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Freight buying has a dirty secret: most bids do a great job of finding the lowest number and a terrible job of finding the best outcome. When shippers reward price without measuring service, good carriers get punished, bad actors slip through, and procurement teams spend weeks in spreadsheets trying to guess what suppliers actually want.

We sit down with Alan Holland from Keelvar to break down a better approach using sourcing optimization, mechanism design, and AI agents. Alan explains how incentive compatible auctions can pull truthful preferences to the surface, why package bids and conditional discounts create space for small fleets to win the lanes they can run best, and how the Google Ads auction is a surprisingly useful model for modern freight procurement. From there we get practical: connecting transportation management system performance data to the sourcing event, weighting on-time performance with price, and designing feedback loops that reward reliable execution instead of “cheap and shaky” promises.

We also zoom out to the bigger AI shift. Massive compute, LLMs, and code generation tools like Claude Code are changing how software gets built, which means logistics technology will evolve faster than most teams are ready for. We talk about risk, uncertainty, penalty cliffs, rebate targets, and the long-term “holy grail” of multi-shipper combinatorial exchanges that could unlock network-level efficiency.

If you care about freight procurement strategy, logistics automation, AI in supply chain, or the future of brokers and carriers, hit play. Subscribe, share this with a friend in logistics, and leave a review with your biggest question about where AI agents help most.

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Thanks to our sponsors:

Stuut Technologies: Your AI coworker that collects your cash automatically.

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Cloneops.ai: Not just AI. Industry-born AI.

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Rapido Solutions Group: Nearshore solutions for logistics companies.

https://www.gorapido.com/

GenLogs: Freight Intelligence on every carrier, shipper, and asset via a nationwide sensor network

https://www.genlogs.io/