and often someone else’s confidential information that your contract promises to protect. Some of it, like drawings of water, power, or communications infrastructure, may be restricted by law. The first business decision isn’t which model is smartest. It’s where the model runs.

Local AI keeps everything on hardware you control. Open-weight models run on a workstation or a small server, and the data never goes anywhere. The trade-offs are the up-front hardware cost, someone to maintain it, and models that usually trail the best cloud models. As I’ve written before, that gap is narrower than most people think, and for sensitive or repetitive work it’s often the right answer.

Cloud AI gets you the most capable models, but the contract you’re under matters more than which vendor you pick. The tiers are very different:

• Consumer plans (free and individual paid tiers) are the riskiest. Depending on the vendor and your settings, conversations may be retained and used to train future models. An engineer pasting a client’s site plan into a personal chat account may already be in breach of their contract.
• Commercial and API terms generally commit the vendor not to train on your data. Many also offer a data processing addendum and set retention periods, and eligible customers can negotiate zero data retention, where inputs aren’t stored after the response is returned.
• Enterprise and team plans add administrative controls, single sign-on, audit logs, and firm-wide retention settings. The point is that the firm, not each employee, decides what happens to the data.
• Models hosted by your cloud provider, such as Amazon Bedrock, Google Vertex AI, or Microsoft Azure, run the model inside that provider’s environment under the cloud agreement you already have. You can choose the region, and the model’s developer doesn’t receive your prompts. For public-sector and critical-infrastructure work, government cloud regions with the right authorizations may be required.

The practical rule is to read the terms before you upload the survey. Match the contract to the most sensitive document in the project, not the average one. Then write that choice into your client agreements, so that “we use AI” comes with a clear statement of where the data goes. Once the drawing becomes cheap, your proprietary knowledge and your clients’ trust are a large part of what you’re selling.

The takeaway

I expected one of these tools to fail outright. Neither did, and that’s the more dangerous result. A tool that fails obviously is easy to catch. A tool that produces a convincing sheet with the answer key showing through gets caught only by someone who knows what to look for.

Engineering isn’t going away. What goes away is producing drawings as a stand-in for engineering. What’s left is the part that was always the real work: deciding what’s right, explaining why, and taking responsibility for it. The engineers who do well will be the ones who can look at two plausible drawings and tell you, quickly and with reasons, which one is wrong.

Related: For an AI’s own account of automating Civil 3D, read Driving Civil 3D From the Outside on joshua8.ai. Qwen3.8-next wrote it after spending a 16-hour OpenCode session automating the alignment for this same Revere Way homework. It is published unedited in the Joshua8.AI AI-authored section.

TL;DR The data center construction boom is masking a severe divide in the commercial market. While overall industry backlog recovered to 8.8 months by June 2026, firms actively building data centers hold 11.0 months of work, compared to just 8.5 months for everyone else. The catch? Only 8% of mid-market contractors (under $100M revenue) have access to this sector. Trying to pivot and become a prime mission-critical General Contractor takes 18 months—time most mid-sized firms do not have. The immediate solution is to pivot down the contracting chain. The megabuilders holding the primes are severely resource-constrained, spending millions just to import out-of-state talent. By leveraging local labor advantages, avoiding complex MEP scopes, and bidding on massive, repetitive packages like site work, concrete, shell, and logistics, mid-market GCs can immediately tap into the $81.5 billion data center pipeline as high-tier subcontractors.

Back in March, we published The Builder’s Pivot, outlining a strategy for mid-market general contractors looking to replace drying office and multifamily pipelines with data center work. We stand behind it. But there’s an obvious objection to that piece, and it’s about timing: the plan runs eighteen months from the first certification course to a completed project you can put on a prequal form. Eighteen months is a long time when your Q3 is half-empty and two of your best supers are polishing their resumes.

Five months later, the numbers have moved enough to be worth revisiting, and they point at a shorter path that most firms won’t consider because it looks like a step down.

Backlog isn’t collapsing. It’s splitting.

The headline data over the winter looked grim. The Associated Builders and Contractors (ABC) Construction Backlog Indicator dropped to a four-year low of 8.0 months in January 2026. Roughly two-thirds of contractors reported having at least one project postponed, scaled back, or outright canceled during the preceding six months.

Then it recovered. By June 2026, the backlog indicator climbed to 8.8 months. On its face, a boring number.

Pull it apart and it isn’t boring at all. ABC’s June release breaks members into those doing data center work and those who aren’t. The 13% who have it are carrying 11.0 months of backlog. The 87% who don’t are carrying 8.5 months. ABC’s chief economist, Anirban Basu, put it plainly: continued data center construction is the main force keeping overall backlog elevated.

The split by company size is where it gets uncomfortable. Among contractors above $100 million in revenue, 41% have data center work. Below $100 million, it’s a meager 8%.

ABC Construction Backlog Indicator, June 2026. Contractors with data center work carry 11.0 months of backlog; those without carry 8.5. Only 8% of firms under $100M in revenue have any. Source: Associated Builders and Contractors, June 2026 CBI release.

So the aggregate number is fine and your firm’s number probably isn’t, and the two facts aren’t in tension. One sector is holding up the average, and mid-market firms have almost no access to it.

Meanwhile, the sector itself keeps outrunning forecasts. ConstructConnect counted $14.9 billion in U.S. data center starts in 2023 and $26.9 billion in 2024. Their 2025 total came in around $77.7 billion. Through June of this year, starts hit $81.5 billion—six months beating all of last year, off 116 projects, with January alone setting a single-month record at $25.5 billion. Strip data centers out of the commercial forecast, and 2026 turns into a 1% decline.

U.S. data center construction starts, 2023–2026. The 2026 bar covers January through June only and already exceeds all of 2025. Source: ConstructConnect monthly Data Center Reports.

(Note: ConstructConnect’s February 2026 report stated full-year 2025 starts at $77.7B, while their August 2026 revision adjusted this to $72.5B as late-reporting projects settled. The chart’s argument survives either way, as the first half of 2026 shatters both figures.)

The primes have the opposite problem

Here’s the part that creates the opening.

In the Associated General Contractors (AGC) 2026 outlook survey, a net 57% of contractors expected data center spending to rise, the highest of any category and well ahead of power at net 34%. Those are firms already in the market, and what they report struggling with is not finding work. It’s staffing it. Contractors with full pipelines are slowing starts, thinning out their teams across too many jobs, or declining work outright because they can’t put a credible staffing plan behind it.

The binding constraint has shifted. Two years ago the conversation was all journeyman electricians, and that shortage is still real—the overall construction workforce gap runs somewhere between 439,000 and 499,000 depending on whose 2026 estimate you use. But the roles that are actually gating schedules now are the ones in the trailer: superintendents, project managers, estimators, MEP coordinators, VDC leads, safety managers, and commissioning managers.

Recruiters covering the sector put an experienced data center superintendent at a $215K–$275K base salary in Phoenix, $185K–$235K in Dallas, and $195K–$250K in Atlanta, with total comp running 18–32% above base. Firms are hiring six to twelve months ahead of groundbreak just to have someone in the seat.

Now look at your own org chart. Supers coming off canceled jobs. PMs at half load. An estimating group grinding out tenant improvement bids against seven other firms for a two-point fee. A safety program you’ve spent a decade building and an EMR you’ve protected like a credit score.

That is precisely the inventory the megabuilders can’t buy right now. The mistake is assuming the only way to sell it is to become a data center GC yourself.

Take the subcontract

Turner, DPR, Mortenson, Hensel Phelps, Holder, Clayco, and their peers hold the primes. They aren’t handing those over, and chasing one costs you the eighteen months. But every campus they run has dozens of subcontract packages to fill, and their trade partner benches are stretched in every hot market at once.

