It was a pleasure to correspond with June Zhu, the visionary CEO of ChargerGoGo, to discuss the evolving landscape of mobile charging and the launch of their ambitious new platform, GoGoSpark. As urban environments become increasingly data-driven, Zhu is positioning ChargerGoGo not just as a hardware provider, but as a critical layer of “connected infrastructure” for local venues.
Our conversation delved into how AI-powered decision support and the distributed charging networks are creating a “post-work” entrepreneurship model, where smart hardware and real-time analytics do the heavy lifting for small business owners.
Top 5 Takeaways from the Interview
- The Network is More Than Hardware: ChargerGoGo defines its “leading network” as a sophisticated ecosystem of multi-slot kiosks, swappable batteries, and a connectivity layer that monitors device health and inventory in real-time to ensure “rental availability” rather than just power status.
- A Shift Toward AI-SaaS: With the introduction of GoGoSpark, the business model is evolving from a transaction-based charging service into an AI-driven enablement platform. It acts as a “business co-pilot,” helping operators optimize everything from equipment placement to promotional timing.
- Data-Driven Venue Optimization: The platform ingests real-time telemetry and foot-traffic proxies to deliver actionable insights to venues. While ChargerGoGo retains network-wide benchmarks, venues receive specific data on station health and revenue contribution to maximize their ROI.
- Privacy-by-Design: In an era of strict compliance, the network deliberately avoids sensitive personal identifiers, such as precise location tracking or biometrics, relying instead on anonymized transactional data and clear, in-flow disclosures to users.
- The Defensible Ecosystem: While charging hardware can be replicated, ChargerGoGo’s competitive moat is built on its dense distribution partnerships, retail media relationships, and a unified software stack that makes it difficult for competitors to displace their local footprints.
The June Zhu Serious Insights Interview
ChargerGoGo calls itself the “leading network” of on-demand portable charging for venues—what does the network look like in operational terms (assets, ownership model, service footprint, uptime targets)?=>Assets (what’s in the network)?
- Ownership / operating model (who owns and runs what)
- Service footprint (where the network exists) Uptime targets (what “reliable network” means in practice)
- If you want a shorter, punchier version for a quote:“
Operationally, our network is a distributed fleet of deployed charging stations and swappable batteries, connected through a monitoring and support platform. Stations are hosted in venues and serviced through a mix of company operations and local distribution partners. We manage performance through device health monitoring, inventory restocking, and field service response, with uptime measured as successful rental availability—not just whether a device is powered on.
- Charging stations/kiosks installed in venues (multi-slot cabinets and compact formats depending on venue layout).
- Swappable portable batteries (the rentable inventory that circulates through stations).
- Connectivity + monitoring layer (cellular/Wi-Fi connectivity where applicable, with cloud monitoring to track station health, battery inventory, and usage in near real time).
- Software layer that ties the physical fleet together: venue/host tools, ops dashboards, support workflows, and device management.
- The fleet is deployed through a distributed operator model:
- Some deployments are company-operated (ChargerGoGo manages installation, inventory, maintenance, and performance).
- Many locations are supported by local distribution partners who deploy and service stations in their territory under ChargerGoGo’s standards, tooling, and support processes.
- Venues typically host the equipment on-site while the network’s inventory, device standards, and operating procedures are managed through ChargerGoGo’s platform.
- Operationally, the “footprint” is defined by active venue locations with deployed stations + circulating batteries, supported by regional field service coverage.
- The network is built for high-traffic, on-premise environments (bars, restaurants, entertainment venues, event sites, and similar “need-to-charge-now” locations), with the ability to surge inventory for events and seasonal peaks.
- We track reliability at two levels:
- Station availability (is the station online and functioning)
- Battery availability (does the station have enough charged inventory to meet demand)
- Operational targets focus on:
- Keeping stations online and transacting (connectivity + device health)
- Keeping locations in-stock (enough batteries on-site, especially during peak hours)
- Fast resolution cycles through remote diagnostics + field service when an issue can’t be fixed remotely
- In practice, “uptime” isn’t only “is the box powered on”—it’s “can a guest successfully rent a charged battery when they need it,” which is why inventory health and service response are core network KPIs.
Walk through the unit economics of a single venue deployment: who pays, when money is collected, and what the cost drivers are (hardware, logistics, maintenance, support, replacements).
At a single venue, ChargerGoGo (or its authorized distribution partner) typically fronts the hardware, batteries, installation, and ongoing operations, while guests pay at the moment they unlock a battery via mobile payment. Rental revenue is collected upfront, processed through payment providers, and then shared with the venue on a periodic settlement cycle after fees through GoGo Host App. In parallel, stations with digital screens generate DOOH advertising revenue, sold either nationally or locally, which is monetized independently of rentals and shared with venues under separate terms.
