There’s a common guideline, in domains that range from investment markets to biology labs, that you need roughly 30 samples of something to produce valid statistics. Since last week’s note here was my thirtieth weekly sharing, this seemed like a good moment to ask some independent readers if my (mostly) headline-triggered items have shown any notable themes or trends.

• If yes: are those topics the right things to be discussing here?
• If no: is there need for a tighter focus, if FfILoE’s priorities are supposed to be reflected (or better, illuminated)?

I consulted a panel of three objective analysts: ChatGPT, Claude, and Adobe. (Others that I also evaluated* either choked or stumbled.) I fed them the entire collection of these posts, through last week’s, and asked them each to summarize that compilation. Their various raw outputs, collected in a single file, are available if you’re interested in their differences of performance and style; since that’s roughly two thousand words of LLM exhaust, though, I won’t show it here. Rather, I’m going to abstract from it to emphasize key results and to preview future directions.

ChatGPT was, overall, the most successful at digesting the whole collection and distilling it to bullet-pointed themes. It’s remarkable that something as brutally simple as a Large Language Model—as Stephen Wolfram has written, merely an engine that “is always fundamentally trying…to produce a ‘reasonable continuation’ of whatever text it’s got so far”—can swallow roughly twenty-eight thousand words, in too few seconds to think about stopwatching it, and report as the “Overall Message” that…

Climate change is not merely an environmental issue but a systems problem involving economics, technology, education, politics, infrastructure, and human behavior. Recurring principles include:

• Resilience over optimization
• Long-term thinking over short-term gains
• Measurement plus storytelling
• Market signals and incentives
• Collective action
• Hope grounded in evidence rather than wishful thinking

Additionally, and this is a feat of unaided abstraction that still surprises me – even after years of this stuff being out there for free public use:

The posts become more urgent and increasingly emphasize how climate, energy, and geopolitical systems are interconnected. Compared with the late-2025 essays, the 2026 writings become less focused on explaining climate science and more focused on:

• Decision-making under uncertainty
• Early warning systems
• Foresight and predictive tools
• Energy security
• Water scarcity
• Methane reduction
• Market signals and incentives
• How political language can obscure physical realities
• Global interdependence and supply-chain vulnerability

A recurring message throughout these months is: Reality eventually reveals itself through physical measurements, prices, resource shortages, and system failures—even when political institutions or markets attempt to hide those signals.

OK, I’m going to say, “Wow.” I’m glad that the algorithm found those ideas; I’m kind of flabbergasted that it can describe them that well. “Reality eventually reveals itself”? I’d feel good to have written that. I’ve rechecked the source text: I did not.

Claude produced quite a different style of result, more of a condensation with examples than a set of recurring topics like those identified in ChatGPT’s output. That doesn’t mean that Claude was less useful: it listed key specifics including

The math of climate change — Coffee repeatedly uses simple arithmetic to make large-scale phenomena tangible: Greenland ice melt translating to 26 feet of sea-level rise, methane plume outputs expressed as “equivalent to 17 million SUVs,” rainfall extremes reframed as distributions rather than averages.

Policy failures and signal distortion — He critically covers COP30’s removal of the word “fossil” from its final text, New York’s proposed methane accounting change to obscure its climate impact, Zillow removing climate risk scores, and the U.S. retiring its billion-dollar disaster database — all framed as deliberate “un-knowing.”

Economic signals — Insurance markets, energy pricing, and supply chain disruptions (especially around the Strait of Hormuz conflict) are presented as real-world consequences forcing climate awareness where politics has failed.

Renewable energy’s rise — Solar and wind are increasingly framed not as idealistic alternatives but as economically superior and geopolitically reliable compared to volatile fossil fuel markets.

Methane as an underappreciated lever — Multiple entries highlight methane’s outsized near-term warming impact and the relative ease and profitability of reducing it.

Talent and education — Coffee argues that solving climate change requires sustained investment in STEM talent pipelines, computational thinking, and STEAM education.

Additionally, and thank you very much kind AI,

The writing blends rigorous sourcing with accessible analogies, consistent urgency, and occasional dry wit.

Yeah, that’s allegedly the output of an algorithm. Are we sure this not a von Kempelenian robot? (Yes, that is a rhetorical question, but again – wow. “Dry wit”? Thanks, I think, but only a four-star review because of that “occasional.”)

Adobe’s results were also impressive, but more suited to producing a presentation slide than to sharing here. You can see them in the output compilation, as mentioned before.

Speaking of slides, though, I asked both Claude and ChatGPT to produce a slide from the prompt, “Why should someone follow this blog?” Claude’s result was gorgeous, but also mildly concerning in that it invented a quotation that is not something I ever wrote or said: “We cannot bend the curve if we first allow the curve to be erased.” That’s clearly derived from last week’s note on the willful destruction or obscuration of key data, and Claude was also kind enough to concentrate that note into one of its three reasons why people should be coming here: “Who else is naming institutions that actively dismantle climate knowledge? Coffee calls it—with receipts—and asks what we intend to do about it.”

I guess I’ll have to try to live up to that assessment, and not merely ask but actively recommend. As Lee Felsenstein wrote in the Berkeley Barb in 1968, “It is my responsibility as a technician not to simply criticize but to offer suggestions.” Thirty down, more to come.

* Google Gemini seemed to be unable to ingest the entire file of notes shared to date, complaining of an “abrupt ending” in the middle of a February post – something that nothing else seemed to encounter, and that I was not able to debug, but Gemini surprised me with this addition to its (incomplete) summary:

To see the author of these posts discuss some of his broader ideas on technology and future trends, you can check out this Keynote presentation by Peter Coffee, which provides context on his approach to strategic foresight and how foundational changes impact global systems.

Of all the videos that Google might have suggested, it picked that one, so perhaps you’ll find it worth your time.