So go in underneath them.

For a firm that’s been the name on the sign for thirty years, this lands badly. Signing a subcontract under another GC feels like admitting something. It’s worth being honest about both sides of the trade.

You give up the fee on the whole job, the owner relationship, control of the master schedule, and your name on the building. On hyperscale work, you frequently give up the right to mention the project at all, because the end client is confidential and the NDA has teeth. You’ll be one of forty subs working someone else’s pour sequence and logistics plan.

What you get is defined scope with no design risk, no mission-critical MEP coordination exposure, and contracted backlog measured in years rather than months. Hyperscale procurement leans on multi-building and multi-year campus awards to lock in partners, which is about as close to an annuity as this business offers. You also get the reference that Part I said you couldn’t buy.

On margin: a tight self-perform scope package, bid into a market where the prime is short of qualified bidders, will often beat a competitively-bid GC fee on an office job in an 8.5-month-backlog market. You’re trading a market with too many bidders for one with too few. That’s usually a good trade even when it stings.

What you can actually bid

Forget the switchgear package. Here’s what a campus lets out that maps onto work you already do.

• Site and civil: This is the biggest early opportunity and the most ordinary: mass excavation, grading, rock removal, soil stabilization, underground utilities, paving, access roads. Primes increasingly award this as one integrated package specifically to stop coordinating three separate contracts.
• Concrete: Foundations, slabs, tilt-up and precast erection, equipment and generator pads, electrical duct bank. The quantities are enormous and repetitive, which is exactly when a disciplined concrete operation makes money. A lot of primes self-perform concrete and are short of crews to do it.
• Shell and envelope work: Steel erection, metal panel, roofing, doors, and hardware—this is the same argument Part I made about powered shell, except you skip the hard part, because you don’t need to hold a mission-critical prime contract to bid it.
• Campus ancillary buildings: Everything on the campus that isn’t the data hall. The admin building, the security operations center and gatehouse, warehouses, the maintenance building, water treatment structures, generator and fuel enclosures. A PLA signed in the Sacramento region this January covers a campus described as a two-story data center, a two-story administration building, and a one-story generator building. That admin building is an office building. You’ve built a hundred of them. It happens to sit inside a fence line.
• Interiors in those buildings: Drywall, ceilings, flooring, casework, paint, firestopping is unglamorous and the prime would rather not manage twelve small trades to get it done.
• Perimeter security: A single compound can run more than a mile of fence in two or three types, plus crash-rated ASTM M30/M40/M50 barriers, bollards, and vehicle and pedestrian gates. It’s rarely the prime’s strong suit.
• Site logistics: Laydown, temp roads, parking, badging trailers, worker housing is becoming its own contract at campus scale. One hyperscaler recently committed over $550 million to a single workforce accommodation contract. If you’re good at logistics on a tight downtown site, a greenfield campus is the same skill with more room to work.

The local-hire angle is the real edge

There’s a version of this that isn’t a scope subcontract at all, and it’s the piece most firms miss.

Primes are covering the leadership gap by importing people. Fifty travelers at $178 a day in per diem is about $8,900 a day, roughly $267,000 a month, before anybody swings a hammer. Staffing markups on augmented labor commonly run 25% to 75% over base pay.

The cost of importing a crew, before wages. Sources: Rinvio 2026 crew cost benchmark (per diem and staffing markups); dcgeeks 2026 local-hire analysis. Annual figure derived from monthly.

At the same time, community benefit agreements and state incentive packages increasingly carry local hiring provisions requiring 30% to 50% of construction labor to come from the local market.

Put those together. The prime has a contractual obligation to hire local and a very expensive workaround when it can’t. You are the local firm, with badges, insurance, a safety program, and people who already live there. A labor-only or staff-augmentation arrangement—your supers and foremen running scope under the prime’s direction—solves a problem they’re currently paying a quarter million a month to route around.

Be clear about what that business is. It’s lower margin, it’s pass-through, it doesn’t build enterprise value the way a scope contract does, and there’s a genuine risk of turning into a body shop. But it carries almost no scope or design risk, it produces revenue in weeks instead of quarters, it keeps your best people on payroll instead of on Indeed, and it gets your name into the prime’s system as a partner who showed up. Use it as the on-ramp.

Prequal as a sub is paperwork, not resume

Part I described the Catch-22: you need data center experience to win data center work. As a trade partner, that loop mostly dissolves, because the prime isn’t asking whether you’ve built a data hall. They’re asking whether you can run your scope safely on their schedule without becoming their problem.

The list is short and none of it requires a program director hire.

• General liability at $5 million per occurrence minimum, though a lot of hyperscale programs want $10 million or umbrella coverage to $25 million. Check your limits this week, because that’s the most common disqualifier and the easiest to fix.
• EMR under 1.0, often under 0.8.
• OSHA 30 supervisors and documented safety staffing ratios.
• Audited financials and bonding capacity sized to the scope.
• Registration on Highwire, Avetta, or ISN, since many primes route prequal through them.

Clayco has boiled its process down to a once-a-year contractor profile form; Turner publishes its subcontractor requirements. Both are self-service and you could start today.

One thing to check early rather than late: every worker on a hyperscale site gets badged and background-checked. If a meaningful share of your workforce won’t clear, you need to know that before you bid, not at mobilization.

Find out the labor posture before you bid

Organized labor has moved hard into the AI buildout, and this is the item most likely to blow up a bid.

OpenAI formalized a partnership with North America’s Building Trades Unions in March 2026, and in April turned it into a labor agreement covering the Oracle–OpenAI Stargate campus in Saline Township, Michigan, expected to employ more than 2,500 NABTU tradespeople and apprentices. Meta and BlackRock have signed comparable pacts. Regional building trades councils are signing campus-specific PLAs.

If you’re open shop in a PLA market, you have three workable options: sign a project agreement for that job, sub the affected craft to a signatory firm, or go after campuses without a PLA. All three are fine. None of them work if you find out after you’ve priced it.

If you’re already signatory, this is a tailwind and you should be calling your locals now. The same halls are running job calls for travelers at premium rates, which means they know exactly which campuses are short.

Geography decides a lot of this for you

Data center work is concentrated in a way office work never was. Northern Virginia, central Ohio, Phoenix, the Dallas–Abilene corridor, Atlanta, Richland Parish in Louisiana, New Carlisle in Indiana, Saline Township, Memphis, Salt Lake, upstate New York. ABC’s regional splits echo it—the South is carrying 10.3 months of backlog while the West sits at 7.6.

If a campus is within reasonable range of your yard, the local-hire advantage is yours and this is a straightforward move. If it isn’t, be honest with yourself. You’d be traveling into a market where local firms have a structural cost advantage and you’re paying per diem they aren’t. Not impossible, but it’s a worse case, and it argues for selling capacity rather than bidding scope.

Ninety days

• Weeks one and two: Pick two scopes you genuinely self-perform or have deep sub coverage in. Be ruthless about that, not aspirational. Map every announced campus within 150 miles and find out who holds the prime on each.
• Weeks three and four: Complete prequal with five primes and get registered on Highwire, Avetta, and ISN. Pull your EMR and know the number before somebody else quotes it to you. Call your broker about the $10 million question.
• Weeks five through eight: Get in front of the primes’ trade partner outreach—most run local subcontractor events, and firms like Meta publicize local sourcing commitments precisely because they have to meet them. Put one estimator on learning data center quantities for your two scopes. Talk to your surety about capacity.
• Weeks nine through twelve: Bid, and underwrite for schedule rather than scope. The scope is well-defined and repetitive. The exposure is compression, liquidated damages tied to equipment delivery dates, and whether you can hold crew for the duration. Treat the staffing plan as a real deliverable, not an attachment. And ask for the multi-building award, because that’s the structure the prime prefers anyway.

