Keith Saunders

written by Deborah Dull, on site at GreenBiz 2026   I spent today in a room full of people who think about waste for a living. And the word that kept coming up had nothing to do with recycling. It was sovereignty. Here is the situation. The United States imports 95% of its critical mineral supply. Lithium, cobalt, rare earth elements, the stuff inside every battery, every semiconductor, every electric motor. We do not make it, we do not mine much of it, and we do not control the supply chain that delivers it. That is not an energy policy problem. That is a national security problem. Now here is the part that should make you put down your coffee. A ton of smartphones contains dramatically more gold than a ton of mined ore. We are talking about concentrations that make urban mining look like a gold rush compared to digging in the ground. And yet the recovery rate for those materials, once a phone leaves its first owner, drops to around 13%. We are losing roughly 80% of the value sitting in devices right now, in drawers, in closets, in landfills. E-waste is also the fastest growing waste stream…

Companies are rapidly integrating generative AI into procurement and sourcing decisions. The promise is obvious. AI can read thousands of pages of supplier proposals faster than any human team, summarize technical requirements in seconds, and create the appearance of consistency and objectivity in evaluation. But there is an important distinction managers are starting to overlook. The same AI system that performs extremely well at identifying whether a supplier meets minimum requirements may perform much less reliably when judging which supplier is truly better. Recent research published in the Journal of Business Logistics examined this issue by comparing how large language models evaluated supplier bids against evaluations completed by experienced procurement professionals. It analyzed 123 supplier proposals tied to 31 public procurement projects conducted by the State of Ohio between 2023 and 2024. The projects involved complex IT services contracts, many containing large, text-heavy bid packages requiring evaluative judgment rather than simple arithmetic comparisons. The researchers tested three reasoning-oriented AI models: OpenAI o3, Grok-3-Mini, and DeepSeek R1. They then compared their evaluations against human procurement scores. The findings revealed a surprisingly clear pattern. AI performed well when evaluating compliance signals. These are signals tied to baseline qualifications and technical requirements. Does…