The core cost drivers per venue are hardware amortization, logistics and installation, connectivity, ongoing maintenance and field service, customer and venue support, and battery replacements due to wear, damage, or loss. Profitability at the unit level depends on rental velocity, screen ad fill rate, inventory availability, and keeping service and replacement costs below modeled thresholds while the station remains online and visible in a high-traffic area.
What are the primary revenue streams today (venue fees, user fees, sponsorships/ads, partnerships), and how does that mix shift as scale increases?
Today, ChargerGoGo’s primary revenue comes from user rental fees, supported by revenue-sharing with venues rather than fixed venue charges, alongside a growing stream of DOOH advertising, sponsorships, and partnership revenue. As the network scales, the revenue mix shifts: based on growth patterns seen in more established Asian markets, increasing network density creates a strong network effect—greater visibility and convenience lead to higher user awareness, which drives more frequent battery rentals, while higher traffic and repeat usage improve audience quality and increase the value of screen-based advertising.
At scale, this network effect makes advertising and sponsorships a larger share of total revenue, while user fees remain the foundational layer, forming a reinforcing loop where density, usage, and monetization compound over time.
How does GoGoSpark change the business model—does it monetize as software (subscriptions/usage), as a take rate on transactions, or as an enablement layer that drives more charging volume?
GoGoSpark initially changes the business model as an enablement layer that improves operator performance and network efficiency—helping partners deploy better locations, keep stations online and stocked, optimize pricing and ad inventory, and resolve issues faster, which directly drives higher charging volume and DOOH ad revenue across the network.
Over time, it evolves into an AI-SaaS and marketplace platform, monetizing through subscriptions, usage-based access, and selective take rates, while also serving as an easy launchpad for additional AI- and smart-hardware merchant services beyond charging. In that model, GoGoSpark becomes both the intelligence layer for the existing network and a scalable distribution, operating, and monetization platform for new connected hardware categories.
What data does GoGo Spark ingest in real time (device telemetry, foot traffic proxies, venue systems, campaign signals), and what gets shared back to venues versus retained by ChargerGoGo?
GoGoSpark ingests real-time data across four layers: device telemetry (station uptime, connectivity, battery inventory, charging cycles, error states), usage and demand signals (rental frequency, dwell time, peak periods as foot-traffic proxies), venue-level inputs where available (operating hours, placement context, event timing), and campaign signals from DOOH ads (impressions, play schedules, engagement proxies).
Data shared back to venues focuses on operational and commercial relevance—station health, inventory status, rental performance, and venue-attributable ad results—so hosts can understand uptime, guest usage, and revenue contribution. ChargerGoGo retains network-level, aggregated, and comparative data, including cross-venue benchmarks, predictive demand models, optimization algorithms, and advertiser-facing insights, which are used to improve pricing, placement, inventory planning, and campaign performance across the broader network without exposing other venues’ proprietary or competitive information.
What are the top decisions GoGoSpark is meant to improve for a local business (staffing, promotions, inventory, equipment placement, partner offers), and what measurable lift has been observed so far (conversion, dwell time, repeat visits, cost reduction)?
GoGoSpark is built to improve a local business’s and operator’s core operating decisions—including equipment placement inside the venue, inventory levels, promotion timing, partner offers, and when staffing or on-site service is actually needed—while also acting as a business co-pilot for hardware operators. It provides tailored training plans, step-by-step guidance, and real-time AI assistance and support based on live device data, usage patterns, and venue context, so operators know what to do and when to do it. Early results show higher rental conversion from better placement, fewer out-of-stock periods, faster issue resolution, and lower service and support costs, alongside gains in repeat usage and ad performance as stations stay visible, stocked, and online during peak demand.
“Interact with connected infrastructure” can mean a lot—what infrastructure integrations matter most (POS, Wi-Fi, digital signage/DOOH, loyalty systems, facilities/IoT), and how hard are they to deploy across heterogeneous venues?
When we talk about “interacting with connected infrastructure,” the most impactful integrations for GoGoSpark are those that improve availability, visibility, and monetization rather than deep system-of-record control. The highest-value integrations are connectivity and facilities/IoT (network health, power status, device uptime), digital signage and DOOH systems (ad scheduling, proof-of-play, campaign measurement), and lightweight venue context signals such as operating hours or event schedules; these are generally moderate in deployment complexity and can be standardized across heterogeneous venues.