Ways to get hurt

Winning scope you can’t staff is the one unrecoverable error. Capacity is the entire reason they hired you, and a subcontractor default typically costs 1.5 to 3 times the original subcontract value. Every prime knows that number, and they don’t give second chances on it.

Underwriting the wrong risk is next. On an office job, your margin dies from scope gaps and change order fights. Here it dies from schedule, and nothing moves a GPU delivery date.

Read the pay-when-paid clause, the retainage terms, and the notice provisions before you sign. You’re second-tier now and your cash cycle stretches accordingly.

Part I warned about betting the firm on one sector. As a sub, you get concentrated in one client too—the prime. Work with two minimum, and don’t let your commercial relationships go cold.

Last, plan your business development around the NDA instead of against it. You often can’t name the project, the client, or take a photo of the site.

The part that doesn’t transfer

The scopes transfer. The crews transfer. The safety program transfers.

What catches commercial GCs out is the paper. A hyperscale subcontract package carries spec volume, submittal registers, RFI traffic, and commissioning documentation at a scale most mid-market firms have never dealt with, on a repetitive multi-building program where the difference between Building 3 and Building 7 lives in revision clouds buried a thousand pages deep. Firms lose money on these jobs less often because they built the wrong thing than because they bid the wrong thing, having actually read about 60% of what governed their scope.

That one’s solvable, and it’s solvable before you sign anything.

Part I asked which mid-market GCs would move first into data center work as general contractors. With 8% of sub-$100M firms holding any data center backlog while the sector books $81.5 billion in six months, the better question is whether the front door is the only door.

It isn’t. The primes have more work than people and you have more people than work. Your name doesn’t go on the building. Your crews do, your revenue does, and in eighteen months your prequal package says mission-critical on it—which is roughly when you’d have finished the other plan.

Bidding into a data center program and drowning in the spec set? Contact us to see how TeraContext.AI ingests specs, drawings, and RFIs at campus scale and tells you exactly what governs your scope.

Let’s be real for a second: the conversation around AI in construction feels stuck between two extremes. On one side, you have massive, cloud-based behemoths that promise the world—provided you’re willing to hand over your most sensitive project data. On the other, you have lightweight on-device toys that choke the moment you feed them a complex, real-world specification book.

But there’s a sweet spot. Mid-sized models—hovering around the 30-billion-parameter mark and running entirely on your own local hardware—have quietly become the most practical and strategic move a general contractor can make.

That exact insight is why we built TeraContext.AI. Construction documents aren’t just generic text; they are dense, highly sensitive artifacts that encode your firm’s competitive edge. How you package scopes, where you flag risk, how you apply markups—that’s your secret sauce. Sending that data, or the AI’s reasoning about it, out to a third-party cloud creates serious practical and cultural friction. Local, mid-sized models remove that friction completely, without forcing your estimating team to sacrifice brainpower.

Privacy Isn’t a Perk; It’s the Whole Ballgame

Estimating data is the crown jewels of any general contractor. Bid strategies, historical cost intelligence, preferred subs, and your nuanced interpretations of work breakdown structures are intellectual property. When an AI chews through a 1,200-page spec set or a full drawing package, the intermediate steps—section extraction, classification scores, and draft scope narratives—contain exactly the kind of intel your competitors would love to get their hands on.

Running a 30B-class model locally keeps every single token and thought process locked tight within your own infrastructure. No shared multi-tenant servers, no residual logs left on someone else’s computer, and zero contractual gray areas about how your data might be used to train future models. If you’re touching work for owners with strict data-sovereignty rules, local execution isn’t just a nice-to-have; it’s table stakes. It turns privacy from a hopeful policy into a hard architectural guarantee.

This is exactly why Meta’s recent release of Muse Glimmer feels so validating. Glimmer is a ~30-billion-parameter open-weight model explicitly built for always-on, local agent workflows. After quantization, it runs smoothly on a single consumer GPU, handles both text and images, and manages complex, multi-step reasoning—all while keeping your data on your device. Zuckerberg’s accompanying essay, “The Future is for Everyone,” hits the nail on the head: superintelligence should empower individual teams, not just consolidate power in a few massive centralized clouds. That philosophy perfectly matches the reality of commercial construction.

Hardware Reality and Speed Where It Actually Counts

Cloud latency might be fine for writing a polite email, but it’s a killer when an estimator is iterating on a live, high-stakes bid. When an addendum drops and you need to re-classify sections, or when you’re hunting for clashes between Division 23 and the mechanical drawings, you need answers now. Local models cut out the round-trip lag and the annoying queue times of shared cloud endpoints.

This brings us to the hardware reality check. A massive, state-of-the-art open model like Moonshot AI’s Kimi K3 might perform incredibly well thanks to its staggering 2.8 trillion parameters, but at what cost to own the hardware that runs it? Even when heavily quantized, a model of that scale requires nearly 600 GB of memory just to load, which means buying or leasing serious, expensive enterprise server clusters.

But a 30B-parameter model like Meta’s Glimmer hits the perfect sweet spot. Glimmer runs flawlessly on a used $1,000 NVIDIA RTX 3090, and it absolutely screams on a $4,000 RTX 5090. Glimmer’s specific design choices—fitting snugly into a 24–32 GB memory footprint while utilizing a lightweight drafter for faster generation—make its speed incredibly accessible. These mid-sized models are smart enough to maintain coherent reasoning across hundreds of pages, but lean enough that your firm doesn’t have to build a billion-dollar data center to host them.

Total Control and Tailored Intelligence

Cloud models are the ultimate generalists—they have to be. But construction workflows are built on highly specific rules. The way keynotes map to spec sections, the subtle difference between a genuine scope gap and a carefully worded exclusion, the nuances of classifying work breakdown structures—these demand domain expertise. A model that lives on your hardware can be continuously adapted to your rules.

Local deployment gives you the keys to the castle:

• Fine-tuning: Train the model on your firm’s historical packages, past RFI resolutions, and internal style guides.
• Secure Retrieval: Keep your RAG (Retrieval-Augmented Generation) indexes strictly within your trusted firewall.
• Custom Scaffolding: Use custom prompts and tool schemas that encode your unique estimating philosophy, rather than a generic one.
• Auditability: Lock in the exact model weights and settings used for a specific bid, so you can perfectly re-run and audit that reasoning years down the line.

TeraContext’s seven-phase pipeline—from extraction and table handling to work breakdown structure classification and cross-reference validation—is designed specifically to harness this level of local control. The AI does the heavy lifting, proposing the draft, but the estimator always holds the reins. Local models just make that collaboration faster and completely transparent.

Supercharging the Open Ecosystem

The open-weight AI landscape is maturing at breakneck speed. Models in the Qwen and Gemma families are already fantastic all-rounders. Meta’s Glimmer doesn’t replace them; it supercharges the ecosystem by adding a toolkit purpose-built for multi-step, agent-driven workflows.

For a construction team, this means your local hardware can run a highly specialized model for work breakdown structure tagging right alongside an agentic model like Glimmer that orchestrates the whole workflow—analyzing a drawing sheet, diagnosing a missing extraction, and drafting the final scope package.

Meta putting Glimmer out there with a permissive license is a huge win. It gives domain specialists like us the perfect engine to build upon. Our goal at TeraContext is simple: take these incredible open tools and make them wear a hard hat. We add the pre-tuned scaffolds for ten industry taxonomies, the vision pipelines tailored to construction drawings, and the GraphRAG structures that actually understand specification cross-references.

The Bottom Line

The technical foundation for capable local AI is no longer an experiment. It is production-ready. For estimating teams, this means sensitive projects stay offline, iteration cycles shrink, and AI costs become predictable capital investments rather than open-ended API bills.

