I'm validating a B2B idea and want honest reactions.
Problem: data center developers sink millions into land that later gets blocked in local permitting or can't get grid power for years. $130B in projects stalled this year alone.
Idea: an AI that scores any US site on approval probability and time to power before they buy, as a paid per site service, with a free national risk map as the front door.
Two questions:
Would a developer or infra investor pay for this, or is it a "nice to know" they'd never buy?
What would make it a must have vs a curiosity?
Not selling anything, genuinely want to know if this is real.
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The hard part isn't the tool, it's mapping who feels the pain vs who controls the budget. Data center developers feel the risk, but often land decisions are driven by capital availability / financing windows rather than site intelligence.
Strongest validation signal: Find someone who lost $20M+ on a bad site choice, show them your tool would have caught it, see if they'd pay per site. If that person doesn't buy, it's a curiosity. If they do, you have your wedge.
Second validation: Talk to the finance teams approving $200M+ projects. Do they mandate site risk review before land acquisition? That's your must-have indicator.
The willingness to pay probably depends less on the score itself and more on whether the buyer can defend the decision internally. For a multi-million-dollar site, an opaque AI probability will feel like a curiosity. A report that shows the underlying permitting records, grid constraints, assumptions, confidence range, comparable projects, and when each source was last updated could become part of diligence.
I would position it as an early screening tool rather than a replacement for feasibility work: eliminate weak sites before exclusivity or expensive studies. The fastest validation may be to produce five reports manually for developers and ask whether each report changes a real shortlist or next action. If it does not change a decision, improving the model will not create urgency. I would also identify who has to defend the recommendation internally, because that person may be the real buyer.
You're right that the score on its own is the weak version, nobody's going to defend a multi million-dollar call on top of a number they can't see inside. So the deliverable isn't the probability, it's the record behind it: the actual permitting history, the grid constraints, the assumptions, a confidence range, comps, and a last updated date on every source. The score is just the cover page.
Early screening rather than a feasibility replacement is exactly where I've landed too, kill the weak sites before anyone spends real money on exclusivity or studies. And "who has to defend the recommendation internally" is the line I'm taking away from this. That person is probably the actual buyer, not whoever requests the report.
So the test you're describing, five reports done by hand, does each one change a real shortlist or a next action, that's what I'm going to go do. If it doesn't change a decision, no amount of model tuning fixes it. Appreciate you laying it out that clearly.
The evidence over the score point in the comments is the real unlock here. I would go one step further and skip the survey question entirely. Find three developers who are mid diligence on a real site right now, hand them a full detailed report for free, and watch what they actually do with it instead of asking if they would pay. If it changes which properties they pursue or how hard they push a term in negotiation, that is your signal it is worth paying for, and if they read it and keep doing what they were already going to do, no price point fixes that.
You know what, this is the exact test I'm going to run, and you've said it better than I had it in my head. No more "would you pay." Find developers mid diligence on a real site right now, hand them the full report free, and watch what they do, drop it, push a term, order the study sooner, or read it and change nothing. If behavior doesn't move, no price fixes it. Going to go get those three sites. Appreciate you cutting straight to it.
A few good angles already covered here (existing spend, backtesting against
known sites, concierge reports). One thing I'd add: "would they pay" often
depends less on total spend and more on whether there's one specific person
inside the developer's org who gets blamed when a permit falls through months
after land is bought — the site selection lead, or whoever signed off on the
purchase. If that person exists and can point to a deal that went sideways on
their watch, they'll fight internally for a budget line even without one
existing today. If it's more of a shared/diffuse risk with no clear owner, it
tends to stay a "nice to know."
I'm chasing a similar question for a different kind of data product right now,
so genuinely curious how this goes for you.
It's not about total spend but it's whether there's a single named person who takes the hit when a permit falls through after the land's bought: the site-selection lead, whoever signed the purchase. If that person exists and can point to a deal that went sideways on their watch, they'll push for budget even where none exists today. If the risk is diffuse with no owner, it stays a curiosity. So my job is to find the person with the scar, not the org with the problem. Good luck with your data product, im also curious how yours goes too!
Risk map doesn't seem valuable here. But if you somehow help them choose proper sites with evidence and reasoning, this may help a lot. They may even pay for that.
Fair hit, the bare map on its own is the weak version, and I don't think anyone defends a multi milion dollar buy on top of a shaded state. Where you landed is exactly right: the value isn't the map, it's helping them choose a specific site with the evidence and reasoning attached, permitting record, grid constraints, comps, the whole file. The map's just the front door that gets them to send me a real parcel. That's the part im going to test with a few manual reports on live sites. Appreciate you cutting to it.
