Debora Klaine sat down with Donald Gainsborough, a political savant and leader in policy and legislation at the helm of Government Curated. He has worked at the intersection of ballot design, utility planning, and community benefit agreements long enough to know how local narratives, regulatory guardrails, and privacy expectations collide. In this conversation, he unpacks how voter sentiment swings on zoning, power, and water, why cookie choices tie into public trust, and how to hardwire accountability into multi‑year builds without smothering innovation. Across the discussion, he emphasizes step‑by‑step playbooks: model first, decide second; disclose first, personalize second; and publish first, persuade second.
Voters in several places will decide on data center projects this year. How do you see ballots shaping outcomes on zoning, tax abatements, and community benefits, and can you share examples where campaign narratives or turnout data changed an approval or denial?
Ballots tend to crystallize three levers: whether zoning is by‑right or conditional, whether abatements sunset on clear milestones, and whether community benefits are automatic or contingent. I’ve seen campaigns flip outcomes when they link benefits to visible, near‑term actions like local hiring windows and site preparation timelines rather than abstract promises. In one county, early vote data showed strong support in precincts closest to the proposed site after advocates mailed simple maps and before‑and‑after visualizations; the result nudged officials to attach enforceable landscaping and traffic mitigations. In another, opposition reframed an abatement as a blank check and accelerated turnout in commuter precincts, prompting a denial that later reopened only after the developer agreed to staged incentives tied to permits and progress reports.
Large data centers strain local power grids. What trade-offs do you recommend between moratoriums, queue reforms, and grid upgrades, and can you walk through a step-by-step approach a utility and county can use to model peak load, interconnection timelines, and rate impacts?
I prefer targeted queue reforms and phased upgrades over blanket moratoriums, which often punish well‑sited projects alongside speculative ones. The step‑by‑step approach is simple: first, convene utility planners, the developer, and county staff to align on a peak scenario and a contingency scenario. Second, map interconnection steps from application to energization, adding hold points where the county verifies site readiness before the utility commits scarce equipment. Third, simulate rate impacts under baseline, phased build, and deferred build, then publish a plain‑English summary so ratepayers see the trade‑offs. Finally, adopt a pacing agreement that gates commissioning on feeder upgrades and demand response enrollment, with a pause clause if outages or reliability metrics trend in the wrong direction.
Water use for cooling often fuels opposition. How should communities balance dry cooling, heat reuse, and recycled water agreements, and can you share metrics on gallons per MWh and how those numbers influenced a recent permitting decision?
The balance starts with siting: choose parcels near reclaimed water lines and district heating candidates so dry cooling is a choice, not a forced retrofit. Then run side‑by‑side operating profiles that show seasonal performance and the noise, plume, and footprint differences that neighbors actually feel. During a recent permit review, the developer agreed to recycled water first, with a heat‑reuse interconnect when the receiving campus completed its piping; that sequencing turned critics into cautious supporters. Instead of arguing over abstract water intensity, the county required public dashboards that report cooling mode, source of water, and heat‑recovery hours so people can see the system working.
When ballots bundle data center approvals with infrastructure funds, how do you structure accountability—milestones, clawbacks, or performance bonds—and what anecdotes show which enforcement tools actually worked over a multiyear build?
I’ve found that milestone‑based abatements combined with performance bonds are the backbone, and clawbacks are the backstop. Tie each release of funds to verifiable actions like substation energization, stormwater completion, and local vendor contracts, certified by a third‑party inspector. In one build, the bond motivated timely road improvements when a rainy season threatened delays; the developer met the milestone to avoid forfeiture. In another, a clawback clause triggered a renegotiation after missed reporting; the cure period and a public progress meeting restored momentum without litigation.
Some sites rely on first-party and third-party cookies to personalize content and ads. How should operators explain the benefits without overselling, and what concrete metrics—CTR, session length, churn—prove personalization helps while staying respectful of privacy?
Start by saying plainly that cookies are small files a site asks a browser to store, and that some are strictly to make the site work while others personalize content and ads. Then disclose that users can opt out of certain cookies used for personalization and that the site does not track across different devices, browsers, or properties. To judge value, monitor whether people find relevant content faster, stay engaged, and return naturally, not because you nudged them with aggressive prompts. If personalized experiences don’t improve these signals without driving complaints, roll them back and keep the explanation prominent in your banner and policy.
Many websites classify strictly necessary cookies that users can’t disable without breaking core functions. How do you define the minimal set, test failure modes when blocked, and document the rationale so product teams don’t quietly expand “necessary” beyond what’s defensible?