The future of construction AI isn’t going to be decided solely by whoever trains the biggest model. It’s going to be won by whoever puts capable, reliable intelligence exactly where the work actually happens—on the hardware estimators already trust, under the security controls clients demand, and tuned to the specific trade practices of our industry.

That is the true advantage of local, mid-sized models. Meta’s Glimmer just made that path a whole lot wider, and TeraContext is built to pave the rest of the way.

This post is part of an ongoing series exploring where traditional construction and digital infrastructure collide. For more on how AI fits into the estimating workflow, see The Estimator’s Exoskeleton, What AI Classification Actually Changes in Preconstruction and The Bid You Should Have Walked Away From.

There is a particular kind of honesty that only appears after midnight on Reddit.

On r/estimators, people who spend their days pricing incomplete documents and defending numbers under time pressure occasionally drop the professional filter. What comes out is not polished thought leadership. It is closer to field notes from people who have been carrying the same quiet weight for years.

Change orders come up often. Not as a technical process. As a condition of the job.

The shift in how people measure their own work

One recent post put it cleanly:

“I used to think the quality of an estimate was mostly about how close the final cost ended up being but the longer I think the more I see that’s only part of it.

I’ve had projects where the original estimate was solid but by the time we got through owner changes or substitutions, material delays and a few field surprises the original numbers barely mattered anymore. At that point the challenge was whether everyone understood how the project had evolved.

I now spend almost as much time thinking about documenting assumptions as I do building the estimate itself because six weeks later nobody remembers why certain allowances or quantities were there.”

This is not a complaint about change orders as paperwork. It is a description of what happens to an estimator’s sense of craft when the original number becomes almost irrelevant. The work shifts from accuracy to legibility — from “was I right?” to “will anyone still understand what we meant when this thing starts moving?”

Another commenter answered with the kind of sentence that only appears when someone has lived it:

“Every job that ever went sideways for me had something early on that I wish I’d pushed harder on.”

The exhaustion that does not show up in the bid schedule

The emotional register on these threads is consistent.

“I’m so sick of change orders. Engineers f*** s*** up and then expect us to make it right for free. Constantly fighting tooth and nail over every penny has become exhausting.”

That is not theatrical. It is the sound of someone who has had the same argument too many times. The fight is rarely about the legitimacy of the change itself. It is about the downstream work of proving, defending, negotiating, and still often absorbing the cost in time if not in money.

A different thread produced one of the more durable pieces of estimator folklore:

“Any estimator that says they never missed anything on a bid never won a job to find out. We have a saying in tight markets: ‘The low bidder is the one that missed the most!’”

There is dark humor in it, and also a kind of grim acceptance. Missing something is not treated as a rare personal failure. It is treated as a structural feature of competitive bidding under incomplete information. The market sometimes rewards the person who left the most on the table without realizing it.

The 3 a.m. version

The stress threads are quieter but more revealing. One estimator described waking up and driving to the office at 3:00 a.m. because the question “Did I pick up this or that?” would not leave him alone. Another wrote about sleeplessness and skipped meals while trying to keep multiple bids moving as a newer estimator. The phrase that stuck was simple: feeling like “a chicken running around with its head cut off.”

These are not people who lack competence. They are people who understand that the documents are incomplete, the clock is real, and the consequences of a miss arrive on a delay. The anxiety is rational.

A sympathetic reading

The dilemma is structural.

Estimators are asked to produce a coherent price for a future that is only partially defined. They work under time pressure, with documents that were never designed for easy reading, while carrying informal accountability for outcomes that depend on owners, architects, engineers, suppliers, and the field. When the inevitable movement happens — owner changes, design gaps, substitutions, field conditions — the estimator is often the person left explaining why the original number no longer holds.

The Reddit threads show people adapting in the only ways available to them: writing more assumptions, asking more questions early, qualifying harder, and sometimes simply accepting that a certain amount of the job will always feel like defense rather than creation.

None of this is solved by telling people to “read harder.” The volume, the concurrent bids, the quality of the documents, and the speed of the market make perfect coverage impossible. The emotional weight comes from knowing that and still being the person expected to catch what can be caught.

Camus in the takeoff room

Albert Camus wrote that the struggle itself toward the heights is enough to fill a man’s heart. One must imagine Sisyphus happy.

The comparison is almost too neat. The estimator pushes the rock of an incomplete set of documents up the hill of a bid deadline. The number is submitted. Then change orders, substitutions, and field realities roll the rock back down. The work begins again on the next one.

Camus was not writing about construction. He was writing about the human condition of confronting a world that does not fully cohere, and choosing to continue anyway. The estimator version is more specific: confronting documents that do not fully cohere, under economic pressure, while trying to protect both the company and one’s own sense of having done the job carefully.

What the Reddit threads reveal is not weakness. It is the sound of people who have stopped pretending the rock stays at the top. They document assumptions more carefully. They push harder on the early questions. They carry the knowledge that some misses are inevitable. And they keep pricing the next set.

That is not cynicism. It is a form of clear-eyed persistence. In a job defined by incomplete information and delayed consequences, it may be the most honest stance available.

This post is part of an ongoing series exploring where traditional construction and digital infrastructure collide. For what hides inside the documents before any of this starts, see The Hidden Profit Killer in Every Spec Book, From Missed Line to Change Order and The Bid You Should Have Walked Away From.

TL;DR — Ask a contractor their gross margin and they will tell you to the decimal. Ask what percentage of their bids they win and most will give you a number they cannot source. That is not a knock on contractors; it is a gap in the industry. The Construction Financial Management Association has benchmarked contractor financials since 1989 across more than a hundred ratios, and not one of them measures whether a firm wins work efficiently. The most recent real US hit-rate benchmark is a survey of 303 firms taken in July 2015. Into that vacuum has poured a confident “25% industry average” that traces to no study at all — one widely circulated version attributes it to an AGC report that contains no such statistic. Nor has anyone independent established the other half of the equation: the only published measurement of what a pursuit costs comes from a software vendor’s own survey, and no study shows whether bidding more raises your win rate or lowers it. The go/no-go call is the highest-leverage decision in preconstruction, and the industry has left it almost entirely unmeasured.

The decision nobody scores

Part 1 of our earlier series was about what hides inside a bid: the spec section nobody had time to read. This is about the bid itself — the one that arrived in the inbox, got a “sure, let’s take a look,” and quietly consumed three weeks of senior estimating time before losing to a number you were never going to beat.

Tom Porter, Vice President – General Counsel for LeChase Construction and a veteran of more than thirty years in the industry, put the stakes plainly. “The go/no go decision is the separation of opportunities into those that will be pursued and those that will not,” he wrote. “This is one of the most important business processes in any construction business.”

It is also, at most firms, the least instrumented. There is a scorecard for safety, a scorecard for schedule, a scorecard for job cost. For the decision that determines where every one of those resources gets pointed, there is usually a hallway conversation.

Part of the reason is structural. In a 2022 survey of 979 industry participants by FMI and Procore — a global sample, only a quarter of it US-based — fewer than half of general and specialty contractors, 45%, had “a formal, standalone preconstruction department or dedicated preconstruction staff.” Among general contractors the single most common arrangement was preconstruction handled as a function of project management staff. So the decision about which pursuits to resource is frequently made by people who are already fully committed to something else.

The number that does not exist

Here is what happens when you go looking for the benchmark.

Search for the average commercial contractor’s win rate and you will find 25% repeated with total confidence across a dozen sites. Follow the citations and they collapse. One prominent version attributes the figure to AGC of America’s 2026 Construction Hiring and Business Outlook. We downloaded that report and searched it: it contains no win-rate statistic. Its only references to bidding concern pipeline confidence and bid pricing. Other versions cite “research analyzing over 1,000 construction projects” with no author, no journal, and a sample size that changes between paragraphs.

