Kristin Orman, Research Director, The Oxford Club Publisher's Note: Alexander Green recently uncovered seven small companies set to lead what he calls the biggest technological buildout in American history. Alex reveals how these microcap stocks could deliver 1,500% or more in the months ahead as trillions flow into AI infrastructure. Click here to learn more. - Stephen Prior, Publisher
Dear Reader, When I started at Avalon Research more than 20 years ago, markets felt different. Desks had screens and data - but also things you rarely see now. Traders used turrets (multi-line phones with direct lines to floor brokers). Almost every station had stacks of print - newspapers, trade magazines, and niche newsletters. My first must-read came from a small group called Williams Inference. Long before we stared at feeds, their network clipped articles, circled odd facts, and mailed them in. The method was simple: read widely, notice anomalies, connect thin clues into a theme you can act on. Not secret data - pattern sense. You didn't wait for a glossy report. You built your own. A single story about a new plant was noise. But when permits, supplier quotes, job postings, and longer lead times started to rhyme, that became signal. Jim Williams called it inferential thinking. Others later used the term "reduced-cue analysis." Different labels, same habit: pay attention to the small stuff and let it add up. That habit matters again - right now - because AI is shifting from labs to daily use. Phase one was training giant models. Phase two will run them everywhere. (Yes, the AI world calls that inference too.) Every answer an AI provides uses power, creates heat, and moves data. Those are physical needs, not marketing lines. They trigger real orders for real companies. If you keep tallies like Williams did, you can watch the buildout take shape. Teaching AI to "Think" like Williams Inference Here's what's new. Teams are training AI to spot weak signals at scale - not to guess prices tick-by-tick, but to notice oddities, link them, and write a clear, testable claim: "Spending is about to shift here because of these cues." Here's how it works (in plain English): - Feed the model messy inputs: news blurbs, permit filings, supplier comments, shipping data, call snippets.
- The model flags what's out of pattern: a spike in data center permits in one county; longer transformer lead times; a jump in 400G/800G optical orders; a cooling pilot that quietly expanded.
- It links the blips by place, vendor, product, and time. What took humans days with a highlighter takes minutes.
It drafts the first sentence an analyst can live with - "Liquid cooling is moving from pilot to rollout in these sites" - plus a short checklist: backlog rising, book-to-bill above 1, small margin lift, new field-service hiring. If those confirmations aren't there, the thesis will wait. That's Williams Inference for the AI age: lots of weak cues, tight linking, one clean claim, and a few facts that can prove or kill it. | 
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