This sounds valuable, but the buyer probably won’t pay for a “score.” They may pay for reducing the risk of wasting millions on the wrong site. I’d validate it with the people responsible for site selection and due diligence and ask what evidence they currently need before approving a purchase.
Another sharp version of what I keep hearing: nobody buys a score, they buy not blowing millions on the wrong parcel. So I'm leading with the risk it removes, not the number. And your validation step is exactly the one I'm running, going straight to the people who own site selection and due diligence and asking what evidence they already need in hand before a purchase gets approved, instead of asking if they'd like my thing. Clarifying, thank you Nadia
The free national risk map as a front door is the right instinct — that's exactly how you separate tire-kickers from buyers. The person who spends 30 minutes on your risk map exploring sites they're already evaluating is the person who'd pay for the full report.
On your core question: "nice to know" vs "must have" comes down to whether it replaces something they already pay for. If data center developers currently spend $5K–$50K on site feasibility consultants per project, your AI isn't a new budget line — it's a cheaper alternative to an existing expense. That's an easier sale than creating a new behavior.
One thing I'd test: instead of asking "would you pay?", ask "what do you pay today to solve this?" If the answer is "consultants" or "6 months of due diligence," you have a market. If the answer is "we just take the risk," you have an education problem, not a product problem.
Two things I'm taking from this. First, "what do you pay today to solve this?" is now my opener on every call instead of "would you pay?". If the answer is a $5K–$50K feasibility consultant or six months of diligence, I'm a cheaper line against a budget that already exists, a far easier sale than inventing a new one. If the answer is "we just eat the risk," that's an education problem and I want to know now. Second: the person who actually opens the map on sites they're already chasing is the buyer; everyone else is browsing. That changes who I chase. Thanks.
This sounds buyable, but only if the score changes an actual acquisition decision. “Approval probability” alone risks becoming an interesting report that nobody trusts enough to stake millions on.
To make it a must-have, I’d show the evidence behind the score: grid interconnection constraints, permitting history, zoning conflicts, water availability, local opposition signals, and comparable projects nearby. Then turn that into a clear recommendation such as proceed, renegotiate, investigate further, or walk away.
The strongest validation would be getting a few developers to give you sites they already evaluated and testing whether your system would have identified the same risks earlier. If it catches one expensive mistake before land acquisition, the value is obvious. The harder question is liability: how will you position the product so buyers use it as decision support rather than treating the score as a guarantee?
You're right that a bare probability becomes a report nobody stakes millions on, the deliverable has to be the evidence: interconnection constraints, permitting history, zoning conflicts, water, local opposition signals, nearby comps, resolving into one of four calls: proceed, renegotiate, investigate further, or walk away. I'm adopting those four verbs directly.
The validation you described is the one I'm actually going to run: get developers to hand me sites they've already evaluated and test whether the system flags the same risks earlier than they found them. If it catches one expensive mistake before land is tied up, the value stops being theoretical.
On liability, I'm positioning it explicitly as decision support and early screening, never a guarantee. Confidence ranges on every score, a last updated date on every source, and language that says "this changes what you investigate next," not "this is safe to build." Thanks for laying it out this cleanly.
The "would they pay" question turns on whether the score changes a decision or just describes one. Land teams already know permitting is the risk; what they'd pay for is a number they can put in front of an IC or a lender. That's the difference between curiosity and must-have, and why the supports for such numbers are crucial.
There are a few more pieces I'd add to your formulation, the most salient of which is the potential of power generation on site. This already creates an optimisation for geographies that support solar arrays, wind turbines, geothermal, etc. Beyond this, however, there is a question of the larger power usage in a certain municipality's grid, because an over served grid is more likely to support a data centre effectively.
Another thing to note: data centres are themselves somewhat overbuilt, especially as hardware continues to optimise. So there are several considerations: power use, cost for the data centre itself, and the most difficult, that of water. Water management relating to data centres concerns not only environmental issues and the protection of surrounding groundwater and water supplies but the water usage of the data centre, which requires additional optimisation.
All of these things can influence the score, but what I'd suggest if you are going to build this out is to create several sub-domains, each of which you develop individually: power (grid and generation), water, land, and zoning/political considerations on a local level.
For each sub-domain, you can develop hybridised approaches that increasingly utilise AI to conduct and check research, integrating new tools as they become available. However, developing them as separate sub-domains provides a much more compelling offer for investors. Each of the sub-domains, when developed adequately, can be applied to additional use cases and is potentially an acquisition target in its own right.
So the must-have version is probably the one where a lender or IC actually accepts the number — which is why the substantiation matters more than the breadth of coverage.
Good luck!