POS and loyalty systems are selectively integrated where it makes sense—for example, enabling promotions or attribution—but are intentionally not required for core operations, as POS stacks vary widely by venue and are costly to maintain at scale. Overall, GoGoSpark is designed to work with minimal required integrations, relying on its own connected hardware and software first, while layering optional integrations where they deliver clear ROI. This approach allows the platform to scale across diverse venues without long or brittle deployment cycles.
What AI approaches are being used (rules + analytics, predictive models, LLMs/agents), and where do hallucination risk and explainability become operational constraints in GoGo Spark?
GoGoSpark uses a layered AI approach rather than a single model: rules and analytics handle safety-critical and operational logic (device states, alerts, thresholds), predictive models are used for demand forecasting, inventory planning, placement optimization, and uptime risk, and LLMs and agent-style systems power the business co-pilot layer—training, guidance, troubleshooting, and decision support for operators.
Hallucination risk is treated as an operational constraint, which is why LLMs are never the system of record and are bounded by real telemetry, predefined actions, and permissioned workflows; they recommend, explain, and guide, but do not autonomously execute irreversible actions. Explainability matters most when outputs affect revenue, service actions, or partner trust, so GoGoSpark emphasizes transparent reasoning (what signal triggered a recommendation, what data was used, and what alternatives exist) and defaults to deterministic logic whenever ambiguity or liability is high. In practice, AI is used to augment judgment and speed decisions, not replace hard operational controls.
Privacy and compliance: what identifiers are used (or deliberately avoided), how is consent handled in public venues, and how does the platform align with state privacy laws and venue-specific policies?
GoGoSpark is designed with privacy-by-design principles: it deliberately avoids collecting or relying on sensitive personal identifiers such as precise location tracking, biometric data, or persistent cross-app user profiles. At the user level, interactions are limited to transactional identifiers required to complete a rental or payment, which are handled through compliant payment processors, while analytics are primarily aggregated and anonymized. In public venues, consent is handled through clear, in-flow disclosures at the point of interaction (e.g., during rental or on-screen messaging), aligned with venue policies and standard consumer expectations for on-demand services.
Data shared with venues is scoped to their own operational and commercial performance, while network-level insights are retained in aggregate. From a compliance standpoint, the platform is built to align with state privacy laws such as CCPA/CPRA, supports data access and deletion requests, limits data retention to operational needs, and adapts to venue-specific requirements through configurable policies—ensuring operators can participate in the network without assuming additional regulatory or privacy risk.
Competitive barriers: with charging hardware and venue access both replicable, what becomes defensible—distribution partnerships, data network effects, enterprise integrations, retail media relationships, or something else?
While charging hardware and venue access are individually replicable, the defensible advantage increasingly sits in the ecosystem built around the network. For ChargerGoGo, that includes distribution partnerships that create dense, hard-to-dislodge local footprints; data network effects from aggregated usage, uptime, and demand signals that continuously improve placement, inventory, pricing, and ad performance; and retail media and advertiser relationships that value scale, consistency, and measurable outcomes across many venues.
Layered on top is the strength of our tech ecosystem—a unified software, connectivity, payments, and AI stack (GoGoSpark) that standardizes operations, integrates enterprise partners, and makes it easier to deploy, optimize, and monetize connected hardware at scale. Together, this combination of density, data, ecosystem partnerships, and operating infrastructure becomes increasingly defensible over time, even as individual components remain easy to copy.
About June Zhu, CEO, ChargerGoGo
June Zhu is the CEO of ChargerGoGo, a leading mobile charging and smart-hardware platform serving merchants, venues, and entrepreneurs across the United States.
Before founding ChargerGoGo, June built extensive experience in cross-border mergers and acquisitions (M&A) andearly-stage investment, working across the United States and Asia to help companies scale, deploy capital and connect global technology ecosystems. She is also the founder of Nutopia, a blockchain-based platform focused on entertainment content and independent films, where she explored decentralized distribution models, creator monetization and the intersection of technology and media.
Her work and leadership have been recognized by her inclusion in Who’s Who in America and by her designation as Entrepreneur of the Year by the Asian Chamber of Commerce (ACC) Las Vegas.
Under her leadership, ChargerGoGo has grown into a nationwide merchant infrastructure platform. With the launch of GoGoSpark, June is expanding the company into a broader AI-powered merchant services and smart hardware ecosystem, enabling small and medium-sized businesses (SMBs) to discover, launch, and operate new business opportunities through AI-driven decision support, operational tools, and connected hardware built on ChargerGoGo’s real-world network.
June is a strong advocate for redefining the relationship between humans and work. Through ChargerGoGo and GoGoSpark, she is helping lead the transition into a post-work economy, where technology works alongside people, lowers barriers to entrepreneurship, and empowers everyday operators to build scalable, sustainable income through intelligence—not just labor.
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