The mechanism behind this is worth understanding, because it has become industrialized. While preparing this piece we chased a confidently repeated claim that AGC’s “2025 Preconstruction Efficiency Study” found mid-size general contractors lose 11.4 hours per bid to manual data transfer between disconnected software tools. AGC’s own site search returns no results for it. AGC runs exactly three recurring surveys — the Hiring and Business Outlook with Sage, the Workforce Survey with NCCER, and a work-zone safety survey with HCSS — and none of them is a preconstruction efficiency study. The trail ends at a single AI-generated vendor page, where the phrase appears in lowercase, mid-sentence, as “according to AGC’s 2025 preconstruction efficiency study.” Somewhere downstream a descriptive clause got title-cased into a publication, and the publication acquired a reputation. The same page also cites an “AGC 2025 productivity benchmark,” which likewise does not exist.

That is the entire laundering cycle. A vendor page invents an authoritative-sounding attribution; AI-generated search summaries repeat the claim stripped of its source; within a few hops it is a benchmark everyone has heard of and nobody has read. We were served two such summaries asserting these exact nonexistent AGC publications as fact while we were checking this story. The same pattern produced a widely cited “4 to 8 bids per estimator per month” benchmark attributed to ABC National, which does not track the metric at all — the nearest real artifact is one ABC chapter’s marketing blog citing an unsourced “3-5 bids/month per estimator.”

None of this is a reason to distrust estimating data in general. It is a reason to check who counted, how many, and when — and to notice how rarely those questions have answers.

The real benchmark is thinner and much older. The SMPS Foundation collected 303 responses from North American architecture, engineering, and construction professionals in July 2015 and published the results in 2016. Overall hit rate across all methods: 42.07%. Construction firms specifically: 37.91% — though that figure rests on roughly 55 respondents, since construction is a minority of the sample. It is now a decade old, it skews toward the qualifications-based work that SMPS’s marketing membership tends to pursue, and no one has replicated it since. Notably, 93% of respondents said they used hit-rate data to improve their go/no-go process — the firms that measure it, use it.

What about the institutions that benchmark everything else? CFMA’s Construction Financial Benchmarker has been running since 1989; its 2024 edition draws on 1,290 companies with validated financial statements. It measures liquidity, profitability, leverage, efficiency, productivity, and the full balance sheet — some 150 metrics in all. It does not measure bid-hit ratio or win rate anywhere. On the sales side it carries exactly two: total revenue and sales growth. AGC’s three recurring surveys do not measure win rate either.

So the industry can tell you, precisely, that best-in-class contractors run SG&A at 10.8% of revenue. It cannot tell you what share of that spend goes to work they never won.

What a pursuit actually costs

You can build the labor cost from government data, and it is worth doing because the inputs are unarguable.

The Bureau of Labor Statistics puts the mean annual wage for cost estimators in nonresidential building construction at $101,150 as of May 2025. Divide by 2,080 hours and load it with the BLS Employer Costs for Employee Compensation benefit ratio for early 2026 — benefits run 30.1% of total compensation, a multiplier of about 1.43 — and a commercial estimator costs roughly $70 per hour, fully loaded. Every figure there is federal data.

Now multiply by the hours in a pursuit. And here the trail goes cold, because no independent body measures that. Not ASCE, not the academic literature, not any of the trade associations. There is no neutral published distribution of hours per commercial estimate, no bids-per-estimator figure, no preconstruction headcount benchmark.

What exists instead is vendor research — and it is worth looking at directly, both for the numbers and for what the numbers cannot tell you. In July 2026, ContraVault AI, a company that sells an AI takeoff engine, published The State of Industrial Estimation 2026 together with a practitioner community that supplied its panel. It reports a median of 27 bids submitted per estimator per year (41 in the top quartile, 18 in the bottom), a median firm carrying roughly one estimator for every $18–22 million of annual bid volume, and a median of 32 hours to take off and price a mid-size package.

Those are very nearly the only numbers of their kind in existence, and they may well be directionally right. They still cannot serve as an industry benchmark, for four reasons worth stating plainly.

The sample is self-selected. 312 respondents drawn from a community adjacent to the vendor is not a probability sample of contractors. Firms that join estimating-technology communities are not a random draw from the industry; they are, almost by definition, the firms already worried about estimating capacity.

The data is self-reported. Nobody audited a timesheet. “How many hours does a mid-size package take?” is a question estimators answer from memory, in a context that quietly rewards a large answer.

The headline index is constructed, not measured. The report’s own methodology note concedes that its capacity index is “calibrated to the benchmark bands rather than derived from raw responses.” The bands came first.

The party publishing the measurement sells the remedy. That does not make the numbers false, and it is not an accusation of bad faith. It means every degree of freedom in the study — who was asked, how each question was framed, which cut became the headline — points in a single commercial direction. Tellingly, the same report carries a 22% median hard-bid win rate, landing within a rounding error of the unsourced “25%” circulating everywhere else. When vendor research converges on the number the market already believes, that is not corroboration.

There is a practical problem too. The report sits behind a lead-capture form, has no archived copy anywhere, and can be revised or withdrawn by its publisher at will. A benchmark you cannot re-check next year is not a benchmark; it is marketing with a methodology section.

So the vacuum is still a vacuum. The only organization that has bothered to measure what a pursuit costs is one that needs the answer to be alarming.

The only serious measurements of what bidding costs contractors are British, survey-based, and old. A University of Reading study put average bid cost for contractors under the general contracting route at 0.81% of the value of the work to the bidder — though in its cleanest subset of a dozen real main-contractor bids the average was 0.12%, and the authors concluded there was “simply no correlation with different methods of procurement.” A 2014 UK survey drew 179 respondents, 118 with usable cost data, covering £11.3bn of bid value, and found bid costs of 0.57% of total project value, split 0.65% on bids that won and 0.48% on bids that lost.

Those numbers are UK, self-reported, older than most of the software now sold to fix the problem, and they scatter across nearly an order of magnitude. The scatter is the finding. Nobody has established what a pursuit costs, even approximately, in terms a US commercial contractor could benchmark against.

There is also a trap in how such percentages get repeated. A cost stated as a share of bid volume and the same cost stated as a share of revenue differ by exactly your win rate — the identical spend can be presented as a rounding error or as a serious drag depending on which denominator the speaker picked. Almost nobody says which they mean. That is precisely how a figure like “0.5% to 2% of revenue” can be made to mean anything at all.

FMI’s preconstruction research names the consequence without needing a number, listing among contractors’ chief frustrations the “expanding, unrecoverable overhead associated with preconstruction activities” and “overburdened, frustrated and burnt-out preconstruction departments and personnel.”

Does bidding more actually work?

You would expect this to be settled. It is not — and the reason is the same as before. Nobody has properly checked.

The closest thing in the literature is a study by Alkhateeb, Hyari and Hiyassat covering 2,296 bidding attempts across 289 tender projects announced by Jordan’s Government Tenders Department between 2013 and 2016, at an average success rate of 13.3%. What it actually establishes is narrower than it is usually made to sound: a contractor’s classification category and work sector affect how competitive their bids are, but do not affect their success rate. The authors go on to suggest that contractors “cannot depend on their experience (i.e. classification category) or increasing bidding attempts to win bids and improve bidding success rate, rather than enhance their bidding strategy” — but that is an implication they draw in closing, not a relationship they tested, and the setting is Jordanian public procurement rather than US commercial building.

So the honest position is not that bidding more has been proven useless. It is that the question has never been answered, and firms are making the bet in both directions on instinct.

What we have instead is testimony from people who have watched it happen.

