First Things First: When A.I. Reveals a Broken Process

Cheaper Tokens, Bigger Bills: Why Construction A.I. Costs Are Skyrocketing

Episode summary: Per-token A.I. prices have fallen sharply, yet construction firms are spending more than ever. This episode traces the gap to consumption, not price, and reads the May 14, 2026 repricing moves from Anthropic and OpenAI through what builders are already doing that drives the meter up every month. What you'll learn: - Why per-token prices can fall 80 to 90 percent while your monthly A.I. bill keeps climbing, and which number actually matters. - What changes when an assistant stops being a chatbot and becomes a doer that plans and executes a full workflow. - How heavy ingestion of spec books, sub quotes, and project data quietly drives token consumption in construction. - What the May 14 Anthropic and OpenAI subscription changes mean for the A.I. tools your teams are already using. Who this is for: - Construction executives, innovation/digital leaders, operations, and IT/security teams who want a pragmatic way to prioritize A.I. pilots. Full report & free sample: https://www.placersolutions.io/product/agi-in-construction

A.I. at Scale: The Big-Firm Advantage Is Getting Cheap

Episode summary: The capabilities that used to require a big firm's budget are getting cheap. Across Placer Solutions A.I. peer groups, smaller builders are retiring legacy estimating software outright, collapsing contract and safety review into a generalist plus a model, and shipping production tools in hours. What you'll learn:- Why a twelve-person preconstruction team fully retired On-Screen Takeoff for Togal after back-testing it to near-exact quantities, and what "retire, don't pilot" really signals.- How a CFO and a superintendent built a safety-review GPT in about four hours that now saves four to six hours per subcontractor plan.- What it means that a specialty sub with no estimating department built a production bid-intake workflow in roughly a day.- Where the advantage gets fragile: workarounds, governance gaps, and the builds firms abandon.Who this is for:- Construction executives, innovation/digital leaders, operations, and IT/security teams navigating A.I. adoption decisions. Full report & free sample: https://www.placersolutions.io/product/agi-in-construction Learn about the peer groups: https://www.placersolutions.io/peer-groups

Bending the Stick: The Chart Is Already Out of Date

Episode summary: When we published the 2026 A.I. Excellence in Construction report, we placed construction workflows on METR's pace-of-change chart. Six months later, the chart has been outpaced. We unpack what's changed and the harder question that follows. What you'll learn:- Why the original METR chart on page 15 has been outpaced and which construction workflows now fall inside the capability envelope of frontier A.I.- How Stanford's 2026 A.I. Index and OpenAI's GDPval benchmark independently corroborate the trajectory from different methodologies.- What Princeton's peer-reviewed Kapoor et al. research shows about the 50x cost gap between benchmark-winning agents and deployable ones.- Why the binding constraint for construction firms is no longer capability but the discipline to turn it into deployment. Who this is for:- Construction executives, innovation/digital leaders, operations, and IT/security teams who want a pragmatic way to prioritize A.I. pilots. Full report & free sample: https://www.placersolutions.io/product/agi-in-construction

Goal Drift: What's Actually Risky About A.I. Agents

Episode summary: The risk profile for A.I. in construction has changed. In a recent study of 847 production agent deployments, hallucinations were only 9.3 percent of observed vulnerabilities. We look at what dominates the rest, and what construction governance needs to do about it. What you'll learn:- Why the biggest agent failures in production aren't hallucinations, with the categories that actually dominate making up nearly two-thirds of the observed risk surface.- What goal drift, tool misuse, and state manipulation mean in plain language, with construction examples that make the new categories concrete.- How a sequence of individually safe tool calls can compose into a harmful outcome, illustrated with a worked example of the kind of chain construction firms are wiring up.- Why governance built for chatbots checks individual outputs, and why agents need governance built for sequences instead. Who this is for:- Construction executives, innovation/digital leaders, operations, and IT/security teams who want a pragmatic way to prioritize A.I. pilots. Full report & free sample: https://www.placersolutions.io/product/agi-in-construction

Six Months In: Three Rooms, Same Lesson

Episode summary: In three of the four Strategic A.I. Peer Groups for Builders this spring, the LLM-vendor conversation matured. Builders moved past the question of which model wins. They're building portfolios now: a primary LLM chosen for ecosystem fit and governance, paired with selective use of others where capability matters most. What you'll learn:- Why an innovation director at a West Coast general contractor stood by his Microsoft Copilot standardization and opened up Claude for his marketing team in the same 30-day window.- How an ecosystem advantage that finds the schedule for a random project instantly is keeping Copilot at the center of most construction firms' A.I. stacks.- What two other peer group cohorts independently concluded about building a harness around models rather than betting on a single vendor.- How to write a one-page two-model policy that says which model goes where in your firm, and why.Who this is for:- Construction executives, innovation/digital leaders, operations, and IT/security teams navigating A.I. adoption decisions. Full report & free sample: https://www.placersolutions.io/product/agi-in-construction Learn about the peer groups: https://www.placersolutions.io/peer-groups