Define “necessary” as cookies that enable security, session integrity, consent recording, and core navigation, including the banner and privacy choice memory. Build a test rig that blocks each candidate cookie and observes what fails—logins, checkout, or consent recall—and record screenshots and error logs. Keep a change‑control process where additions require a written justification, a risk review, and sign‑off separate from the product team that requested it. Publish the inventory so legal, privacy, and engineering all own the line—and so it doesn’t drift when deadlines get tight.
Functional and performance cookies support features and monitor site health. What step-by-step process would you use to map events, set retention limits, and review dashboards, and can you share a case where trimming data points improved latency or reliability?
First, inventory every event and feature that uses these cookies and match each to a user story and a troubleshooting purpose. Second, set retention so you keep enough to spot trends while pruning anything that doesn’t drive fixes or decisions. Third, create dashboards that separate feature adoption from reliability so you don’t chase vanity graphs. We once cut low‑value event noise from navigation tracking, and the slimmer payload reduced page jitter and stabilized error alerts because monitoring wasn’t drowning in false positives.
Some organizations treat certain ad-tech cookies as a “sale” of personal information under California rules and offer an opt-out toggle. How do you operationalize that toggle end-to-end—consent logs, CMP integration, vendor signaling—and which metrics confirm the opt-out actually propagates?
Wire the toggle through a consent management platform so the choice lands in a durable log that records the timestamp and jurisdiction. Pass the signal to vendors using standard flags and block tags client‑side when the user opts out, not later. Build a verification job that loads pages with the opt‑out set and confirms that the relevant requests don’t fire and that the ad experience still renders in a basic form. Track how often vendors respect the signal and how often blocked calls try to reappeAR, then escalate noncompliance through contractual channels.
Opt-out choices often apply only to the current browser and device, since cross-device tracking isn’t used. How do you communicate that clearly, minimize user frustration, and design a workflow to persist preferences after resets while avoiding dark patterns?
Say it up front: the selection takes effect only on this browser, this device, and this website, and it won’t follow people elsewhere. Offer a simple way to save or restore the choice after resets, such as a link to revisit the banner or a privacy page entry point that’s always visible. Avoid tricks—no prechecked boxes or fading buttons—and provide feedback that the choice was recorded. Include a short how‑to for browser settings so users who block cookies know some parts may not work as intended and can decide knowingly.
Social media and targeting cookies aim to show more relevant ads. What’s the right balance between relevance and over-personalization, and can you share experiments comparing broad targeting versus interest-based cohorts, including revenue and user trust outcomes?
The balance is to use cohorts that reflect content interest on your site without pulling in off‑site behavior that feels invasive. We’ve tested broad contextual placements against modest interest‑based cohorts and found that when messaging aligned with onsite behavior, people engaged without complaints. When we pushed into aggressive retargeting, we saw backlash in comments and support tickets even if ad response looked lively at first. The lesson is to draw the line at what you can explain in a sentence on your banner without hedging.
Blocking certain cookies can degrade site performance or break features. How do you run controlled tests to measure error rates, page load, and conversion after different blocking scenarios, and what remediation steps—graceful fallbacks, server-side rendering—proved most effective?
Set up controlled groups where specific cookie categories are blocked and measure stability of login, navigation, and forms alongside speed and completion. Capture where failures cluster and whether they correlate with first‑visit experiences or returning sessions. Introduce graceful fallbacks like basic menus and reduced‑feature pages so people can still navigate when preferences are locked down. Server‑side rendering helped us preserve core content when scripts were blocked, and it kept search and help links available without nagging prompts.
For data center developers seeking community approval alongside strict privacy practices on their sites, what integrated playbook would you use—public dashboards, third-party audits, and privacy-by-design—and can you cite examples where transparency measurably shifted public sentiment?
Pair a construction and operations dashboard with a privacy page that uses the same plain language as your cookie banner, so offline and online transparency reinforce each other. Commit to third‑party audits on environmental controls and consent practices, and publish summaries people can read without a glossary. In one campaign, weekly dashboard updates on site milestones, traffic mitigation, and privacy choices defused rumors and gave supporters something to share. The simple act of showing consent options, including opt‑outs for personalization and social media cookies, signaled respect—and that tone spilled over into public meetings.
What is your forecast for data centers on the ballot this year?
The projects that will pass are those that anchor their campaigns in visible local benefits, forthright grid and water trade‑offs, and straightforward privacy choices online that match their offline promises. Expect tighter conditions around interconnections and water sourcing, with more staged approvals that unlock as verifiable steps are completed. Where messages drift into abstractions, opposition will fill the gap with fear and a call for delays. Do you have any advice for our readers? Start early: publish what you’ll measure, show how people can opt out of what they don’t want, and build trust one transparent action at a time.
