| 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. Why Inference Matters Now AI "use" pushes money into four spend lines first: - Power (conversion, protection, reliable supply)
- Cooling (from air to liquid systems at scale)
- Interconnects and speed between data centers (100G → 400G → 800G upgrades)
- On-site build/serve (install, commission, maintain)
You often see the turn in small places: a rushed substation permit, a fiber backlog note, a vendor adding a second shift for manifold assembly. These are thin cues - exactly the kind AI can sift across thousands of sources. In short, we're using AI inference to track the AI-inference buildout. When usage climbs, the same names repeat; the confirms show up, and orders follow. AI is tireless at collecting and clustering. It reads what you can't and remembers all of it. It also forces structure - who/where/when, what changed, what's unproven. But it will also spin a sharp story from a shallow pool if you let it. Humans add three guardrails... 1) Context: "That project was announced last year; this is just a permit step." 2) Risk: "Single-customer exposure is high." 3) Common sense and logic: "Even if the theme is right, this company's balance sheet can't handle delays." That's why I treat AI-assisted inference as a theme engine, not a Magic 8-Ball. Use it to surface candidates - and to disqualify pretenders. So where does our scan keep pointing? Consider... - Using an AI-first, Williams-style read of the current cycle, the same cluster lights up again and again
- Power conversion and protection
- Liquid cooling moving from pilots to fleets
- 400G everywhere, with 800G entering production
- A handful of niche installers winning follow-on work
From that, we narrowed to seven small names that fit the confirmations - not just the story. One is our lead pick, which we're sharing for free at our AI Supremacy Summit on Monday, December 22 at 1 p.m. ET. If you want the full lineup - and why each name earned a slot - this event will walk you through the cues and confirmations. If you like the idea of AI that thinks like Williams Inference, that's your next step. Reserve Your Free Spot Here Good investing, Kristin | 
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