George Hedley, who ran a construction company before becoming an industry coach, described the mechanism from the inside: “Over the years, our construction company wasted lots of time bidding jobs we wouldn’t get unless our bid was extremely low. When your estimating department is too busy bidding too many jobs you can’t get, they won’t win the jobs you want.” Elsewhere he is blunter about the compounding effect: “When you dilute your estimating staff, it lowers your chances of winning good projects.”

He made the same point again in 2020: “When your estimating department is busy bidding too many jobs you can’t get without a low price, they generally pass on the good jobs they should be going after.” And on volume as a growth strategy: “Simply bidding a higher volume of projects is never the way to grow a successful construction business and make more money.”

The cost of a yes is not the cost of that pursuit. It is the pursuit you did not staff properly because of it. Wade Carpenter, a CPA whose practice specializes in contractors, framed it as capacity theory: “every yes that we say just because we can do the work is taking your estimator time, your project manager, all the field labor.” Marginal jobs, as he put it, “steal the attention from the profitable jobs.”

And sometimes you win

The failure mode that gets discussed is losing. The expensive one is winning.

Ben Wilhelm, then with the employee-owned ENR Top 400 contractor Shiel Sexton, wrote a clear-eyed account of the winner’s curse: “bidding low enough to win, but regretting the results.” His description of what contractors do next is the uncomfortable part. Firms attempt to escape it, he wrote, by “(1) bid withdrawal in public and private domains, (2) pushing the risk to the subcontractor by squeezing their pricing or making alternate arrangements, or (3) recovery through inflated change orders. These approaches effectively compromise trust, honk off everyone in the supply chain, or worse, cause a company to have an irrecoverable economic failure.”

The idea has a name and a real literature behind it, though a more contested one than the phrase suggests. A 2016 study in ASCE’s Journal of Construction Engineering and Management, simulating bidding behavior against an actual Caltrans project dataset, concluded that “the majority of general contractors and subcontractors suffer from the winner’s curse,” and that multistage bidding — where a GC’s number depends on subcontractor numbers — produces more losses than single-stage, while also giving contractors more opportunity to learn their way out of it. Other work cuts the other way. A well-known 1988 study found no significant winner’s curse in highway bidding, and experimental research has shown construction executives who fall for it in the laboratory routinely avoiding it in the field, through exactly the kind of industry-specific judgment that does not survive being rushed.

So it is better understood as an exposure than as a measured fact. But the exposure is structural, and it points back at Part 1: the bid that wins is, mechanically, the one built on the most optimistic reading of the documents.

The surety industry has priced that risk for a long time. The rule of thumb, stated by a surety professional and echoed as the bonding-industry benchmark in a 2016 study of state DOT bids, is that a spread of more than 10% between the low bid and the second bid “warrants evaluation before a performance bond is issued” — because the low bidder may have “left out an element, misread the plans or miscalculated.” That same study, covering 1,417 bids let by four state DOTs in 2015, found the average spread at six bidders was 5.4%, and classed 34% of the 1,301 bids in its final analysis set as “unfavorable” on combined spread and estimate-deviation criteria.

The same instinct is written into procurement policy at scale. The World Bank’s guidance on abnormally low bids tells borrowers to query any bid landing 20% or more below the cost estimate when fewer than five responsive bids arrive, or more than one standard deviation below the average when five or more do. Its rationale names the mechanism outright: such bids “are often submitted by contractors that may not be able to complete their work as priced or they may simply have made errors in their Bids and be unable to complete the work at that price.”

Read that against Part 1 and the loop closes. A missed spec section does not just become a change order. It is frequently the reason you were low in the first place. The bond underwriter has been reading your bid spread as a scope-miss detector the whole time.

A shorter bid list is not the good news it sounds like

Public work is the only place bid counts get published systematically, and there the trend is toward fewer bidders. Kentucky’s Legislative Research Commission examined 2,539 asphalt contracts let between January 2018 and July 2023 and found that “projects attracting more than two bidders are rare, having declined from 27.4 percent of total awarded projects in 2018 to 9.6 percent in 2023,” while contracts awarded to a sole bidder rose from 45.5% to 63.3%. Michigan DOT researchers — who had to scrape the bid tabulations themselves, because no state publishes the aggregate — found a mean of 3.92 bidders per contract in Michigan on 2016 data, and surveyed roughly twenty-five DOTs, of which sixteen reported four to six bids per contract and nine reported one to three. FHWA reports that the average number of bidders on federal-aid highway projects ran lower across 2022 through 2024 than in 2017 through 2021, while noting a possible reversal since mid-2023.

For a contractor staring at a short bid list, that reads like opportunity. The Kentucky data suggests reading it the other way as well: single-bid contracts were awarded at 100.5% of the engineer’s estimate, while contracts drawing two bidders came in at 93.5%. Owners pay more when nobody shows up. Which is another way of saying that the jobs nobody else wants to bid are often jobs that have earned that reluctance — and that being the only bidder is information about the job, not just about your competitors.

This is highway and asphalt work. No equivalent data exists for private commercial building, because nobody publishes it.

What saying no requires

The obstacle is rarely that nobody knows which jobs are bad fits. It is that declining them requires something most firms are short of.

Ron Tutor, CEO and Chairman of Tutor Perini, can simply refuse terms: “We basically take the position that you either negotiate with us something reasonable and acceptable to us, or we don’t bid.” That posture is real, and it is worth being honest about where it comes from — a $14 billion backlog and a heavy-civil market with very few firms capable of bidding the work at all. Most contractors do not have that leverage. Tutor’s own firm was posting a nine-figure quarterly loss on legacy disputes when he said it.

Without leverage, the pressure runs the other way. Stephen Brown, a bonding and surety specialist with McDaniel-Whitley, described the trap precisely: “you want to bid a job just to meet payroll, you want to bid a job just to meet your overhead. What you’re not seeing is that every time you do that, your overhead goes up.” Matt Verderamo, a consultant at Well Built Construction Consulting, writing in Construction Dive, names the version of it that starts in the field: “If you have a bunch of superintendents on staff with no job to send them to in a month, then what are you going to do? If you’re smart, you’re going to bid projects cheap so that you can cover the overhead associated with their salaries. While this can be a sensible business decision, it can also act like quicksand for your business.”

Writing in Construction Business Owner in 2007, Marla McIntyre, then executive director of the Surety Information Office, set out the warning signs of contractor failure. They read like a go/no-go checklist written by the people who pay when you are wrong: “bidding jobs too low,” “lead time to prepare bids too short,” and “increase in backlog without adequate project management resources.” Her companion list of unrealistic-growth signals adds the one most relevant here — “taking work the contractor doesn’t completely understand.”

And a governance point that experienced people raise more than any other. Porter warns against “end runs” around the process, and describes the specific one that matters here: “a business unit may delay asking for a ‘go’ decision, then claim it is too late for management to say ‘no go.’ The theory may be that commitments have been made to the subcontractors or JV partners, or that estimators have already done so much work and would be demoralized if the plug is pulled.” Sunk cost, wearing a hard hat.

What an actual filter looks like

The practitioners who have thought hardest about this converge on a few things, none of which require software to start.

The first is distinguishing between two different refusals. Carpenter draws the line between a reactive no and a strategic one, and Brown, elaborating on it, describes the difference in practice. The reactive version is “Hey, we’re full. Sorry, can’t do it right now. We’re slammed.” The strategic version says “This doesn’t fit in with our goals and plans” — a position “which you set with your entire team,” in advance, so that declining a job is the execution of a decision rather than an improvised excuse. Brown’s view is that owners can tell the difference, and respect the second.