Thank you for the depth. The sub domain structure, power (grid and generation), water, land, zoning/political, is something I hadn't laid out cleanly, and you're right that building each as its own module is both a stronger product and a better story for investors, since each one stands alone and could matter beyond this use case. The on site generation angle, solar, wind, geothermal shifting the optimization by geography, and the over vs under served grid point are going straight into how I think about the power module. Water I'd underweighted, both the groundwater exposure and the facility's own usage as an optimization problem. And the through line I'm keeping above all of it: the must have version is the one where a lender or IC actually accepts the number, which means substantiation beats breadth. Genuinely grateful :)
The interesting part is that the product could potentially influence a multi-million dollar decision before capital is committed.
What would convince you that developers would treat this as a required diligence step rather than just another data point they consider alongside existing feasibility studies?
The only thing that would convince me is behavior. A developer sends me a real site they're actually evaluating, gets the score, and then does something different because of it: drops it, moves faster, renegotiates price, orders the study sooner. If the score never changes what they do next, it's a "nice to know" they'll nod at and ignore, and I'd rather find that out now.
The "required diligence step" version probably only happens if it slots into a gate they already have to clear, something an investment committee or a lender expects to see before capital's committed. So the real question I'm chasing isn't "is this interesting," it's "does it change your next move, and does anyone above you require it." Going to test exactly that with a few free reports on live sites.
This is a real problem, and I think the honest answer is: it depends entirely on which half of your idea you lead with.
The free national risk map is a nice to know infra investors and developers already have in-house GIS/permitting teams for that, or pay for existing data providers like, grid interconnection queue trackers, zoning data aggregators. A map alone won't get paid.
The per-site score is where it gets interesting, but only if you can answer the diligence-grade question: "Why should I trust this score over my own team's judgment?" For a $50M+ land decision, "an AI scored it 72/100" isn't enough they need to see your inputs (interconnection qu3ue position, local permitting precedent, zoning history, utility capacity studies) and be able to defend the number to their investment committee.
Love the idea. With your startup can you identify locations and then start recommending.
Yes, that's exactly where it's headed. The map is just the front door. The real product points a developer at specific parcels worth pursuing with the evidence attached: permitting record, grid headroom, comps. Not "here's a shaded map," but "these three sites clear the bar, here's why, and the one you're chasing doesn't." A recommendation, not just information. Appreciate it.
Building on the must-have thread — I'd look hard at which of your two metrics is actually the wedge. "Approval probability" is contestable; the first time it's wrong, a skeptical analyst discounts the whole tool. "Time to power," though, plugs straight into a pro forma — it's a schedule input that moves IRR. If you can defend a months-to-energization number, that's the line that ends up in the model, and de-risking the model is what people pay for.
On the free national risk map: watch that it doesn't anchor the category as free. I'd keep the free layer at region/county resolution (directional only) and put parcel-level precision — the part they'd stake millions on — behind the paywall. The free map earns trust and SEO; the paid report is where "nice to know" turns into "can't close without it."
You're right that approval probability is the contestable one, the first time it's wrong, a skeptical analyst writes off the whole tool. Time to power plugs straight into a pro forma and moves IRR, so that's the number that survives in the model. I'm leading with months- o energization as the defensible line and treating approval odds as supporting evidence, not the headline. And keeping the free map directional at county resolution while parcel level precision stays paid, that stops the category from anchoring as free. Taking all of it. Thank you very much :)
The 'nice to know vs must have' question is the real crux here. With PLEBS — a verified luxury club I'm building — we hit something similar: everyone loves the idea of proof/verification in the abstract, but paying for it is a different commitment. What made the difference for us was targeting the people who get hurt by the lack of it (in your case, probably the investors who eat the $130B in stalled projects, not the developers who caused it). Who bears the cost of getting it wrong in your model — the buyer of the report, or someone else downstream?"
Congrats on PLEBS, sharp parallel, everyone loves verification in the abstract until the invoice shows up. Your question is the one I have to answer: who eats the cost when a site goes sideways. In my model it's split, the developer sinks the land spend, the fund or lender eats the delay on their return. But you're right that the person who got personally burned is the one who fights for a budget line. So I'm going to find whoever at a developer signed off on a site that later got blocked. That person already has the scar. Thanks for this.
This feels real if you can turn uncertainty into a decision, not just a report. Developers and infra investors pay when the output helps them kill bad sites early, prioritize the right ones, or negotiate with confidence before they spend serious money. The free map is a good front door, but the paid product becomes a must-have only if it saves them months of diligence or one expensive mistake.
"Turn uncertainty into a decision, not just a report" is the line I needed. The output can't stop at information, it has to kill bad sites, rank the good ones, or give someone the confidence to negotiate. And the bar you set is the right one: it has to save months of diligence or catch one expensive mistake, or it stays a curiosity. Building toward exactly that test. Appreciate it
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