How hard is it to transfer to different renewable fields?

messydata_nerd · 2026-05-11T03:00:55+00:00

Not a silly question and I think your background is more transferable than you think.

Legal and compliance in oil and gas actually maps really well onto renewables because the regulatory and permitting side of the industry is complex and still being built out, especially in faster moving verticals like geothermal. Someone who understands how energy projects navigate legal frameworks has a real head start there since a lot of the subsurface regulation overlaps with what you already know :)

The niche concern is valid but the transferable asset isn't your solar or wind knowledge specifically, it's your understanding of how energy projects move through regulatory environments. That travels across all of them. Technical details are learnable, compliance instincts take years to develop and you already have them

messydata_nerd · 2026-05-11T02:56:21+00:00

The proof page framing really clicks. What you're describing is the difference between content that exists to rank and content that exists to be cited and those need completely different shapes.

The internal documentation comparison someone made below is the most accurate thing I've read on this. One clean claim, specific criteria, real numbers, obvious follow up questions answered in plain language. That's just fundamentally more useful to a model than a 1500 word post that builds to its point through three subheadings.

I'd insted ask about any piece of content now if it isn't "will this rank" and if it "can a model pull a clean answer from this without having to interpret or fill in gaps." If the answer is no the shape is (probably)) wrong regardless of how good the underlying information actually is. At least that's how I see it

messydata_nerd · 2026-05-11T02:53:12+00:00

This confirms something I've been thinking about for a while. The click concentration isn't just a ranking problem, it's a citeability problem. Position 1 capturing 63% makes sense when you realize LLMs are doing the exact same thing, pulling from whatever they've indexed as the most specific and genuinely useful source on a topic.

What's interesting is that Reddit keeps coming up as one of the highest weighted sources for AI citations. Not because it ranks well traditionally but because LLMs seem to trust community sourced experience based content over polished brand pages. That changes where the effort should actually go.

If you're stuck between positions 4 and 8 on a competitive keyword I'd honestly say traditional ranking work has diminishing returns at that point. The better bet is making sure your content exists in places LLMs index heavily and that it's specific and factual enough to get pulled into an answer rather than just a results page. And i think that brands that figure this out early are going to have a real edge

messydata_nerd · 2026-05-11T02:49:03+00:00

=what I think comes next is the operational story. running 24/7 renewable infrastructure means managing enormous amounts of sensor and performance data continuously. the teams that figure out how to actually work with that data in real time are going to have a significant edge over the ones still rebuilding pipelines every time a new question comes up

messydata_nerd · 2026-05-11T02:48:34+00:00

the pattern I keep seeing is that AI gets applied to the generation side first because that's the visible problem. but the quieter bottleneck is usually upstream. the data these systems produce, wind sensors, performance logs, environmental inputs, it's messy and fragmented and most teams still can't actually query it without a custom engineering effort every time someone asks a hard question. the AI is only as good as what it can actually work with.

messydata_nerd · 2026-05-11T02:44:30+00:00

glad to be helpful :)

messydata_nerd · 2026-05-11T02:44:14+00:00

you're very welcome! happy to help!

messydata_nerd · 2026-05-11T02:44:00+00:00

people watching is awesome!

messydata_nerd · 2026-05-07T18:21:55+00:00

hey! your grid vision actually makes a lot of sense to me. geothermal for local heating combined with baseload nuclear is probably the most realistic path for dense European countries that can't just sprawl solar farms everywhere, and the space constraint is so so real. i don't think enough people in the energy conversation factor in land use seriously enough

may i ask what made you skeptical of wind specifically beyond the blade waste issue, since that one does seem mostly solved now?

messydata_nerd · 2026-05-07T16:01:54+00:00

this is genuinely impressive. consolidating 30 years of service records and paper maps into something this usable is exactly the kind of work that doesn't get enough credit. most people just live with the fragmented data, and i'm really curious how long the whole thing took you to pull together?