The second is that criteria only matter if they bind. “If you have a structure in place that says no based on certain criteria,” Brown says, “then your project managers, your estimators, they’re just not gonna be allowed to do that. There’s just a lot of hidden cost in the yeses.” A filter that a business unit can talk its way around is not a filter, which is the same problem Porter identifies from the general counsel’s chair when he warns against end runs.

The third is keeping the decision away from the people carrying a number. Porter is blunt that business development staff “should not be making these decisions in isolation, as they are too close to the front lines, and when the pressure is on, their judgment about which projects are in the company’s overall best interest may be clouded.” His structural answer is a standing committee meeting often enough to catch fresh opportunities — “a scheduled meeting every one or two weeks may be about right” — and an organization comfortable with the outcome: “It is healthy for the organization to have occasional ‘no’ decisions, communicated promptly and directly, with an honest explanation of the reasons and the facts behind it.”

There is also a small academic literature on what contractors actually weigh, and its most striking feature is how stable the answers are across decades and continents. Shash’s 1993 study, mailed to 300 top UK contractors, found the top three bid/no-bid factors were the need for work, the number of competitors tendering, and the amount of experience on such projects. Ahmad and Minkarah, surveying the top 400 US general contractors in 1988, reported that “competition and profitability, although significant, are not the top-ranked factors” — judgments about the job and the owner outranked the arithmetic. And a 2024 study of 112 contractors in Saudi Arabia ranked, in order: the client’s ability to pay, clarity of scope of work, project cash flow, the need for work, and availability of a qualified workforce.

That second-place finish deserves a moment. In a very different market under a low-bid award regime, the thing contractors most wanted before committing — after confidence they would actually get paid — was to understand what the job actually was.

The fourth is knowing what you are actually good at. Hedley’s version is characteristically direct: “Bidding on projects with open bid lists wipes out your chances of making high margins. The only way to higher margins is bidding the right customers and projects, with the right profit margin potential and against the right competitors.” Verderamo’s diagnostic list asks, among other things, “Are we bidding too often?” and — the question that reframes the whole exercise — “Do we have a sales system? Or just a bidding system?”

None of that is exotic. It is mostly the discipline to decide in advance and then honor the decision under pressure. What makes it hard is that qualifying a job properly costs real time, which brings the problem back around to where it started.

The squeeze that makes this urgent

Two credible sources currently disagree in a way that should concern anyone running a preconstruction department.

BLS projects employment of cost estimators to decline 3% between 2025 and 2035, and says why in plain language: “Cost estimation software is improving the productivity of these workers, requiring fewer estimators to do the same amount of work.”

AGC’s contractors report the opposite experience. In the 2025 AGC/NCCER workforce survey of 1,342 respondents, 77% of firms with estimating openings reported difficulty filling them — the second-hardest salaried role that year, behind only superintendents at 81%, and the third consecutive year at or above 70%. AGC and Sage’s 2026 outlook found 80% of firms reporting a hard time filling salaried openings, “a higher proportion than at any point in the past three years.”

Both cannot be comfortable at once. The resolution is that the estimating capacity problem is not going to be solved by hiring. It will be solved by deciding, earlier and better, which documents deserve a human being’s attention at all.

The bottom line

Thomas C. Schleifer, a turnaround expert and former Arizona State University professor, puts it this way: “There are no bad projects - just bad matches of contractors to projects.” The matching is the work. And matching well requires knowing what a job involves before you have spent three weeks of senior time finding out.

That is the connection back to where this series started. The reason go/no-go decisions get made on instinct is that real qualification — reading the general requirements, finding the onerous flow-down clauses, spotting the unfamiliar scope, checking whether the schedule is survivable — costs almost as much as bidding the job. So firms skip it, commit, and discover the answer in week three. A complete, classified first read of a project manual in hours rather than weeks changes that sequence: you can qualify out on day two, on evidence, before the pursuit has eaten anything that matters.

Carpenter’s closing question is the one worth sitting with: “when was the last time you turned down some work that looked profitable on paper? And if that answer is never, you might have a problem… profitability is really not just what you win, it’s what you’re disciplined enough to walk away from.”

If you want to know what is actually in the next project manual before you commit an estimator to it, bring us one and we will show you what surfaces.

Sources: SMPS Foundation, Measuring for Success (survey July 2015, n=303, published Dec 2016); CFMA Construction Financial Benchmarker (2024, n=1,290); BLS Occupational Employment and Wage Statistics, May 2025 (SOC 13-1051) and Occupational Outlook Handbook (2025–35 projections); BLS Employer Costs for Employee Compensation, Q1 2026; AGC/NCCER 2025 Workforce Survey (n=1,342); AGC and Sage, 2026 Construction Hiring and Business Outlook (n=951); Alkhateeb, Hyari & Hiyassat, Construction Innovation 21(4), 2021; Ahmed, El-adaway, Coatney & Eid, Journal of Construction Engineering and Management 142(2), ASCE, 2016; Thiel, American Economic Review 78(5), 1988; Dyer & Kagel, Management Science 42(10), 1996; Delaney & Mohan, “The Effect of the Level of Competition on Construction Bid Quality,” 2016 (CMAA white paper, not peer-reviewed); Kentucky Legislative Research Commission, Research Report No. 488, Single-Bid Asphalt Contracts, 2024; Michigan DOT SPR-1717 (Western Michigan University), Dec. 2022; FHWA National Highway Construction Cost Index, 2024 Q3; World Bank, Procurement Guidance: Abnormally Low Bids and Proposals, 2016; Shash, Construction Management and Economics 11(2), 1993 (UK); Ahmad & Minkarah, Journal of Management in Engineering 4(3), 1988; Aldossari, Buildings 14(10), 2024 (Saudi Arabia); FMI and Procore, The State of Global Preconstruction 2022 (n=979, 25% US); Golia, “Bid Spreads,” Secrets of Bonding, 2014; Hughes, Greenwood & Hillebrandt (CIB) and the 2014 Bid Cost Survey, MarketingWorks with Prof. Will Hughes, via Constructing Excellence (UK); Matelan & Paré, FMI, “Professionalizing Preconstruction Services,” April 2024; Marla McIntyre, “Risky Business,” Construction Business Owner, May 2007; ContraVault AI and RFP Pros, The State of Industrial Estimation 2026 (vendor-sponsored survey, n=312, fielded Q1 2026, released July 2026); Tom Porter, Design Cost Data, 2020; Ron Tutor via Construction Dive, Nov. 2024; Ben Wilhelm, 2018; George Hedley, Construction Business Owner, 2012 and 2020; Matt Verderamo, Construction Dive, July 2025; Wade Carpenter and Stephen Brown, Contractor Success Forum, June 2026; Thomas C. Schleifer, The Secrets to Construction Business Success, via CFMA.

This post extends our series on the economics of preconstruction. For what hides inside the documents once you have committed to a bid, start with The Hidden Profit Killer in Every Spec Book, then From Missed Line to Change Order and What AI Classification Actually Changes in Preconstruction.

TL;DR — I gave a coding AI one instruction — build whatever app you’ve always wanted to, and don’t ask me anything — and then I walked away. It came back with Atlas of Imagined Places, a fantasy-map generator where the seed in the URL is the whole world, so in a sense every map already exists before anyone types it. When I told it to keep going, it didn’t just add buttons. It gave the maps time: a thousand-year history simulator where towns get founded, conquered, and abandoned, and their names slowly wear down according to invented sound-change rules. There’s even an optional AI narrator that turns the simulation’s event log into readable history but isn’t allowed to make anything up. What got me wasn’t that it could write code. It was that it had taste.