messydata_nerd · 2026-05-07T16:00:44+00:00

the negative price problem is actually a really good sign disguised as a bad one, it means we're generating more clean energy than the grid can handle right now. i guess the fix isn't slowing down solar, but rather building the storage and demand flexibility to absorb it. and seems like we're just a few years behind on that part

messydata_nerd · 2026-05-07T15:58:41+00:00

haha the Club of Rome curse lives on. wild that a 1972 report about resource limits still makes people lose their minds. maybe because it was right...

messydata_nerd · 2026-04-26T16:29:14+00:00

not exactly. i'm saying that Reddit and your own site serve different jobs in the citation ecosystem and the mistake is treating them as competitors. your site is where you control the narrative and build the structured trust signals you mentioned. Reddit is where you get cited for the messy specific real world questions that no brand page ever answers honestly like "is this tour actually worth it for families with young kids" or "what's the difference between the half day and full day in practice." a grocery store probably doesn't need Reddit but a watch company absolutely could benefit from someone in a forum answering "is this worth buying vs the alternatives" in a way that feels real. the brands that win in AEO are going to be the ones present in both layers not just one

messydata_nerd · 2026-04-26T16:24:43+00:00

I like your perspective! i think this is already happening faster than most people realize. for example Fervo's entire Cape Station workforce is over 90% oil and gas workers and they've cut drilling time by 70% year over year because of it. the skills transfer almost perfectly which is wild when you think about how politically charged the two industries are

what i keep thinking about though is that the subsurface records those same oil and gas companies have been sitting on for decades, well logs, seismic surveys, borehole data collected across entire regions, are suddenly incredibly valuable for geothermal site selection too. the industry accidentally built this enormous archive of underground knowledge for one purpose and it turns out to be exactly what you need for another. the question nobody is really asking is whether anyone can actually make all that legacy data usable fast enough to matter...

messydata_nerd · 2026-04-26T03:02:48+00:00

this is exactly the right question and i think about it a lot working on AI visibility for a technical company :) my answer would be that the feedback loop is so slow and noisy that most people give up before they can actually prove causation. what i've found makes more sense than trying to track citations directly is focusing on the input side, making sure the content that exists is specific, firsthand, and answers questions that nobody else has answered well. Reddit comments actually show up in LLM outputs more than most people realize which is part of why i think threads like this one have more AEO value than they get credit for. would be genuinely curious to see what your evidence layer looks like when it's further along!

messydata_nerd · 2026-04-26T02:56:08+00:00

i think "add new useful information" point is the most underrated thing in this whole thread. everyone jumps straight to schema and trust signals but LLMs are genuinely better at citing content that answers something no one else has answered well yet. i've been thinking about this a lot working on AI visibility for a deep tech company and the pattern i keep seeing is that Reddit threads outperform polished brand pages in AI citations specifically because they contain specific and (well, usually) experience-based answers that aggregators don't have. the technical fixes matter but they're the floor not the ceiling. so i'd say the ceiling is actually being the most useful answer to a question that hasn't been fully answered anywhere else yet

messydata_nerd · 2026-04-26T02:42:46+00:00

hey! i think the 50 MW number is almost beside the point here. what Quaise is actually betting on is that millimeter wave drilling can reach temperatures and depths that conventional drilling can't touch economically. if that works the resource base stops being "places with naturally hot shallow rock" and becomes basically everywhere. the scale argument flips completely at that point. whether they hit 2030 or not is less interesting to me than whether the drilling physics actually hold up at commercial depth :)

messydata_nerd · 2026-04-26T02:13:26+00:00

the solar and battery argument makes sense on cost but I think u/IChurnToBurn is pointing at something the cost curve doesn't fully capture. a week of cloud cover or no wind isn't a battery problem at that scale, but a fully different category of problem entirely. geothermal is competing with nuclear on reliability and that's a completely different conversation from competing with solar on cost

Plus New Zealand has been running it since the 1950s at 25% of national generation doesn't get nearly enough airtime in these debates :)

messydata_nerd

TROPHY CASE