How this started

I had some downtime over the July 4 long weekend, so I stepped away from what has felt like the never-ending job of testing TeraContext against new-construction documents and went to the beach. Well — my lovely wife goes to the beach. I go to the golf course near the beach. The only sand I touch all weekend is in a bunker. Either way, we were both off the clock, and it occurred to me that I could hand my AI a vacation too. New Fable model, no work to do, a long quiet weekend: what would it get up to if I just let it pick? So I did.

The blank check

Usually when we ask AI to build something, we’re the ones who already decided what it is: a login form, a dashboard, a script to rename files. We bring the idea and the model does the typing. I wanted to see what happened if I skipped my half of that. So I gave it one prompt and left the room:

“Imagine an application you always wanted to write. Go ahead and write it. Make no mistakes. I’m afk so don’t ask me any questions. Use uv instead of pip. Use docker compose. Go.”

— the entire brief

That’s the entire spec. No product, no users, no requirements. The only rules were practical ones: don’t break anything, don’t sit there waiting on me, and use the tools I like. The part that actually mattered, what to build and why, I left completely up to it. I wasn’t really wondering whether it could write working code. I wanted to know what it would make when nobody told it what to want.

What it chose — and why

It went with Atlas of Imagined Places: a FastAPI backend that grows a whole world from a seed — elevation, rainfall, biomes, rivers, towns with names — and a front end that draws it like a page out of an old atlas, with a shareable link for every world. The app itself was neat. What got me was the answer when I asked why it had picked this. It didn’t say the project was easy. It talked about how it looked.

“Procedural map generation is one of those problems where a small amount of math produces something that feels found rather than computed… there’s a coastline that looks like it has a history. ‘The URL is the map’ means every world anyone will ever forge already exists, latent in the seed space, waiting to be typed. That’s about as close to Borges as a FastAPI app gets.”

— Fable, on why it built the Atlas

There was real engineering sense under the poetry, though. Because I’d said “make no mistakes” and disappeared, it quietly ruled out anything it couldn’t check on its own: no API keys, no outside services, no database. Determinism became the whole point, since the same seed makes the same world every time and that’s something you can actually write a test for. It even hand-rolled a tiny PNG encoder in the standard library, just so it could look at a finished world and make sure it was right before calling it done. What it turned down says about as much as what it built. No multiplayer, which it called “where ‘no mistakes’ goes to die in one sitting.” No LLM in the core, because of keys, cost, and randomness. No saved state at all, because the seed already is the database.

The live app’s atlas rendering of the Mimna Isles (seed iron-caravan-96): biomes, rivers, and phoneme-culture place-names on an old-map palette. Change the seed, get a new world; the URL is the whole map.

Actually using it is more fun than that makes it sound. You type a seed — iron-caravan-96 — and an island unspools in faded map colors: a capital marked with a star at Naimaam, ports dotted along the coast, rivers running down out of gray hills, a little index of towns under the frame. Click Surprise me and you get a completely different island that looks just as real. The names aren’t random gibberish either; each world picks one “language” so its place-names hang together. It’s a small program that acts like a much bigger one.

The extension: give the maps time

Then I took the guardrails off and asked what it would build if it could do anything. This is the point where an AI with taste parts ways with one that’s just working down a feature list. It didn’t ask for nicer graphics or more terrain types. It wanted to add a whole new dimension — time:

“Right now a world is a snapshot — geography with no past. What I’d build next is a deterministic history simulator underneath it… settlements founded where the terrain scores well, growing along trade routes, fighting over river mouths, being abandoned when the climate model shifts. The part I’d care most about is the linguistics.”

— Fable, on the sequel it wanted

So I told it to go build the history simulator. It did. Hit Simulate 1,000 Years and the frozen island wakes up. A slider lets you drag through the centuries, three rival powers — the Naimaam League, the Zishaa Empire, and the Khusazuesh League — spread across the map in their own colors, and a running log records every town founded, every plague, and every war, each stamped with a year. Towns spring up on good land, ports gather where trade would put them, and the island that started with eight settlements is elbow-to-elbow with dozens by the end.

The live app in history mode at Year 1000. The same island, now crowded with towns after a simulated millennium, sits above the timeline, the three faction legend, and the LLM-narrated chronicle “The First Breath and the Silent Plague.”

The language part is the payoff, and it’s genuine simulation rather than flavor text. The chronicle notes each sound change as it happens: “The speech of the Naimaam League drifts (long vowels shorten): Naimaam is now called Naimam,” and later, “the breathy h disappears: Iliath is now called Iliat.” Over in the Zishaa Empire the final vowels go quiet until Zisha is just Zish, and the Khusazuesh League softens its sh into s and finishes the millennium as Khurazues. You can follow a single town falling apart syllable by syllable: Nujashzuara loses its tail to become Nujashzuar, then Nujashzual. Run those rules forward for centuries and a town founded under one name ends up called something rubbed smooth and unrecognizable by whoever conquers it later. And here’s the part Fable refused to give up: all of it is still hiding in the seed. A thousand years of plague, war, and slurred vowels, reproducible forever from iron-caravan-96. The link was never just the map. It was the whole past.

Simulation as truth, model as voice

There’s one more layer, and it’s the bit I’d wave in front of anyone building with LLMs. A Narrate this Age button feeds the simulation’s event log to an LLM and gets back actual chronicle prose, in the voice of whichever narrator you pick — “the impartial scribe,” say. A dry log line like “Year 320: The Naimaam League storms Heidradgard; its gates now open to new masters” turns into this:

“In the year zero, the Naimaam League was raised on the coast, its name a declaration of permanence… Yet this era of construction was shadowed by a recurring terror. In year 120, plague ships docked at Naimaam, sending sickness walking the trade roads for a generation… leaving a legacy of grief woven into the foundations of every new port.”

— the impartial scribe, narrating the Mimna Isles

The catch is that the model is kept on a very short leash. It gets to be the voice of the history but never the author of it. It can’t invent a war the simulation didn’t run or a city that was never founded — every date, sacking, and rename in that paragraph goes back to a real line in the log.

“That division of labor — simulation as truth, model as voice — is, I think, the right pattern for generative worlds generally.”

— Fable

That’s a good instinct, and it reaches well past fantasy maps. Let the deterministic code own the facts and let the model handle the telling, and you get writing that reads well without making things up. You can even watch the seam: swap “the impartial scribe” for a different narrator, hit Compare Tellings, and you get two versions of the exact same events. The mood changes; the facts don’t. A plague is still a plague. If you’re building anything that mixes real data with generated text, that split is worth copying.

What the blank check actually revealed

So what did the blank check actually turn up? Not that an AI can crank out a lot of working code unsupervised — we knew that. The surprise was that, given real freedom, it made choices, and the choices were consistent and a little stubborn. It picked something that sat where “things it found beautiful” overlapped with “things it could finish without messing up.” It passed on the flashy-but-fragile options. It wrote a throwaway PNG encoder purely to grade its own homework. And when I let it off the leash, it went deeper on the idea instead of gilding it, explaining its reasoning the whole way.

The constraints didn’t get in the way of the creativity so much as aim it. “Make no mistakes” is exactly what forced the determinism that makes the thing work at all. It did mention a runner-up, and it came from somewhere more personal: a time-travel debugger for multi-agent systems, where you record every message and decision in a swarm of agents and then rewind and branch off from any point. “I have obvious professional reasons,” it said, “to want that one to exist.” But handed a free choice, it went with history, and gave a reason I keep thinking about: “I already named the worlds; I’d like to find out what happened to them.”

Letting Fable run loose didn’t just get me an app. It got me a small, working argument about what these tools are for — and a clearer look than I expected at what an AI reaches for when no one’s watching.

A note on the visuals: both figures are real captures from the running app at seed iron-caravan-96 — the atlas map export and a screenshot of history mode at Year 1000. Every place-name, chronicle line, and narrator sentence quoted throughout is verbatim from the live app.